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# Hypothesis-Generating Brief: Brain age MRI — full paper ## Abstract This paper synthesizes evidence on Brain age MRI across 56 accepted source papers and 894 high-confidence extracted claims. The evidence profile contains 1 direct clinical source, 55 adjacent clinical sources, and no sources classified primarily as mechanistic or model-system evidence, with 56 cross-study disagreements across the evidence base. Positive study-level signals are summarized in the cardiometabolic outcome class, null signals in the contextual adjacent evidence, safety and comorbidity, frailty outcome classes, and negative signals in no dominant outcome class. The paper therefore interprets the corpus as a tiered evidence profile rather than as a single pooled effect. The conclusion is that Brain age MRI remains a bounded geroscience case: the retained clinical and adjacent evidence profile defines the scope for targeted testing, while mixed and null findings limit any unqualified anti-aging claim. ## Methods ### Review type and protocol This manuscript is reported as a Evidence brief. A deterministic protocol governed source retrieval, screening, extraction, and synthesis; the protocol was frozen before manuscript rendering. The full audit trail is in the supplementary `methods_pack.json` and the timestamped submission directory `synthesis-brain_age_mri-v06-DAILY-2026-06-21T12-03-47Z`. ### Information sources Sources were retrieved across PubMed, Europe PMC, OpenAlex, Semantic Scholar, Crossref, DOAJ, OpenAIRE, PMC OAI, bioRxiv, medRxiv, arXiv, and ClinicalTrials.gov. Retrieval window: 2026-06-21. ### Search strategy The following topic-anchored queries were executed against the information sources listed above: - `brain age MRI AND aging AND human` - `brain age MRI AND older adults` - `brain age MRI AND randomized controlled trial` - `brain age AND aging AND human` - `brain age AND older adults` - `brain age AND randomized controlled trial` - `MRI brain age AND aging AND human` - `MRI brain age AND older adults` - `MRI brain age AND randomized controlled trial` - `neuroimaging aging AND aging AND human` ### Eligibility criteria - Sources whose primary content addresses brain age mri. - Sources with extractable quantitative or qualitative findings. - Peer-reviewed primary research, systematic reviews, or meta-analyses; preprints accepted only when source-traceable. - Sources with verifiable bibliographic identifiers (DOI / PMID / canonical handle). ### Selection of sources of evidence The synthesis did not begin from an unfiltered database export. It began from a pre-curated receipt-candidate set generated by the retrieval and claim-binding pipeline. Of 404 records in the receipt-candidate union, 156 were classified as source candidates and 56 were admitted as traceable synthesis sources. Mixed partial-or-none and partial-only rows are separate claim-binding audit buckets, not additive exclusion totals. No additional records were excluded after final source admission. ### source admission funnel | Admission bucket | n | |---|---:| | Receipt candidate union | 404 | | Classified source candidates | 156 | | No extractable claims | 39 | | None-only claim binding | 15 | | Mixed partial-or-none claim-binding candidates | 152 | | Partial-only claim-binding candidates | 29 | | Strict high-confidence sources | 13 | | Admitted final sources | 56 | ### Exclusion reasons - No records were excluded at the gates instrumented for this run: the eligibility criteria above were applied during retrieval and claim-binding but produced no post-screening exclusions with recorded counts for this corpus. ### Data items The following fields were extracted from each included source: study design, population / cohort, intervention or exposure, comparator, outcome class, effect direction, effect size, confidence interval or credible interval, p-value, sample size, follow-up duration, risk-of-bias rating. Under the calibration rule, source verification in the public bundle is limited to reference-level metadata; exact statistics and effect directions are drawn from these structured extraction artifacts (the synthesis manifest, risk-of-bias sidecar when populated, and claim registry) rather than from re-parsed full text. ### Risk-of-bias appraisal Risk-of-bias framework assignment follows study design (RoB-2 for RCTs, ROBINS-I for non-randomised studies, AMSTAR-2 for systematic reviews / meta-analyses). Public appraisal claims are limited to populated `risk_of_bias.json` rows; when no populated ratings are present, interpretation remains bounded by source tier and directness rather than formal RoB certification. ### Synthesis approach Evidence-tension synthesis: claims grouped by outcome class (cardiometabolic, cognitive, contextual adjacent evidence, frailty, immune and inflammation, muscle function, safety and comorbidity); within-class agreement, disagreement, and directness gaps surfaced explicitly. Quantitative pooling applied only where ≥3 sources reported a comparable endpoint with extractable effect estimates. ### AI-use disclosure Source retrieval, claim extraction, evidence routing, and prose drafting were assisted by large language models under a