# Mechanisms of physiological brain aging

## Conceptual Framework of Cognitive Aging

The human central nervous system undergoes continuous structural, functional, and molecular transformations across the lifespan. Historically, research conflated age-related cognitive decline with early-stage pathological neurodegeneration. However, physiological brain aging is a distinct biological trajectory characterized by a gradual decline in select cognitive domains—such as cognitive processing speed, working memory, and executive function—while maintaining largely intact activities of daily living and semantic knowledge [cite: 1, 2]. Pathological neurodegeneration, encompassing conditions such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), involves catastrophic cellular loss, extensive brain atrophy, and the accumulation of neurotoxic protein aggregates [cite: 3, 4].

In stark contrast, normal cognitive aging is driven by subtle, widespread, and interconnected alterations in synaptic integrity, epigenetic regulation, glial state transitions, white matter microstructure, and cerebrovascular hemodynamics [cite: 3, 4]. Isolating these mechanisms requires examining the specific variables that mediate biological decline independently of overt disease. This analysis explores the multi-scalar determinants of physiological brain aging, moving from intracellular molecular networks to systemic physiological influences.

## Cellular Proteostasis and Mitochondrial Dynamics

### Proteostatic Collapse

At the intracellular level, physiological aging is precipitated by a progressive decline in protein homeostasis (proteostasis). Proteostasis relies on a tightly regulated equilibrium between protein synthesis, folding, and degradation. Experimental models of accelerated aging, such as the turquoise killifish (*Nothobranchius furzeri*), demonstrate that normal aging intrinsically disrupts the central machinery responsible for protein manufacturing [cite: 5]. During physiological aging, there are observable dysregulations in amino acid concentrations, transfer RNA (tRNA) availability, and messenger RNA (mRNA) translation dynamics, leading to increased aggregation and dysfunction in synthesis processes [cite: 5].

Unlike AD, where specific hallmark proteins (amyloid-beta and hyperphosphorylated tau) form dense extracellular plaques and intracellular tangles, normal aging involves a generalized, low-grade accumulation of misfolded proteins. This occurs due to age-related impairments in the unfolded protein response (UPR) and the induction of chronic endoplasmic reticulum (ER) stress [cite: 6, 7]. Furthermore, nutrient-sensing pathways that govern cellular metabolism undergo significant shifts; while the mechanistic target of rapamycin (mTOR) signaling pathway and growth hormone levels naturally decrease during physiological aging, pathological aging is often associated with hyperactivated mTOR pathways and localized brain insulin resistance [cite: 3].

### Mitochondrial Dysfunction and Oxidative Stress

Mitochondrial integrity is central to maintaining the high bioenergetic demands of neural tissue. Because the brain accounts for a disproportionate amount of reactive oxygen species (ROS) consumption, it exhibits extreme susceptibility to oxidative stress [cite: 8]. In normal aging, neural mitochondria undergo profound structural modifications, including morphological enlargement and progressive fragmentation, alongside a measurable reduction in oxidative phosphorylation and the accumulation of mitochondrial DNA (mtDNA) mutations [cite: 3]. 

The age-related impairment of mitophagy—the selective macroautophagic clearance of damaged mitochondria—results in severe bioenergetic insufficiency [cite: 9]. The consequent overproduction of ROS damages critical cellular components, including genomic DNA, leading to genomic instability. In normal aging, accumulated DNA damage predominantly affects the promoter regions of genes associated with synaptic plasticity, mitochondrial function, and neuronal survival, thereby diminishing their baseline expression [cite: 3]. 

## Genomic Instability and Epigenetic Clocks

### Epigenetic Modifications in Neural Tissue

Epigenetic modifications provide a mechanism by which environmental, metabolic, and chronological factors influence brain aging without altering the underlying DNA sequence. Normal brain aging is associated with highly specific epigenetic shifts, including an increase in histone methylation and a concurrent decrease in histone acetylation in the promoter regions of synaptic genes [cite: 3]. For example, healthy aging is associated with a significant accumulation of the histone marker H4K16ac, whereas pathological neurodegeneration entails a marked loss of H4K16ac near genes essential for cellular survival [cite: 3]. 

