Declarative memory formation critically relies on the synchronization of brain oscillations in the theta frequency band (4-8 Hz) within specific brain networks. The development of this capacity is closely linked to the functional organization of these networks already at rest. However, the relationship between theta-band resting-state functional connectivity and declarative memory abilities remains unexplored in children. Here, using magnetoencephalography, we examined the association between declarative memory performance and pre-learning resting-state functional connectivity across frequency bands in 32 school-aged children. Declarative memory was assessed as the percentage of correct retrieval of 50 new associations between non-objects and magical functions, while resting-state functional connectivity was measured through power envelope correlation of the theta, alpha, low beta, and high beta frequency bands. We found that stronger theta-band resting-state functional connectivity within occipito-temporo-frontal networks correlated with better declarative memory retrieval, while no correlation was observed in the alpha and beta frequency bands. These findings suggest that the functional brain architecture at rest, specifically involving theta-band oscillations, supports declarative memory in children. This mechanism may facilitate the subsequent rapid transformation of sensory input into visuo-semantic representations, highlighting the critical role of theta-band connectivity in early cognitive development.

Children, particularly at school age, have to quickly learn large amounts of novel information about their environment (Hart et al., 2007). The declarative memory system is particularly at play to this aim, as it allows children to acquire and store broad conceptual representations and explicit knowledge about facts and events (Eichenbaum, 1997; Hart et al., 2007; Squire, 2004; Tulving, 2002).

Over the past decades, functional magnetic resonance imaging (fMRI) studies have characterized the brain regions underlying declarative memory processes in adults (review in Kim, 2011). They demonstrated the key role of (para-)hippocampal and neocortical (e.g., prefrontal or temporo-parietal) interactions in the storage and the “binding” of declarative memory representations (Jenkins & Ranganath, 2010; Moscovitch et al., 2016; Ofen, 2012; Squire, 2004; Staresina & Davachi, 2010; Tang et al., 2018; Tulving, 2002; Vincent et al., 2006). In particular, it was suggested that hippocampo-neocortical functional connectivity (FC) processes allow the binding of a priori arbitrarily related elements (e.g., face- or object-name; object-function; words-meaning) into unique memory traces or engrams as well as their transfer into pre-existing memory systems (Cohen et al., 1997; Eichenbaum, 2001; Squire, 2004).

During childhood, these hippocampo-neocortical networks are continuously maturing (Blankenship et al., 2017; Ghetti & Bunge, 2012; Menon, 2013; Ofen, 2012; Uddin et al., 2011), in parallel with the development of complex learning and cognitive functions such as declarative memory. Studying school-aged children offers a unique opportunity to examine how functional brain architecture supports memory during a stage of development characterized by flexible and adaptable neural connections (Johnson, 2001). Unlike adults, whose memory processes depend on well-established brain networks shaped by years of experience, children rely on less specialized networks, introducing valuable variability for investigating brain–behavior relationships. Investigating these networks in typically developing children can also inform clinical research by providing critical insights into learning disabilities and neurodevelopmental disorders (Karmiloff-Smith, 1998).

Interestingly, studies have shown that, already at rest (i.e., in the absence of explicit or goal-directed task practice), the functional organization of memory-related networks could play a critical role in the development of learning abilities (Gerraty et al., 2014; Wang, LaViolette, et al., 2010). Resting-state FC (rsFC) is considered as a marker of functional brain network integrity or efficiency (Smith et al., 2009) and has been shown to be predictive of functionally related performance or abilities in adults (Schlaffke et al., 2017; Stillman et al., 2013; Wang, Negreira, et al., 2010). RsFC has been usually assessed using fMRI through the detection of interregional correlations of spontaneous blood-oxygen-level-dependent (BOLD) fluctuations, which are thought to reflect the synchronized neuronal activity and communication between distinct regions. As these networks identified at rest have been progressively shown to overlap with the patterns of activations observed in task-based (e.g., sensorimotor, language, memory) studies (Kahn et al., 2008; Vincent et al., 2006), rsFC is suggested to likely influence the extent to which brain regions can coordinate their activity during task performance (Schlaffke et al., 2017). Regarding declarative memory, few fMRI studies reported an association between interindividual variability in episodic memory performance (i.e., declarative memory for events) and rsFC across declarative memory-related regions of interest (ROI) such as the hippocampus and cortical regions (e.g., cingulate, precuneus, retrosplenial cortex, inferior parietal lobule), in adults (Persson et al., 2018; Vincent et al., 2006; Wang, LaViolette, et al., 2010; Wang, Negreira, et al., 2010). Similarly, three fMRI studies showed that hippocampal ROI-based rsFC was correlated with declarative memory abilities in pre-school-aged children (i.e., aged 4 to 6 years) and in adolescents (Geng et al., 2019; Riggins et al., 2016; Warren et al., 2021). Precisely, these studies showed that episodic memory performance is associated with hippocampal-dependent rsFC processes in networks involving the precuneus, the superior frontal gyrus (SFG), the superior temporal gyrus (STG) (Riggins et al., 2016), as well as the orbital frontal gyrus (OFG) (Geng et al., 2019) in young children and the inferior parietal lobule (IPL) in adolescents (Warren et al., 2021).

Still, none of the above-mentioned studies have investigated declarative memory-related rsFC processes using a broader brain approach, which do not constrain their purview to the study of hippocampal-dependent rsFC. In addition, due to the sluggishness of fMRI responses (Heeger & Ress, 2002; Logothetis, 2008), these past studies could not investigate theta frequency band (4–8 Hz) oscillatory activity, which has been reported as critical for learning and memory processes (Fell & Axmacher, 2011; Solomon et al., 2017; Staresina & Wimber, 2019). Accordingly, using intracranial or magnetoencephalographic (MEG) recordings, previous studies have reported the involvement of long-range theta-band coupling between medial temporal lobe (MTL) and prefrontal cortex (PFC) in successful declarative memory encoding or retrieval in adults (Anderson et al., 2010; Backus et al., 2016; Kaplan et al., 2014) and children (Johnson et al., 2022). Theta-band oscillatory coupling between fronto-temporal brain regions was thus understood as the possible electrophysiological mechanism that enables the transfer of information during memory formation.

Unlike fMRI, which provides an indirect measure of neuronal activity through BOLD activity recordings, MEG directly measures the magnetic fields generated by electric currents in the brain at the millisecond (ms) level with good spatial resolution, enabling it to analyze long-range functional brain connectivity processes in specific frequency bands (Engel et al., 2013; Hipp et al., 2012). MEG thus provides an exquisite opportunity to investigate how long-range theta-band rsFC prior to learning relates to subsequent declarative memory performance in school-aged children, as previously shown in the context of procedural learning and working memory tasks (Barnes et al., 2016; Van Dyck et al., 2021). Based on existing fMRI studies exploring the link between interindividual rsFC and declarative memory performance in young children (Geng et al., 2019; Riggins et al., 2016) and the established role of theta-band oscillations in declarative memory processes (Backus et al., 2016; Fell & Axmacher, 2011; Kaplan et al., 2014), we hereby hypothesized that stronger theta-band rsFC within temporo-frontal brain networks is associated with better declarative memory performance at school age.

2.1 Participants

Thirty-seven typically developing children aged from 7 to 12 years old participated in this study. All children were right handed, based on parental reports, and were native French speakers with self-reported normal or corrected vision. They had no history of neurological/psychiatric issues or learning, cognitive or language disabilities. The “Sleep Disturbances Scale for Children” (SDSC) (Bruni et al., 1996) allowed us to ensure that all children had normal sleep quality and sleep habits over the preceding months (SDSC total score < 67) and that they had maintained a regular sleep schedule for the three nights preceding the experiment (“St Mary’s Hospital Sleep Questionnaire”) (Ellis et al., 1981). Vigilance was also measured before the MEG recording sessions (see below) using the psychomotor vigilance task (PVT, 5 min-duration version) (Dinges & Powell, 1985) to control for potential variations in sustained attention abilities between participants (Chun & Turk-Browne, 2007).

Overall, five children had to be excluded from the analyses due to excessive movements during MEG recordings (n = 3), poor sleep quality (n = 1), or poor vigilance (n = 1). The final sample, therefore, included 32 school-aged children (15 females and 17 males, mean age ± SD: 10.0 ± 1.1 years) with normal sleep quality (mean ± SD: 37.7 ± 6.8) and regular sleep schedule (mean ± SD last 6 months-nights: 9.7 ± 0.6 h; night-3: 9.8 ± 0.9 h; night-2: 9.9 ± 0.9 h; night-1: 9.5 ± 0.7 h; p = 0.19).

The study was approved by the Ethics Committee of the HUB-Hôpital Erasme (Brussels, Belgium; Reference: P2018/335). Participants and their parents gave written informed consent.

2.2 Material and design

2.2.1 Material

Complete details regarding the learning material features and task procedure can be found in Urbain et al. (2013), Urbain, De Tiège, et al. (2016), and Peiffer et al. (2020).

Briefly, 100 2D colored drawings of unknown non-objects were used in the declarative memory task (Fig. 1A). All non-objects were randomly split between 50 to-be-learned non-objects and 50 control (i.e., not learned) non-objects. Each to-be-learned non-object was randomly associated with a magical (imaginary) function that had to be learned by the participants (e.g., “paints the sky in all colors”, “opens all doors”, “stops the rain”, etc.). All definitions were in French and included three to seven words.

Fig. 1.

Experimental task and procedure design. (A) Sample illustrations of the 50 non-objects used. (B) Declarative memory task: At each trial, children were asked to provide the definition of the non-object presented on the screen, or skip it if the non-object was not previously defined. Responses had to be given after the appearance of a question mark (1 s after stimulus onset). (C) Experimental procedure: After a psychomotor vigilance task (PVT) and two 5-min resting-state MEG recording sessions, children underwent the declarative memory task separated into a learning session and an immediate retrieval session. A final structural high-resolution brain 3D T1-weighted magnetic resonance image (MRI) was acquired.

Fig. 1.