deterministic audit-trail protocol. Every manuscript claim is traceable to a source record in the supplementary `manifest.json`. Final eligibility and interpretation decisions are author-verified. ### Accountability Accountability is established through reproducible artifacts: a deterministic protocol (`methods_pack.json`), a complete claim and citation registry, extracted numeric trace, deterministic gates (`full_paper.journal_surface.json`, `pre_submit_gate.json`, `artifact_consistency.json`), and a versioned correction path documented in the run's submission record. Certification under the `researka_agent_certified` model verifies that the manuscript is machine-verifiable, internally consistent, provenance-traced, and format-checked against these artifacts; it does not adjudicate domain correctness, corpus fit, or novelty, which remain subject to expert and reader review. ## Results **Outcome-class note:** Contextual Adjacent Evidence denotes background, boundary-condition, or adjacent-outcome sources. It is not pooled with direct outcome evidence; these sources bound scope, safety, methods, and translation rather than serving as equal-weight support for the main efficacy claim. | Evidence domain | Corpus slice | Strongest signal | Directness | Main limitation | |---|---|---|---|---| | Contextual Adjacent Evidence | n=43; claims=615 | no extracted directional signal in 42/43 sources | 1 direct; 40 indirect; 2 review | limited corpus depth in this outcome class | | Safety and Comorbidity | n=5; claims=109 | no extracted directional signal in 5/5 sources | 5 indirect | limited corpus depth in this outcome class | | Cardiometabolic | n=2; claims=72 | positive signal in 1/2 sources | 2 indirect | limited corpus depth in this outcome class | | Frailty | n=2; claims=15 | no extracted directional signal in 2/2 sources | 2 indirect | limited corpus depth in this outcome class | | Muscle Function | n=2; claims=2 | no extracted directional signal in 2/2 sources | 2 indirect | limited corpus depth in this outcome class | | Cognitive | n=1; claims=21 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | | Immune and Inflammation | n=1; claims=60 | no extracted directional signal in 1/1 sources | 1 indirect | single-source slice; hypothesis-generating | This evidence brief reports outcome packets as a map of retained evidence rather than as a full journal Results narrative or pooled effect estimate. ### Contextual Adjacent Evidence Outcomes 43 included sources were assigned to this outcome class. Directional coding: null=42, unclear=1. Directness coding: direct=1, indirect=40, review=2. ### Safety Comorbidity Outcomes 5 included sources were assigned to this outcome class. Directional coding: null=5. Directness coding: indirect=5. ### Cardiometabolic Outcomes 2 included sources were assigned to this outcome class. Directional coding: null=1, positive=1. Directness coding: indirect=2. ### Frailty Outcomes 2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2. ### Muscle Function Outcomes 2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2. ### Cognitive Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ### Immune Inflammation Outcomes 1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1. ## Limitations **Verification note:** Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim. The corpus is dominated by observational cohorts and methodological/deep-learning development studies rather than by intervention trials, and it contains no long-term mortality or hard-outcome randomized trial in a non-diabetic adult population. The remaining evidence is indirect on clinically meaningful outcomes, so any synthesis-level statement that brain age improvement translates into reduced incidence of dementia, falls, or mortality cannot be supported by the present corpus. Surrogate-endpoint caveats apply throughout (Ioannidis 2005), and the absence of long-term mortality RCTs in this corpus leaves the boundary between a favorable brain-age signal and a favorable clinical outcome undefined. Several clinically attractive claims rest on a single trial or a single cohort within the corpus and therefore cannot be internally replicated. Conclusions anchored to these single sources should be treated as preliminary. The population covered by the corpus is narrow in ways that constrain external validity. Generalizing the brain-age construct to populations outside the chronic-disease and community-dwelling adult frame sampled here is therefore not warranted by the evidence assembled. For several outcome classes the corpus supplies only mechanistic, methodological, or indirect evidence where a clinically actionable claim is being made, and this mechanism-to-clinic gap is not bridged by the present evidence base. The single RCT in the corpus (Haudry 2025) is a direct mechanistic/biomarker study and cannot be fused with indirect observational findings on a different outcome (e. For example, Huang 2025 on immune/inflammation markers, Stefaniak 2024 on cognitive correlates, Yu 2025 on chronic medical conditions, Motaghi 2025 on frailty, or Raji 2025 on muscle volume), as the cross-domain tensions in the matrix make explicit. The closing claim should therefore be read as a map of what the retained