Skip to content
Frontiers
Search

Mini Review

Frontiers in Aging Neuroscience ·

Volume 18 - 2026 | https://doi.org/10.3389/fnagi.2026.1957901

Molecular mechanisms underpinning cognitive reserve and resilience

  • Amber Chen1
  • Sreenithi Bhuvaneshwaran1
  • Yash Akkara1
  • Thomas Burke1
  • Robert Perneczky2,3
  • Magdalena Sastre1*corresponding author
  1. 1Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, United Kingdom
  2. 2Department of Psychiatry and Psychotherapy, LMU Hospital, Ludwig-Maximilians University Munich, Munich, Germany
  3. 3German Centre for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
Article metrics0 viewsView details
PDFCiteMetrics

Abstract

The concept of cognitive reserve (CR) and resilience originates from clinicopathological observations that amyloid-β deposition, tau pathology and neurodegeneration do not uniformly translate into clinical dementia, highlighting inter-individual differences in the brain’s capacity to tolerate pathology. Although CR and resilience lack a single accepted definition, they broadly describe the ability to maintain cognition in the presence of age-related or pathological brain changes. This review synthesises current molecular, cellular, and brain network mechanisms of cognitive reserve and critically examines key controversies. We also identify convergent biological pathways across multiple levels of organisation. Available evidence implicates that CR is not attributable to a single factor but reflects interactions among environmental exposures, genetic background, epigenetic regulation, cellular homeostasis, glial function, and large-scale brain network organisation. Lifestyle factors are associated with reduced pathology accumulation, preserved brain structure, and maintenance of cognition despite pathology, depending on the mechanism and outcome considered and are understood to exert their effects through epigenetic modulation of neuroplasticity, neurotrophic signalling, metabolic regulation, and inflammatory pathways. At the cellular level, preservation of dendritic spine integrity, synaptic signalling balance, mitochondrial energetic flexibility, and adult hippocampal neurogenesis supports efficient circuit function under pathological stress. Glial cells play central roles in mediating adaptive plasticity, neuroinflammatory control, and metabolic support, with dysregulation accelerating cognitive decline. Collectively, these findings frame CR as an emergent property of coordinated biological processes operating across molecular, cellular, and network scales, with important implications for developing reserve-enhancing therapeutic and lifestyle-based interventions that complement traditional pathology-focused approaches.

Keywords

1 Introduction

The concept of cognitive reserve (CR) and resilience emerged from observations such as those from the longitudinal Nun Study (Clarke et al., 2025), in which some individuals with Alzheimer’s disease (AD) pathology maintain relatively preserved cognitive function. Post-mortem and in vivo studies have consistently shown that amyloid-β deposition, tau pathology, and neurodegeneration do not uniformly translate into clinical dementia (Stern, 2012). This suggests that inter-individual differences in the brain’s capacity to tolerate pathology influence cognitive outcomes, forming the basis of the resilience framework. In this line, recent research in cognitively healthy centenarians in the 100-plus Study has demonstrated that some are able to maintain cognitive health despite substantial AD pathology, whereas others showed very limited pathology. Particularly, these observations suggest that centenarians preserve cognition either through resistance to Aβ accumulation or resilience to Aβ-induced p-tau pathology (Hulsman et al., 2026). The essential distinction is that resistance concerns whether pathology develops, whereas resilience concerns the cognitive consequences once pathology is present.

Clinically, the threshold and decompensation model suggests that reserve shifts the relationship between pathology and symptoms rather than slowing pathological progression (Pettigrew et al., 2020). Individuals with greater reserve can tolerate higher pathological burden before symptoms emerge (Li et al., 2021). However, once compensatory capacity is exhausted, decline may be more rapid because clinical expression occurs at a more advanced disease stage (Soldan et al., 2017). This process reflects an initial adaptive response involving compensatory neural activity and other protective mechanisms, which eventually become overwhelmed, leading to neuroinflammation, metabolic dysfunction, network breakdown, and clinical deterioration (Stern, 2012; Hillary and Grafman, 2017; Penalba-Sánchez et al., 2022; Liu et al., 2025).

Although the terms are widely used, CR and resilience lack a single, universally accepted definition and are often used interchangeably across the literature. To avoid conceptual ambiguity, we distinguish mechanisms that influence the accumulation of brain ageing or disease, from mechanisms that modify the cognitive consequences of these changes. Brain maintenance refers to reduced or delayed age-related brain change, whereas resistance refers more specifically to reduced accumulation or spread of neuropathology. Brain reserve denotes quantitative or structural brain features that increase the amount of damage that can be sustained before clinical impairment becomes apparent. CR refers to active adaptability of cognitive and neural processes, including neural efficiency, capacity, flexibility and compensatory network recruitment (Stern et al., 2023). Here, resilience is used as an overarching clinical phenotype, denoting better-than-expected cognitive function given the observed burden of pathology or neurodegeneration. These constructs are related but not interchangeable, and individual biological mechanisms may contribute to more than one construct. A given pathway may reduce pathology accumulation, preserve structural substrate, and facilitate functional adaptation. Its classification therefore depends on the level and outcome being examined rather than on the molecular pathway alone (Stern et al., 2020) (see Supplementary Table S1).

CR research has been driven by the belief that identifying its underlying biological mechanisms could lead to novel therapeutic targets (Ma and Pop, 2024). Various lifestyle and environmental factors, such as education, occupation, activity, social engagement, and cognitive stimulation, have been associated with CR. However, these are likely proxies for underlying biological processes, such as brain-derived neurotrophic factor (BDNF) release, which is modified by diet and exercise and enhances synaptic density, neuronal survival, and neurogenesis (Alqahtani et al., 2025). Research has increasingly shifted towards identifying the molecular, cellular, and systems-level pathways that enable neural compensation for neurodegenerative pathology. Advances in neuroimaging, genomics, epigenomics, proteomics, and single-cell transcriptomics have provided new insight into the biological basis of CR.

Despite these advancements, the field remains fragmented, with findings often examined in isolation and occasionally producing conflicting results. Perhaps the biggest controversy is that CR is a theoretical construct rather than a directly measurable biological entity. Moreover, the molecular mechanisms that drive CR remain incompletely understood, limiting translation of these findings into effective disease-modifying therapies.

In this review, we examine how environmental and genetic factors influence resistance and brain maintenance, how cellular and structural mechanisms contribute to brain reserve, and how synaptic and network adaptations generate cognitive reserve and ultimately cognitive resilience.

A central proposition of this review is that cognitive reserve is not a property of any single biological level but an emergent phenomenon arising from interactions across levels. Genetic and environmental factors set the operating range of cellular maintenance pathways, which influence synaptic and circuit efficiency and ultimately determine network-level adaptability. This framework predicts that biomarkers measured across levels will explain resilient cognitive performance better than markers derived from any single scale alone.

A detailed mechanistic understanding of CR could offer a pathway towards developing novel therapeutic strategies aimed at preserving cognition.

2 Environmental exposures and epigenetic regulation on CR

Lifetime environmental exposures contribute to cognitive reserve (CR) by shaping cognitive outcomes through modifiable factors, unlike genetic susceptibility, which is largely fixed. Epigenetic regulation provides a plausible mechanism linking environmental experiences to CR, as exposure-dependent changes in gene expression and neural networks have been observed in pathways related to learning and memory (Koulouri and Zannas, 2024). While most research has focused on the relationship between epigenetic modifications and neurodegenerative burden or structural brain reserve, growing evidence suggests that epigenetic processes may also support the functional adaptations characteristic of CR.