Experimental task and procedure design. (A) Sample illustrations of the 50 non-objects used. (B) Declarative memory task: At each trial, children were asked to provide the definition of the non-object presented on the screen, or skip it if the non-object was not previously defined. Responses had to be given after the appearance of a question mark (1 s after stimulus onset). (C) Experimental procedure: After a psychomotor vigilance task (PVT) and two 5-min resting-state MEG recording sessions, children underwent the declarative memory task separated into a learning session and an immediate retrieval session. A final structural high-resolution brain 3D T1-weighted magnetic resonance image (MRI) was acquired.

Close modal

2.2.2 Experimental design

The experimental protocol lasted half a day and occurred as follows (see Fig. 1C for details). Of note, this study was part of a larger research project and experimental protocol aiming at investigating various aspects of memory processes in children with typical and atypical development. Participants first performed the PVT (5 min-duration version) (Dinges & Powell, 1985), during which they had to press a response button as fast as possible each time a digital counter stimulus was presented. The mean reciprocal reaction time (RRTs = 1/Reaction Time(s)) was used as vigilance score as recommended by Basner & Dinges (2011). Then, they underwent two successive 5 min resting-state (RS) MEG recordings, which aimed at characterizing pre-learning rsFC processes in each individual. To do so, children were asked to lay down and remain as still as possible in the MEG scanner to prevent excessive motions, while focusing on a fixation cross placed on the ceiling of the magnetically shielded room. Immediately after, declarative memory performance was assessed in a behavioral session based on the declarative memory task previously developed by Urbain et al. (2013), which lasted approximately 1 h and included a 40-min learning session and a 15-min immediate retrieval session. During the learning session, children had to learn the magical functions associated with 50 non-objects (i.e., to-be-learned non-objects). The learning session included 10 learning blocks of 5 non-objects. For each learning trial, a non-object was presented on the computer screen by the experimenter who mentioned its magical function aloud to the participant. Then, each non-object was presented during 150 ms followed by 850 ms of a white screen and finally a question mark indicating to the participant to repeat the function they had just been taught. After each five non-objects, a recapitulative test (including the five non-objects) was administered. Feedback with correct responses was given to the participant during this five-by-five learning session but not during the immediate retrieval session. Participants had to reach a specific learning criterion (i.e., 60% of correctly learned associations) to ensure that all participants properly learned the material while keeping children motivated during the learning session and allowing for sufficient variability in interindividual scores. If the participant did not reach this criterion, the learning session was repeated including only the presentation of the unlearned non-objects. Once the participant reached the criterion, the immediate retrieval session started. All 50 to-be-learned non-objects and 50 control non-objects were presented twice in a random order and children had to retrieve the functions associated with each to-be-learned non-object and to correctly skip the 50 control non-objects. Each non-object was presented for 150 ms followed by an 850 ms blank screen with a fixation cross, and then a question mark prompting the child to verbally provide aloud the correct object’s function or say “skip” if unknown, with the question mark remaining on the screen until a response was given. The next trial began after a 1000-ms inter-stimulus interval corresponding to a blank screen with a fixation cross (Fig. 1B). A performance score was calculated for each participant as the percentage of to-be-learned non-objects correctly recalled during the immediate retrieval session. The protocol ended by the acquisition of a structural 3D T1-weighted brain magnetic resonance image (MRI) to allow for MEG source reconstruction.

2.3 Data acquisition, preprocessing, and analyses

2.3.1 MEG and MRI data acquisition

MEG data (signals band-pass filtered at 0.1–330 Hz and sampled at 1 kHz) were recorded inside a magnetically shielded room (Maxshield, MEGIN, Helsinki, Finland; see De Tiège et al. (2008) for details) using a 306-channel whole-scalp neuromagnetometer (MEG) system (Triux, MEGIN, Helsinki, Finland) installed at the HUB–Hôpital Erasme. The head position of each participant was continuously monitored inside the MEG helmet using four head-tracking coils (Taulu et al., 2005). In addition, 3 landmark positions (left and right tragi and nasion) and at least 400 additional head-surface points (on scalp, nose, and face) were digitized using an electromagnetic tracker system (Fastrak, Polhemus, Colchester, VT, USA). MEG-compatible bipolar electrodes were used to monitor ocular, cardiac, and mouth muscle artifacts, placed vertically around the eyes (electrooculography, EOG), on the back (electrocardiography, ECG) and vertically on the chin (electromyography, EMG), respectively.

After the two 5-min resting-state MEG sessions, a structural 3D T1-weighted MRI scan was acquired in all participants (MRI, 1.5T, Intera, Philips, Best, The Netherlands) except six for whom MRI acquisition could not be performed successfully. For each of these participants, we used a linear deformation of the structural MRI of an age-matched child to best match head-surface points, using the CPD toolbox (Myronenko & Xubo Song, 2010) embedded in FieldTrip (Donders Institute for Brain Cognition and Behaviour, Nijmegen, The Netherlands, RRID:SCR_004849) (Oostenveld et al., 2011).

2.3.2 MEG data preprocessing

MEG data were filtered using the temporal extension of signal space separation (tSSS; correlation coefficient, 0.98; window length, 10 s) (Taulu et al., 2005) to remove external environmental noise and correct for head movements (Maxfilter, MEGIN, Helsinki, Finland; version 2.2 with default parameters). At this stage, three subjects were excluded due to excessively noisy data. In the remaining sample, including 32 participants, no bad MEG channel was identified.

Independent component analysis (FastICA algorithm with dimension reduction to 30 and hyperbolic tangent non-linearity contrast) (Vigario et al., 2000) was applied to band-pass filtered (1–40 Hz) MEG signals to remove remaining ocular and cardiac artifacts. Components related to artifacts were visually detected and regressed out of the full rank data of each session (number of components removed: 3.5 ± 0.8, range: 2–5). The cleaned MEG data were then filtered into the theta frequency band (4–8 Hz), which was chosen for its specific role in declarative memory processes (Backus et al., 2016; Fell & Axmacher, 2011; Kaplan et al., 2014) and in the alpha (8–12 Hz), low beta (12–21 Hz), and high beta (21–30 Hz) bands to control for the specificity of the theta-band results.

2.3.3 MEG source reconstruction

Participants’ structural brain MRI was preprocessed to compute the MEG forward model, which is necessary to proceed with MEG source reconstruction. The brain MRI was anatomically segmented using the FreeSurfer software (Martinos Center for Biomedical Imaging, Massachusetts, USA) (Fischl, 2012). MEG functional and brain MRI structural data were coregistered manually using the digitized fiducials and head-surface points (Mrilab, MEGIN Helsinki, Finland). A volumetric source grid (cubic with 5 mm edges) was defined in the Montreal Neurological Institute (MNI) template MRI and deformed onto each subject’s MRI using a non-linear deformation in Statistical Parametric Mapping Software (SPM12, Wellcome Trust Centre for Neuroimaging, London, UK) (Ashburner & Friston, 1999). The MEG forward model was then computed using the one-layer boundary element method implemented in the MNE-C suite (Martinos Center for Biomedical Imaging, Massachusetts, USA) (Gramfort et al., 2014).

Neuronal source activity in each frequency band was reconstructed using minimum norm estimation (MNE) (Dale & Sereno, 1993). A 10-min empty room recording preprocessed with SSS and filtered in each frequency band was used to estimate the noise covariance matrix. The MNE regularization parameter was determined using the consistency condition described in a previous study (Wens et al., 2015). The resulting three-dimensional dipole time series were further processed by projecting them onto their direction of maximum variance and obtaining their analytic signal via Hilbert transformation, as previously described by Sjøgård et al. (2019) and Wens et al. (2014).

2.3.4 Functional connectivity analyses

Theta-band rsFC between each pair of sources was calculated through power envelope correlation (Brookes et al., 2011; Hipp et al., 2012; Wens et al., 2014) after orthogonalization in order to correct for spatial leakage (Brookes et al., 2012). The power envelope correlation technique involves correlating the power envelopes of neural oscillatory time courses in the frequency band of interest (i.e., theta frequency band) of two spatially separate brain sources. This connectivity measure has previously been recognized for its ability to identify functional resting-state networks as it reliably detects the same networks widely observed with fMRI (Brookes et al., 2011; de Pasquale et al., 2010; Liu et al., 2010), while also providing frequency specificity. In fact, power envelope correlation reflects the temporal coordination in the spontaneous bursting of transient neural oscillations (Cordier et al., 2024; Seedat et al., 2020). This makes power envelope correlation particularly suited to the investigation of brain networks associated with neural oscillations such as theta oscillations. To control for the specificity of the theta frequency band connectivity in declarative memory processes, this procedure was repeated for other frequency bands showing classical brain functional networks (i.e., alpha, low beta, and high beta bands; Hipp et al., 2012; Wens et al., 2014). Power envelopes were low-pass filtered at 1 Hz before being correlated (Hipp et al., 2012). The temporal correlation between each pair of the envelope signals was calculated separately for each resting-state MEG recording (5 min) and then averaged over the two sessions to improve the stability of the power envelope correlation estimation, as recommended in previous studies (e.g., see Liuzzi et al., 2017). This measure of connectivity was estimated at the connectome level (i.e., for each pair of nodes) using a customized parcellation of the human brain, including 75 nodes (MNI coordinates of all nodes can be found in memory atlas developed by Peiffer et al. (2021)). This parcellation involves 47 nodes from the Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002) and 28 additional nodes distributed over the whole brain that were selected from the literature for their partial or specific role in declarative memory processes (Bastin et al., 2019; Ranganath & Ritchey, 2012; Takashima et al., 2009; Urbain, De Tiège, et al., 2016). The resulting 75-by-75 connectivity matrices were calculated for each subject and for each frequency band yielding an image of their functional connectome in the frequency bands of interest (Fig. 2A). Of note, no node averaging procedure was applied nor needed given the intrinsic smoothness of MEG source reconstruction (i.e., spatial leakage; see Wens, 2015; Wens et al., 2015). To prevent potential asymmetries that could arise after pairwise orthogonalization, we followed the approach described in Hipp et al. (2012) and symmetrized the matrices by taking their average with their own transpose. To exclude a potential impact of power on functional connectivity estimates (Muthukumaraswamy & Singh, 2011), we calculated the variance of the source signal at each node with depth bias correction by noise standardization (Pascual-Marqui, 2002). The influence of signal power from each node pair on the power envelope correlation of the correction connection was suppressed alongside other covariates of no interest (see below) using multivariate regression modeling.

