Disruption of hippocampal-prefrontal neural dynamics and risky decision-making in a mouse model of Alzheimer’s disease

Eun Joo Kim; Sanggeon Park; Bryan P Schuessler; Harry Boo; Jeiwon Cho; Jeansok J Kim

doi:10.1016/j.celrep.2025.116081

. Author manuscript; available in PMC: 2025 Sep 23.

Published in final edited form as: Cell Rep. 2025 Jul 29;44(8):116081. doi: 10.1016/j.celrep.2025.116081

Disruption of hippocampal-prefrontal neural dynamics and risky decision-making in a mouse model of Alzheimer’s disease

Eun Joo Kim ^1,^6,^*, Sanggeon Park ^3,^4,⁶, Bryan P Schuessler ^1,⁵, Harry Boo ¹, Jeiwon Cho ^3,^*, Jeansok J Kim ^1,^2,^7,^*

PMCID: PMC12451995 NIHMSID: NIHMS2107271 PMID: 40742807

SUMMARY

This study investigates how amyloid pathology influences hippocampal-prefrontal neural dynamics and decision-making in Alzheimer’s disease (AD) using 5XFAD mice, a well-established model system characterized by pronounced early amyloid pathology. Utilizing ecologically relevant “approach food-avoid predator” foraging tasks, we show that 5XFAD mice exhibit persistent risk-taking behaviors and reduced adaptability to changing threat conditions, indicative of impaired decision-making. Multi-regional neural recordings reveal rigid hippocampal CA1 place cell fields, decreased sharp-wave ripple (SWR) frequencies, and disrupted medial prefrontal-hippocampal connectivity, all of which correspond with deficits in behavioral flexibility during spatial risk scenarios. These findings highlight the critical role of SWR dynamics and corticolimbic circuit integrity in adaptive decision-making, with implications for understanding cognitive decline in AD in naturalistic contexts. By identifying specific neural disruptions underlying risky decision-making deficits, this work provides insights into the neural basis of cognitive dysfunction in AD and suggests potential targets for therapeutic intervention.

Graphical Abstract

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In brief

Kim et al. show that 5XFAD mice exhibit persistent risk-taking and reduced behavioral flexibility in naturalistic “approach food-avoid predator” foraging paradigms. Simultaneous neural recordings reveal rigid place cell coding, impaired SWR-associated corticolimbic coordination, and disrupted hippocampal-prefrontal synchrony, linking Alzheimer’s disease pathology to deficits in adaptive decision-making.

INTRODUCTION

Alzheimer’s disease (AD) research has traditionally focused on memory impairment, yet growing evidence shows that AD also disrupts decision-making under risk and ambiguity,^1–10 functions essential for daily life. Notably, decision-making deficits may precede memory loss,^11–20 with early signs like reduced scam awareness and impaired financial judgment linked to amyloid accumulation and heightened AD risk.^11,19,21 Neuroeconomic studies in patients with early- to moderate-stage AD show impairments in risky decision-making,^20,22 suggesting that this domain may serve as an early behavioral marker. Unlike basic memory processes, decision-making integrates spatial context, emotional regulation, and experiences.^23–25 However, preclinical models remain limited²⁶ due to a lack of naturalistic behavioral paradigms.

Although AD pathology is generally defined by β-amyloid (Aβ) plaques and tau tangles, how these changes affect real-time neural circuit functions during behavior remains poorly understood. This gap persists despite the limited success of Aβ-targeted therapies^27–29 and evidence of cognitive resilience despite amyloid pathology.^30–32 These findings highlight the need to examine how AD disrupts neural connectivity during behavior.^33–35

The medial prefrontal cortex (mPFC) and the dorsal hippocampus (dHPC) form a key circuit for decision-making. Damage or inactivation of either region impairs risk-based behavior in rodents, monkeys, and humans.^36–41 fMRI studies in humans and single-unit recordings in animals performing probabilistic/delay-discounting tasks link decision-making to mPFC and dHPC activity.^38,42 Patients with AD also show deficits in financial decision-making²² and set-shifting tasks,³ attributed to Aβ plaques and tau tangles in the dHPC and mPFC–regions particularly vulnerable to AD.

Despite these insights, risky decision-making within ecologically relevant paradigms and mPFC-dHPC dynamics remain unexplored in AD models. Our prior work in rats documented neural activity during goal-directed foraging in risky environments.^43,44 We found that dHPC place cells encoded spatial-danger gradients, with unstable firing near a predatory robot but stable firing near the safe nest, highlighting the dHPC’s role in spatial-danger coding. Concurrently, mPFC neurons tracked food procurement and exhibited heightened activity during cautious approaches to a stationary predator, underscoring the mPFC’s role in threat assessment and decision-making. These findings highlight the value of naturalistic foraging paradigms to investigate risky choices^3,5,45 and mPFC-dHPC activity in AD models.

Here, we apply this approach to the 5XFAD transgenic mouse model, which develops Aβ pathology from ∼4 months due to overexpression of amyloid precursor protein (APP) and presenilin 1 (PS1) mutations.^46–49 We focus on dHPC sharp-wave ripples (SWRs)–brief, high-frequency (150–250 Hz) oscillations linked to memory, emotion, and deliberation^50,51–and their coordination with mPFC activity.^52–54 SWRs are reduced in APP/PS1,⁵⁵ apolipoprotein E4 knockin (KI),^56,57 and 5XFAD⁵⁸ models, with recent evidence linking these deficits to hippocampal inhibitory synaptic dysfunction.⁵⁹

By leveraging naturalistic environments that influence and reshape neural activity and behavior,^60–63 we adapted the rat “approach food-avoid predator” conflict paradigm^64,65 to the 5XFAD model. This platform enables a comprehensive analysis of how AD affects cognition and mPFC-dHPC coordination during risky decision-making. Our study aims to provide new insights into the neural dynamics and behavioral alterations associated with risky decision-making in AD.

