Hippocampal maturity promotes memory distinctiveness in childhood and adolescence

Abstract

Adaptive learning systems need to meet two complementary and partially conflicting goals: detecting regularities in the world versus remembering specific events. The hippocampus (HC) keeps a fine balance between computations that extract commonalities of incoming information (i.e., pattern completion) and computations that enable encoding of highly similar events into unique representations (i.e., pattern separation). Histological evidence from young rhesus monkeys suggests that HC development is characterized by the differential development of intrahippocampal subfields and associated networks. However, due to challenges in the in vivo investigation of such developmental organization, the ontogenetic timing of HC subfield maturation remains controversial. Delineating its course is important, as it directly influences the fine balance between pattern separation and pattern completion operations and, thus, developmental changes in learning and memory. Here, we relate in vivo, high-resolution structural magnetic resonance imaging data of HC subfields to behavioral memory performance in children aged 6-14 y and in young adults. We identify a multivariate profile of age-related differences in intrahippocampal structures and show that HC maturity as captured by this pattern is associated with age differences in the differential encoding of unique memory representations.

Keywords: child development; episodic memory; hippocampal subfields; pattern separation; specificity.

Fig. 1.

Age-related differences in hippocampal structures suggest differential maturational trends that extend well into middle childhood and beyond. (A) Three ROIs, limited to HC body, comprising the CA regions 1 and 2, CA3 and DG, and Sub, and one ROI comprising EC were manually demarcated on MR slices to obtain volumetric measures for HC subfields (Methods and SI Methods). (B) Linear regression models fitted on the relationship of ROI volumes with age reveal a complex pattern with differential and often nonlinear maturational tracks in the different ROIs. Including a quadratic term improved fit for DG-CA3 and Sub. Confidence bounds capture 95% confidence intervals.

Fig. 2.

A specific multivariate profile of HC subfields is associated with age. (A) Example ROIs defined by manual tracings. (B) Latent variable weights (brain saliences) for each ROI used to transform original volumetric data of each participant into one latent variable expressing the largest amount of information common to the multivariate pattern of ROI volumes and age. Z score-like values of stability suggest a positive relationship between DG-CA3 and CA1-2 and age, and a negative relationship between EC and Sub and age, and also show that DG-CA3 and EC are the most stable elements of the weight vector. (C) The resulting latent variable, termed HC maturity score, plotted against age with least-squares line (dashed). The large overlap of HC-maturity scores between age groups (defined arbitrarily for illustrative purposes) underscores that chronological age only partly relates to differences in HC maturity.

Fig. S2.

Related to Figs. 1 and 2. Voxel-based morphometry reveals widespread cortical but no mediotemporal areas showing age-related differences in gray matter volume. Gray matter volume estimates were extracted by using the standard preprocessing pipeline of the CAT12 toolbox (dbm.neuro.uni-jena.de/cat) run in SPM12 (Wellcome Trust Centre for Neuroimaging, London, www.fil.ion.ucl.ac.uk/spm) (version 6906). In brief, T1-weighted (FOV: 256 mm, TR: 2,500 ms; TE: 3.69 ms; voxel size: 1 mm × 1 mm × 1 mm) images of each participant were normalized to template space, then segmented into tissue classes of gray and white matter, and cerebrospinal fluid. After checking sample homogeneity, we excluded one brain that had extremely low average pairwise correlation to all other brains (2 SDs below mean) and visual quality check confirmed that the T1 image was too noisy to be used. Resulting images were then smoothed with a kernel of 8 mm (FWHM) by using SPM12. Voxelwise gray matter volumetric estimates were ICV corrected by using the same approach as for our HC analysis (see description in the main text section Delineating ROIs in the MTL). Similarly to the extraction of HC-maturity and frontal maturity scores, we extracted one significant LV (P < 0.001) that optimally (in a least-squares sense) represents the associations between age and voxelwise gray matter volume estimates. BSR calculated for the resulting LV for each voxel in the brain was mapped to and is visualized here on MNI space. The scale represents voxelwise BSR. Importantly, BSR values indicate that despite the widespread age-related differences in several frontal and parietal regions, no voxels showed reliable age-related difference in the MTL. Suggesting that total HC measures do not show age-related change in our sample. A similar analysis restricted to bilateral HC ROIs defined by the lpba40 Atlas (54) was performed to further explore any potential age-related differences in total HC. Again, PLSC failed to extract an optimal and generalizable latent variable structure expressing the association between age and HC (P = 0.23).