Fig. 2.

Functional connectivity and statistical analyses. (A) Neuronal theta-band source signal was reconstructed for 75 nodes from a customized parcellation of the human brain. Theta-band rsFC between each pair of nodes was calculated through power envelope correlation (PEC) of the theta-band source signals. The resulting 75-by-75 connectivity matrices were calculated for each subject. (B) The link between interindividual rsFC and behavioral retrieval performance was computed for each connection in the functional brain connectome, using a regression analysis implemented in Network-Based Statistic (NBS). Significant brain networks were then visualized using the BrainNet Viewer Connectivity Toolbox.

Fig. 2.

Functional connectivity and statistical analyses. (A) Neuronal theta-band source signal was reconstructed for 75 nodes from a customized parcellation of the human brain. Theta-band rsFC between each pair of nodes was calculated through power envelope correlation (PEC) of the theta-band source signals. The resulting 75-by-75 connectivity matrices were calculated for each subject. (B) The link between interindividual rsFC and behavioral retrieval performance was computed for each connection in the functional brain connectome, using a regression analysis implemented in Network-Based Statistic (NBS). Significant brain networks were then visualized using the BrainNet Viewer Connectivity Toolbox.

Close modal

2.3.5 Statistics and reproducibility

Behavioral declarative memory performance scores were calculated for each participant as the percentage of learned non-objects correctly recalled during the immediate retrieval session. We checked for outliers considered here as exceeding three median absolute deviations from the median declarative memory performance score (Leys et al., 2013), removing them from the analyses where appropriate. We also examined whether there were correlations between declarative memory performance, age, and the children’s vigilance (RRTs) using Pearson correlations run in Jamovi 2022 (Version 2.3).

We used a regression analysis to infer statistical relationships between behavioral declarative memory retrieval performance and MEG pre-learning theta-band rsFC for each connection across the connectome. To test the specificity of the theta frequency band, we also replicated these regression analyses with alpha-, low beta- and high beta-bands rsFC. Performance score was inserted as covariate of interest. Age, sex, and number of trials needed for each participant to reach the 60% learning criterion were used as covariates of no interest and were regressed out of the analysis alongside node signal power (see above). The vigilance score (i.e., RRTs) was further added post hoc as covariate of no interest in order to check for potential confounding contribution of vigilance performance to actual declarative memory retrieval performance. A regression analysis with vigilance as the covariate of interest was also performed to confirm the findings of this post hoc analysis. Positive regression coefficients indicated that higher rsFC was associated with better behavioral performance, while a negative regression coefficient indicated that higher rsFC was associated with poorer behavioral performance. The statistical significance of the regression coefficients was assessed using the non-parametric Network-Based Statistic (NBS) toolbox (Zalesky et al., 2010). NBS allowed us to identify network components, that is, contiguous sets of brain connections with significant correlation between rsFC and behavioral performance, while keeping control of the Family-Wise Error Rate (FWER). An FWER-corrected p-value was assigned to each network component using permutation testing (n = 10000). A conservative univariate threshold of t ≥ 4.5 was applied on the rsFC analyses, ensuring that only the most robust and statistically significant connections were considered, thereby minimizing the risk of false positives and increasing the reliability of the results. Significant brain networks (i.e., associated with pcorr < 0.05) were then visualized using the BrainNet Viewer Connectivity Toolbox (Xia et al., 2013) (Fig. 2B).

3.1 Behavioral performance related to declarative memory and vigilance

No participant was identified as a behavioral outlier based on declarative memory performance score (i.e., percentage of correct responses for the learned non-objects). During the immediate retrieval session, children successfully recalled 64 ± 13% (mean ± SD percentage of correct responses) of the functions associated with the 50 to-be-learned non-objects. The number of trials needed to reach the criterion (60%) in the learning session was on average two and did not exceed three.

One child was excluded from the analyses due to excessively low vigilance (score exceeding three median absolute deviations from the median RRT score). The mean RRT over all children was 2.70/s (SD = ± 0.32/s).

No significant correlation was found between vigilance (RRTs) and declarative memory performance (Pearson correlation test, p > 0.3), nor between age and declarative memory performance or vigilance (Pearson correlation test, p > 0.3).

3.2 Link between theta-band resting-state functional brain connectivity and declarative memory performance

Regression analyses revealed significant positive correlations between pre-learning theta-band rsFC and subsequent declarative memory performance that emerged within two, mainly right-lateralized, neural networks (NBS, pcorrs < 0.02). The first network included five connections among six nodes, namely connections between the right superior occipital gyrus and the medial superior frontal gyrus, the right medial prefrontal cortex, the left orbital frontal gyrus and the right amygdala, and a connection between the medial superior frontal gyrus and the right inferior temporal gyrus (pcorr = 0.001). The second network included a connection between the anterior part of the right inferior temporal gyrus and the right fusiform gyrus (pcorr = 0.019; see all components regrouped in Fig. 3A, upper panel for the axial view and lower panel for the sagittal view, and the scatter plots for each significant connection in Fig. 3B). No negative correlation was found.

Fig. 3.

Significant correlations between pre-learning theta-band resting-state functional connectivity and subsequent declarative memory performance. (A) Connections positively correlating with declarative memory performance, represented on the MNI brain (viewed from the top (up) or the right (down)) with nodes obtained from the customized parcellation. These plots were realized using the BrainNet Viewer Connectivity Toolbox (Xia et al., 2013). (B) Scatter plots for each significant connection from the two network components. L/R = left/right hemisphere, %IR = percentage of correctly recalled objects during immediate retrieval.

Fig. 3.

Significant correlations between pre-learning theta-band resting-state functional connectivity and subsequent declarative memory performance. (A) Connections positively correlating with declarative memory performance, represented on the MNI brain (viewed from the top (up) or the right (down)) with nodes obtained from the customized parcellation. These plots were realized using the BrainNet Viewer Connectivity Toolbox (Xia et al., 2013). (B) Scatter plots for each significant connection from the two network components. L/R = left/right hemisphere, %IR = percentage of correctly recalled objects during immediate retrieval.

Close modal

Repeating these analyses (post hoc) with vigilance score as additional covariate of non-interest, we observed that the significant positive correlations between pre-learning theta-band rsFC and subsequent declarative memory performance were reduced to two significant connections (pcorrs = 0.021) including the right superior occipital gyrus connected to the left orbital inferior frontal gyrus, and the right inferior temporal gyrus connected to the medial superior frontal gyrus. Worth noticing, this latter result was similar to the result obtained with our initial analysis when the NBS threshold was reduced (t < 4.5), suggesting that vigilance is not a primary contributor to the declarative memory-related networks. This was further confirmed by the absence of significant correlation between vigilance score and theta-band rsFC (NBS, pcorr > 0.05).

Complementary regression analyses in the alpha (8–12 Hz), low-beta (12–21 Hz), and high-beta (21–30 Hz) frequency bands revealed no significant correlation between rsFC and declarative memory performance (NBS, pcorrs > 0.05), suggesting that declarative memory performance is specifically related to theta-band rsFC.

This study aimed at better understanding how the functional architecture of the developing brain, sustained by theta-band cortical oscillations at rest, is associated with subsequent declarative memory abilities at school age.

Results showed that 7- to 12-year-old children with stronger theta-band rsFC within two mainly right-lateralized occipito-temporo-frontal networks were also better at retrieving newly learned declarative memory information. More precisely, better declarative memory performance was associated with stronger theta-band rsFC between (i) the right superior occipital gyrus, the medial and the left orbito-frontal cortices, the right amygdala and the right inferior temporal gyrus, but also between (ii) the right inferior temporal gyrus and the right fusiform gyrus. Altogether, these findings show that the strength of offline theta-band connections between specific brain network nodes influences subsequent declarative memory performance in children.

While this study establishes a link between the functional organization of the resting brain of school-aged children and declarative memory performance, similar associations have been observed with procedural memory at school age (Van Dyck et al., 2021). Still, this past study revealed different underlying networks sustained by neural oscillations in other frequency bands. It was shown, using MEG, that procedural learning performance correlated with stronger pre-learning alpha-band rsFC within an interhemispheric sensorimotor network, encompassing the bilateral inferior parietal cortices and the primary somatosensory and motor cortices. In contrast, our study showed that declarative memory performance was associated with stronger theta-band rsFC in two mainly right-lateralized occipito-temporo-frontal networks, suggesting a dissociation of underlying neural correlates between memory domains in the resting brain in school-aged children. Supporting the hypothesis of a specific resting brain network for declarative memory abilities, our results also indicate that the theta-band rsFC processes were not directly related to attentional abilities, as no significant correlation was observed between theta-band rsFC and the vigilance score.

Among other connections, better declarative memory performance was associated with stronger rsFC between the right fusiform gyrus and the anterior part of the right inferior temporal gyrus in our study. As temporal areas are known to contribute to high-level processing and to the recognition of visual information (Geng et al., 2019), these results suggest that stronger functional connections between these regions at rest may have supported a better recognition of the learned non-objects during subsequent declarative memory retrieval in our participants. This is in line with the known functional role of the right inferior temporal gyrus in the short-term storage and recognition processes of visual inputs, which allows comparing the incoming visual information with stored memory representations (Foxe et al., 2020; Ranganath et al., 2004) as well as of the right fusiform gyrus, which facilitates the transformation of the processed sensory input (e.g., visual input) into internal to-be-stored representations through the ventral stream of extra-striate visual systems (Kim, 2011). Moreover, increases in bilateral fusiform gyri activity have been frequently reported in the context of declarative memory tasks in adults, particularly for successful encoding that helps later memory recognition processes (Garoff et al., 2005; Ofen, 2012). Hence, our results suggest that stronger rsFC within the right postero-anterior temporal areas may have improved the processing, as well as the encoding and/or the recognition of visual declarative memory information in children.

