. 2025 Sep 9;48(9):zsaf117.

doi: 10.1093/sleep/zsaf117.

Individual differences in medial temporal lobe functional network architecture predict the capacity for sleep-related consolidation of emotional memories in older adults

Miranda G Chappel-Farley^{1

2}, Jenna N Adams^{1

2}, Richard F Betzel³, Negin S Sattari⁴, Destiny E Berisha^{1

2}, Novelle J Meza^{1

2}, John T Janecek^{1

2}, Hamid Niknazar⁵, Soyun Kim^{1

2}, Abhishek Dave^{4

5}, Ivy Y Chen⁴, Kitty K Lui⁶, Ariel B Neikrug⁴, Ruth M Benca^{1

2

4

7

8

9

10}, Michael A Yassa^{1

2

4

10

11}, Bryce A Mander^{2

4

5

10

12}

Affiliations

¹ Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, USA.
² Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA.
³ Department of Psychological and Brain Sciences, University of Indiana Bloomington, Bloomington, IN, USA.
⁴ Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.
⁵ Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA.
⁶ Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, CA, USA.
⁷ Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA.
⁸ Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA.
⁹ Department of Psychiatry and Behavioral Medicine, Wake Forest University, Winston-Salem, NC, USA.
¹⁰ Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, CA, USA.
¹¹ Department of Neurology, University of California Irvine, Irvine, CA, USA.
¹² Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, CA, USA.

PMID: 40442306
PMCID: PMC12417020
DOI: 10.1093/sleep/zsaf117

Individual differences in medial temporal lobe functional network architecture predict the capacity for sleep-related consolidation of emotional memories in older adults

Miranda G Chappel-Farley et al. Sleep. 2025.

. 2025 Sep 9;48(9):zsaf117.

doi: 10.1093/sleep/zsaf117.

Authors

Affiliations

¹ Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, USA.
² Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA.
³ Department of Psychological and Brain Sciences, University of Indiana Bloomington, Bloomington, IN, USA.
⁴ Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.
⁵ Department of Cognitive Sciences, University of California Irvine, Irvine, CA, USA.
⁶ Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, CA, USA.
⁷ Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA.
⁸ Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA.
⁹ Department of Psychiatry and Behavioral Medicine, Wake Forest University, Winston-Salem, NC, USA.
¹⁰ Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, CA, USA.
¹¹ Department of Neurology, University of California Irvine, Irvine, CA, USA.
¹² Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, CA, USA.

PMID: 40442306
PMCID: PMC12417020
DOI: 10.1093/sleep/zsaf117

Abstract

Study objectives: Memory consolidation during non-rapid eye movement slow wave sleep (NREM SWS) involves reactivation of a hippocampal index which integrates distributed cortical representations of an experience. This study examined whether individual differences in brain network properties that align with this theoretical organization were associated with interindividual variability in memory consolidation during sleep.

Methods: Thirty-six older adults (μage = 72.9 ± 5.6) completed overnight polysomnography with pre- and post-sleep emotional memory testing. Slow oscillation (SO) power and SO-sleep spindle (SO-SP) coupling were computed. Participants also completed 3T structural and resting-state functional magnetic resonance imaging (rsfMRI). Resting-state fMRI network modularity (Q), hippocampal and amygdala eigenvector centrality (EC), and betweenness centrality (BC) were calculated.

Results: Greater Q was associated with reduced emotional memory retention and greater SWS. Hippocampal and amygdala EC were associated with better emotional memory retention. Hippocampal BC was associated with immediate test performance, whereas amygdala BC was associated with delayed test and memory retention. Hippocampal centrality measures were associated with SO power and SO-SP coupling. Random forest classification revealed distinct network measure combinations predict different stages of memory processing.

Conclusions: Network integration supports overnight emotional memory retention. Individuals with more influential medial temporal lobe nodes exhibited better memory consolidation, suggesting functional network architecture contributes to memory processing. These measures were associated with sleep oscillations implicated in consolidation. Individual differences in functional network organization may predict the capacity of older adults to consolidate memories.

Keywords: consolidation; graph theory; memory; networks; resting-state; sleep.