### DNA Methylation Clocks and Biological Age Acceleration

Advancements in DNA methylation (DNAm) have enabled the development of "epigenetic clocks" that quantify biological aging as distinct from chronological aging. First-generation clocks (e.g., Horvath and Hannum) were trained strictly on chronological age, while second- and third-generation clocks, such as DNAm PhenoAge, GrimAge, AgeAccelGrim2, and DunedinPACE, incorporate physiological biomarkers and mortality risk factors to estimate the precise pace of biological decline [cite: 10, 11]. 

Accelerated biological aging, as measured by these advanced clocks, strongly correlates with structural brain atrophy, reduced cognitive fitness, and a statistically higher risk of developing mild cognitive impairment (MCI) [cite: 11, 12]. Longitudinal cohorts, such as the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) comprising 2,671 participants, demonstrate that between-visit increases in age acceleration for GrimAge and PhenoAge are significantly associated with a greater risk of global cognitive decline [cite: 13, 14].

The application of machine learning to 1,397 healthy human cortex samples has further refined these tools, yielding brain-specific epigenetic clocks based on 347 distinct methylation sites calibrated specifically to central nervous system tissue [cite: 15]. Utilizing these cell-type resolution clocks, researchers have discovered that different neural cell populations age at disparate rates. Glial cells, for instance, exhibit accelerated epigenetic aging compared to neurons, suggesting that non-neuronal cells may act as primary drivers of the epigenetic aging trajectory within the brain [cite: 16].

## Synaptic Degradation and Structural Connectivity

### Debunking the Neuronal Cell Death Model

A pervasive and historical misconception regarding brain aging is the assumption that the process inevitably involves massive, widespread neuronal cell death. Modern neuroanatomical studies and stereological counting methods have conclusively debunked this notion. If an individual ages without developing a neurodegenerative disorder, they do not lose a statistically significant number of cortical neurons [cite: 4, 17]. Postmitotic, terminally differentiated neurons generally survive throughout the human lifespan under normal physiological conditions [cite: 6, 7].

Instead, the cognitive decline observed in healthy aging is predominantly driven by alterations at the synapse [cite: 4, 7]. Normal aging is characterized by a significant reduction in dendritic branching, decreased spine density, and the functional downregulation of synaptic genes, particularly within the prefrontal cortex and the hippocampus [cite: 3].

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 The disruption of intercellular communication and micro-circuit integrity, rather than the outright apoptosis of neuronal cell bodies, accounts for observed deficits in working memory, attention, and executive function [cite: 3]. This distinction is therapeutically critical: because the neuronal soma remains alive in physiological aging, interventions aimed at restoring synaptic health and plasticity possess a viable target, whereas reversing cell death in advanced neurodegeneration remains an insurmountable barrier [cite: 4, 17].



### The Adult Hippocampal Neurogenesis Debate

The functional relevance of adult hippocampal neurogenesis (AHN) in humans remains one of the most fiercely debated topics in neurobiology. While it is well-established that neurogenesis persists in the dentate gyrus of adult rodents, providing a continuous source of new neurons that integrate into memory circuits, evidence in humans is highly contested [cite: 6, 18]. 

The advent of single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) was initially anticipated to resolve this controversy by identifying precise transcriptomic signatures of neurogenic populations. However, recent studies have yielded profoundly conflicting conclusions [cite: 19, 20]. Certain profiling studies have identified sparse populations of cells with neural stem cell (NSC) or immature neuronal characteristics in the adult human hippocampus [cite: 19, 21]. Conversely, rigorous cross-species comparisons have reported a sharp developmental drop in neurogenesis during infancy, reaching undetectable levels in adult human tissue [cite: 18].

These contradictions stem from several profound methodological and conceptual challenges. First, traditional immunohistochemical markers for immature neurons, such as doublecortin (DCX), are not exclusively specific to neurogenic lineages in humans. DCX expression has been detected in non-neurogenic cell types, including mature astrocytes and interneurons, leading to false positives [cite: 21]. Second, adult radial glial-like cells (RGLs), the purported NSCs, exhibit transcriptomic profiles that overlap heavily with mature astrocytes. Identifying human-specific marker genes to segregate these populations remains difficult, as there is minimal overlap between human and rodent neurogenic markers [cite: 19]. 

Finally, the quality of postmortem human brain tissue introduces significant confounding variables. Postmortem delay (PMD), tissue processing methodologies, and the inherent neuroinflammatory state of the donor prior to death can significantly alter RNA stability and marker expression [cite: 18, 19]. Given these constraints, current consensus reflects high uncertainty, suggesting a highly restricted or negligible rate of *de novo* AHN in adult humans. This indicates that the human hippocampus relies primarily on other forms of synaptic plasticity, rather than the generation of new neurons, to maintain cognitive function during aging [cite: 18, 21].