This study also revealed that better declarative memory performance was associated with stronger theta-band rsFC between bottom-up striate visual processing and top-down (pre)frontal brain areas. Among these top-down (pre)frontal regions, whose recruitment is known to depend on the content of the memory task (Wagner et al., 2001), rsFC involved the left orbito-frontal cortex (OFC) connected to the right superior occipital gyrus, the latter being mostly involved in the early stage of visual processing (Foxe et al., 2020; Geng et al., 2019). This finding aligns with previous studies reporting a role of the orbito-frontal regions in providing a rapid global estimate of the stimulus and its top-down control processing within relevant visual occipital regions (Bar, 2003; Bar et al., 2006; Engel et al., 2001; O’Shea & Walsh, 2006). Interestingly, this process may have been strengthened by the parallel functional connections of the right amygdala with the superior occipital gyrus observed in our results. Being particularly sensitive to rewarding or emotionally salient stimuli (Bar, 2003), we suggest that occipito-dependent brain processes related to the OFC and the amygdala support more efficient visual processing of learned information and, consequently, better interindividual declarative memory performance. One could thus interpret that, from a phylogenetic perspective, these top-down mechanisms may have been progressively integrated into rsFC processes to enable the subsequent rapid transformation of salient sensory inputs into internal representations and their comparison with pre-existing memory traces.

The declarative memory task used in our study not only required the recognition of visual information (i.e., non-object) but also triggered the retrieval of associated episodic and semantic information (i.e., the non-object’s magical function). This dual requirement is consistent with our results showing that a better declarative memory performance was associated with stronger rsFC between occipital-temporal regions and medial (pre)frontal regions (i.e., the right mPFC and the superior medial frontal gyrus), which play a key role in the top-down access of semantic representations (Bokde et al., 2001; Jackson, 2021; Whatmough et al., 2002). Accordingly, our results suggest that stronger theta-band mPFC-dependent rsFC may have helped children in successfully retrieving the semantic (i.e., magical functions) information associated with the visual stimulus (i.e., non-object). This interpretation is also supported by previous fMRI and MEG studies that have linked increased activity in mPFC and occipital-temporal regions to the optimal retrieval of semantic information, including information about an object’s function in children and adults (Urbain et al., 2013; Yee et al., 2010). However, it cannot be claimed that these connectivity patterns exclusively support the retrieval of associated semantic functional representations, as other episodic elements may have been learned at the same time (e.g., the context of the personal experience that accompanied the learning), possibly reflecting an interplay between episodic and semantic memory retrieval processes (De Brigard et al., 2022).

Worth noticing, despite the verbal semantic contribution of our declarative memory task, which has often been described as mainly relying on the left hemisphere (Binder et al., 2009), the resting-state brain networks associated with declarative memory performance in our study were predominantly right-lateralized. This lateralization is most likely due to the nature of our learning task requiring to bind idiosyncratic associations between novel visual, complex non-objects and their imaginary function and aligns with previous studies reporting rather right- than left-hemispheric processing in the context of complex picture learning tasks (Golby et al., 2001; Wagner et al., 1998), and especially in new situations for which no previous representation is available in long-term memory (Goldberg & Podell, 1994, 1995; Urbain et al., 2013). Indeed, in this study, non-objects were used to reduce potential inter-individual differences in prior semantic knowledge, allowing us to investigate the link between rsFC processes and newly learned (i.e., idiosyncratic) associations. However, it should be noted that this type of learning may not fully reflect typical daily learning experiences of school-aged children. Nonetheless, acquiring idiosyncratic associations between novel objects and their functions plays a crucial role in the development of semantic representations, onto which children can later map lexical words (Clark, 2004; Jaswal, 2006), an essential process in children’s language development.

Altogether, our results suggest that declarative memory performance in children is supported by specific offline occipito-temporo-frontal theta-band functional brain connectivity processes, which may facilitate the encoding and the retrieval of new complex visuo-semantic representations (Staresina & Wimber, 2019). However, it is important to acknowledge certain limitations in this study. First, due to the correlational nature of our research design, we cannot establish causal relationships between rsFC and declarative memory performance. Secondly, while the implementation of a learning criterion (i.e., a 60% learning threshold) ensured that all participants adequately learned the experimental material and remained motivated throughout the learning process, it inevitably reduced the variability of declarative memory performance in our study. Yet, as recently highlighted by Nebe et al. (2023), low variability, where participants exhibit more similar values, restricts the range of data points and weakens the ability to detect significant associations. It remains thus possible that a less restrictive learning criterion would have led to greater between-subject variability, potentially revealing additional or stronger rsFC associations. That being said, our results still showed valuable interindividual variability in performance scores during the immediate retrieval session (mean ± SD percentage of correct responses: 64 ± 13%; range of scores: min = 28%; max = 83%). Future studies may explore the impact of different learning criteria on the associations between rsFC processes and memory performance. Finally, as this study was based on a cross-sectional sample, it limits our ability to draw definitive conclusions about developmental trajectories and individual differences. Yet, examining declarative learning and memory processes from a developmental perspective may offer an ideal avenue to achieve a deeper understanding of fundamental cognitive processes (Karmiloff‐Smith, 1994). Future studies using experimental designs such as longitudinal investigations across multiple time points would be valuable in assessing the stability and predictive value of theta-band rsFC processes for memory abilities.

Still, our results are in line with previous studies suggesting that theta-band oscillations specifically support the functional basis of long-range brain communication, in particular, between temporal areas and neocortical (mostly prefrontal) areas, required for memory formation in children (Johnson et al., 2022). Moreover, we did not observe a similar relationship between declarative memory performance and rsFC in the alpha and beta frequency bands, strengthening the key specificity of theta-band FC processes for declarative memory.

Unexpectedly, MEG rsFC results did not include (para)hippocampal or medial temporal brain areas, despite their known role in learning and declarative memory processes (Kim, 2011), including those involved in the specific task underused (see Urbain, De Tiège, et al., 2016). This is also surprising as theta-band oscillations have been repeatedly associated with hippocampal-dependent declarative memory processes (Griffiths et al., 2021; Johnson et al., 2022; Staresina & Wimber, 2019), and hippocampal-related rsFC has been previously highlighted as a possible support for declarative memory performance in young children (Geng et al., 2019; Riggins et al., 2016). Still, these latter studies were ROI hippocampal-based studies, which may have increased the chances of identifying an effect including medial temporal brain regions in past results. Methodologically, it remains possible that the apparent lack of hippocampal involvement in our rsFC results may be due to difficulties in recording magnetic fields emerging from deep sources, at least in the resting state (Wens, 2023). On the other hand, several task-based studies have nowadays demonstrated the ability of MEG to capture hippocampal activity or hippocampal-related functional brain connectivity (López-Madrona et al., 2022; Quraan et al., 2011; Urbain, De Tiège, et al., 2016; Urbain, Vogan, et al., 2016; Urbain et al., 2015, 2017). The question of whether MEG is sensitive enough to detect spontaneous fluctuations in hippocampal theta oscillations and their connectivity thus remains largely open. Alternatively, we suggest that our results might indicate that rsFC processes do not influence the specific process of binding memory traces, as this function is repeatedly associated with (para)hippocampal brain regions (Geng et al., 2019). Rather, our MEG results indicate that pre-learning rsFC would specifically support sensory input transformation processes, which are underpinned by larger brain networks. Precisely, our data suggest that stronger theta-band rsFC processes would facilitate the subsequent rapid processing and transformation of visuo-semantic information into internal representations during a declarative memory task at school age. Consequently, stronger theta-band rsFC within these networks could lead to a more efficient communication between these brain areas during declarative memory processes and thus to efficient visuo-semantic declarative memory retrieval performance. This idea fits with the hypothesis that resting-state networks would ensure responsiveness to possible future tasks (Deco & Corbetta, 2011) by forming a critical pathway for communication when relevant. However, our study remains correlational and a formal causal relationship between rsFC processes and subsequent memory abilities remains to be investigated in the context of future studies.

Furthermore, the identification of stronger theta-band rsFC as a correlate of better declarative memory performance provides valuable insights into the potential underlying mechanisms contributing to learning difficulties. This could offer potential support for the early and easy (resting-state-dependent) identification of children at risk for declarative learning impairments, particularly in the context of low cognitive load protocols (Riggins et al., 2016; Uddin, 2010), and possible insights regarding clinical routines and procedures.

The data that support the findings of this study are available on request from the corresponding author and after acceptance by institutional authorities (Hôpital Universitaire de Bruxelles and Université libre de Bruxelles). The data are not publicly available due to ethical restrictions.

S.G.: behavioral, MEG, and MRI data analyses; interpretation of the results, writing - original draft, review & editing. C.R.: MEG and MRI data analyses, revising the manuscript. A.P.: investigation, revising the manuscript. V.W.: software, MEG, and MRI data analyses, interpretation of the results, writing - original draft, review & editing, funding acquisition. X.D.T.: conceptualization, interpretation of the results, writing - original draft, review & editing, funding. C.U.: conceptualization, interpretation of the results, writing - original draft, review & editing, funding.

The authors declare no competing interests.

S.G. is supported by the Fonds pour la formation à la recherche dans l’industrie et l’agriculture [FRIA, Fonds de la Recherche Scientifique (FRS-FNRS), Brussels, Belgium]. C.R. was supported by The Belgian Kids’ Fund for Pediatric Research and is supported by Fund iris-Research managed by the King Baudouin Foundation. A.P. wa supported by the Fonds de la Recherche Scientifique (F.R.S. F.N.R.S., Aspirant Research Fellowship). X.D.T. is clinical researcher at the Fonds de la Recherche Scientifique (F.R.S.–FNRS). The other authors received no additional funding.