2.1 Environmental enrichment

Cognitively stimulating lifestyles, including higher educational and occupational attainment, are consistently associated with preserved cognition despite ageing and neurodegeneration (Melendez et al., 2026). Because of this relationship, education is often used as a proxy measure of CR (Stern et al., 1999). In rodent models, environmental enrichment enhances learning, memory and hippocampal neurogenesis (Khalil, 2024), although the persistence of these effects depends on the duration and timing of exposure. However, CR cannot be inferred from education alone. It is most appropriately operationalised as inter-individual variation in cognition that remains after accounting for demographic factors and objectively measured brain ageing or pathology, or as moderation of the relationship between pathology and longitudinal cognitive change.

Environmental enrichment induces widespread epigenetic changes within the hippocampus, including DNA hypomethylation and increased expression of neuroplasticity-related genes such as BDNF, EGR1 (Early Growth Response Protein 1) and ARC (Activity-Regulated Cytoskeleton-Associated Protein) (Puang et al., 2020; Costa et al., 2023). Age-related methylation changes at the MECP2 (Methyl-CpG-Binding Protein 2) locus can also be partially reversed (Huang et al., 2021; Zocher et al., 2021), supporting theories of “epigenetic rejuvenation,” whereby stimulating environments mitigate aspects of biological ageing. However, these modifications persist for only a limited period after removal from the enriched environment (Zocher et al., 2020), suggesting that sustained cognitive engagement may be necessary to maintain long-term benefits.

2.2 Lifestyle exposures

Lifestyle factors such as diet, physical activity, and psychosocial stress may influence CR through epigenetic mechanisms. Dietary factors can regulate DNA methylation and histone modifications in genes involved in synaptic function, neuroinflammation, oxidative stress, and neuronal plasticity (Li et al., 2024).

Caloric restriction is of particular interest because it activates epigenetic regulators, including Sirtuin-1 (SIRT1) and CREB-binding protein (CREB-BP), increasing chromatin accessibility and promoting expression of neuroplasticity-related genes such as BDNF (Koulouri and Zannas, 2024; Alkurd et al., 2024). Conversely, high-fat diets induce maladaptive epigenetic changes associated with cognitive decline. However, the causal relationship between diet, BDNF signalling and CR remains unclear (Xie et al., 2023; Sequeira-Cordero et al., 2025).

Physical activity appears to promote CR through epigenetic regulation of neurotrophic, synaptic, and mitochondrial pathways, increasing expression of BDNF, MEF2 (Myocyte Enhancing Factor 2), PPAR-δ (Peroxisome Proliferator-Activated Receptor-δ), PGC-1α (PPARγ Coactivator-1α) and (TFAM Mitochondrial Transcription Factor A), while also altering microRNA profiles involved in synaptic remodelling and neuronal metabolism (Benite-Ribeiro et al., 2023; Steinke et al., 2023).

In contrast, chronic psychosocial stress has been linked to increased dementia risk through epigenetic alterations of the glucocorticoid receptor gene NR3C1, (Nuclear Receptor Subfamily 3, Group C, Member 1), leading to disrupted cortisol regulation, impaired hippocampal neurogenesis, and poorer memory performance (Gassen et al., 2017).

Together, these findings suggest that lifestyle exposures contribute to CR through epigenetic pathways, although causal relationships remain to be fully established.

3 Genetic influences on CR

Rising evidence supports a polygenic model for the genetic influence on mechanisms underlying CR and resilience. Several population-level genome-wide association studies (GWAS) highlight gene sets mapping to biological pathways with potential mechanistic relevance. The effect of AD-risk genes and specific mutations is also relevant to understanding the association between gene expression and resilience.

Large-scale GWAS demonstrate that resilience-associated variants show minimal overlap with clinical AD risk, including weak or absent associations with Apolipoprotein (APOE) ε4. This dissociation supports a dual-pathway idea, whereby AD risk genes influence pathology accumulation, while resilience genes, such as the APO ε3 Christchurch mutation, modify the cognitive consequences of pathology. Table 1 details key genetic loci implicated in CR and resilience.

Table 1. Summary of genetic loci or pathways associated with cognitive reserve/resilience, grouped by mechanism, key variants and type of evidence.

Mechanism contributing to cognitive reserve/resilienceLocus/pathwayFunctionKey variantStudy typeReference(s)
DNA repair and oxidative stressNucleotide excision repairDNA repair, reactive oxygen species removal–GWASKo et al. (2022)
MetallothioneinReactive oxygen species removal–Transcriptomicde Vries et al. (2024)
Lipid metabolism and protein traffickingATP8B1Bile acid metabolism, prefrontal cortex methylation–GWASDumitrescu et al. (2020)
APoELipid metabolism, synapse plasticity, membrane repair–Cohort studiesPettigrew et al. (2023)
APOE ε3 ChristchurchCase report/animal modelArboleda-Velasquez et al. (2019) and Naguib et al. (2025)
NeuroinflammationPLCG2Modifies neuroinflammation, microglial phagocytosisP522RGWASTesi et al. (2024)
GATA binding protein 3Neuronal survival and development, microglial gene expression–GWASEissman et al. (2022)
Synaptic signalling and plasticityTumour growth factor-βSynaptic plasticity and neuronal survival–GWASPhillips et al. (2025)
APPSynaptic plasticity, neurite growth–TWAS/GWASMustafa et al. (2025)
PICALMSynapse function, endothelial function–Candidate genePonomareva et al. (2020)
rs3851179GWASEissman et al. (2022)
RAB10Synaptic vesicle traffickingrs142787485Pedigree WGSRidge et al. (2017)
CLUSynaptic maintenancers11136000Multi-omicsMa and Qiu (2022)
BDNFSynaptic plasticity, neurotrophic supportVal66MetCandidate geneWard et al. (2015)
Neural network organisation and synapse connectivityNPTX2Synaptic connectivity, network integrity marker–ProteomicOh et al. (2025)
RELNNeuronal migration, network organisation–GWASKramer et al. (2011)
RELN-COLBOSCase reportLopera et al. (2023)
Endothelial and mitochondrial metabolismEndothelial and mitochondrial lociBlood-brain-barrier integrity; neuronal energy supply–PRSXu et al. (2024)

APP: Amyloid Precursor protein; ATP8B1: ATPase phospholipid transporting 8B1; CLU: Clusterin; PLCG2: Phospholipase C gamma 2; PICALM: Phosphatidylinositol binding clathrin assembly protein; RAB10: Ras-related protein Rab-10; BDNF: Brain-derived neurotrophic factor; NPTX2: Neuronal pentraxin 2; RELN: Reelin; GWAS: genome-wide association study, TWAS: transcriptome-wide association study, WGS: whole genome sequencing, PRS: polygenic risk score; - represents a lack of identified key variant.

Collectively, these analyses support the roles of DNA repair, oxidative stress regulation, synaptic and neuronal maintenance, and glial-neuronal signalling in CR and resilience, concepts revisited in subsequent sections.

4 Intracellular and neuronal mechanisms as surrogate drivers of reserve and mesoscale synaptic/circuit dynamics

CR is supported by the ability of neurons to maintain synaptic integrity, energetic homeostasis and neurogenesis despite ageing and neuropathology. Cellular and intracellular processes that preserve these functions may therefore act as surrogate drivers of CR, enabling neural circuits to sustain cognition under adverse conditions (see summary in Figure 1) (Anacker and Hen, 2017; Chang and Hen, 2024; Taddei and Duff, 2025).