studies can support, not as a clinical recommendation or a general anti-aging endorsement. Positive signals identify hypotheses and candidate contexts; null, mixed, or adverse signals identify the boundaries that future work must test directly. The evidence hierarchy remains load-bearing here: direct interventional hard-endpoint records carry more interpretive weight than adjacent clinical evidence, and both carry more translational weight than mechanistic or model systems. A stronger future conclusion would require larger direct human samples, prespecified endpoints, longer follow-up, comparable intervention characterization, transparent safety capture, and a consistent direction of effect across clinically proximate outcomes. Until that evidence exists, the paper's conclusion is that the topic is worth structured follow-up only within the boundaries defined by the included source set. That boundary is not a weakness in the paper; it is the main claim that keeps the synthesis reusable. Readers should carry forward the evidence classes separately: favorable mechanistic or surrogate findings can motivate experiments, indirect human findings can prioritize populations and endpoints, and direct clinical findings define the current ceiling for applied interpretation. The current corpus may support Brain age MRI as a general health or lifestyle intervention where otherwise indicated, but does not justify marketing it as a standalone geroprotective or anti-aging intervention with proven hard-longevity effects. Any downstream use should preserve that tiered reading rather than compressing the corpus into a simple yes/no verdict for clinical practice or public messaging. ## What This Synthesis Adds This synthesis maps 56 included sources on Brain Age MRI across 7 outcome classes and 56 cross-study disagreements. It separates endpoint-specific evidence from broad geroprotection claims so that favorable biomarker signals are not treated as proof of durable healthspan benefit. Across 56 curated reference papers, the evidence base for Brain shows a context-dependent profile. Positive signals appear in: cardiometabolic. Null findings dominate: contextual other, safety comorbidity. The synthesis surfaces cross-study disagreements across outcome classes — see Cross-Domain Synthesis. The Brain anti-aging case as currently constituted is incomplete: mechanistic plausibility coexists with mixed or sparse human-RCT evidence, and the boundary conditions remain to be established. The strongest unresolved contrast is the null vs positive between Levakov 2023 and Derboghossian 2024 on cardiometabolic (severity 4/5), which defines the boundary condition future studies must test rather than smooth over. This synthesis adds a design-level evidence-weighting layer and an explicit cross-study disagreement map, keeping boundary conditions visible instead of averaging them away in narrative summary. ### Boundary-Condition Matrix | Evidence domain | Direct sources | Indirect / mechanism sources | Direction profile | Interpretation boundary | |---|---:|---:|---|---| | cardiometabolic | 0 | 2 | null, positive | conflict-resolution gap | | cognitive | 0 | 1 | null | direct interventional hard-endpoint gap | | frailty | 0 | 2 | null | direct interventional hard-endpoint gap | | muscle function | 0 | 2 | null | direct interventional hard-endpoint gap | | immune and inflammation | 0 | 1 | null | direct interventional hard-endpoint gap | | safety and comorbidity | 0 | 5 | null | direct interventional hard-endpoint gap | | contextual adjacent evidence | 1 | 42 | null, unclear | replication gap | ### Evidence-Gap Priority | Priority | Gap | Rationale | |---|---|---| | P1 | cardiometabolic: conflict-resolution gap | 0 direct and 2 indirect sources; direction profile: null, positive | | P2 | cognitive: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | | P3 | frailty: direct interventional hard-endpoint gap | 0 direct and 2 indirect sources; direction profile: null | | P4 | muscle function: direct interventional hard-endpoint gap | 0 direct and 2 indirect sources; direction profile: null | | P5 | immune and inflammation: direct interventional hard-endpoint gap | 0 direct and 1 indirect source; direction profile: null | ### Next-Study Design Recommendation The next high-yield study for Brain Age MRI should target the **cardiometabolic** evidence gap, pre-register the primary endpoint, separate clinical from mechanistic endpoints, preserve safety and adherence capture, and include an analysis plan that can falsify the current boundary-condition claim rather than only confirming a favorable direction. Minimum useful design: at least 200 participants per arm, a priority population of adults or older adults with baseline risk in the target outcome domain, and follow-up lasting at least 24 weeks; shorter or smaller studies should be treated as hypothesis-generating. ## Evidence Snapshot The manuscript foregrounds the load-bearing evidence; the full evidence tables remain in the supplement. ### Load-Bearing Included Studies - Haudry 2025; tier=A1; directness=direct; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.14. - Huang 2025; tier=B2; directness=indirect; endpoint=immune inflammation; direction=null. - Levakov 