This MEG project conducted at the Hôpital Universitaire de Bruxelles and Université libre de Bruxelles is financially supported by the FRS-FNRS (Excellence Of Science, EOS-MEMODYN: 30446199; CREDIT DE RECHERCHE: n° 29149840) and the Fonds Erasme (Convention “Les Voies du Savoir”, Brussels, Belgium). The authors would also like to warmly thank all the children and their parents for their participation.

Anderson
,
K. L.
,
Rajagovindan
,
R.
,
Ghacibeh
,
G. A.
,
Meador
,
K. J.
, &
Ding
,
M.
(
2010
).
Theta oscillations mediate interaction between prefrontal cortex and medial temporal lobe in human memory
.
Cerebral Cortex
,
20
,
1604
1612
. https://doi.org/10.1093/cercor/bhp223
Ashburner
,
J.
, &
Friston
,
K. J.
(
1999
).
Nonlinear spatial normalization using basis functions
.
Human Brain Mapping
,
7
(
4
),
254
266
. https://doi.org/10.1002/(SICI)1097-0193(1999)7:4<254::AID-HBM4>3.0.CO;2-G
Backus
,
A. R.
,
Schoffelen
,
J.-M.
,
Szebényi
,
S.
,
Hanslmayr
,
S.
, &
Doeller
,
C. F.
(
2016
).
Hippocampal-prefrontal theta oscillations support memory integration
.
Current Biology
,
26
(
4
),
450
457
. https://doi.org/10.1016/j.cub.2015.12.048
Bar
,
M.
(
2003
).
A cortical mechanism for triggering top-down facilitation in visual object recognition
.
Journal of Cognitive Neuroscience
,
15
(
4
),
600
609
. https://doi.org/10.1162/089892903321662976
Bar
,
M.
,
Kassam
,
K. S.
,
Ghuman
,
A. S.
,
Boshyan
,
J.
,
Schmid
,
A. M.
,
Dale
,
A. M.
,
Hämäläinen
,
M. S.
,
Marinkovic
,
K.
,
Schacter
,
D. L.
,
Rosen
,
B. R.
, &
Halgren
,
E.
(
2006
).
Top-down facilitation of visual recognition
.
Proceedings of the National Academy of Sciences of the United States of America
,
103
(
2
),
449
454
. https://doi.org/10.1073/pnas.0507062103
Barnes
,
J. J.
,
Woolrich
,
M. W.
,
Baker
,
K.
,
Colclough
,
G. L.
, &
Astle
,
D. E.
(
2016
).
Electrophysiological measures of resting state functional connectivity and their relationship with working memory capacity in childhood
.
Developmental Science
,
19
(
1
),
19
31
. https://doi.org/10.1111/desc.12297
Basner
,
M.
, &
Dinges
,
D. F.
(
2011
).
Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss
.
Sleep
,
34
(
5
),
581
591
. https://doi.org/10.1093/sleep/34.5.581
Bastin
,
C.
,
Besson
,
G.
,
Simon
,
J.
,
Delhaye
,
E.
,
Geurten
,
M.
,
Willems
,
S.
, &
Salmon
,
E.
(
2019
).
An integrative memory model of recollection and familiarity to understand memory deficits
.
Behavioral and Brain Sciences
,
42
,
e281
. https://doi.org/10.1017/S0140525X19000621
Binder
,
J. R.
,
Desai
,
R. H.
,
Graves
,
W. W.
, &
Conant
,
L. L.
(
2009
).
Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies
.
Cerebral Cortex
,
19
(
12
),
2767
2796
. https://doi.org/10.1093/cercor/bhp055
Blankenship
,
S. L.
,
Redcay
,
E.
,
Dougherty
,
L. R.
, &
Riggins
,
T.
(
2017
).
Development of hippocampal functional connectivity during childhood
.
Human Brain Mapping
,
38
(
1
),
182
201
. https://doi.org/10.1002/hbm.23353
Bokde
,
A. L. W.
,
Tagamets
,
M.-A.
,
Friedman
,
R. B.
, &
Horwitz
,
B.
(
2001
).
Functional interactions of the inferior frontal cortex during the processing of words and word-like stimuli
.
Neuron
,
30
(
2
),
609
617
. https://doi.org/10.1016/S0896-6273(01)00288-4
Brookes
,
M. J.
,
Woolrich
,
M.
,
Luckhoo
,
H.
,
Price
,
D.
,
Hale
,
J. R.
,
Stephenson
,
M. C.
,
Barnes
,
G. R.
,
Smith
,
S. M.
, &
Morris
,
P. G.
(
2011
).
Investigating the electrophysiological basis of resting state networks using magnetoencephalography
.
Proceedings of the National Academy of Sciences of the United States of America
,
108
(
40
),
16783
16788
. https://doi.org/10.1073/pnas.1112685108
Brookes
,
M. J.
,
Woolrich
,
M. W.
, &
Barnes
,
G. R.
(
2012
).
Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage
.
NeuroImage
,
63
(
2
),
910
920
. https://doi.org/10.1016/j.neuroimage.2012.03.048
Bruni
,
O.
,
Ottaviano
,
S.
,
Guidetti
,
V.
,
Romoli
,
M.
,
Innocenzi
,
M.
,
Cortesi
,
F.
, &
Gianotti
,
F.
(
1996
).
The Sleep Disturbance Scale for Children (SDSC) Construct ion and validation of an instrument to evaluate sleep disturbances in childhood and adolescence
.
Journal of Sleep Research
,
5
(
4
),
251
261
. https://doi.org/10.1111/j.1365-2869.1996.00251.x
Chun
,
M. M.
, &
Turk-Browne
,
N. B.
(
2007
).
Interactions between attention and memory
.
Current Opinion in Neurobiology
,
17
(
2
),
177
184
. https://doi.org/10.1016/j.conb.2007.03.005
Clark
,
E. V.
(
2004
).
How language acquisition builds on cognitive development
.
Trends in Cognitive Sciences
,
8
(
10
),
472
478
. https://doi.org/10.1016/j.tics.2004.08.012
Cohen
,
N. J.
,
Poldrack
,
R. A.
, &
Eichenbaum
,
H.
(
1997
).
Memory for items and memory for relations in the procedural/declarative memory framework
.
Memory
,
5
(
1–2
),
131
178
. https://doi.org/10.1080/741941149
Cordier
,
A.
,
Mary
,
A.
,
Vander Ghinst
,
M.
,
Goldman
,
S.
,
De Tiège
,
X.
, &
Wens
,
V.
(
2024
).
The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks
.
Imaging Neuroscience
,
2
,
1
15
. https://doi.org/10.1162/imag_a_00231
Dale
,
A. M.
, &
Sereno
,
M. I.
(
1993
).
Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach
.
Journal of Cognitive Neuroscience
,
5
(
2
),
162
176
. https://doi.org/10.1162/jocn.1993.5.2.162
De Brigard
,
F.
,
Umanath
,
S.
, &
Irish
,
M.
(
2022
).
Rethinking the distinction between episodic and semantic memory: Insights from the past, present, and future
.
Memory & Cognition
,
50
(
3
),
459
463
. https://doi.org/10.3758/s13421-022-01299-x
de Pasquale
,
F.
,
Della Penna
,
S.
,
Snyder
,
A. Z.
,
Lewis
,
C.
,
Mantini
,
D.
,
Marzetti
,
L.
,
Belardinelli
,
P.
,
Ciancetta
,
L.
,
Pizzella
,
V.
,
Romani
,
G. L.
, &
Corbetta
,
M.
(
2010
).
Temporal dynamics of spontaneous MEG activity in brain networks
.
Proceedings of the National Academy of Sciences of the United States of America
,
107
(
13
),
6040
6045
. https://doi.org/10.1073/pnas.0913863107
De Tiège
,
X.
,
de Beeck
,
M. O.
,
Funke
,
M.
,
Legros
,
B.
,
Parkkonen
,
L.
,
Goldman
,
S.
, &
Van Bogaert
,
P
. (
2008
).
Recording epileptic activity with MEG in a light-weight magnetic shield
.
Epilepsy Research
,
82
(
2–3
),
227
231
. https://doi.org/10.1016/j.eplepsyres.2008.08.011
Deco
,
G.
, &
Corbetta
,
M.
(
2011
).
The dynamical balance of the brain at rest
.
The Neuroscientist
,
17
(
1
),
107
123
. https://doi.org/10.1177/1073858409354384
Dinges
,
D. F.
, &
Powell
,
J. W.
(
1985
).
Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations
.
Behavior Research Methods, Instruments, & Computers
,
17
(
6
),
652
655
. https://doi.org/10.3758/bf03200977
Eichenbaum
,
H.
(
1997
).
Declarative memory: Insights from cognitive neurobiology
.
Annual Review of Psychology
,
48
(
1
),
547
572
. https://doi.org/10.1146/annurev.psych.48.1.547
Eichenbaum
,
H.
(
2001
).
The hippocampus and declarative memory: Cognitive mechanisms and neural codes
.
Behavioural Brain Research
,
127
(
1–2
),
199
207
. https://doi.org/10.1016/S0166-4328(01)00365-5
Ellis
,
B. W.
,
Johns
,
M. W.
,
Lancaster
,
R.
,
Raptopoulos
,
P.
,
Angelopoulos
,
N.
, &
Priest
,
R. G.
(
1981
).
The St. Mary’s Hospital Sleep Questionnaire: A study of reliability
.
Sleep
,
4
(
1
),
93
97
. https://academic.oup.com/sleep/article/4/1/93/2750218
Engel
,
A. K.
,
Fries
,
P.
, &
Singer
,
W.
(
2001
).