RESULTS

5XFAD mice exhibit risk-prone foraging with escalating predatory risk

5XFAD and wild-type (WT) mice were tested in a foraging task in which predatory risk increased with distance from a safe nest (Figure 1). The task consisted of three phases: nest habituation, baseline foraging, and predator testing. During baseline foraging, both 5XFAD and WT mice left the nest and retrieved food pellets in a large open arena (104 × 45 × 61 cm) with similar efficiency (Figure S1A). Notably, during the very first baseline trial–when mice encountered the open arena for the first time–trial-by-trial latencies showed similar hesitation following gate opening, as reflected in comparable nest-exit latencies. This suggests no genotype differences in baseline anxiety levels in response to a novel environment.

The day after the final baseline session, animals were exposed to a predator (a surging weasel) in the testing arena (Figures 1A and 1B). Behavioral patterns were consistent across sexes and age groups, allowing data to be pooled (Figures S1B and S1C). On the first day of predator testing, both groups initially fled to the nest in response to the surging weasel, with significantly longer pellet retrieval latencies at the longest distance (L; 38.1 cm) compared to baseline (Figures 1C and 1D). At the medium distance (M; 25.5 cm), WT mice displayed risk-averse behavior, whereas 5XFAD mice continued foraging, indicating risk-prone behavior. At the shortest distance (S; 12.7 cm), both groups successfully retrieved the pellet; WT latencies matched baseline, while 5XFAD mice showed shorter latencies. Across all distances, 5XFAD mice had shorter mean retrieval latencies than WT mice (Figure 1E).

Importantly, these latency differences were not accompanied by group differences in running speed (velocity), latency to enter the foraging area, or the number of pellet attempts (Figures 1F and S1D–S1F), suggesting preserved predator responsiveness and pellet motivation.

Histological analysis confirmed significantly elevated Aβ plaque deposits in the dCA1 and mPFC of 5XFAD mice relative to WT controls (Figures 1G–1I).

Together, these findings suggest that Aβ pathology in 5XFAD mice impairs the ability to assess threat levels and discern safety boundaries under escalating predatory risk, leading to risk-prone foraging behavior.

5XFAD mice exhibit inflexible foraging and rigid place cell coding in a conditional predatory task

To examine how Aβ pathology affects risky decision-making and hippocampal spatial coding, 5XFAD and WT mice were implanted with tetrode arrays in the dHPC (targeting the CA1 sub-region) and mPFC ipsilaterally (Figure S2A). Mice were tested in a conditional foraging task within a T-shaped maze, where narrow pathways enhanced visit map reliability, and predator visibility was restricted to the pellet zone, enabling analysis of place cell responses to internal fear (threat anticipation) states. Mice progressed through nest habituation, baseline foraging, and predator testing, with distinct pellet types (grain based and chocolate flavored) placed in separate goal arms (Figure 2A). Neural recordings were collected during both pre-predator and predator sessions (Figure 2B).

Figure 2. — (A) Image of a mouse tethered for neural recording in the T maze apparatus.

(B) Schematic of the behavioral procedure consisting of successive pre-predator and predator sessions.

(C) Comparison of successful procurement of preferred (P) and non-preferred (NP) pellets between pre-predator and predator sessions in WTs (6 mice, 16 recording days) and 5XFADs (11 mice, 29 recording days).

(D) PI (ratio of preferred pellet choices to total choices) during the predator session in WT and 5XFAD mice.

(E) Overall movement speed during pre-predator and predator sessions in WT and 5XFAD mice.

(F) Tetrode recordings of dHPC place cells (top) and designated zones in the T maze (bottom): nest, center zone, risky arm, and safe arm.

(G) Representative waveforms and place fields from nest, center-, and risky-arm cells during pre-predator and predator sessions in WT (left) and 5XFAD (right) mice. The color scale (red, maximal firing; blue, no spikes) represents the firing rate for each unit; numerical values indicate peak firing rates per session.

(H and I) Spatial correlations (H) and peak distances (I) between pre-predator and predator sessions within the nest, center-, and risky-arm zones.

Data are presented as mean ± SEM. *p < 0.05, **p < 0.01, ****p < 0.0001. See also Figures S2 and S3.

During the predator session, the preferred pellet arm was rendered “risky” by pairing it with a looming predator. WT mice adapted by switching to the non-preferred, safe arm to avoid the threat (Figure 2C). In contrast, 5XFAD mice continued to visit the predator-associated arm, as reflected by significantly higher preference index (PI) values compared to WT mice (Figure 2D). Despite divergent foraging strategies, both groups reduced their movement speed during the predator session relative to pre-predator levels, indicating a generalized caution response. However, movement speeds did not differ significantly between genotypes in either session (Figure 2E). Histological analysis confirmed elevated Aβ plaque deposits in both dHPC and mPFC of 5XFAD mice (Figures S2B and S2C). These findings suggest that Aβ pathology disrupts behavioral flexibility under threat without affecting baseline locomotion or motivation to forage.

Building on previous research showing place cell remapping in proximity to predatory threats,^44,66 we examined how Aβ pathology affects spatial coding in 5XFAD mice (Figure 2F). Recordings were obtained from 6 WT (94 units) and 11 5XFAD (112 units) mice. Place cells were categorized by peak firing locations during the pre-predator session: nest cells (WT, n = 18; 5XFAD, n = 35), center-arm cells (WT, n = 24; 5XFAD, n = 24), risky-arm cells (WT, n = 14; 5XFAD, n = 19), and safe-arm cells (WT, n = 13; 5XFAD, n = 10) (Figure 2G).