Schematic overview of how coordinated neuronal processes support cognitive reserve (CR) under ageing and disease. (A) Mitochondrial energetic flexibility and regulated Ca2+ handling enable neurons to meet metabolic demands without accumulating damage. (B) Preserved dendritic spine stability, (C) adult hippocampal neurogenesis, and (D) efficient synaptic signalling sustain memory despite synaptic loss, while enhanced inhibitory control limits network hyperexcitability. Adult hippocampal neurogenesis further contributes by increasing circuit flexibility under high cognitive demand, highlighting CR as an emergent property of interacting intracellular mechanisms. ([www.canva.com](https://www.canva.com)).
Figure 1. Schematic overview of how coordinated neuronal processes support cognitive reserve (CR) under ageing and disease. (A) Mitochondrial energetic flexibility and regulated Ca2+ handling enable neurons to meet metabolic demands without accumulating damage. (B) Preserved dendritic spine stability, (C) adult hippocampal neurogenesis, and (D) efficient synaptic signalling sustain memory despite synaptic loss, while enhanced inhibitory control limits network hyperexcitability. Adult hippocampal neurogenesis further contributes by increasing circuit flexibility under high cognitive demand, highlighting CR as an emergent property of interacting intracellular mechanisms. (www.canva.com).

4.1 Mitochondrial energetics and Ca2+ regulation

The capacity of neurons to sustain compensation also depends on mitochondrial function. Rather than absolute oxidative capacity, CR appears to be associated with energetic flexibility, including efficient phosphocreatine buffering and preserved mitochondrial complex I activity (Mahapatra et al., 2023; Shatalina et al., 2024; Lopez et al., 2025). These processes enable neurons to meet fluctuating energy demands while limiting cumulative cellular damage.

Controlled transfer of Ca2+ between synapses and mitochondria supports synaptic plasticity, whereas chronic Ca2+ overload promotes oxidative stress, mitochondrial dysfunction and synaptic failure (Arnsten et al., 2025; Ebrahimi et al., 2025). Regulation of endoplasmic reticulum-mitochondria coupling may therefore represent an intracellular reserve mechanism that preserves neuronal function during stress. However, direct evidence linking mitochondrial Ca2+ regulation to CR in humans remains limited, with most findings derived from animal and in vitro studies (Popugaeva et al., 2017).

4.2 Adult hippocampal neurogenesis

Adult hippocampal neurogenesis (AHN), the generation of new granule neurons in the adult dentate gyrus, may support CR by improving the efficiency of hippocampal information processing rather than replacing lost neurons (Anacker and Hen, 2017; Chang and Hen, 2024). Newly generated neurons undergo a period of heightened excitability and plasticity that enhances memory encoding and discrimination (Akers et al., 2014; Frechou et al., 2024).

By improving pattern separation and reducing interference between memories, AHN may enable hippocampal networks to operate more efficiently under cognitive stress, effectively expanding reserve capacity (Danielson et al., 2016; McAvoy et al., 2016). However, its contribution is constrained by the marked decline of neurogenesis with ageing and the requirement for successful integration of new neurons into existing circuits. Furthermore, much of the evidence derives from murine studies, and the extent of AHN in humans remains debated (Boldrini et al., 2018; Sorrells et al., 2018).

Overall, these findings suggest that CR emerges from coordinated intracellular processes that preserve synaptic signalling, energetic adaptability and neurogenesis. Together, these complementary mechanisms provide a biologically plausible explanation for why some individuals maintain cognitive function despite substantial neuropathology.

4.3 Dendritic spines and synaptic signalling

Dendritic spines, the principal postsynaptic sites of excitatory transmission, play a key role in linking structural plasticity with synaptic efficacy. Their preservation may support CR by maintaining communication within neural networks (Guptarak et al., 2024; Bhembre et al., 2025). Across ageing, cognitive performance has been associated with spine morphology and stability, particularly the maintenance of GluN2B-containing receptors that facilitate long-term potentiation (Pegasiou et al., 2020; Guptarak et al., 2024). Emerging evidence also identifies synapse-related biomarkers, such as the YWHAG:NPTX2 ratio, as predictors of cognitive impairment independent of pathological burden, reinforcing synaptic integrity as a substrate of reserve (Oh et al., 2025).

Preservation of spine diameter, postsynaptic density and mature mushroom spines appears capable of sustaining memory performance despite overall spine loss, potentially accounting for compensatory processes observed in ageing and dementia (Aguilar-Hernández et al., 2025; Monroy et al., 2025). However, these effects are often region- and task-specific, and evidence in humans remains largely correlational.

The effectiveness of structural compensation depends on intracellular signalling. Individuals demonstrating resilience frequently exhibit preserved presynaptic function and inhibitory control, supported by GABAergic signalling and inhibitory interneuron activity (Morgan and Carlyle, 2024; van Dyck et al., 2024). These mechanisms may limit network hyperexcitability, maintain signal fidelity and reduce metabolic strain, thereby contributing to CR.

4.4 Mesoscale synaptic/circuit dynamics

Mesoscale circuit dynamics are critical regulators of CR, enabling adaptive neural reorganisation in response to pathology. CR depends on maintaining excitatory-inhibitory balance, with stable cortical excitatory and hippocampal inhibitory ensembles preventing amyloid-driven hyperconnectivity (Chen et al., 2026). Disruption of inhibitory signalling, including parvalbumin interneuron dysfunction and reduced GABA_A receptor α1 expression, promotes neuronal hyperactivity, whereas targeted activation of inhibitory microcircuits can reverse early cognitive deficits (Shu et al., 2023). Early Aβ-related circuit dysfunction is also reflected by interictal epileptiform spikes during low-arousal states, which may predict later memory decline (Shoob et al., 2023).

At the systems level, these adaptations extend across distributed functional brain networks.

4.5 Functional brain networks

CR relies on functional network integrity despite neuropathology. Stronger default mode network (DMN)-visual network coupling may compensate for hippocampal dysfunction and support cognition (Jiang et al., 2025). Reserve-related activity also involves inferior temporal and occipital regions, including the fusiform gyrus (Vockert et al., 2024). Dynamic connectivity studies further show that favourable risk profiles are associated with strongly connected and DMN-linked network states, whereas weaker connectivity patterns are linked to lower cognitive engagement and greater vascular or mood-related risk (Dautricourt et al., 2022). Together, these findings suggest that resilience depends on the brain's ability to maintain and redistribute connectivity across networks in response to pathology.

5 Glial cells

In addition to cellular mechanisms operating within neurons, glial cells are increasingly recognised as important mediators of CR and resilience. By regulating neural activity, metabolism, and inflammation, these cells influence the brain’s capacity to maintain cognition despite ageing and pathology.

5.1 Astrocytes

Astrocytes are increasingly recognised as key contributors to cognitive resilience, functioning as molecular and metabolic integrators rather than passive support cells. Supporting this view, a recent plasma proteomic study identified astrocyte ageing as the strongest cellular predictor of incident AD, with accelerated astrocyte ageing associated with greater disease risk (Ding et al., 2026).

Astrocytes support CR by maintaining synaptic signalling through neurotransmitter recycling, neurotrophic factor release, and Ca2+-mediated regulation of neural network activity (Zonca et al., 2025). They also act as metabolic buffers (Mason, 2017) and regulate cerebral blood flow via neurovascular coupling, suggesting that resilience may depend not only on neuronal capacity but also on astrocytic metabolic and vascular support (Attwell et al., 2010; Iadecola, 2017). However, the contribution of neurovascular function to CR remains relatively understudied.