Dynamic predictions: Oscillations and synchrony in top–down processing
.
Nature Reviews Neuroscience
,
2
(
10
),
704
716
. https://doi.org/10.1038/35094565
Engel
,
A. K.
,
Gerloff
,
C.
,
Hilgetag
,
C. C.
, &
Nolte
,
G.
(
2013
).
Intrinsic coupling modes: Multiscale interactions in ongoing brain activity
.
Neuron
,
80
(
4
),
867
886
. https://doi.org/10.1016/j.neuron.2013.09.038
Fell
,
J.
, &
Axmacher
,
N.
(
2011
).
The role of phase synchronization in memory processes
.
Nature Reviews Neuroscience
,
12
(
2
),
105
118
. https://doi.org/10.1038/nrn2979
Fischl
,
B.
(
2012
).
FreeSurfer
.
NeuroImage
,
62
(
2
),
774
781
. https://doi.org/10.1016/j.neuroimage.2012.01.021
Foxe
,
D.
,
Irish
,
M.
,
Roquet
,
D.
,
Scharfenberg
,
A.
,
Bradshaw
,
N.
,
Hodges
,
J. R.
,
Burrell
,
J. R.
, &
Piguet
,
O.
(
2020
).
Visuospatial short-term and working memory disturbance in the primary progressive aphasias: Neuroanatomical and clinical implications
.
Cortex
,
132
,
223
237
. https://doi.org/10.1016/j.cortex.2020.08.018
Garoff
,
R. J.
,
Slotnick
,
S. D.
, &
Schacter
,
D. L.
(
2005
).
The neural origins of specific and general memory: The role of the fusiform cortex
.
Neuropsychologia
,
43
(
6
),
847
859
. https://doi.org/10.1016/j.neuropsychologia.2004.09.014
Geng
,
F.
,
Redcay
,
E.
, &
Riggins
,
T.
(
2019
).
The influence of age and performance on hippocampal function and the encoding of contextual information in early childhood
.
NeuroImage
,
195
,
433
443
. https://doi.org/10.1016/j.neuroimage.2019.03.035
Gerraty
,
R. T.
,
Davidow
,
J. Y.
,
Wimmer
,
G. E.
,
Kahn
,
I.
, &
Shohamy
,
D.
(
2014
).
Transfer of learning relates to intrinsic connectivity between hippocampus, ventromedial prefrontal cortex, and large-scale networks
.
Journal of Neuroscience
,
34
(
34
),
11297
11303
. https://doi.org/10.1523/JNEUROSCI.0185-14.2014
Ghetti
,
S.
, &
Bunge
,
S. A.
(
2012
).
Neural changes underlying the development of episodic memory during middle childhood
.
Developmental Cognitive Neuroscience
,
2
(
4
),
381
395
. https://doi.org/10.1016/j.dcn.2012.05.002
Golby
,
A. J.
,
Poldrack
,
R. A.
,
Brewer
,
J. B.
,
Spencer
,
D.
,
Desmond
,
J. E.
,
Aron
,
A. P.
, &
Gabrieli
,
J. D. E.
(
2001
).
Material-specific lateralization in the medial temporal lobe and prefrontal cortex during memory encoding
.
Brain
,
124
(
9
),
1841
1854
. https://doi.org/10.1093/brain/124.9.1841
Goldberg
,
E.
, &
Podell
,
K.
(
1994
).
Lateralization of frontal lobe functions and cognitive novelty
.
The Journal of Neuropsychiatry and Clinical Neurosciences
,
6
(
4
),
371
378
. https://doi.org/10.1176/jnp.6.4.371
Goldberg
,
E.
, &
Podell
,
K.
(
1995
).
Lateralization in the frontal lobes: Searching the right (and left) way
.
Biological Psychiatry
,
38
(
9
),
569
571
. https://doi.org/10.1016/0006-3223(95)00445-8
Gramfort
,
A.
,
Luessi
,
M.
,
Larson
,
E.
,
Engemann
,
D. A.
,
Strohmeier
,
D.
,
Brodbeck
,
C.
,
Parkkonen
,
L.
, &
Hämäläinen
,
M. S.
(
2014
).
MNE software for processing MEG and EEG data
.
NeuroImage
,
86
,
446
460
. https://doi.org/10.1016/j.neuroimage.2013.10.027
Griffiths
,
B. J.
,
Martín-Buro
,
M. C.
,
Staresina
,
B. P.
, &
Hanslmayr
,
S.
(
2021
).
Disentangling neocortical alpha/beta and hippocampal theta/gamma oscillations in human episodic memory formation
.
NeuroImage
,
242
,
118454
. https://doi.org/10.1016/j.neuroimage.2021.118454
Hart
,
J.
,
Anand
,
R.
,
Zoccoli
,
S.
,
Maguire
,
M.
,
Gamino
,
J.
,
Tillman
,
G.
,
King
,
R.
, &
Kraut Michael
A
. (
2007
).
Neural substrates of semantic memory
.
Journal of the International Neuropsychological Society
,
13
(
5
),
865
880
. https://doi.org/10.1017/S135561770707110X
Heeger
,
D. J.
, &
Ress
,
D.
(
2002
).
What does fMRI tell us about neuronal activity?
Nature Reviews Neuroscience
,
3
(
2
),
142
151
. https://doi.org/10.1038/nrn730
Hipp
,
J. F.
,
Hawellek
,
D. J.
,
Corbetta
,
M.
,
Siegel
,
M.
, &
Engel
,
A. K.
(
2012
).
Large-scale cortical correlation structure of spontaneous oscillatory activity
.
Nature Neuroscience
,
15
(
6
),
884
890
. https://doi.org/10.1038/nn.3101
Jackson
,
R. L.
(
2021
).
The neural correlates of semantic control revisited
.
NeuroImage
,
224
,
117444
. https://doi.org/10.1016/j.neuroimage.2020.117444
Jaswal
,
V. K.
(
2006
).
Preschoolers favor the creator’s label when reasoning about an artifact’s function
.
Cognition
,
99
(
3
),
B83
B92
. https://doi.org/10.1016/j.cognition.2005.07.006
Jenkins
,
L. J.
, &
Ranganath
,
C.
(
2010
).
Prefrontal and medial temporal lobe activity at encoding predicts temporal context memory
.
Journal of Neuroscience
,
30
(
46
),
15558
15565
. https://doi.org/10.1523/JNEUROSCI.1337-10.2010
Johnson
,
E. L.
,
Yin
,
Q.
,
O’Hara
,
N. B.
,
Tang
,
L.
,
Jeong
,
J. W.
,
Asano
,
E.
, &
Ofen
,
N.
(
2022
).
Dissociable oscillatory theta signatures of memory formation in the developing brain
.
Current Biology
,
32
(
7
),
1457.e4
1469.e4
. https://doi.org/10.1016/j.cub.2022.01.053
Johnson
,
M. H.
(
2001
).
Functional brain development in humans
.
Nature Reviews Neuroscience
,
2
(
7
),
475
483
. https://doi.org/10.1038/35081509
Kahn
,
I.
,
Andrews-Hanna
,
J. R.
,
Vincent
,
J. L.
,
Snyder
,
A. Z.
, &
Buckner
,
R. L.
(
2008
).
Distinct cortical anatomy linked to subregions of the medial temporal lobe revealed by intrinsic functional connectivity
.
Journal of Neurophysiology
,
100
(
1
),
129
139
. https://doi.org/10.1152/jn.00077.2008
Kaplan
,
R.
,
Bush
,
D.
,
Bonnefond
,
M.
,
Bandettini
,
P. A.
,
Barnes
,
G. R.
,
Doeller
,
C. F.
, &
Burgess
,
N.
(
2014
).
Medial prefrontal theta phase coupling during spatial memory retrieval
.
Hippocampus
,
24
(
6
),
656
665
. https://doi.org/10.1002/hipo.22255
Karmiloff‐Smith
,
A.
(
1994
).
Beyond modularity: A developmental perspective on cognitive science
.
International Journal of Language & Communication Disorders
,
29
(
1
),
95
105
. https://doi.org/10.3109/13682829409041485
Karmiloff-Smith
,
A.
(
1998
).
Development itself is the key to understanding developmental disorders
.
Trends in Cognitive Sciences
,
2
(
10
),
389
398
. https://doi.org/10.1016/S1364-6613(98)01230-3
Kim
,
H.
(
2011
).
Neural activity that predicts subsequent memory and forgetting: A meta-analysis of 74 fMRI studies
.
NeuroImage
,
54
(
3
),
2446
2461
. https://doi.org/10.1016/j.neuroimage.2010.09.045
Leys
,
C.
,
Ley
,
C.
,
Klein
,
O.
,
Bernard
,
P.
, &
Licata
,
L.
(
2013
).
Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median
.
Journal of Experimental Social Psychology
,
49
(
4
),
764
766
. https://doi.org/10.1016/j.jesp.2013.03.013
Liu
,
Z.
,
Fukunaga
,
M.
,
de Zwart
,
J. A.
, &
Duyn
,
J. H.
(
2010
).
Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography
.
NeuroImage
,
51
(
1
),
102
111
. https://doi.org/10.1016/j.neuroimage.2010.01.092
Liuzzi
,
L.
,
Gascoyne
,
L. E.
,
Tewarie
,
P. K.
,
Barratt
,
E. L.
,
Boto
,
E.
, &
Brookes
,
M. J.
(
2017
).
Optimising experimental design for MEG resting state functional connectivity measurement
.
NeuroImage
,
155
,
565
576
. https://doi.org/10.1016/j.neuroimage.2016.11.064
Logothetis
,
N. K.
(
2008
).
What we can do and what we cannot do with fMRI
.
Nature
,
453
(
7197
),
869
878
. https://doi.org/10.1038/nature06976
López-Madrona
,
V. J.
,
Medina Villalon
,
S.
,
Badier
,
J. M.
,
Trébuchon
,
A.
,
Jayabal
,
V.
,
Bartolomei
,
F.
,
Carron
,
R.
,
Barborica
,
A.
,