WT mice exhibited significant remapping in risky- and center-arm cells, as indicated by lower spatial correlations (Z’) compared to nest cells (Figure 2H), and increased peak distances in risky-arm cells (Figure 2I), consistent with flexible spatial coding in response to threat. In contrast, 5XFAD mice showed minimal remapping in these regions, reflected by higher spatial correlations and reduced peak shifts (Figures 2H, 2I, S2D, and S2E), indicating reduced neural flexibility and impaired spatial-fear encoding. Although the place field size in risky-arm cells was larger in 5XFAD mice during the pre-predator session (Figure S3A), it did not correlate with spatial correlation or peak distance (Figures S3B and S3C), suggesting that field size alone does not account for remapping deficits.

In the safe arm, place field stability resembled that of nest cells, with similar spatial correlation and peak distance values (Figures S3D and S3E). However, due to limited occupancy, especially in the safe arm during the pre-predator session (in most mice from both groups) and the predator session (in most 5XFAD mice), further analysis was not conducted for this zone.

In summary, 5XFAD mice persistently preferred risky zones and failed to adapt their foraging behavior in response to threat. Correspondingly, their place cells showed rigid spatial coding and failed to remap under dynamic threat conditions. These findings suggest that Aβ pathology impairs hippocampal encoding of threat-relevant spatial information and disrupts adaptive decision-making in ecologically relevant contexts.

5XFAD mice exhibit altered SWRs and disrupted SWR-associated dHPC activity during foraging under predatory risks

We analyzed SWRs and dHPC neuronal activity during foraging under predatory risks. Both 5XFAD and WT mice exhibited SWRs (Figure 3A), but 5XFAD mice showed a significantly reduced dCA1 SWR frequency across all sessions (Figure 3B, left). SWR durations were similar between groups, with both showing longer SWRs during predator sessions compared to pre-predator sessions (Figure 3B, right). The majority of SWRs were detected in the nest area, with relatively few events recorded elsewhere (Figure S4A), indicating that the reported SWR differences primarily reflect dynamics within the nest. Given prior evidence that dHPC SWR-associated reactivation supports general, rather than trial-specific, information processing,⁶⁷ our findings suggest that SWR-related integration may occur continuously throughout the session.

To assess SWR modulation of dHPC firing, we analyzed peri-SWR activity (±250 ms around SWR onset) during both pre-predator and predator sessions (Figure 3C). The proportion of dHPC units exhibiting significant peri-SWR firing (Z > 3) during the predator session was similar for WT (56.5%) and 5XFAD (43.1%) mice. Both groups displayed higher firing rates within the ±250 ms and ±100 ms windows during predator sessions compared to pre-predator levels (Figures 3D–3G). However, key differences emerged in the temporal alignment of firing to SWRs; WT dHPC neurons exhibited peak firing closely aligned with SWR onset (Figure 3H), whereas 5XFAD dHPC neurons showed delayed peak firing (Figure S4B). Moreover, peak firing rates of dHPC cells during both time windows were significantly higher in WT mice (Figures 3I, S4C, and S4D). These results indicate disrupted SWR-spike coordination in 5XFAD mice, suggesting impaired dHPC synchronization during risky decision-making.

We next examined SWR activity and dHPC firing during a post-predator rest period in the nest (Figure S5A). Consistent with prior observations,^58,59 5XFAD mice exhibited fewer (Figure S5B, left) and shorter (Figure S5C) dCA1 SWRs than WT mice. Movement speed did not differ between groups (Figure S5D), confirming that SWR frequency differences were not movement related. When categorized by behavioral state–moving (velocity ≥5 cm/s) vs. stationary (velocity <5 cm/s)–5XFAD mice showed a reduced SWR frequency in both states (Figure S5B, middle and right). Given that SWRs during movement and immobility have been linked to future planning and memory consolidation, respectively,^68–72 these reductions suggest broader disruptions in decision-relevant cognitive processes.

Analysis of peri-SWR dHPC activity (±250 ms window) revealed that WT mice had more SWR-modulated units and shorter SWR-firing lags than 5XFAD mice (Figures S5E and S5F). Peak firing relative to SWR onset was also delayed in 5XFAD mice (Figure S5G). However, maximal firing rates during the ±250 ms and ±100 ms periods were comparable between groups (Figure S5H). These findings suggest Aβ pathology disrupts SWR-associated dHPC firing coordination during post-threat processing.

Finally, we examined the relationship between amyloid burden and SWR activity. While dHPC Aβ plaque levels did not correlate with SWR frequency during the pre-predator session, we observed significant negative correlations during both predator and post-predator sessions (Figure S6, top). Additionally, mPFC Aβ levels were negatively correlated with SWR frequency during the post-predator session (Figure S6, bottom). Together, these findings identify SWR-associated hippocampal activity as a potential in vivo biomarker for AD-related dysfunction and highlight its relevance to cognitive impairments, particularly in risky decision-making contexts.

5XFAD mice exhibit altered dHPC-mPFC dynamics during foraging under predatory risks

To examine how Aβ pathology affects hippocampal-prefrontal interactions, we analyzed dHPC SWRs and their influence on mPFC firing during foraging under predatory risks (Figure 4A). Comparable proportions of mPFC neurons exhibited significant peri-SWR firing (Z > 3) during predator sessions in WT (38.3%) and 5XFAD (31.9%) mice. In WT mice, mPFC cells showed increased firing within ±500 and ±250 ms of SWR onset during predator compared to pre-predator sessions (Figures 4B, S7A, and S7B). This modulation was absent in 5XFAD mice, whose mean mPFC firing rates in response to SWRs did not differ between sessions (Figures 4C and S7C).

Although the proportion of mPFC cells with peak firing near SWR onset was similar across genotypes (Figure 4D), peak Z values during the ±500 ms and ±250 periods relative to SWR onset were significantly higher in WT mice (Figure 4E). These findings indicate that dHPC SWRs modulate mPFC activity in WT but not 5XFAD mice, suggesting impaired dHPC-mPFC coordination during risky decision-making in AD models.