5.2 Microglia

Microglia also play a key role in CR through both neuroprotective and immune-related functions. Adaptive microglial responses may promote resistance by limiting tau propagation (Jury-Garfe et al., 2024), supporting brain maintenance by reducing chronic inflammatory injury, and indirectly supporting cognitive resilience by preserving synaptic and circuit integrity. In addition to secreting anti-inflammatory cytokines and neurotrophic factors such as BDNF, microglia regulate synaptic pruning, refine neural circuits, and potentially enhance network efficiency (Schafer et al., 2012; Favuzzi et al., 2019; Li et al., 2023). However, excessive or chronic microglial activation is associated with greater amyloid and tau pathology and poorer cognition (Rossano et al., 2024).

Together, these findings suggest that adaptive microglial responses support CR, whereas chronic neuroinflammation undermines resilience.

5.3 Oligodendrocytes

Oligodendrocytes contribute to neural reserve by maintaining myelin integrity, processing speed and network integration (Nave, 2010). Although the causal role of demyelination in cognitive impairment remains uncertain, remyelination improves learning and memory in rodent models, suggesting that preserved myelin may enable more efficient cognitive processing (Pan et al., 2020; Steadman et al., 2020).

6 Discussion: therapeutics/translational perspectives

Despite increasing evidence linking these mechanisms to CR, several challenges limit their therapeutic translation. A major issue is the lack of standardised definitions and measurements of CR, which hinders reproducibility and cross-study comparisons (Stern et al., 2020). Furthermore, reliance on proxies such as education and occupation may introduce socioeconomic and cultural confounds (Melendez et al., 2026). The impact of reserve mechanisms is likely moderated by age, sex, socioeconomic factors and disease stage, variables that remain insufficiently addressed in current mechanistic studies. Another limitation is that most human evidence remains observational, whereas mechanistic support derives predominantly from experimental animal studies.

The field is also dominated by cross-sectional and correlational research. Longitudinal and interventional studies are needed to establish causality, particularly for potentially reversible mechanisms such as epigenetic regulation and glial modulation. Such work will clarify whether targeting these processes can enhance resilience and CR.

Future strategies may focus less on modifying pathology and more on preserving the compensatory mechanisms underlying CR (Zammit et al., 2024). Lifestyle interventions are promising, with exercise activating FNDC5/irisin and BDNF-TrkB signalling pathways that support hippocampal plasticity and cognitive resilience (Lourenco et al., 2019; Cheng et al., 2025). Dietary polyphenols similarly promote neurogenesis and reduce oxidative stress, while combined exercise and dietary interventions may further improve cognitive outcomes (Carrillo et al., 2025; Komulainen et al., 2021).

Emerging pharmacological therapies include TREM2 agonists, BDNF mimetics, APOE-targeted gene therapies, histone deacetylase inhibitors, NAD+ precursors, and GLP-1 receptor agonists (Jackson et al., 2024; Li et al., 2025; Numakawa and Kajihara, 2025; Qader et al., 2025). Circuit-level neuromodulation approaches, including transcranial magnetic stimulation, transcranial direct current stimulation, and sensory flicker stimulation, also show promise for enhancing synaptic plasticity and resilience (Adaikkan et al., 2019; Yu et al., 2019; Tang et al., 2021).

The most effective approaches will likely be multi-faceted, combining reserve-enhancing molecular, neuromodulatory and behavioural interventions. Future research should adopt multimodal and multi-omic approaches integrating molecular, genetic, epigenetic and neuroimaging markers of neural efficiency (Yang et al., 2025), including biomarkers of neurodegeneration and structural and functional measurements of network efficiency by imaging.

The framework generates several testable predictions. First, individuals with comparable amyloid and tau burden should differ in cognition according to multiscale reserve signatures combining genetic, glial, synaptic and network biomarkers. Second, reserve-enhancing lifestyle interventions should produce measurable changes in molecular and network markers before detectable cognitive effects emerge. Third, biomarkers of inhibitory network stability and synaptic integrity should predict resilience more strongly than pathology measures alone. So far, no biomarker has achieved broad acceptance. Finally, interventions targeting multiple levels simultaneously may produce larger effects than interventions focused on a single mechanism.

7 Conclusion

In conclusion, elucidating the mechanisms that underpin CR and resilience is essential for the development of targeted therapeutic strategies that may transform the current approach to neurodegenerative diseases, such as AD treatment. The factors discussed in this review should not be viewed as isolated processes but rather as components of an integrated mechanistic continuum, spanning genetic and environmental modifiers, molecular and cellular pathways, synaptic and network-level adaptations, and ultimately clinical phenotypes (Graphical Abstract). BDNF signalling illustrates a potential cross-level pathway whereby environmental exposures influence epigenetic regulation, synaptic plasticity, network efficiency, and ultimately resilient cognition. Importantly, although many mechanisms described here are related to AD, emerging CR-focused therapeutic approaches have the potential to extend beyond AD, offering novel avenues for promoting resilience and modifying disease trajectories across a broad range of neurological disorders. As our understanding of these interconnected mechanisms advances, harnessing CR may become a central strategy for preserving cognitive function and improving clinical outcomes in neurodegenerative disease.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2026.1957901/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Gessica Sala, University of Milano-Bicocca, Italy

Reviewed by: Minghui Wang, Air Force Medical University, China

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. We thank the Faculty Education Office (FEO) at Imperial College London for funding the publication costs.

Author contributions

AC: Writing – original draft. SB: Writing – original draft. YA: Writing – original draft. TB: Writing – original draft. RP: Conceptualization, Supervision, Validation, Writing – review & editing. MS: Conceptualization, Supervision, Validation, Writing – review & editing.