2023; tier=B2; directness=indirect; endpoint=cardiometabolic; direction=positive; representative statistic=P < 0.001. - Tanner 2025; tier=B2; directness=indirect; endpoint=safety comorbidity; direction=null; representative statistic=P = 0.061. - Narula 2026; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P > 0.05. - Selitser 2025; tier=B2; directness=review; endpoint=contextual adjacent evidence; direction=null. - Liew 2023; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.386. - Gemein 2024; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P = 0.13. - Sun 2026; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null; representative statistic=P > 0.05. - Lu 2024; tier=B2; directness=indirect; endpoint=contextual adjacent evidence; direction=null. ### Source Classification Map Each retained source is mapped to its public evidence role so the evidence landscape can be checked without opening the supplement. - Impact of meditation on brain age derived from multimodal neuroimaging in experts and older adults from a randomized trial: outcome=contextual adjacent evidence; directness=direct; tier=A1; direction=null; claims=17. - Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods: outcome=immune inflammation; directness=indirect; tier=B2; direction=null; claims=60. - The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity: outcome=cardiometabolic; directness=indirect; tier=B2; direction=positive; claims=56. - More than chronic pain: behavioural and psychosocial protective factors predict lower brain age in adults with/at risk of knee osteoarthritis over two years: outcome=safety comorbidity; directness=indirect; tier=B2; direction=null; claims=50. - The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=43. - Cardiometabolic risk factors and brain age: a meta-analysis to quantify brain structural differences related to diabetes, hypertension, and obesity: outcome=contextual adjacent evidence; directness=review; tier=B2; direction=null; claims=43. - Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=40. - Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=34. - Machine Learning–Based Sleep Electroencephalographic Brain Age Index and Dementia Risk: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=33. - MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=32. - Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=32. - Increased MRI-based Brain Age in chronic migraine patients: outcome=safety comorbidity; directness=indirect; tier=B2; direction=null; claims=31. - Brain age gap reduction following exercise mirrors clinical improvements in schizophrenia spectrum disorders: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=unclear; claims=29. - Brain age in genetic and idiopathic Parkinson's disease: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=27. - Genome-wide analysis of brain age identifies 59 associated loci and unveils relationships with mental and physical health: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=25. - A deep learning model for brain age prediction using minimally preprocessed T1w images as input: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=24. - Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study: outcome=safety comorbidity; directness=indirect; tier=B2; direction=null; claims=24. - Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets: outcome=contextual adjacent evidence; directness=review; tier=B2; direction=null; claims=24. - Association between low‐frequency oscillations in blood pressure variability and brain age derived from neuroimaging: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=22. - Brain age gap, dementia risk factors and cognition in middle age: outcome=cognitive; directness=indirect; tier=B2; direction=null; claims=21. - Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=16. - ASSOCIATIONS BETWEEN CARDIORESPIRATORY FITNESS, BRAIN AGE, AND NEURODEGENERATION AMONG OLDER ADULTS: outcome=cardiometabolic; directness=indirect; tier=B2; direction=null; claims=16. - Decoding MRI-informed brain age using mutual information: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=15. - Longitudinal accelerated brain age in mild cognitive impairment and Alzheimer’s disease: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=14. - Meditation Linked to Enhanced MRI Signal Intensity in the Pineal Gland and Reduced Predicted Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=13. - Increased Brain Age Among Psychiatrically Healthy Adults Exposed to Childhood Trauma: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=11. - Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=11. - MRI-based whole-brain elastography and volumetric measurements to predict brain age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=10. - Investigating the Association of Frailty Score and Diabetes with Relative Brain Age : Insights from the UK Biobank: outcome=frailty; directness=indirect; tier=B2; direction=null; claims=9. - Sleep Patterns in Midlife and Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=8. - Plasma‐based Brain Age as a Biomarker for Cognitive Health and Risk of Brain‐Related Diseases: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=8. - Brain age gap