## Microglial Trajectories and State Transitions

### From Homeostasis to Dystrophy

Microglia, the resident innate immune macrophages of the central nervous system, act as the primary surveyors of the brain microenvironment. During youth, microglia exist in a homeostatic state characterized by a highly ramified morphology, executing vital functions such as synaptic pruning, tissue repair, and debris phagocytosis [cite: 22, 23]. However, physiological aging induces a progressive, irreversible loss of this homeostatic profile.

As the brain ages, microglia transition into intermediate, stress-responsive states before adopting a fully "primed" or dystrophic phenotype [cite: 22, 24]. Single-cell RNA-sequencing and pseudotime trajectory analyses have intricately mapped these intermediate states. Initially, mitochondrial dysfunction and systemic inputs trigger a stress response, leading to the transient upregulation of immediate early genes (e.g., *Jun*, *Fos*, *Klf2*) and the purinergic receptor gene *P2ry12* [cite: 25, 26]. Products of this stress response, notably transforming growth factor beta 1 (TGFβ1), operate through autocrine signaling mechanisms in an attempt to return the microglial cells to homeostasis [cite: 22, 24, 27]. 

### The Phenotype of the Primed Microglia

Over time, this compensatory stress response fails. Aged microglia experience a downregulation of essential homeostatic transcription factors (such as RUNX1, IRF8, and PU.1) and an upregulation of genes associated with innate immune activation, including *B2m*, *Apoe*, *Cd48*, *Lyz2*, and major histocompatibility complex (MHC) presentation pathways [cite: 25, 28]. 

Morphologically, these cells become dystrophic—exhibiting deramified, thickened, and retracted processes [cite: 23]. Functionally, primed microglia exhibit severely diminished phagocytic capacity but become hyper-reactive to secondary inflammatory stimuli. They chronically release pro-inflammatory cytokines such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) [cite: 23, 28]. 

This microglial activation is not uniform across the brain. Quantitative analyses of the lysosomal marker CD68 and the pro-inflammatory transcription factor NFKB p65 reveal striking spatial heterogeneity within the aging hippocampus. Regions such as the granule cell layer (GC), hilus, CA3, and outer CA1 show robust, age-related microglial activation, whereas the molecular layer (ML) and inner CA1 remain relatively devoid of these age-related inflammatory changes [cite: 24]. This chronic, low-grade, region-specific neuroinflammation—often termed "inflammaging"—directly contributes to the synaptic dysfunction underlying cognitive decline, independent of specific disease pathology [cite: 22, 23, 26].

## Cerebrovascular Integrity and Fluid Dynamics

### Blood-Brain Barrier Permeability

The neurovascular unit (NVU)—comprising cerebral endothelial cells, pericytes, astrocytes, and the basement membrane—forms the blood-brain barrier (BBB), a highly selective physiological filter maintaining brain homeostasis [cite: 29, 30]. Normal aging exerts a profound, deleterious impact on both the structural and functional integrity of the BBB.

Age-related BBB breakdown is initiated by the reduced expression of key tight junction proteins, specifically claudin-5, occludin, and zonula occludens-1 (ZO-1) [cite: 8, 29]. Concurrently, the capillary network undergoes morphological degradation, involving the detachment of pericytes, the swelling of astrocytic end-feet, and a reorganization of the endothelial actin cytoskeleton. This cellular disruption is heavily driven by chronic oxidative stress acting through the RhoA, PI3, and PKB/Akt signaling pathways [cite: 8]. 

Beyond paracellular tight junction failure, physiological aging disrupts tightly regulated transcellular transport mechanisms. There is a documented age-dependent downregulation of major facilitator superfamily domain-containing protein 2A (MFSD2A), a critical lipid transporter that normally suppresses transcytosis [cite: 29, 30]. The loss of MFSD2A derepresses caveolin-1, leading to an abnormal increase in non-specific vesicular transport across the endothelium. Additionally, aging reduces the expression of essential transporters like GLUT1 (affecting glucose delivery), LRP1, and P-glycoprotein, while upregulating RAGE receptors [cite: 29]. 