To further assess decision-related dHPC-mPFC interactions, we examined spike synchrony in the “decision zone” before animals turned toward either the preferred (risky) or non-preferred (safe) arm (Figure 4F). Cross-correlograms (CCs) were computed using mPFC spikes as reference during 3-s pre-turn epochs for pre-predator and predator decision points. We recorded 1,060 dHPC-mPFC neuron pairs (WT. 462; 5XFAD, 598) across sessions and analyzed 222 WT and 251 5XFAD pairs with firing rates >0.2 Hz.

At predator decision points, a higher proportion of WT pairs (20.3%, 45 of 222) showed spike synchrony than 5XFAD pairs (10.4%, 26 of 251), indicating reduced dHPC-mPFC synchrony in 5XFAD mice (Figure 4G). Analysis of dHPC firing rates relative to mPFC spikes during the ±100 ms period revealed elevated mean and peak dHPC firing rates during predator vs. pre-predator sessions in both groups (Figures 4H, 4I, and S7D–S7F). However, reduced synchrony in 5XFAD mice highlights a breakdown in dHPC-mPFC functional coordination during risky decision-making.

Given these findings and prior work implicating theta rhythms in hippocampal-prefrontal interactions during decision-making, we examined theta (6–10 Hz)^44,73–75 dynamics during the 3-s pre-turn decision window. In WT mice, dHPC theta power increased under predatory threat (Figure S8A), consistent with its role in guiding decision-making and processing emotional context.^44,76–78 This increase was absent in 5XFAD mice (Figure S8B), and the overall theta power ratio across sessions was significantly lower in 5XFAD mice across sessions (Figure S8C). In contrast, mPFC theta power did not differ significantly by genotype or threat level (Figures S8D–S8F).

To assess functional coupling, we analyzed phase-locking of mPFC units to dHPC theta oscillations during the 3-s pre-turn epoch (the decision window) (Figures S8G and S8H). The mean resultant length, a standard measure of phase-locking strength,^79,80 was comparable between WT (n = 31 neurons) and 5XFAD (n = 41 neurons) mice across both the pre-predator and predator sessions (Figure S8I). Due to the limited number of trials per session (pre-predator, 8–14; predator, 3–10), fewer cells met the inclusion criterion of >40 spikes^81–83 for phase-locking analysis. While the proportion of significantly phase-locked mPFC neurons (p < 0.05, Rayleigh test) did not differ between groups in the pre-predator session (Figure S8J), a greater percentage of WT mPFC neurons was phase-locked compared to 5XFAD neurons during the predator session (Figure S8K), indicating reduced theta-mediated dHPC-mPFC coordination in 5XFAD mice.

To rule out general deficits in fear learning, we assessed contextual fear conditioning following the predator task (Figure 4J). 5XFAD and WT mice exhibited similar post-shock and contextual freezing levels across days (Figures 4K, 4L, S9A, and S9B), suggesting intact fear learning and foot shock sensitivity. Thus, altered risky foraging in 5XFAD mice is not due to general impairment in aversive processing.

Taken together, 5XFAD mice exhibit reduced mPFC modulation by dHPC SWRs and diminished dHPC-mPFC synchrony and theta coupling during threat-based decision making. These disruptions in hippocampal-prefrontal dynamics may underlie impaired risk-based choices in AD and highlight a potential mechanistic link between Aβ pathology and decision-making deficits.

DISCUSSION

Using two distinct predatory threat scenarios and simultaneous mPFC-dHPC recordings, this study reveals cognitive and neural alterations in 5XFAD mice that mirror risky decision-making deficits observed in patients with AD.^5,8,22,84 Behaviorally, 5XFAD mice exhibited heightened risk-taking, marked by longer foraging distances and inflexible strategies, despite intact contextual fear conditioning and predator-evoked flight responses. This selective deficit, observed in both 4- to 5- and 7- to 9-month-old 5XFAD mice, supports the utility of ecologically relevant foraging paradigms in potentially detecting early-stage AD-related cognitive dysfunction.

At the neural level, risk-prone behavior in 5XFAD mice was accompanied by reduced dCA1 SWR frequency and disrupted mPFC-dHPC connectivity. SWRs are established “cognitive biomarkers” of memory consolidation, retrieval, and planning,^67,85,86 and their decline has been linked to aging^87,88 in AD mouse models.^58,55 Real-time interference with SWRs impairs memory,⁵⁴ while prolonging SWRs improves it,⁸⁹ underscoring their cognitive relevance. Our findings linking Aβ plaques with SWR activity and risky decision-making suggest that dysfunctional SWRs contribute to cognitive decline in 5XFAD mice. This aligns with prior work on SWR contributions to spatial memory, navigation, and decision-making under risk.^{52,67,85,86,89,90}

Although 5XFAD and WT mice showed similar proportions of dHPC neurons firing during SWRs in predator sessions, 5XFAD hippocampal cells exhibited delayed peak firing relative to SWR onset, suggesting reduced temporal synchrony, a finding consistent with previous reports of weakened hippocampal co-activation during SWRs in AD mice.⁵⁹ This temporal misalignment may underlie inefficient hippocampal cell activity and contribute to impaired cognition.

mPFC-dHPC interactions, including coordinated replay, oscillatory coupling, and phase-locked firing, are critical for decision-making.^41,52,78 For instance, distinct CA1-mPFC replay patterns encode either past experiences or future decisions during spatial learning.^91,92 Oscillatory coupling between the hippocampus and mPFC occurs across multiple frequency bands, including theta, gamma, and SWRs, during goal-directed behavior.^80,93–98 Notably, mPFC neurons increase phase-locking to hippocampal theta oscillations during decision-making epochs^99–101 and anxiety-related behaviors.^102,103 In WT mice, predator exposure increased mPFC firing within 500 ms of SWR onset, a response absent in 5XFAD mice. This blunted mPFC reactivity, combined with reduced dHPC-mPFC synchrony at decision regions, points to disrupted circuit-level integration. Additional deficits in hippocampal theta power and mPFC theta phase-locking further support a breakdown in inter-regional coordination. Given the role of the mPFC in rule switching, adaptive action selection, and memory-guided decisions,^53,104,105 impaired dHPC-mPFC coordination likely underlies the inflexible foraging observed in 5XFAD mice. These findings suggest that strategies to restore hippocampal-prefrontal synchrony or enhance SWR activity via life-style interventions (e.g., exercise and diet)^106,107 or therapeutic approaches (e.g., pharmacological,¹⁰⁸ genetic,¹⁰⁹ neuromodulation,¹¹⁰ or gut microbiome¹¹¹ interventions) may mitigate cognitive decline in AD.