Vulliémoz
,
S.
,
Alario
,
F. X.
, &
Bénar
,
C. G.
(
2022
).
Magnetoencephalography can reveal deep brain network activities linked to memory processes
.
Human Brain Mapping
,
43
(
15
),
4733
4749
. https://doi.org/10.1002/hbm.25987
Menon
,
V.
(
2013
).
Developmental pathways to functional brain networks: Emerging principles
.
Trends in Cognitive Sciences
,
17
(
12
),
627
640
. https://doi.org/10.1016/j.tics.2013.09.015
Moscovitch
,
M.
,
Cabeza
,
R.
,
Winocur
,
G.
, &
Nadel
,
L.
(
2016
).
Episodic memory and beyond: The hippocampus and neocortex in transformation
.
Annual Review of Psychology
,
67
,
105
134
. https://doi.org/10.1146/annurev-psych-113011-143733
Muthukumaraswamy
,
S. D.
, &
Singh
,
K. D.
(
2011
).
A cautionary note on the interpretation of phase-locking estimates with concurrent changes in power
.
Clinical Neurophysiology
,
122
(
11
),
2324
2325
. https://doi.org/10.1016/j.clinph.2011.04.003
Myronenko
,
A.
, &
Song
,
X.
(
2010
).
Point set registration: Coherent point drift
.
IEEE Transactions on Pattern Analysis and Machine Intelligence
,
32
(
12
),
2262
2275
. https://doi.org/10.1109/TPAMI.2010.46
Nebe
,
S.
,
Reutter
,
M.
,
Baker
,
D. H.
,
Bölte
,
J.
,
Domes
,
G.
,
Gamer
,
M.
,
Gärtner
,
A.
,
Gießing
,
C.
,
Gurr
,
C.
,
Hilger
,
K.
,
Jawinski
,
P.
,
Kulke
,
L.
,
Lischke
,
A.
,
Markett
,
S.
,
Meier
,
M.
,
Merz
,
C. J.
,
Popov
,
T.
,
Puhlmann
,
L. M. C.
,
Quintana
,
D. S.
,…
Feld
,
G. B
. (
2023
).
Enhancing precision in human neuroscience
.
eLife
,
12
,
e85980
. https://doi.org/10.7554/eLife.85980
Ofen
,
N.
(
2012
).
The development of neural correlates for memory formation
.
Neuroscience & Biobehavioral Reviews
,
36
(
7
),
1708
1717
. https://doi.org/10.1016/j.neubiorev.2012.02.016
Oostenveld
,
R.
,
Fries
,
P.
,
Maris
,
E.
, &
Schoffelen
,
J.-M.
(
2011
).
FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data
.
Computational Intelligence and Neuroscience
,
2011
,
1
9
. https://doi.org/10.1155/2011/156869
O’Shea
,
J.
, &
Walsh
,
V.
(
2006
).
Cognitive neuroscience: Trickle-down theories of vision
.
Current Biology
,
16
(
6
),
R206
R209
. https://doi.org/10.1016/j.cub.2006.02.030
Pascual-Marqui
,
R. D.
(
2002
).
Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details
.
Methods and Findings in Experimental and Clinical Pharmacology
,
24
Suppl D,
5
12
. https://www.uzh.ch/keyinst/loreta
Peiffer
,
A.
,
Brichet
,
M.
,
De Tiège
,
X.
,
Peigneux
,
P.
, &
Urbain
,
C.
(
2020
).
The power of children’s sleep—Improved declarative memory consolidation in children compared with adults
.
Scientific Reports
,
10
(
1
),
9979
. https://doi.org/10.1038/s41598-020-66880-3
Peiffer
,
A.
,
Roshchupkina
,
L.
,
Gander
,
S.
,
Rouge
,
C.
,
Peigneux
,
P.
, &
Urbain
,
C.
(
2021
).
Memory atlas
. https://osf.io/axhmw/?view_only=c6f4c94a2c0545cca332d3330f440769
Persson
,
J.
,
Stening
,
E.
,
Nordin
,
K.
, &
Söderlund
,
H.
(
2018
).
Predicting episodic and spatial memory performance from hippocampal resting-state functional connectivity: Evidence for an anterior-posterior division of function
.
Hippocampus
,
28
(
1
),
53
66
. https://doi.org/10.1002/hipo.22807
Quraan
,
M. A.
,
Moses
,
S. N.
,
Hung
,
Y.
,
Mills
,
T.
, &
Taylor
,
M. J.
(
2011
).
Detection and localization of hippocampal activity using beamformers with MEG: A detailed investigation using simulations and empirical data
.
Human Brain Mapping
,
32
(
5
),
812
827
. https://doi.org/10.1002/hbm.21068
Ranganath
,
C.
,
DeGutis
,
J.
, &
D’Esposito
,
M.
(
2004
).
Category-specific modulation of inferior temporal activity during working memory encoding and maintenance
.
Cognitive Brain Research
,
20
(
1
),
37
45
. https://doi.org/10.1016/j.cogbrainres.2003.11.017
Ranganath
,
C.
, &
Ritchey
,
M.
(
2012
).
Two cortical systems for memory-guided behaviour
.
Nature Reviews Neuroscience
,
13
(
10
),
713
726
. https://doi.org/10.1038/nrn3338
Riggins
,
T.
,
Geng
,
F.
,
Blankenship
,
S. L.
, &
Redcay
,
E.
(
2016
).
Hippocampal functional connectivity and episodic memory in early childhood
.
Developmental Cognitive Neuroscience
,
19
,
58
69
. https://doi.org/10.1016/j.dcn.2016.02.002
Schlaffke
,
L.
,
Schweizer
,
L.
,
Rüther
,
N. N.
,
Luerding
,
R.
,
Tegenthoff
,
M.
,
Bellebaum
,
C.
, &
Schmidt-Wilcke
,
T.
(
2017
).
Dynamic changes of resting state connectivity related to the acquisition of a lexico-semantic skill
.
NeuroImage
,
146
,
429
437
. https://doi.org/10.1016/j.neuroimage.2016.08.065
Seedat
,
Z. A.
,
Quinn
,
A. J.
,
Vidaurre
,
D.
,
Liuzzi
,
L.
,
Gascoyne
,
L. E.
,
Hunt
,
B. A. E.
,
O’Neill
,
G. C.
,
Pakenham
,
D. O.
,
Mullinger
,
K. J.
,
Morris
,
P. G.
,
Woolrich
,
M. W.
, &
Brookes
,
M. J.
(
2020
).
The role of transient spectral ‘bursts’ in functional connectivity: A magnetoencephalography study
.
NeuroImage
,
209
,
116537
. https://doi.org/10.1016/j.neuroimage.2020.116537
Sjøgård
,
M.
,
De Tiège
,
X.
,
Mary
,
A.
,
Peigneux
,
P.
,
Goldman
,
S.
,
Nagels
,
G.
,
van Schependom
,
J.
,
Quinn
,
A. J.
,
Woolrich
,
M. W.
, &
Wens
,
V.
(
2019
).
Do the posterior midline cortices belong to the electrophysiological default-mode network?
NeuroImage
,
200
,
221
230
. https://doi.org/10.1016/j.neuroimage.2019.06.052
Smith
,
S. M.
,
Fox
,
P. T.
,
Miller
,
K. L.
,
Glahn
,
D. C.
,
Fox
,
P. M.
,
Mackay
,
C. E.
,
Filippini
,
N.
,
Watkins
,
K. E.
,
Toro
,
R.
,
Laird
,
A. R.
, &
Beckmann
,
C. F.
(
2009
).
Correspondence of the brain’s functional architecture during activation and rest
.
Proceedings of the National Academy of Sciences of the United States of America
,
106
(
31
),
13040
13045
. https://doi.org/10.1073/pnas.0905267106
Solomon
,
E. A.
,
Kragel
,
J. E.
,
Sperling
,
M. R.
,
Sharan
,
A.
,
Worrell
,
G.
,
Kucewicz
,
M.
,
Inman
,
C. S.
,
Lega
,
B.
,
Davis
,
K. A.
,
Stein
,
J. M.
,
Jobst
,
B. C.
,
Zaghloul
,
K. A.
,
Sheth
,
S. A.
,
Rizzuto
,
D. S.
, &
Kahana
,
M. J.
(
2017
).
Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition
.
Nature Communications
,
8
(
1
),
1704
. https://doi.org/10.1038/s41467-017-01763-2
Squire
,
L. R.
(
2004
).
Memory systems of the brain: A brief history and current perspective
.
Neurobiology of Learning and Memory
,
82
(
3
),
171
177
. https://doi.org/10.1016/j.nlm.2004.06.005
Staresina
,
B. P.
, &
Davachi
,
L.
(
2010
).
Object unitization and associative memory formation are supported by distinct brain regions
.
Journal of Neuroscience
,
30
(
29
),
9890
9897
. https://doi.org/10.1523/JNEUROSCI.0826-10.2010
Staresina
,
B. P.
, &
Wimber
,
M.
(
2019
).
A neural chronometry of memory recall
.
Trends in Cognitive Sciences
,
23
(
12
),
1071
1085
. https://doi.org/10.1016/j.tics.2019.09.011
Stillman
,
C. M.
,
Gordon
,
E. M.
,
Simon
,
J. R.
,
Vaidya
,
C. J.
,
Howard
,
D. V.
, &
Howard
,
J. H.
(
2013
).
Caudate resting connectivity predicts implicit probabilistic sequence learning
.
Brain Connectivity
,
3
(
6
),
601
610
. https://doi.org/10.1089/brain.2013.0169
Takashima
,
A.
,
Nieuwenhuis
,
I. L. C.
,
Jensen
,
O.
,
Talamini
,
L. M.
,
Rijpkema
,
M.
, &
Fernández
,
G.
(
2009
).
Shift from hippocampal to neocortical centered retrieval network with consolidation
.
Journal of Neuroscience
,
29
(
32
),
10087
10093
. https://doi.org/10.1523/JNEUROSCI.0799-09.2009
Tang
,
L.
,
Shafer
,
A. T.
, &
Ofen
,
N.
(
2018
).
Prefrontal cortex contributions to the development of memory formation
.