Consequently, neurotoxic blood-derived proteins (such as albumin and fibrinogen) leak into the brain parenchyma, provoking localized neuroinflammation and vasogenic edema. These microvascular leakages are detectable as white matter hyperintensities on dynamic contrast-enhanced magnetic resonance imaging (MRI). Because BBB permeability frequently manifests earlier than observable structural brain atrophy, microvascular dysfunction serves as a primary, upstream driver of emerging cognitive impairment [cite: 8, 31].

### The Glymphatic System and Fluid Clearance

The glymphatic system is a brain-wide perivascular network responsible for the exchange of subarachnoid cerebrospinal fluid (CSF) and brain interstitial fluid (ISF), facilitating the clearance of metabolic waste products, including soluble amyloid-beta, tau, and lactate [cite: 32, 33]. This convective bulk flow is primarily mediated by aquaporin-4 (AQP4) water channels densely localized on the astrocytic end-feet that ensheath the cerebrovasculature [cite: 33, 34].

Recent advanced neuroimaging techniques, specifically diffusion-based intravoxel incoherent motion (IVIM) and perfusion-based multi-echo arterial spin labeling (ASL), have successfully differentiated the specific effects of physiological aging from those of sleep disruption on human glymphatic dynamics [cite: 32]. 

Research indicates that older adults exhibit significantly reduced cortical cerebral blood flow (CBF) and a shortened trans-vascular water exchange time (Tex) between the vasculature and the perivascular space, pointing to an impaired fluid transfer efficiency into the brain parenchyma [cite: 32]. Interestingly, while acute sleep deprivation primarily reduces the mobility of CSF within the large subarachnoid spaces (measured by pseudodiffusion coefficient, D*), the microvascular perfusion deficits and altered transendothelial exchange are uniquely driven by chronological aging [cite: 32]. The impairment of this fluid transport system over decades results in a sluggish clearance of metabolic byproducts, contributing heavily to the sterile neuroinflammation and proteostatic collapse characteristic of the aging brain [cite: 33, 35].

## Intracortical Myelin Degradation

Myelin, the lipid-rich sheath insulating axonal tracts, is critical for the rapid transduction of action potentials and the precise synchrony of neural networks. The myelination of the human cerebral cortex is not a static developmental event limited to childhood, but a lifelong plastic process responsive to learning and memory [cite: 36]. Utilizing ultra-high field (7 Tesla) quantitative MRI to measure longitudinal relaxation rates (R1) and transverse relaxation rates (R2), researchers have accurately mapped the trajectory of intracortical myelin across the human lifespan [cite: 36, 37].

The trajectory of cortical myelination follows a distinct quadratic, inverted-U shape. Myelin volume fractions increase progressively through adolescence and early adulthood, reaching a global peak in the late 30s to early 40s, before undergoing a protracted decline beginning in the fifth and sixth decades of life [cite: 37, 38, 39]. 

Crucially, this age-related demyelination is anatomically heterogeneous. The precise timeline of myelin maturation and eventual degradation heavily depends on the functional role of the specific cortical region, as outlined in the data comparison below.

### Comparative Timelines of Regional Myelin Density

| Cortical Region | Maturation Profile | Peak Age of Myelin Density | Age-Related Degradation Pattern |
| :--- | :--- | :--- | :--- |
| **Visual Cortex (e.g., Splenium)** | Early-myelinating | ~34 years | Remains relatively stable during normal aging; highly resistant to breakdown [cite: 37, 40, 41]. |
| **Motor & Premotor Areas** | Intermediate-myelinating | ~38 years | Gradual decline following peak; follows standard global brain averages [cite: 37, 41]. |
| **Prefrontal Cortex (e.g., Genu)** | Late-myelinating | Early to Mid 40s | Exceptionally vulnerable; undergoes the steepest and most significant age-related degradation [cite: 37, 40]. |

Because the prefrontal cortex governs higher-order executive function, working memory, and cognitive processing speed (CPS), the degradation of myelin integrity in this specific topography acts as a direct biological mediator of age-related cognitive slowing. Statistical models confirm that the breakdown of late-myelinating white matter (LMWM) mediates the relationship between advanced age and slowing CPS, indicating that myelin loss is a primary structural mechanism driving normative cognitive decline [cite: 36, 40].