References

  1. Adaikkan Chinnakkaruppan, Middleton Steven J., Marco Asaf ‘ Gamma Entrainment Binds Higher-Order Brain Regions and Offers Neuroprotection ’. Neuron 102, no. (2019): 929 – 943.e8. doi:, doi:10.1016/j.neuron.2019.04.01
  2. Aguilar-Hernández L., Flores-Gómez G. D., Nacher J., Morales-Medina J. C., Flores G. (2025). Cerebrolysin ameliorates age-induced dendritic spine degeneration and memory decline in C57BL6 mice. Neurochem. Res. 51: 23. doi:,. doi:10.1007/s11064-025-04627-0
  3. Akers K. G., Martinez-Canabal A., Restivo L., Yiu A. P., De Cristofaro A., Hsiang H.-L. (2014). Hippocampal neurogenesis regulates forgetting during adulthood and infancy. Science 344, 598 – 602. doi:,. doi:10.1126/science.1248903
  4. Alkurd R., Mahrous L., Zeb F., Khan M. A., Alhaj H., Khraiwesh H. M.. (2024). Effect of calorie restriction and intermittent fasting regimens on brain-derived neurotrophic factor levels and cognitive function in humans: a systematic review. Med. (Mex.) 60: 191. doi:,. doi:10.3390/medicina60010191
  5. Alqahtani S. M., Al-kuraishy H. M., Al Gareeb A. I., Albuhadily A. K., Alexiou A., Papadakis M.. (2025). Unlocking Alzheimer’s disease: the role of BDNF signaling in neuropathology and treatment. Neuromolecular Med. 27: 36. doi:,. doi:10.1007/s12017-025-08857-x
  6. Anacker C., Hen R. (2017). Adult hippocampal neurogenesis and cognitive flexibility—linking memory and mood. Nat. Rev. Neurosci. 18, 335 – 346. doi:,. doi:10.1038/nrn.2017.45
  7. Arboleda-Velasquez Joseph F., Lopera Francisco, O’Hare Michael ‘ Resistance to Autosomal Dominant Alzheimer’s in an APOE3-Christchurch Homozygote: A Case Report ’. Nature Medicine 25, no. (2019): 1680 – 83. doi:,. doi:10.1038/s41591-019-0611-3
  8. Arnsten A. F. T., Perone I., Wang M., Yang S., Uchendu S., Bolat D.. (2025). Dysregulated calcium signaling in the aged primate association cortices: vulnerability to Alzheimer’s disease neuropathology. Front. Aging Neurosci. 17. doi: doi:10.3389/fnagi.2025.1610350
  9. Attwell D., Buchan A. M., Charpak S., Lauritzen M., Macvicar B. A., Newman E. A. (2010). Glial and neuronal control of brain blood flow. Nature 468, 232 – 243. doi:,. doi:10.1038/nature09613
  10. Benite-Ribeiro S. A., Barbosa H. C., Ramadan W., dos Santos J. M. (2023). Exercise-mediated increase in PGC1α and MEF2 expression in type 2 diabetes mellitus. Gene Rep. 31: 101758. doi: doi:10.1016/j.genrep.2023.101758
  11. Bhembre N., Boskovic Z., Willshaw J. L., Castello-Waldow T., Bonthron C., Paolino A.. (2025). Direct evidence for dendritic spine compensation and regeneration in Alzheimer’s disease models. Alzheimers Dement. 21: e70829. doi: doi:10.1002/alz.70829
  12. Boldrini M., Fulmore C. A., Tartt A. N., Simeon L. R., Pavlova I., Poposka V.. (2018). Human hippocampal neurogenesis persists throughout aging. Cell Stem Cell 22, 589 – 599.e5. doi:, doi:10.1016/j.stem.2018.03.015
  13. Carrillo Juan Ángel, Arcusa Raúl, Xandri-Martínez Raquel, Cerdá Begoña, Zafrilla Pilar, Marhuenda Javier. ‘ Impact of Polyphenol-Rich Nutraceuticals on Cognitive Function and Neuroprotective Biomarkers: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial ’. Nutrients 17, no. (2025): 601. doi:,. doi:10.3390/nu17040601
  14. Chang W., Hen R. (2024). “ Adult neurogenesis, context encoding, and pattern separation: a pathway for treating overgeneralization,” in Engrams: A Window into the Memory Trace, eds. Gräff J., Ramirez S. (Cham: Springer International Publishing), 163 – 193.
  15. Cheng Yang, Liu Yuxiang, Ma Jing, Li Zhen, Han Enli, Bo Shumin. ‘ Effects of Three Aerobic Exercise Modalities (Walking, Running, and Cycling) on Circulating Brain-Derived Neurotrophic Factor in Older Adults: A Systematic Review and Meta-Analysis ’. Frontiers in Aging Neuroscience 17 (September 2025). doi:,. doi:10.3389/fnagi.2025.1673786
  16. Chen Keying, Pineau Emma, Koletar Margaret ‘ Stable Neuronal Representations Underlie Cognitive Resilience to Alzheimer’s Disease Pathology ’. Alzheimer’s \u0026amp; Dementia 22, no. (2026): e71570. doi: doi:10.1002/alz.71570
  17. Clarke K. M., Etemadmoghadam S., Danner B., Corbett C., Ghaseminejad-Bandpey A., Dopler M.. (2025). The Nun Study: insights from 30 years of aging and dementia research. Alzheimer’s Dement. 21: e14626. doi:,. doi:10.1002/alz.14626
  18. Costa G. A., de Gusmão Taveiros Silva N. K., Marianno P., Chivers P., Bailey A., Camarini R. (2023). Environmental enrichment increased Bdnf transcripts in the prefrontal cortex: implications for an epigenetically controlled mechanism. Neuroscience 526, 277 – 289. doi:,. doi:10.1016/j.neuroscience.2023.07.001
  19. Danielson N. B., Kaifosh P., Zaremba J. D., Lovett-Barron M., Tsai J., Denny C. A.. (2016). Distinct contribution of adult-born hippocampal granule cells to context encoding. Neuron 90, 101 – 112. doi:,. doi:10.1016/j.neuron.2016.02.019
  20. Dautricourt Sophie, Gonneaud Julie, Landeau Brigitte ‘ Dynamic Functional Connectivity Patterns Associated with Dementia Risk ’. Alzheimer’s Research \u0026amp; Therapy 14, no.(2022): 72. doi:. doi:10.1186/s13195-022-01006-7
  21. Ding D. Y., Bot V. A., Chen K. L., Groves J. W., Pálovics R., Masuda D.. (2026). Plasma proteomic signatures of cellular aging predict human disease. Nat. Med. 32, 2060 – 2072. doi:,. doi:10.1038/s41591-026-04446-y
  22. Dumitrescu Logan, Mahoney Emily R., Mukherjee Shubhabrata ‘ Genetic Variants and Functional Pathways Associated with Resilience to Alzheimer’s Disease ’. Brain 143, no. (2020): 2561 – 75. doi:, doi:10.1093/brain/awaa209
  23. Ebrahimi R., Hatami S., Hashempoor A., Oraee S., Salarvandian S., Faramarzi A.. (2025). Role of mitochondrial calcium dysregulation in Alzheimer’s disease pathogenesis. Mol. Neurobiol. 63: 214. doi:,. doi:10.1007/s12035-025-05465-5
  24. Eissman Jaclyn M., Dumitrescu Logan, Mahoney Emily R. ‘ Sex Differences in the Genetic Architecture of Cognitive Resilience to Alzheimer’s Disease ’. Brain 145, no. (2022): 2541 – 54. doi:,. doi:10.1093/brain/awac177
  25. Favuzzi E., Deogracias R., Marques-Smith A., Maeso P., Jezequel J., Exposito-Alonso D.. (2019). Distinct molecular programs regulate synapse specificity in cortical inhibitory circuits. Science 363, 413 – 417. doi:,. doi:10.1126/science.aau8977
  26. Frechou M. A., Martin S. S., McDermott K. D., Huaman E. A., Gökhan Ş., Tomé W. A.. (2024). Adult neurogenesis improves spatial information encoding in the mouse hippocampus. Nat. Commun. 15: 6410. doi: doi:10.1038/s41467-024-50699-x