Cerebral Cortex
,
28
(
9
),
3295
3308
. https://doi.org/10.1093/cercor/bhx200
Taulu
,
S.
,
Simola
,
J.
, &
Kajola
,
M.
(
2005
).
Applications of the signal space separation method
.
IEEE Transactions on Signal Processing
,
53
(
9
),
3359
3372
. https://doi.org/10.1109/TSP.2005.853302
The jamovi project
. (
2022
).
jamovi
. (Version 2.3) [Computer Software]. Retrieved from https://www.jamovi.org.
Tulving
,
E.
(
2002
).
Episodic memory: From mind to brain
.
Annual Review of Psychology
,
53
(
1
),
1
25
. https://doi.org/10.1146/annurev.psych.53.100901.135114
Tzourio-Mazoyer
,
N.
,
Landeau
,
B.
,
Papathanassiou
,
D.
,
Crivello
,
F.
,
Etard
,
O.
,
Delcroix
,
N.
,
Mazoyer
,
B.
, &
Joliot
,
M.
(
2002
).
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
.
NeuroImage
,
15
(
1
),
273
289
. https://doi.org/10.1006/nimg.2001.0978
Uddin
,
L. Q.
(
2010
).
Typical and atypical development of functional human brain networks: Insights from resting-state fMRI
.
Frontiers in Systems Neuroscience
,
4
,
21
. https://doi.org/10.3389/fnsys.2010.00021
Uddin
,
L. Q.
,
Supekar
,
K. S.
,
Ryali
,
S.
, &
Menon
,
V.
(
2011
).
Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development
.
Journal of Neuroscience
,
31
(
50
),
18578
18589
. https://doi.org/10.1523/JNEUROSCI.4465-11.2011
Urbain
,
C.
,
Bourguignon
,
M.
,
Op de Beeck
,
M.
,
Schmitz
,
R.
,
Galer
,
S.
,
Wens
,
V.
,
Marty
,
B.
,
De Tiège
,
X.
,
Van Bogaert
,
P.
, &
Peigneux
,
P.
(
2013
).
MEG correlates of learning novel objects properties in children
.
PLoS One
,
8
(
7
),
e69696
. https://doi.org/10.1371/journal.pone.0069696
Urbain
,
C.
,
De Tiège
,
X.
,
Op De Beeck
,
M.
,
Bourguignon
,
M.
,
Wens
,
V.
,
Verheulpen
,
D.
,
Van Bogaert
,
P.
, &
Peigneux
,
P.
(
2016
).
Sleep in children triggers rapid reorganization of memory-related brain processes
.
NeuroImage
,
134
,
213
222
. https://doi.org/10.1016/j.neuroimage.2016.03.055
Urbain
,
C.
,
Pang
,
E. W.
, &
Taylor
,
M. J.
(
2015
).
Atypical spatiotemporal signatures of working memory brain processes in autism
.
Translational Psychiatry
,
5
(
8
),
e617
e617
. https://doi.org/10.1038/tp.2015.107
Urbain
,
C.
,
Sato
,
J.
,
Pang
,
E. W.
, &
Taylor
,
M. J.
(
2017
).
The temporal and spatial brain dynamics of automatic emotion regulation in children
.
Developmental Cognitive Neuroscience
,
26
,
62
68
. https://doi.org/10.1016/j.dcn.2017.05.004
Urbain
,
C.
,
Vogan
,
V. M.
,
Ye
,
A. X.
,
Pang
,
E. W.
,
Doesburg
,
S. M.
, &
Taylor
,
M. J.
(
2016
).
Desynchronization of fronto-temporal networks during working memory processing in autism
.
Human Brain Mapping
,
37
(
1
),
153
164
. https://doi.org/10.1002/hbm.23021
Van Dyck
,
D.
,
Deconinck
,
N.
,
Aeby
,
A.
,
Baijot
,
S.
,
Coquelet
,
N.
,
Trotta
,
N.
,
Rovai
,
A.
,
Goldman
,
S.
,
Urbain
,
C.
,
Wens
,
V.
, &
De Tiège
,
X
. (
2021
).
Resting-state functional brain connectivity is related to subsequent procedural learning skills in school-aged children
.
NeuroImage
,
240
,
118368
. https://doi.org/10.1016/j.neuroimage.2021.118368
Vigario
,
R.
,
Sarela
,
J.
,
Jousmiki
,
V.
,
Hamalainen
,
M.
, &
Oja
,
E.
(
2000
).
Independent component approach to the analysis of EEG and MEG recordings
.
IEEE Transactions on Biomedical Engineering
,
47
(
5
),
589
593
. https://doi.org/10.1109/10.841330
Vincent
,
J. L.
,
Snyder
,
A. Z.
,
Fox
,
M. D.
,
Shannon
,
B. J.
,
Andrews
,
J. R.
,
Raichle
,
M. E.
, &
Buckner
,
R. L.
(
2006
).
Coherent spontaneous activity identifies a hippocampal-parietal memory network
.
Journal of Neurophysiology
,
96
(
6
),
3517
3531
. https://doi.org/10.1152/jn.00048.2006
Wagner
,
A. D.
,
Paré-Blagoev
,
E. J.
,
Clark
,
J.
, &
Poldrack
,
R. A.
(
2001
).
Recovering meaning: Left prefrontal cortex guides controlled semantic retrieval
.
Neuron
,
31
(
2
),
329
338
. https://doi.org/10.1016/S0896-6273(01)00359-2
Wagner
,
A. D.
,
Poldrack
,
R. A.
,
Eldridge
,
L. L.
,
Desmond
,
J. E.
,
Glover
,
G. H.
, &
E.
Gabrieli
,
J.
D
. (
1998
).
Material-specific lateralization of prefrontal activation during episodic encoding and retrieval
.
NeuroReport
,
9
(
16
),
3711
3717
. https://doi.org/10.1097/00001756-199811160-00026
Wang
,
L.
,
LaViolette
,
P.
,
O’Keefe
,
K.
,
Putcha
,
D.
,
Bakkour
,
A.
,
Van Dijk
,
K. R. A.
,
Pihlajamäki
,
M.
,
Dickerson
,
B. C.
, &
Sperling
,
R. A.
(
2010
).
Intrinsic connectivity between the hippocampus and posteromedial cortex predicts memory performance in cognitively intact older individuals
.
NeuroImage
,
51
(
2
),
910
917
. https://doi.org/10.1016/j.neuroimage.2010.02.046
Wang
,
L.
,
Negreira
,
A.
,
LaViolette
,
P.
,
Bakkour
,
A.
,
Sperling
,
R. A.
, &
Dickerson
,
B. C.
(
2010
).
Intrinsic interhemispheric hippocampal functional connectivity predicts individual differences in memory performance ability
.
Hippocampus
,
20
(
3
),
345
351
. https://doi.org/10.1002/hipo.20771
Warren
,
D. E.
,
Rangel
,
A. J.
,
Christopher-Hayes
,
N. J.
,
Eastman
,
J. A.
,
Frenzel
,
M. R.
,
Stephen
,
J. M.
,
Calhoun
,
V. D.
,
Wang
,
Y. P.
, &
Wilson
,
T. W.
(
2021
).
Resting-state functional connectivity of the human hippocampus in periadolescent children: Associations with age and memory performance
.
Human Brain Mapping
,
42
(
11
),
3620
3642
. https://doi.org/10.1002/hbm.25458
Wens
,
V.
(
2015
).
Investigating complex networks with inverse models: Analytical aspects of spatial leakage and connectivity estimation
.
Physical Review E—Statistical, Nonlinear, and Soft Matter Physics
,
91
(
1
),
012823
. https://doi.org/10.1103/PhysRevE.91.012823
Wens
,
V.
(
2023
).
Exploring the limits of MEG spatial resolution with multipolar expansions
.
NeuroImage
,
270
,
119953
. https://doi.org/10.1016/j.neuroimage.2023.119953
Wens
,
V.
,
Marty
,
B.
,
Mary
,
A.
,
Bourguignon
,
M.
,
Op de Beeck
,
M.
,
Goldman
,
S.
,
Van Bogaert
,
P.
,
Peigneux
,
P.
, &
De Tiège
,
X
. (
2015
).
A geometric correction scheme for spatial leakage effects in MEG/EEG seed-based functional connectivity mapping
.
Human Brain Mapping
,
36
(
11
),
4604
4621
. https://doi.org/10.1002/hbm.22943
Wens
,
V.
,
Mary
,
A.
,
Bourguignon
,
M.
,
Goldman
,
S.
,
Marty
,
B.
,
Op de Beeck
,
M.
,
Van Bogaert
,
P.
,
Peigneux
,
P.
, &
De Tiège
,
X
. (
2014
).
About the electrophysiological basis of resting state networks
.
Clinical Neurophysiology
,
125
(
8
),
1711
1713
. https://doi.org/10.1016/j.clinph.2013.11.039
Whatmough
,
C.
,
Chertkow
,
H.
,
Murtha
,
S.
, &
Hanratty
,
K.
(
2002
).
Dissociable brain regions process object meaning and object structure during picture naming
.
Neuropsychologia
,
40
(
2
),
174
186
. https://doi.org/10.1016/S0028-3932(01)00083-5
Xia
,
M.
,
Wang
,
J.
, &
He
,
Y.
(
2013
).
BrainNet viewer: A network visualization tool for human brain connectomics
.
PLoS One
,
8
(
7
),
e68910
. https://doi.org/10.1371/journal.pone.0068910
Yee
,
E.
,
Drucker
,
D. M.
, &
Thompson-Schill
,
S. L.
(
2010
).
fMRI-adaptation evidence of overlapping neural representations for objects related in function or manipulation
.
NeuroImage
,
50
(
2
),
753
763
. https://doi.org/10.1016/j.neuroimage.2009.12.036
Zalesky
,
A.
,
Fornito
,
A.
, &
Bullmore
,
E. T.
(
2010
).
Network-based statistic: Identifying differences in brain networks
.
NeuroImage
,
53
(
4
),
1197
1207
. https://doi.org/10.1016/j.neuroimage.2010.06.041
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.