## Systemic Milieu and Heterochronic Parabiosis

### The Influence of Circulating Factors

The brain does not age in isolation; it is continuously exposed to a systemic milieu of circulating proteins, lipids, and extracellular vesicles. The profound influence of these systemic factors on brain aging has been elucidated through heterochronic parabiosis—a surgical experimental model wherein the circulatory systems of young and old animals are conjoined to share a common blood supply [cite: 42, 43, 44]. 

These studies conclusively demonstrate that the systemic environment is capable of driving or reversing cellular aging mechanisms. Exposing an aged brain to young blood can restore BBB integrity, enhance neurovascular coupling, increase capillary density, and reduce the burden of senescent cells [cite: 42, 44, 45]. Conversely, exposing a young animal to aged blood rapidly induces vascular dysfunction, reduces neurogenesis, and triggers neuroinflammation, driven heavily by pro-geronic factors circulating in older plasma, such as CCL11, osteopontin, and beta-2 microglobulin (B2M) [cite: 42, 43]. 

### The GDF11 Controversy and Novel Rejuvenating Factors

The search for specific "rejuvenating" anti-geronic factors has yielded several candidates, though the field is marked by ongoing controversy. Growth Differentiation Factor 11 (GDF11), a member of the TGF-β superfamily, was initially identified as a potent systemic factor that declines with age. Early reports suggested that exogenous administration of recombinant GDF11 could reverse age-related cardiac hypertrophy and stimulate neurogenesis and blood vessel density in the aging brain [cite: 46, 47, 48]. 

However, subsequent studies generated highly conflicting results regarding its safety, efficacy, and its true age-related trajectory. A major source of this controversy lies in the biochemical structure of GDF11, which shares 90% homology with GDF8 (myostatin), differing by only 11 amino acids in their mature signaling domains [cite: 46, 49]. Because many commercial antibodies cross-react with both proteins, initial measurements of age-related decline were called into question. While recent rigorous assays confirm that GDF11 is biochemically distinct and significantly more potent at inducing SMAD2 phosphorylation than GDF8, the dose-dependent effects and exact therapeutic viability of GDF11 for human brain rejuvenation remain unresolved [cite: 46, 49].

More recently, pigment epithelium-derived factor (PEDF) has emerged as a novel, less contested circulating protein that declines with mammalian aging. Utilizing *in vitro* heterochronic parabiosis screening systems, researchers discovered that PEDF extended the replicative lifespan of primary human fibroblasts. When systemically administered to aged mice, PEDF reversed age-related functional decline across multiple tissues and measurably improved cognitive function, positioning it as a highly promising mediator of systemic homeostasis [cite: 45].

## Diagnostic Biomarkers of Neurodegeneration Versus Aging

Differentiating physiological brain aging from the preclinical stages of neurodegeneration is a critical clinical challenge. While normal aging features generalized structural atrophy and mild memory changes, pathological aging is defined by the localized, toxic accumulation of specific misfolded proteins. 

Recent breakthroughs in blood-based and CSF biomarkers allow for highly sensitive, minimally invasive stratification of these divergent trajectories. The table below outlines the differential behavior of primary biomarkers in normal aging versus early Alzheimer's disease.

| Biomarker Profile | Physiological Brain Aging | Preclinical / Early Neurodegeneration (AD) |
| :--- | :--- | :--- |
| **Plasma p-tau217 & p-tau231** | Stable; remains well below established diagnostic thresholds (e.g., < 0.09 pg/mL) [cite: 50]. | Highly elevated; indicates active tau phosphorylation and correlates strongly with early amyloidosis before PET positivity [cite: 50, 51]. |
| **Neurofilament Light (NfL)** | Gradual, subtle increase reflecting normal, age-related myelin and axonal turnover [cite: 52, 53]. | Sharp, significant elevation reflecting acute neuroaxonal injury and rapid neuronal death [cite: 52, 53]. |
| **Aβ42 / Aβ40 Ratio (CSF)** | Remains normal or shows minor, non-pathological shifts reflecting steady clearance [cite: 54, 55]. | Significantly decreased, indicating abnormal brain retention and the formation of Aβ plaques [cite: 54, 55]. |
| **Glial Fibrillary Acidic Protein (GFAP)** | Mild elevation corresponding to normal microglial priming and stress responses [cite: 53]. | Highly elevated, indicating severe, chronic astrogliosis and advanced neuroinflammation [cite: 53]. |

Plasma phosphorylated tau 217 (p-tau217) and p-tau231 have emerged as the most specific indicators of early Alzheimer's disease pathology, rising significantly even in the preclinical, cognitively unimpaired stages [cite: 50, 51, 52]. P-tau217 strongly correlates with early neocortical amyloid-beta (Aβ) accumulation and can accurately distinguish AD from typical age-related cognitive decline with high sensitivity and specificity [cite: 50, 51]. In contrast, Neurofilament light chain (NfL) is a highly sensitive but disease-agnostic marker of neuroaxonal injury; it will rise in AD, ALS, or following a stroke, making it an excellent measure of generalized brain damage but insufficient for diagnosing specific proteinopathies alone [cite: 51, 52, 53].