  27. Gassen N. C., Chrousos G. P., Binder E. B., Zannas A. S. (2017). Life stress, glucocorticoid signaling, and the aging epigenome: implications for aging-related diseases. Neurosci. Biobehav. Rev. 74, 356 – 365. doi:,. doi:10.1016/j.neubiorev.2016.06.003
  28. Guptarak J., Scaduto P., Tumurbaatar B., Zhang W. R., Jupiter D., Taglialatela G.. (2024). Cognitive integrity in non-demented individuals with Alzheimer’s neuropathology is associated with preservation and remodeling of dendritic spines. Alzheimers Dement. 20, 4677 – 4691. doi: doi:10.1002/alz.13900
  29. Hillary F. G., Grafman J. H. (2017). Injured brains and adaptive networks: the benefits and costs of hyperconnectivity. Trends Cogn. Sci. 21, 385 – 401. doi:,. doi:10.1016/j.tics.2017.03.003
  30. Huang J., Zhang F., Su M., Li J., Yi W., Hou L.. (2021). MeCP2 prevents age-associated cognitive decline via restoring synaptic plasticity in a senescence-accelerated mouse model. Aging Cell 20: e13451. doi:,. doi:10.1111/acel.13451
  31. Hulsman M., Ganz A. B., Zhang M., Koopmans F., Li K. W., Miedema S. S. M.. (2026). Cognitively healthy centenarian brains resist tau accumulation and exhibit lower APOE, early proteasome response and maintained β-oxidation. 26343581. doi: doi:10.64898/2026.01.08.26343581
  32. Iadecola C. (2017). The neurovascular unit coming of age: a journey through neurovascular coupling in health and disease. Neuron 96, 17 – 42. doi: doi:10.1016/j.neuron.2017.07.030
  33. Jackson Rosemary J., Keiser Megan S., Meltzer Jonah C. ‘ APOE2 Gene Therapy Reduces Amyloid Deposition and Improves Markers of Neuroinflammation and Neurodegeneration in a Mouse Model of Alzheimer Disease ’. Molecular Therapy 32, no. (2024): 1373 – 86. doi:,. doi:10.1016/j.ymthe.2024.03.024
  34. Jiang Lanli, Zhao Shaokun, Li Ting ‘ Cognitive Reserve in Subjective Cognitive Decline with Worry: DMN–VN Connectivity Supports Episodic Memory under Structural Vulnerability ’. BMC Geriatrics 25 (July 2025): 452. doi:,. doi:10.1186/s12877-025-06072-8
  35. Jury-Garfe N., Redding-Ochoa J., You Y., Martínez P., Karahan H., Chimal-Juárez E.. (2024). Enhanced microglial dynamics and a paucity of tau seeding in the amyloid plaque microenvironment contribute to cognitive resilience in Alzheimer’s disease. Acta Neuropathol. (Berl.) 148: 15. doi: doi:10.1007/s00401-024-02775-1
  36. Khalil M. H. (2024). Environmental enrichment: a systematic review on the effect of a changing spatial complexity on hippocampal neurogenesis and plasticity in rodents, with considerations for translation to urban and built environments for humans. Front. Neurosci. 18: 1368411. doi:,. doi:10.3389/fnins.2024.1368411
  37. Ko Hyunwoong, Kim Soyeon, Kim Kiwon ‘ Genome-Wide Association Study of Occupational Attainment as a Proxy for Cognitive Reserve ’. Brain 145, no. (2022): 1436 – 48. doi:, doi:10.1093/brain/awab35
  38. Komulainen Pirjo, Tuomilehto Jaakko, Savonen Kai ‘ Exercise, Diet, and Cognition in a 4-Year Randomized Controlled Trial: Dose-Responses to Exercise Training (DR’s EXTRA) ’. The American Journal of Clinical Nutrition 113, no. (2021): 1428. doi:, doi:10.1093/ajcn/nqab018
  39. Koulouri A., Zannas A. S. (2024). Epigenetics as a link between environmental factors and dementia risk. J. Alzheimer’s Dis. Rep. 8, 1372 – 1380. doi:,. doi:10.1177/25424823241284227
  40. Kramer Patricia L., Haiyan Xu, Woltjer Randall L. ‘Alzheimer Disease Pathology in Cognitively Healthy Elderly:A Genome-Wide Study’. Neurobiology of Aging 32, no. (2011): 2113 – 22. doi:. doi:10.1016/j.neurobiolaging.2010.01.010
  41. Li L., Chen R., Zhang H., Li J., Huang H., Weng J.. (2024). The epigenetic modification of DNA methylation in neurological diseases. Front. Immunol. 15: 1401962. doi:,. doi:10.3389/fimmu.2024.1401962
  42. Li Ling, Zheng Xiaoxiao, Ma Hongyue ‘ TREM2 in Neurodegenerative Diseases: Mechanisms and Therapeutic Potential ’. Cells 14, no. (2025): 1387. doi:,. doi:10.3390/cells14171387
  43. Li L., Jiang W., Yu B., Liang H., Mao S., Hu X.. (2023). Quercetin improves cerebral ischemia/reperfusion injury by promoting microglia/macrophages M2 polarization via regulating PI3K/Akt/NF-κB signaling pathway. Biomed. Pharmacother. 168: 115653. doi: doi:10.1016/j.biopha.2023.115653
  44. Liu H., Chen Y., Zhang J., Chen X. (2025). Adaptive immunity in the neuroinflammation of Alzheimer’s disease. Chin. Med. J. (Engl.) 138, 2116 – 2129. doi:,. doi:10.1097/CM9.0000000000003695
  45. Li X., Song R., Qi X., Xu H., Yang W., Kivipelto M.. (2021). Influence of cognitive reserve on cognitive trajectories. Neurology 97, e1695 – e1706. doi:,. doi:10.1212/WNL.0000000000012728
  46. Lopera Francisco, Marino Claudia, Chandrahas Anita S. ‘ Resilience to Autosomal Dominant Alzheimer’s Disease in a Reelin-COLBOS Heterozygous Man ’. Nature Medicine 29, no. (2023): 1243 – 52. doi:,. doi:10.1038/s41591-023-02318-3
  47. Lopez F. V., O’Shea A., Huo Z., DeKosky S. T., Trouard T. P., Alexander G. E.. (2025). Neurocognitive correlates of cerebral mitochondrial function and energy metabolism using phosphorus magnetic resonance spectroscopy in older adults. GeroScience 47, 2223 – 2234. doi:,. doi:10.1007/s11357-024-01403-w
  48. Lourenco Mychael V., Frozza Rudimar L., de Freitas Guilherme B. ‘ Exercise-Linked FNDC5/Irisin Rescues Synaptic Plasticity and Memory Defects in Alzheimer’s Models ’. Nature Medicine 25, no. (2019): 165 – 75. doi:,. doi:10.1038/s41591-018-0275-4
  49. Ma J., Qiu S. (2022). Genetic variant rs11136000 upregulates clusterin expression and reduces Alzheimer’s disease risk. Front. Neurosci. 16: 926830. doi:,. doi:10.3389/fnins.2022.926830
  50. Ma D., Pop N. (2024). Harnessing therapeutic potential of induced pluripotent stem cell–derived endothelial cells for remyelination in the central nervous system. Neural Regen. Res. 20, 1715 – 1716. doi:,. doi:10.4103/NRR.NRR-D-24-00209
  51. Mahapatra G., Gao Z., Bateman J. R. III, Lockhart S. N., Bergstrom J., DeWitt A. R.. (2023). Blood-based bioenergetic profiling reveals differences in mitochondrial function associated with cognitive performance and Alzheimer’s disease. Alzheimers Dement. 19, 1466 – 1478. doi: doi:10.1002/alz.12731
  52. Mason S. (2017). Lactate shuttles in neuroenergetics—homeostasis, allostasis and beyond. Front. Neurosci. 11: 43. doi:,. doi:10.3389/fnins.2017.00043
  53. McAvoy K. M., Scobie K. N., Berger S., Russo C., Guo N., Decharatanachart P.. (2016). Modulating neuronal competition dynamics in the dentate gyrus to rejuvenate aging memory circuits. Neuron 91, 1356 – 1373. doi:,. doi:10.1016/j.neuron.2016.08.009