Fig. 3.

Multidimensional structural maturity of the hippocampus is associated with memory processes enabling the unique encoding of similar representations. (A) Mnemonic similarity task used to assess pattern separation/completion bias. After incidentally encoding pictures of everyday objects, in a recognition task, participants saw the same target pictures intermixed with highly similar lures and novel foils. Their task was to identify image types by responding “old,” “similar,” or “new.” Pattern separation/completion bias was calculated by subtracting the proportion of similar responses to foils from the proportion of similar responses to lures. The resulting score weighs the tendency to encode two highly similar inputs into separate mnemonic representations against the tendency to assimilate the incoming information to already existing mnemonic representations. Trials that were responded either similar or old were followed by a source memory decision trial (not depicted; see SI Methods for details of material, design, and procedure). (B) Increasing HC maturity is associated with a shift in bias toward pattern separation. (C) The faces-and-names task used to assess item and associative memory. After incidentally encoding face-name pairs, participants performed an old/new recognition task composed of three tests administered in a counterbalanced order. Performance on the two item tests was merged to provide one item memory score. Hits and false alarms were calculated for both item and associative memory (SI Methods for details). (D) Increasing HC maturity is related to a decrease in false item recognition. (B and D) Dashed lines represent least-square lines. Different shades of gray represent different age segments to illustrate that the HC maturity–behavior associations hold across age.

Fig. 4.

(A) Six frontal ROIs defined by the lpba40 atlas (54). SupFroG, MidFroG, InfFroG: Superior, Middle, and Inferior frontal gyrus; MidOrbG, LatOrbG: Middle and Lateral orbitofrontal gyrus, RecG: gyrus rectus. (B) Latent variable weights (brain saliences) for each ROI used to transform individual GM volumetric estimates, extracted using VBM, into one latent variable expressing the largest amount of information common to the multivariate pattern of GM and age. Z score-like values of stability suggest a negative relationship between all ROIs and age. (C) The resulting latent variable, termed frontal maturity score, plotted against age. Increasing frontal maturity related to an increase in source memory accuracy (D) and increase in correct item recognition (E). (C–E) Dashed lines represent least-square lines.

Fig. S1.

Related to Fig. 3, univariate analyses reveal a complex picture of age-related differences in various memory processes. Scatterplots showing performance (in percent) on memory scores derived from the two behavioral tasks (Fig. 3 and Methods). Red lines represent linear regression models fit on the relationship of performance with age. Dashed red lines represent 95% confidence intervals. (A) Pattern separation/completion bias showed a marginally significant linear age trend (R² = 0.03, p_β = 0.054), and a significant age trend when a quadratic term was included (R² = 0.1, p_{β linear} = 0.002, p_{β quadratic} = 0.004). (B) Source memory (percent of correct source identifications for items classified as either old or similar) showed a significant linear age trend (R² = 0.09, p_β = 0.001). (C) Hits in item memory recognition (percent of old items correctly identified as old) showed a significant linear age trend (R² = 0.21, p_β < 0.001). Adding a quadratic term to the regression (R² = 0.24, p_{β linear} = 0.005, p_{β quadratic} = 0.03) increased fit significantly. (D) False alarms in item memory recognition (percent of new items incorrectly identified as old) showed a significant negative linear age trend (R² = 0.04, p_β < 0.02). (E) Hits in associative memory (percent of old pairs correctly identified as old) showed a significant age trend when both linear and quadratic terms were included (R² = 0.07, p_{β linear} = 0.008, p_{β quadratic} = 0.015). (F) False alarms in associative memory (percent of rearranged pairs incorrectly identified as old) also showed a significant age trend when both linear and quadratic terms were included (R² = 0.04, p_{β linear} = 0.032, p_{β quadratic} = 0.047).