Place cell coding properties in Aβ mouse models are variable. For example, while Tg2576 mice (16 months old) exhibit reduced spatial information and enlarged place fields,¹¹² 5XFAD mice (4–14 months old) show the opposite.¹¹³ Place field stability also varies; APP KI (7–13 months old) and J20 APP (7 months old) mice retain stable maps in familiar contexts,^114,115 whereas 3xTg mice (8–9 months old, which develop both Aβ and tau pathology) show less stable place fields.¹¹⁶ Aβ models also display experience-dependent coding deficits; APP-PS1 place cells fail to adapt to learned changes,^114,115 and APP KI mice struggle with remapping on a different track.¹¹⁷ Here, we found rigid place cell firing in 5XFAD mice, particularly in predator-exposed zones. This inflexibility contrasts with WT mice (Figures 2H and 2I), which showed remapping not only in the threat zone but also across adjacent maze regions, suggesting that spatial representations in WT mice are shaped by internal states such as fear anticipation. In 5XFAD mice, by contrast, this integration of spatial and emotional information was impaired, revealing a potential mechanism for decision-making deficits.

Although 5XFAD mice showed intact contextual fear conditioning, a hippocampus-dependent task,^118,119 reduced innate fear could still influence foraging. Previous reports showed impaired contextual fear in older (>6 months) but not younger (<4 months) 5XFAD mice.¹²⁰ However, our data from 8- to 11-month-old animals showed no genotype differences, consistent with findings in APP-KI (12- to 13-month-old)¹¹⁴ and Tg2575 (16- to 18-month-old) models.¹²¹ It is possible that the standard fear conditioning, with limited chamber cues and aversive foot shocks, masks subtle hippocampal dysfunction. As Thorndike (1899) noted, animals may solve tasks in constrained environments through simple associations.¹²² In contrast, naturalistic foraging tasks require complex spatial navigation and dynamic threat assessment, making them more sensitive to early AD-related changes.

In conclusion, these results collectively highlight the value of ecologically valid tasks in detecting risky decision-making and neural alterations in AD. In 5XFAD mice, Aβ pathology was associated with impaired risk-based foraging, reduced SWR activity, disrupted place coding, and weakened mPFC-dHPC coordination. As decision-making deficits may precede memory loss in clinical settings, ecological behavioral assays and network-level analyses could serve as sensitive tools for early detection, mechanistic insights, and targeted intervention development. Future studies should explore strategies to enhance SWR activity and cortico-hippocampal coordination as potential avenues to slow or reverse cognitive decline in AD.

Limitations of the study

First, while prior studies^113,114 report general spatial inflexibility in AD models, our findings suggest more specific threat-related remapping deficits. In our task, the predator was visible only in the distal zone, yet WT mice exhibited remapping across broader maze regions (e.g., the center arm). This indicates that internal states, such as fear or vigilance, modulated spatial coding in WT but not in 5XFAD mice. Future work using neutral or non-aversive manipulations could clarify whether the observed remapping deficits reflect global spatial inflexibility or a specific disruption in threat-modulated coding. Second, decision-making impairments in 5XFAD mice may stem from broader corticolimbic dysfunction beyond the dHPC-mPFC circuit. The amygdala and ventral hippocampus, both affected in AD,^123–126 are critical for emotion, salience, and risk evaluation.^{43,66,102,103,127} Future studies should examine whether these regions also contribute to disrupted dHPC-mPFC interactions and risk-related decision-making in AD.

RESOURCE AVAILABILITY

Lead contact

Requests for further information and resources should be directed to the lead contact, Jeansok J. Kim (jeansokk@uw.edu).

Materials availability

This study did not generate new unique reagents.

Data and code availability

All data are available from the lead contact upon request.
Code is publicly available at GitHub: https://github.com/KimLab-UW-NSB/5xfad-hpc-pfc-neural-dynamics.git.
Additional details to reproduce analyses are available upon request.

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Animals

5XFAD transgenic mice, which overexpress amyloid precursor protein (APP) and presenilin 1 (PS1), along with their WT littermate mice counterparts (aged 4–8 weeks, both sexes), were used in this study. Mouse strains B6SJL-Tg(APPSwFlLon, PSEN1*M146L* L286V)6799Vas/Mmjax (RRID: MMRRC_034840-JAX) and B6.Cg-Tg(APPSwFlLon, PSEN1*M146L*L286V)6799Vas/Mmjax (RRID: MMRRC_034848-JAX) were obtained from the NIH-funded MMRRC at The Jackson Laboratory, originally donated by Dr. Robert Vassar, Northwestern University.⁴⁷ Initially, these animals were group-housed in a climate-controlled vivarium under a reversed 12-h light/dark cycle (lights on at 7 PM), accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Upon initiation of experimental procedures, 4–9-month-old mice were individually housed and placed on a standard food-deprivation schedule, with ad libitum access to water, to maintain ∼85% of their normal body weight. A total of 17 5XFAD and 17 WT mice were assigned to the escalating predatory risk task (Figure 1), while a separate cohort (5XFAD, n = 11; WT, n = 6) underwent combined tetrode recording and conditional predatory risk testing (Figure 2), as detailed below. All experiments were conducted during the dark phase in strict accordance with the guidelines of the University of Washington Institutional Animal Care and Use Committee (IACUC protocol #: 4040-01).