  54. Melendez J. C., Venegas L. C., de la Fuente C. P. (2026). Cognitive reserve and its relationship with memory changes: an analysis of the Survey of Health, Aging, and Retirement in Europe (SHARE). Int. J. Geriatr. Psychiatry 41: e70190. doi:,. doi:10.1002/gps.70190
  55. Monroy E., Aguilar-Hernandez L., de la Cruz-López F., Flores G., Morales-Medina J. C. (2025). Dendritic spine degeneration is associated with age-related decline in recognition and spatial memory in male mice. Brain Struct. Funct. 230: 142. doi:,. doi:10.1007/s00429-025-03002-7
  56. Morgan G. R., Carlyle B. C. (2024). Interrogation of the human cortical peptidome uncovers cell-type specific signatures of cognitive resilience against Alzheimer’s disease. Sci. Rep. 14: 7161. doi:,. doi:10.1038/s41598-024-57104-z
  57. Mustafa Yousef, Main Leighanne R., Mews Makaela ‘Monocyte Inflammation and Resilience to Alzheimer’s Disease: Novel Genetic Risk Genes Identified by Transcriptome-Wide Association Study’. Translational Psychiatry 15, no. (2025): 481. doi:. doi:10.1038/s41398-025-03698-5
  58. Nave K.-A. (2010). Myelination and the trophic support of long axons. Nat. Rev. Neurosci. 11, 275 – 283. doi: doi:10.1038/nrn2797
  59. Numakawa Tadahiro, Kajihara Ryutaro. ‘ The Role of Brain-Derived Neurotrophic Factor as an Essential Mediator in Neuronal Functions and the Therapeutic Potential of Its Mimetics for Neuroprotection in Neurologic and Psychiatric Disorders ’. Molecules 30, no. (2025): 848. doi:,. doi:10.3390/molecules30040848
  60. Naguib Sarah, Chloe Lopez-Lee, Eileen Ruth Torres ‘The R136S Mutation in the APOE3 Gene Confers Resilience against Tau Pathology via Inhibition of the cGAS-STING-IFN Pathway Immunity 58 15, no. 8 (2025): 1931 – 1947. doi: doi:10.1016/j.immuni.2025.05.023
  61. Oh H. S.-H., Urey D. Y., Karlsson L., Zhu Z., Shen Y., Farinas A.. (2025). A cerebrospinal fluid synaptic protein biomarker for prediction of cognitive resilience versus decline in Alzheimer’s disease. Nat. Med. 31, 1592 – 1603. doi:,. doi:10.1038/s41591-025-03565-2
  62. Pan S., Mayoral S. R., Choi H. S., Chan J. R., Kheirbek M. A. (2020). Preservation of a remote fear memory requires new myelin formation. Nat. Neurosci. 23, 487 – 499. doi:,. doi:10.1038/s41593-019-0582-1
  63. Pegasiou C. M., Zolnourian A., Gomez-Nicola D., Deinhardt K., Nicoll J. A. R., Ahmed A. I.. (2020). Age-dependent changes in synaptic NMDA receptor composition in adult human cortical neurons. Cereb. Cortex 30, 4246 – 4256. doi:,. doi:10.1093/cercor/bhaa052
  64. Penalba-Sánchez L., Oliveira-Silva P., Sumich A. L., Cifre I. (2022). Increased functional connectivity patterns in mild Alzheimer’s disease: a rsfMRI study. Front. Aging Neurosci. 14: 1037347. doi:,. doi:10.3389/fnagi.2022.1037347
  65. Pettigrew Corinne, Nazarovs Jurijs, Soldan Anja ‘ Alzheimer’s Disease Genetic Risk and Cognitive Reserve in Relationship to Long-Term Cognitive Trajectories among Cognitively Normal Individuals ’. Alzheimer’s Research \u0026amp; Therapy 15, no. (2023): 66. doi:. doi:10.1186/s13195-023-01206-9
  66. Pettigrew C., Soldan A., Zhu Y., Cai Q., Wang M.-C., Moghekar A.. (2020). Cognitive reserve and rate of change in Alzheimer’s and cerebrovascular disease biomarkers among cognitively normal individuals. Neurobiol. Aging 88, 33 – 41. doi:,. doi:10.1016/j.neurobiolaging.2019.12.003
  67. Phillips Jared M., Dumitrescu Logan C., Archer Derek B. ‘ Novel Modelling Approaches to Elucidate the Genetic Architecture of Resilience to Alzheimer’s Disease ’. Brain 148, no. (2025): 2714 – 29. doi:,. doi:10.1093/brain/awaf106
  68. Ponomareva Natalya, Andreeva Tatiana, Protasova Maria ‘ Genetic Association Between Alzheimer’s Disease Risk Variant of the PICALM Gene and EEG Functional Connectivity in Non-Demented Adults ’. Frontiers in Neuroscience 14 (April 2020). doi:,. doi:10.3389/fnins.2020.00324
  69. Popugaeva E., Pchitskaya E., Bezprozvanny I. (2017). Dysregulation of neuronal calcium homeostasis in Alzheimer’s disease—a therapeutic opportunity?. Biochem. Biophys. Res. Commun. 483, 998 – 1004. doi: doi:10.1016/j.bbrc.2016.09.053
  70. Puang S. J., Elanggovan B., Ching T., Sng J. C. G. (2020). MEF2C and HDAC5 regulate Egr1 and Arc genes to increase dendritic spine density and complexity in early enriched environment. Neuronal Signal. 4: NS20190147. doi: doi:10.1042/NS20190147
  71. Qader Musaab Abdulrazzaq, Hosseini Leila, Abolhasanpour Nasrin ‘ A Systematic Review of the Therapeutic Potential of Nicotinamide Adenine Dinucleotide Precursors for Cognitive Diseases in Preclinical Rodent Models ’. BMC Neuroscience 26, no. (2025): 17. doi:,. doi:10.1186/s12868-025-00937-9
  72. Ridge Perry G., Karch Celeste M., Hsu Simon ‘ Linkage, Whole Genome Sequence, and Biological Data Implicate Variants in RAB10 in Alzheimer’s Disease Resilience ’. Genome Medicine 9, no. (2017): 100. doi:, doi:10.1186/s13073-017-0486-1
  73. Rossano S. M., Johnson A. S., Smith A., Ziaggi G., Roetman A., Guzman D.. (2024). Microglia measured by TSPO PET are associated with Alzheimer’s disease pathology and mediate key steps in a disease progression model. Alzheimers Dement. 20, 2397 – 2407. doi: doi:10.1002/alz.13699
  74. Schafer D. P., Lehrman E. K., Kautzman A. G., Koyama R., Mardinly A. R., Yamasaki R.. (2012). Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron 74, 691 – 705. doi:,. doi:10.1016/j.neuron.2012.03.026
  75. Sequeira-Cordero A., Brenes J. C., Vindas-Smith R. (2025). The role of BDNF on food intake and overweight: linking neuroplasticity and obesity. Prog. Neuropsychopharmacol. Biol. Psychiatry 141: 111457. doi:,. doi:10.1016/j.pnpbp.2025.111457
  76. Shatalina E., Whitehurst T. S., Onwordi E. C., Gilbert B. J., Rizzo G., Whittington A.. (2024). Mitochondrial complex I density is associated with IQ and cognition in cognitively healthy adults: an in vivo [18F]BCPP-EF PET study. EJNMMI Res. 14: 41. doi:,. doi:10.1186/s13550-024-01099-1
  77. Shoob Shiri, Buchbinder Nadav, Shinikamin Ortal ‘Deep Brain Stimulation of Thalamic Nucleus Reuniens Promotes Neuronal and Cognitive Resilience in an Alzheimer’s Disease Mouse Model’. Nature Communications 14, no. (2023): 7002. doi:. doi:10.1038/s41467-023-42721-5
  78. Shu Shu, Si-Yi Xu, Ye Lei ‘ Prefrontal Parvalbumin Interneurons Deficits Mediate Early Emotional Dysfunction in Alzheimer’s Disease ’. Neuropsychopharmacology 48, no. (2023): 391 – 401. doi:,. doi:10.1038/s41386-022-01435-w
  79. Soldan A., Pettigrew C., Cai Q., Wang J., Wang M.-C., Moghekar A.. (2017). Cognitive reserve and long-term change in cognition in aging and preclinical Alzheimer’s disease. Neurobiol. Aging 60, 164 – 172. doi:,. doi:10.1016/j.neurobiolaging.2017.09.002