## Global Longitudinal Cohorts and Cognitive Resilience

Despite facing highly similar biological and molecular changes, individual humans exhibit vastly different cognitive trajectories as they age. The study of these diverse outcomes relies heavily on massive, multi-decade global longitudinal cohorts. Prominent examples include the Chinese Longitudinal Healthy Longevity Survey (CLHLS), the Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSA), the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA), and the Ibadan Study of Aging in Nigeria [cite: 13, 56, 57, 58, 59].

Group-based trajectory modeling applied to these diverse populations consistently demonstrates that older adults segregate into distinct psychological resilience groups: persistent high resilience, persistent middle resilience, and decreasing resilience [cite: 56, 57]. Individuals whose psychological resilience declines over time experience a drastically higher hazard ratio (HR = 2.46 in the CLHLS cohort) for developing cognitive impairment, entirely independent of their baseline chronological age or basic biological markers [cite: 57, 60]. 

Factors such as higher educational attainment, the rigorous clinical management of vascular risk factors (e.g., hypertension and obstructive sleep apnea), and consistently low depression indices interact directly with the brain's biological state [cite: 31, 61, 62]. These lifestyle and psychological factors build "cognitive reserve," mitigating the neurotoxic effects of microvascular damage, counteracting the slowing of cognitive processing speed caused by prefrontal myelin degradation, and preserving high-level executive function throughout the physiological aging process [cite: 31, 61].