METHOD DETAILS

Surgery

Under isoflurane anesthesia, animals were secured in a stereotaxic instrument (Kopf) for the implantation of a microdrive array containing individually movable tetrodes. The tetrodes targeted the right dHPC (AP: −1.8, ML: +1.7, DV: −1.3; four tetrodes) and mPFC (AP: +1.8, ML: +0.3, DV: −1.2; four tetrodes). The tetrodes were fabricated from formvar-insulated nichrome wire (14 μm diameter; Kanthal) and gold-plated to a final impedance of 100–300 kΩ (measured at 1 kHz). The microdrive assembly was secured with dental cement and bone screws. A recovery period of at least one week preceded the initiation of behavioral experiments and recording sessions.

Escalating predatory risk

This task was conducted in a custom-built apparatus consisting of a nest area (inner dimensions: 14 cm L × 45 cm W × 61 cm H; luminance: 2.2 lux) and an adjacent foraging space (104 cm L × 45 cm W × 61 cm H; luminance: 5.0 lux; background noise: 72-dB white noise). The environment was monitored using the ANY-maze system (Stoelting Co.) and a ceiling-mounted HD webcam (C910; Logitech) for video tracking at 30 fps (Figure 1A). The task consisted of three phases: habituation, baseline foraging, and weasel predator testing.

During habituation, hunger-motivated mice were acclimated by being placed in the nest with 20 food pellets (45 mg each, F0165, Bio-Serv) for 30 min/day over two days. For baseline foraging, mice were provided with two 45-mg pellets in the nest. After consuming these, they were given access to the foraging area via an open gateway to retrieve pellets placed at distances of 12.7 cm (S), 25.4 cm (M), and 38.1 cm (L) from the nest. Each daily session included three trials for four consecutive days.

The predator testing phase began with mice foraging for a pellet placed 38.1 cm from the nest in three consecutive trials. Subsequent trials introduced a taxidermy weasel mounted on a wheeled frame positioned at the opposite end of the foraging space. As the mouse approached the pellet within ∼12.7 cm, the weasel was activated to surge forward 30.4 cm toward the pellet at 60 cm/s using a pneumatic actuator, then automatically returned to its starting position. Each trial ended either when the mouse successfully secured the pellet or after 3 min had elapsed. This process was repeated for pellet placements at 25.2 cm and 12.7 cm.

Conditional predatory risk

Adapted from a figure-eight maze,¹²⁸ this T-maze setup features a nest area at the base (dimensions: 92 cm L × 92 cm W × 79 cm H; luminance: 7 lux) and two lateral goal arms, each equipped with a concealed puppet eagle predator near a designated pellet zone (Figure 2A). Movement was tracked using headstage-mounted LEDs and integrated with the ANY-maze (Stoelting Co.) and Cheetah (NeuraLynx, Inc.) systems. This maze design ensured consistent location visits, enabling optimal place cell analysis.

The task protocol comprised four phases: habituation, shaping, baseline foraging, and predator testing, with tetrodes adjusted toward the target areas as detailed in the ‘Unit recording and analyses’ section. During habituation (two days), animals were placed in the nest for 30 min/day with ten grain (45 mg, F0165, Bio-Serv) and ten chocolate (45 mg, F0299, Bio-Serv) pellets to facilitate acclimation. The shaping phase lasted three days. Mice received one grain and one chocolate pellet in the nest. After consumption, the nest gate opened, guiding them to retrieve a pellet at increasing distances. Each arm was consistently assigned a specific pellet type. After a forced-arm retrieval and pellet consumption, the next trial began on the opposite arm. During baseline foraging, two pellets were provided in the nest. After consumption, the nest gate opened, allowing mice to choose between the left and right arm pellets. Choosing one pellet triggered the closure of the opposite arm gate. Each daily session included 6 trials, lasting for an average of 8.6 ± 1.03 days. Predator testing sessions, during which neural recordings were conducted, included pre-predator (8–14 trials), predator (3–10 trials), and post-predator (10 min nest period) phases. During predator trials, when mice approached the preferred pellet, a puppet eagle surged forward (15.2 cm at 38 cm/s via a linear actuator). In contrast, when mice approached the non-preferred pellet, the predator remained hidden behind a black plastic block. Pellet preference was quantified using a Preference Index (PI), defined as the ratio of preferred pellet choices to total pellet choices.

Contextual fear conditioning

After completing the conditional predatory risk task, 5XFAD and WT mice were placed in a standard operant chamber with a stainless-steel grid floor (5 mm diameter rods spaced 1 cm apart).¹⁰⁰ Following a 3-min baseline period, three unsignaled footshocks (0.7 mA, 1 s) were administered at 1-min intervals. Postshock freezing was assessed between each shock. The next day, mice were returned to the same chamber, and their conditioned freezing response to the context was measured for 3 min before administering another set of 3 unsignaled shocks and assessing postshock freezing. An uninformed observer used a custom-written computer-assisted scoring program (written in C language) to manually record the duration of freezing behavior via keystrokes on a computer keyboard.¹²⁹ Freezing was defined as the absence of any visible movement in the body and vibrissae, except for respiratory movements.¹³⁰ The program applied a continuous inactivity threshold of 2 s or more to identify freezing. The percentage of freezing was calculated by dividing the total duration of freezing by the total observation time and multiplying by 100.

Electrophysiological recording and analysis

Data acquisition

Single-unit and local field potential (LFP) signals were amplified (10,000×), filtered (unit: 600 Hz–6000 Hz; LFPs: 0.1 Hz–1000 Hz), and digitized at 32 kHz using the Cheetah data acquisition system (NeuraLynx). Unit isolation was performed using an automatic spikesorting program (SpikeSort 3D; NeuraLynx) followed by manual cluster cutting, following protocols described in previous studies.^43,44 Raster plots and peristimulus time histograms were generated using NeuroExplorer (Nex Technologies).