  80. Sorrells S. F., Paredes M. F., Cebrian-Silla A., Sandoval K., Qi D., Kelley K. W.. (2018). Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults. Nature 555, 377 – 381. doi:,. doi:10.1038/nature25975
  81. Steadman P. E., Xia F., Ahmed M., Mocle A. J., Penning A. R. A., Geraghty A. C.. (2020). Disruption of oligodendrogenesis impairs memory consolidation in adult mice. Neuron 105, 150 – 164.e6. doi:,. doi:10.1016/j.neuron.2019.10.013
  82. Steinke I., Govindarajulu M., Pinky P. D., Bloemer J., Yoo S., Ward T.. (2023). Selective PPAR-delta/PPAR-gamma activation improves cognition in a model of Alzheimer’s disease. Cells 12: 1116. doi:,. doi:10.3390/cells12081116
  83. Stern Y. (2012). Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurol. 11, 1006 – 1012. doi:,. doi:10.1016/S1474-4422(12)70191-6
  84. Stern Y., Albert M., Barnes C. A., Cabeza R., Pascual-Leone A., Rapp P. R. (2023). A framework for concepts of reserve and resilience in aging. Neurobiol. Aging 124, 100 – 103. doi:,. doi:10.1016/j.neurobiolaging.2022.10.015
  85. Stern Y., Albert S., Tang M. X., Tsai W. Y. (1999). Rate of memory decline in AD is related to education and occupation: cognitive reserve?. Neurology 53, 1942 – 1947. doi:,. doi:10.1212/wnl.53.9.1942
  86. Stern Y., Arenaza-Urquijo E. M., Bartrés-Faz D., Belleville S., Cantilon M., Chetelat G.. (2020). Whitepaper: defining and investigating cognitive reserve, brain reserve and brain maintenance. Alzheimers Dement. J. Alzheimers Assoc. 16, 1305 – 1311. doi: doi:10.1016/j.jalz.2018.07.219
  87. Taddei R. N., Duff K. E. (2025). Synapse vulnerability and resilience underlying Alzheimer’s disease. eBioMedicine 112: 105557. doi:, doi:10.1016/j.ebiom.2025.105557
  88. Tang Alexander D., Bennett William, Bindoff Aidan D. ‘ Subthreshold Repetitive Transcranial Magnetic Stimulation Drives Structural Synaptic Plasticity in the Young and Aged Motor Cortex ’. Brain Stimulation 14, no. (2021): 1498 – 507. doi:,. doi:10.1016/j.brs.2021.10.001
  89. Tesi, Niccolo’, Sven van der Lee, Marc Hulsman ‘ Cognitively Healthy Centenarians Are Genetically Protected against Alzheimer’s Disease ’. Alzheimer’s \u0026amp; Dementia 20, no. (2024): 3864 – 75. doi: doi:10.1002/alz.13810
  90. van Dyck C. H., Mecca A. P., O’Dell R. S., Bartlett H. H., Diepenbrock N. G., Huang Y.. (2024). A pilot study to evaluate the effect of CT1812 treatment on synaptic density and other biomarkers in Alzheimer’s disease. Alzheimer’s Res. Ther. 16: 20. doi:,. doi:10.1186/s13195-024-01382-2
  91. Vries Luuk E., de, Aldo Jongejan, Jennifer Monteiro Fortes ‘Gene-Expression Profiling of Individuals Resilient to Alzheimer’s Disease Reveals Higher Expression of Genes Related to Metallothionein and Mitochondrial Processes and No Changes in the Unfolded Protein Response’. Acta Neuropathologica Communications 12, no. (2024): 68. doi:. doi:10.1186/s40478-024-01760-9
  92. Ward D. D., Summers M. J., Saunders N. L., Ritchie K., Summers J. J., Vickers J. C. ‘ The BDNF Val66Met Polymorphism Moderates the Relationship between Cognitive Reserve and Executive Function ’. Translational Psychiatry 5, no. (2015): e590 – e590. doi:,. doi:10.1038/tp.2015.82
  93. Xie X., Houtz J., Liao G.-Y., Chen Y., Xu B. (2023). Genetic Val66Met BDNF variant increases hyperphagia on fat-rich diets in mice. Endocrinology 164: bqad008. doi:,. doi:10.1210/endocr/bqad008
  94. Xu Xuan, Wang Hui, Bennett David A., Zhang Qing-Ye, Meng Xiang-Yu, Zhang Hong-Yu. ‘ Characterization of Brain Resilience in Alzheimer’s Disease Using Polygenic Risk Scores and Further Improvement by Integrating Mitochondria-Associated Loci ’. Journal of Advanced Research 56 (February 2024): 113 – 24. doi:,. doi:10.1016/j.jare.2023.03.002
  95. Yang Z., Sheng J., Zhang Q., Zhao X., Song Y., Dong G.. (2025). Multimodal imaging markers of cognitive resilience and molecular mechanisms of brain resilience in Alzheimer’s disease. J. Integr. Neurosci. 24: 26584. doi:,. doi:10.31083/JIN26584
  96. Yu Ting-Hsuan, Yi-Jen Wu, Chien Miao-Er, Hsu Kuei-Sen. ‘ Transcranial Direct Current Stimulation Induces Hippocampal Metaplasticity Mediated by Brain-Derived Neurotrophic Factor ’. Neuropharmacology 144 (January 2019): 358 – 67. doi:,. doi:10.1016/j.neuropharm.2018.11.012
  97. Zammit Andrea R., Bennett David A., Buchman Aron S.. ‘ From Theory to Practice: Translating the Concept of Cognitive Resilience to Novel Therapeutic Targets That Maintain Cognition in Aging Adults ’. Frontiers in Aging Neuroscience 15 (January 2024). doi:. doi:10.3389/fnagi.2023.1303912
  98. Zocher S., Overall R. W., Lesche M., Dahl A., Kempermann G. (2021). Environmental enrichment preserves a young DNA methylation landscape in the aged mouse hippocampus. Nat. Commun. 12: 3892. doi:,. doi:10.1038/s41467-021-23993-1
  99. Zocher S., Schilling S., Grzyb A. N., Adusumilli V. S., Bogado Lopes J., Günther S.. (2020). Early-life environmental enrichment generates persistent individualized behavior in mice. Sci. Adv. 6: eabb1478. doi:,. doi:10.1126/sciadv.abb1478
  100. Zonca L., Bellier F. C., Milior G., Aymard P., Visser J., Rancillac A.. (2025). Unveiling the functional connectivity of astrocytic networks with AstroNet, a graph reconstruction algorithm coupled to image processing. Commun. Biol. 8: 114. doi:,. doi:10.1038/s42003-024-07390-0

Citation

Chen, A., et al. (2026). Molecular mechanisms underpinning cognitive reserve and resilience. Frontiers in Aging Neuroscience, Volume 18. https://doi.org/
Received
Accepted
Published
Volume
18 - 2026

Copyright

Copyright © 2026 Chen, Bhuvaneshwaran, Akkara, Burke, Perneczky and Sastre.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Magdalena Sastre

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0

Related articles

ReviewApplication of large language models in early screening of Alzheimer’s disease: a scoping reviewJianan Tian, Weidong Huang, Yongmei Wen, Yifan LiangCase ReportCase Report: Longitudinal evaluation of cerebrospinal fluid Alzheimer’s disease core biomarkers in a case of refractory late-onset epilepsy of unknown etiologyTakafumi Wada, Akira Kuzuya, Kazuma Koda, Sakiho Ueda, Atsushi Shima, Nobukatsu Sawamoto, Akio Ikeda, Ryosuke Takahashi, Riki MatsumotoOriginal ResearchA clustering approach based on neuropsychological testing to detect early signs of probable MCI among community-dwelling older adultsRafael Mauti, Chiraz Chouk, Aymeric Guillot, Arnaud Peronin, Patrick Fargier, Pascal Chabaud