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47. [aging-us.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHxLT1Wbw-K4-5lFzJNcUsAygsDqpfWu_y8ktkekuHxIHl5ffTJpnkoLAmmEeoOFATl3bvTg6-gWj9Gwz2xSyrh63U7oifemcw1M6c5pgItwbuSWonod9qd5zc8luD81Y1UbA==)
48. [elevian.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFbIhyvkgyjTSNZEPiDYUtdd4R4Ayxcd5qmc77I64VdxKlqcmhZ6gNGhSI4sUcFHXOwgzClcdV3eywcwzb4c0kmCst6FKiiyXIgmCYG35z6-ubZFQJcc_gtcQX_5JE=)
49. [alzdiscovery.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHaPRUteVGfvzSfQi1flukVXVvHt-YZ1Q2ClSjC4xWOzcuW2DZdO95ttgzV6GdbEKCsg3Kr6jwg18L9wUmtQ5USrSZ41Ig7CL-Gw-pvU94kjBa7Z51EZqlpQqS_9vhHbdCIoml9dfuVM17jFNK9Cuep30jYVJDf5plkc3OeKZ4a7M2tJTmTb8V7IugNF7WbLTquDgY894FBNd3ZGimMeuQxKQ==)
50. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHQC-Oq-2gEpveOaNIjagseUb0GsMQTFzYVRPHLB5bgDH67CkKicMqY0Dg6KRYOEHMvtLhPureTAAD1uxToetYFYlWyR8ODTzl9MEZd3Lyd5WuN9kZx5p0qVD0x4Zm0heMm-DRDcqtxwA==)
51. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFC_gNNl8XgH3DeNZDPsV_UJwNb0Bt_TQ6U_b99OZx42HBsP8A85ifCvina7GdsATX6Xe5wGVtUDCY3KFdq-ZEvB1DuLTn0VbrQrYwolasgqxHimmt_9zq7X230JXrPxW2FCFORAlDb5Q==)
52. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFeI2MMGajQrmoBFETXcT8haG8BXHKhQkh0q77VVpUvvTrwOrrMsG07yCI6Tiw33mHqnQN1ZT678LyYpFTrPmjMr1ZrC1VZrZdXYxfK973Qp3XALIL5IaYenmLlYGDuJD7YpiUleJoCog==)
53. [mdpi.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFAxrpf34I0MOq5KRrRsgSs7JCizESARNYgq7YILpR0uW4n6lapEs0bopUkTHt_K8GEdBiGdLz7Swea2OQhHYXbC1W8BHB3oMJfiDqUIw8a77MWPwoAxKzKnqeX9ZW2Sg==)
54. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHbxROr-2E6Vo37QcBBQuzwU-F98cWqq86_xlA8ITYay-y01CPEmxagNepr5vu9FpT-DZ9K8frOdDGKBnhu8ftH7eQpdOFmNkH4SfHbe74gj1DrOhOlGGwXH3TqthMzGZT8Rtc3eMCt)
55. [unicatt.it](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEx3jV3R03-6dc375Cinj-04BXJB2JyFyJZV7J3ghOZVo-zIQibAUDmcAOtUisBsHnAgHzuuLgME1DxFl2pyhOev-wE6NC-nTHQaWstxXIikC9MmkjjZUq_K-z8yZY169W7549Sxt3wBXa3Oc2QYPCllEavlHmvS35gomzepmFS2vwp-kbVkGIQCacEjvG7z8alPkbv7F6Dd4rjLoyyna9ort0=)
56. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFUCCd_6Z_IfeoRaLPxDxCXn-_hVkVMw7QKU2Wq6b02ssZPdAqHG1o2TQJCxat34uRmmQqQdVOiJBjAYFjy0VTEGnIqn0YYcpF8Cr9QYatTRjrWdt-FkVPPYGbpTSVbYg==)
57. [medrxiv.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHWo9o9dk6ANdHUEa6Anc4ROrEM17GjUnfuwkXmUtdm1mbsjumWVXu3iLjjGZCAAT7UI_xhblEv77uvuVM546H_AU1ZNAicvDb0DM30e-sgOVH7FIfZOzAv_mnI7w_niknQAHo52mPQ5t7ANFeLbz9iRUmq3HVxSxNh627m)
58. [harvard.edu](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHbISXWGly7okPJ0Nsc7RwuQd3dlDQi7TXbFPLScA33tp1R4pdq0NRltsDkJR30wr3s1EyNbL50MAjHI6vsqIcK51BixSSkNQ5mbB_utVMpVkkiA7NBvYufNfslNTUdF_L2M86ZlLkILZjYzJfT3W3FhrM9nzyLvY5NRqtalZdxQsrQ7F8=)
59. [oup.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGeQ2ogPQFttoASfJiuDobbisoxRrksz3gzEIZKWC51AI_7Oldv0X82If1kZtOU9r9zkb739yTlc_nXZwb4qdSQW6T2uwtYmDtfWTSphpuncQ8MAVjGuIOYkkZ6F7feI9ieQorJhnb90umMNGqd9RO4EfGbpm86)
60. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG7bP_tr_ICwzd2lwHjP6mdD7tR_qngu51SXNnlIZWA3fmeEvSvfQQeNp6ysUmJFIcAejlNkhIxLBV2SDapDYx-4DHjCbA-YSKN7X2u_MBugJQu7guaRQrSyphyuVBqbPAn8EI1W3N9IZKTwlKM2NRxPtxDjVPQtZRt66ee2q7-FhZamm-ZfqW2ZdkxkCv7JsujatyP1MiHSfWrydd72pofmWXgc8xID0PCqAEzSGyISW90fOnVxiRxgp1ZTQ9tS_LefRtynuH_5rq4rmRKPn3_XTQY6k6xWHLXS_w1k0zXasYyw3_119hZFe-CZvWv7eoT)
61. [cogneurosociety.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFtUKa3YSi7_9vMUh7fc2gzid5rX5HSRTchnA9j8Qa584ojTC-KcSU_wjbNFPcvGeAjIGvdeUAK-RrxoCIZNwm5ukgCiyWlYfASm2CXfezN8eVp-h4Fo-eYmF-HYQFMqSB3d7w93WewtccrG7CeCVgytvX5bZVSX8uWg2xr6F37WGrCY9BNiLdePO1Sa5XiMz-hwDvJc-HBUT7NT-Oipn5dSukfdeQwo7hX46U=)
62. [nih.gov](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGAS2m4T_XcnoCDQINlTVJhmJj-ZA4wgbN3NfY3Rp1z7jVQclBW7O804XSXJgPVNSsBD6tB9Dhg467Iwqfjx6GIFcHYhLtw9rOBokoNQu4wAOa1ZtlQXgkRBDcUX622Kg==)