Place cell analysis

Place cells were identified based on criteria established in prior research.^44,66 Units were classified as place cells if they exhibited stable, well-discriminated complex spike waveforms, a refractory period of at least 1 ms, peak firing rates >2 Hz in any session, and spatial information >0.5 bits/s in any session. Place cells were categorized according to the peak positions of their place fields during the pre-predator session as follows: nest cells (maximal firing in the nest), center cells (maximal firing in the center zone), risky arm cells (maximal firing in the maze arm near the threat), and safe arm cells (maximal firing in the maze arm away from the threat). A pixel-by-pixel spatial correlation analysis was performed using a customized R program to calculate the similarity of place maps between the pre-predator and predator sessions for each place cell. The correlation value (r) was transformed into a Fisher Z’ score for parametric comparisons between cell types. Additionally, the program calculated the distance between each cell’s peak firing locations across sessions. The maximal firing locations (x, y coordinates) were identified for each session, and the distance (in cm) between these points was calculated to measure positional shifts of place fields from the pre-predator to predator sessions.

Cross-correlogram (CC) analysis

CC of simultaneously recorded dHPC and mPFC units were generated using NeuroExplorer, following established methods.^43,66 CCs were constructed with dHPC cells as the reference, using a 10-ms bin width for two time epochs: pre-turn epoch_pre (a 3-s epoch before the animal turned at the end of the center zone toward one of the arms during the pre-predator session) and pre-turn epoch_predator (a 3-s epoch before the turn during the predator session). To avoid false correlations due to covariation or nonstationary firing rates between the dHPC and mPFC, a ‘Shift-Predictor’ correction was applied, with trial shuffles performed 100 times.⁴³ Each shift-predictor correlogram was subtracted from its respective raw correlogram, and Z-scores were calculated using the mean and standard deviation of the corrected CCs. A neural pair was deemed significantly correlated if the peak Z score exceeded 3. Additional criteria required that dHPC and mPFC firing rates during pre-turn periods for both the pre-predator and predator sessions be above 0.2 Hz, and the peak of the CC had to fall within a ±100 ms testing window relative to the reference spikes.

LFP and SWR analysis

LFP signals from a single channel of each animal’s dHPC electrodes were band-pass filtered between 150 and 250 Hz using a zero-phase digital filter to minimize phase distortions. The envelope of these high-frequency oscillations was extracted using the Hilbert transform, providing a precise measure of their amplitude. The resulting envelope was smoothed with a Gaussian-weighted moving average over a 40 ms window. SWR events were detected when the smoothed envelope exceeded a threshold of 4 standard deviations above the mean amplitude. The event ended when the amplitude fell below 2 standard deviations, ensuring precise delineation of event boundaries. Only SWR events with durations over 15 ms were included in the analysis. During the post-predator session in the nest, movement states for SWR analysis were classified as moving (velocity ≥5 cm/s) or not moving (velocity <5 cm/s).⁷¹

SWR modulation of unit activity analysis

To assess the modulatory effects of SWRs on dHPC and mPFC unit activity, unit firing rates were normalized to pre-SWR baseline periods (dHPC: −500 to −250 ms; mPFC: −1000 to −500 ms). A neuron was classified as significantly activated if its peak Z-score exceeded 3 within a specified time window around SWR onset (dHPC: ±100 ms or ±250 ms; mPFC: ±250-ms or ±500 ms). All analyses were conducted using MATLAB 2024 Signal Processing Toolbox, which provided the necessary tools for detailed signal examination and processing.

Phase-locking analysis

Hippocampal LFPs were band-pass filtered between 4–12 Hz using a zero-phase-delay Butterworth filter (4th order) implemented with the filtfilt function in MATLAB (MathWorks) to prevent phase distortion. The instantaneous phase of the theta rhythm was extracted using the Hilbert transform. For each prefrontal cortical neuron, spike times were assigned corresponding theta phase via linear interpolation between the LFP time series and spike timestamps. Phase-locking significance was assessed using the Rayleigh test for circular uniformity (via the circ_rtest function, Circular Statistics Toolbox), which evaluates whether spike phases are uniformly distributed around the theta cycle. Neurons with p < 0.05 (Rayleigh test) and at least 40 spikes^81–83 were considered significantly phase-locked to the hippocampal theta rhythm. The strength of phase-locking was quantified by the Mean Resultant Length (MRL), calculated with the circ_r function. MRL values range from 0 (uniform distribution, no phase-locking) to 1 (perfect phase-locking). The preferred phase of firing was determined using the circular mean phase via the circ_mean function.

Histology

After concluding the experiment, electrolytic lesions were created by applying a 10 μA current for 10 s to the tetrode tips to mark electrode placements. Mice were then euthanized with an overdose of Beuthanasia and perfused intracardially with phosphate-buffered saline (PBS), followed by fixation with 4% paraformaldehyde in PBS. Extracted brains were stored overnight at 4°C in fixative, then transferred to 10%, 20%, and 30% sucrose solutions until they sank. For Aβ immunostaining, transverse brain sections (30 μm) were rinsed in PBS and incubated for 1 h in 5% normal goat serum with 0.5% Triton X- in PBS to block non-specific binding. The sections were then incubated overnight at 4°C with Aβ-specific mouse monoclonal antibody (1:1000, MediMabs, McSA1). After thorough washing, sections were incubated with a goat anti-mouse secondary antibody conjugated to Alexa Fluor 568 (1:250, Abcam, ab175473) for 2 h at room temperature. The prepared sections were mounted on slides and cover slipped with Flouromount-G^™ containing DAPI (eBioscience) for nuclear staining. Imaging was performed using a Keyence BZ-X800E microscope. Image analysis was conducted using ImageJ software (NIH, version 1.54d). A separate set of brain sections was mounted onto gelatinized slides and stained with Cresyl violet and Prussian blue to confirm the precise locations of the tetrode tips.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analyses analysis