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Figures

**Figure 1.**
Emotional mnemonic discrimination task. The eMDT consists of three phases: incidental encoding and a surprise immediate test phase prior to sleep, and a delayed test phase following overnight sleep. During the incidental encoding phase, participants are shown a series of emotionally salient images and asked to rate them as positive, negative, or neutral via button press. Following encoding, participants perform an old/new recognition paradigm and are shown targets, foils, and high/low similarity lures modulated by valence. Following overnight sleep, participants are again shown targets, foils, and lures and are asked to indicate via button press whether it is an old or new image. Images from the incidental encoding phase are randomly split into the immediate and delayed test phases. ISI, inter stimulus interval; Neg, negative; Neu, neutral; Pos, positive; LSim, low similarity; HSim, high similarity; Targ, target.

**Figure 2.**
More modular brain networks express more NREM SWS sleep and exhibit worse overnight memory retention in older adults. (A) Schematic of network modularity. (B) Greater network modularity is associated with a greater percentage of sleep time spent in NREM SWS while adjusting for age, sex, and AHI. (C) A greater percentage of sleep time spent in NREM SWS is positively associated with overnight emotional memory retention, adjusting for age, sex, AHI, and Q. (D) Greater network modularity is negatively associated with overnight emotional memory retention, adjusting for age, sex, AHI, and percentage of sleep time spent in NREM SWS. The shaded area corresponds to the 95% confidence interval.

**Figure 3.**
Hippocampal-amygdala functional network hierarchy supports emotional memory retention in older adults. (A) Schematic of eigenvector centrality. (B) Greater hippocampal eigenvector centrality is positively associated with overnight emotional memory retention, adjusting for age, sex, and AHI. (C) Greater amygdala eigenvector centrality is positively associated with overnight emotional memory retention, adjusting for age, sex, and AHI. The shaded area corresponds to the 95% confidence interval.

**Figure 4.**
Betweenness centrality identifies distinct roles for the hippocampus and amygdala in emotional memory acquisition, consolidation, and delayed retrieval in older adults. (A) Schematic of betweenness centrality. (B) Greater hippocampal betweenness centrality is positively associated with emotional memory performance at immediate test, adjusting for age, sex, and AHI. (C) Higher amygdala betweenness centrality (cube-root transformed) is positively associated with overnight emotional memory retention and (D) emotional memory performance at delayed test, adjusting for age, sex, and AHI. The shaded area corresponds to the 95% confidence interval.

**Figure 5.**
Random forest classification identifies the most important graph features for sleep-related memory consolidation in older adults. (A) ROC and Gini Impurity Index output for top 3 predictors of overnight negative memory retention (LDI). (B) ROC and Gini Impurity Index output for delayed test performance for negative LDI. (C) ROC and Gini Impurity Index output for top 3 predictors of immediate test performance for negative LDI. The shaded area corresponds to the 95% bootstrapped confidence intervals. ROC, receiver operating characteristic curve; AUC, area under the curve; EC, eigenvector centrality; BC, betweenness centrality; LDI, Lure discrimination index.

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Update of

Medial temporal lobe functional network architecture supports sleep-related emotional memory processing in older adults.
Chappel-Farley MG, Adams JN, Betzel RF, Janecek JC, Sattari NS, Berisha DE, Meza NJ, Niknazar H, Kim S, Dave A, Chen IY, Lui KK, Neikrug AB, Benca RM, Yassa MA, Mander BA. Chappel-Farley MG, et al. bioRxiv [Preprint]. 2023 Nov 1:2023.10.27.564260. doi: 10.1101/2023.10.27.564260. bioRxiv. 2023. Update in: Sleep. 2025 Sep 9;48(9):zsaf117. doi: 10.1093/sleep/zsaf117. PMID: 37961192 Free PMC article. Updated. Preprint.

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Individual differences in medial temporal lobe functional network architecture predict the capacity for sleep-related consolidation of emotional memories in older adults

Affiliations

Individual differences in medial temporal lobe functional network architecture predict the capacity for sleep-related consolidation of emotional memories in older adults

Authors

Affiliations

Abstract

Figures

Update of

References

MeSH terms

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