Statistical significance was assessed using a range of tests tailored to the diverse data structures in the study. These tests included one-way ANOVAs for group comparisons, mixed-design ANOVAs for analyses incorporating both within- and between-subject variables, Pearson’s correlation, unpaired t-tests, and paired t-tests. Post hoc analyses, when required, were conducted using Tukey’s test or Fisher’s least significant difference test. The Greenhouse-Geisser correction was applied to account for sphericity violations. Comprehensive details for each statistical analysis are provided in Table S1. Statistical significance was defined as p < 0.05. Statistical analyses and graph generation were performed using SPSS (ver. 19), custom MATLAB codes, GraphPad Prism (ver. 9.00), and NeuroExplorer (ver. 5.030).

Supplementary Material

NIHMS2107271-supplement-1.pdf^{(3.7MB, pdf)}

NIHMS2107271-supplement-2.xlsx^{(23.2KB, xlsx)}

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2025.116081.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Mouse Anti-Human Aβ Monoclonal Antibody	Medimabs	RRID: AB_1807985
Goat Anti-Mouse IgG H&L (Alexa Fluor^® 568)	Abcam	RRID: AB_2895153
Experimental models: Organisms/strains
B6SJL-Tg(APPSwFlLon, PSEN1M146LL286V)6799Vas/Mmjax	The Jackson Laboratory	RRID: MMRRC_034840-JAX
B6.Cg-Tg(APPSwFlLon, PSEN1M146LL286V) 6799Vas/Mmjax	The Jackson Laboratory	RRID: MMRRC_034848-JAX
Software and algorithms
MATLAB	The MathWorks	http://www.mathworks.com/products/matlab/; RRID: SCR_001622
Custom MATLAB code for single-unit and LFP data analyses	This paper	https://github.com/KimLab-UW-NSB/5xfad-hpc-pfc-neural-dynamics.git
GraphPad Prism	GraphPad software	http://www.graphpad.com/; RRID: SCR_002798
R	R Foundation	http://www.r-project.org/; RRID: SCR_001905
ImageJ	National Institutes of Health	https://imagej.net/ij/; RRID: SCR_003070
Cheetah	NeuraLynx, Inc.	https://neuralynx.fh-co.com/research-software/cheetah/
Spikesort3D	NeuraLynx, Inc.	https://neuralynx.fh-co.com/research-software/spikesort-3d/; RRID: SCR_014478
NeuroExplorer	Nex Technologies	https://www.neuroexplorer.com/; RRID: SCR_001818
bioRender	bioRender	https://www.biorender.com/

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Highlights.

5XFAD mice exhibit impaired risky decision-making in ecologically valid paradigms
Amyloid pathology disrupts place cell flexibility and reduces sharp-wave ripple activity
SWR-associated corticolimbic coordination is impaired in 5XFAD mice
Hippocampal-prefrontal synchrony is diminished in 5XFAD mice

ACKNOWLEDGMENTS

This study was supported by NIH grants AG067008 (to E.J.K.), MH139585 (to J.J.K.), UW Royalty Research Fund A188443 (to E.J.K.), National Research Foundation of Korea RS-2025-00522887 (to J.C.), and ETRI 25YB1210 (to S.P.). We thank Drs. Min Whan Jung and Jong Won Lee (Korea Advanced Institute of Science and Technology) for generously sharing custom-made microdrive components and electronic interface boards and Nayoung Kim for assistance with experiments and data collection/analysis. Illustrations were created with BioRender.

Footnotes

DECLARATION OF INTERESTS

The authors declare no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS2107271-supplement-1.pdf^{(3.7MB, pdf)}

NIHMS2107271-supplement-2.xlsx^{(23.2KB, xlsx)}

Data Availability Statement

All data are available from the lead contact upon request.
Code is publicly available at GitHub: https://github.com/KimLab-UW-NSB/5xfad-hpc-pfc-neural-dynamics.git.
Additional details to reproduce analyses are available upon request.

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PERMALINK

Disruption of hippocampal-prefrontal neural dynamics and risky decision-making in a mouse model of Alzheimer’s disease

Eun Joo Kim

Sanggeon Park

Bryan P Schuessler

Harry Boo

Jeiwon Cho

Jeansok J Kim

SUMMARY

Graphical Abstract

In brief

INTRODUCTION

RESULTS

5XFAD mice exhibit risk-prone foraging with escalating predatory risk

Figure 1. 5XFAD mice exhibit risk-prone foraging in an escalating predatory task.

5XFAD mice exhibit inflexible foraging and rigid place cell coding in a conditional predatory task

Figure 2. 5XFAD mice exhibit inflexible foraging and rigid place cell coding in a conditional predatory task.

5XFAD mice exhibit altered SWRs and disrupted SWR-associated dHPC activity during foraging under predatory risks

Figure 3. 5XFAD mice exhibit alterations in SWRs and associated dHPC activity during foraging under predatory risk.

5XFAD mice exhibit altered dHPC-mPFC dynamics during foraging under predatory risks

Figure 4. 5XFAD mice exhibit altered dHPC-mPFC interactions during foraging under predatory risk.

DISCUSSION

Limitations of the study

RESOURCE AVAILABILITY

Lead contact

Materials availability

Data and code availability

STAR★METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Animals

METHOD DETAILS

Surgery

Escalating predatory risk

Conditional predatory risk

Contextual fear conditioning

Electrophysiological recording and analysis

Data acquisition

Place cell analysis

Cross-correlogram (CC) analysis

LFP and SWR analysis

SWR modulation of unit activity analysis

Phase-locking analysis

Histology

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analyses analysis

Supplementary Material

Highlights.

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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