estimation using attention-based ResNet method for Alzheimer’s disease detection: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - Advanced brain age prediction using 3D convolutional neural network on structural MRI: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - Examining the reliability of brain age algorithms under varying degrees of participant motion: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - Predicting brain age using Tri-UNet and various MRI scale features: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=7. - A PROTEOMICS-BASED MEASURE OF ACCELERATING AGING IS CORRELATED WITH THE BRAIN AGE GAP IN THE ARIC STUDY: outcome=frailty; directness=indirect; tier=B2; direction=null; claims=6. - The association between a pro‐inflammatory diet and machine learning‐based brain age in middle‐aged and older adults: Findings from the UK Biobank: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=6. - Association between shift work and brain age gap: a neuroimaging study using MRI-based brain age prediction algorithms: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=6. - White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=5. - White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age: outcome=contextual adjacent evidence; directness=indirect; tier=B2; direction=null; claims=5. ### Classification Criteria - **Outcome class** is assigned from the source's bound endpoint, population, and claim text; adjacent/background sources are separated from clinical outcome slices. - **Directness** is coded as direct only when a source tests the topic against a clinically proximate outcome in the relevant population; a qualifying direct source would be a human interventional or hard-endpoint study of the topic itself. Indirect human, review-level, and mechanistic sources are weighted separately. - **Directional signal** is counted within the assigned outcome class only. A `no extracted directional signal` cell means the retained sources in that outcome slice did not yield a coded positive, negative, or mixed direction for that slice; it is not a claim that the source reports no associations anywhere else. - **Evidence tier** follows the deterministic tier/directness taxonomy used in the source builder; the prose writer cannot move a source between classes after sources are frozen. ### Load-Bearing Tensions - Severity 4 null vs positive: Levakov 2023 vs Derboghossian 2024; Levakov 2023 (positive on cardiometabolic) vs Derboghossian 2024 (null on cardiometabolic) — partial conflict - Severity 3 indirectness gap: Liew 2023 vs Haudry 2025; Haudry 2025 (direct, A1) vs Liew 2023 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Jonemo 2023 vs Haudry 2025; Haudry 2025 (direct, A1) vs Jonemo 2023 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Kim 2023 vs Haudry 2025; Haudry 2025 (direct, A1) vs Kim 2023 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Dartora 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Dartora 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Hanson 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Hanson 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Aghaei 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Aghaei 2024 (indirect) on contextual other — direct vs indirect must be kept separate - Severity 3 indirectness gap: Pang 2024 vs Haudry 2025; Haudry 2025 (direct, A1) vs Pang 2024 (indirect) on contextual other — direct vs indirect must be kept separate ## Conclusion Additional corpus sources informed the synthesis without anchoring a foregrounded quantitative claim and are catalogued for completeness: Millar 2023, Navarro-Gonzalez 2023, Yilmaz 2025, Teipel 2024, Jawinski 2025, Ull 2025, Park 2026, Heffernan 2025, Coetzee 2025, Li 2024, Ly 2024, Plini 2025, Hendrikse 2025, Hu 2025, Claros-Olivares 2024, Cavailles 2025, Wang 2025, Ahmadi 2025, Casanova 2024, Dunk 2025, Kim 2025, Tavakoli 2025, Pallapothu 2025, Meysami 2025, Roman 2025, Meysami 2026, Kou 2024, Toraih 2025, Yu 2025b, Rajabli 2025, Satpathi 2025, Rajabli 2026, Dorfel 2024, Aithal 2025, Raji 2026. ## References - **Huang 2025.** _Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods._ International Journal of Surgery (London, England), 2025. DOI: 10.1097/JS9.0000000000002746. PMID: 40561180. - **Levakov 2023.** _The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity._ eLife, 2023. DOI: 10.7554/eLife.83604. PMID: 37022140. - **Tanner 2025.** _More than chronic pain: behavioural and psychosocial protective factors predict lower brain age in adults with/at risk of knee osteoarthritis over two years._ Brain Communications, 2025. DOI: 10.1093/braincomms/fcaf344. PMID: 41020178. - **Selitser 2025.** _Cardiometabolic risk factors and brain age: a meta-analysis to quantify brain structural differences related to diabetes, hypertension, and obesity._ Journal of Psychiatry & Neuroscience : JPN, 2025. DOI: 10.1503/jpn.240105. PMID: 40068862. - **Narula 2026.** _The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis._ Aging Clinical and Experimental Research, 2026. DOI: 10.1007/s40520-026-03322-6. PMID: 41566095. - **Liew 2023.** _Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke._ Neurology, 2023. DOI: 10.1212/WNL.0000000000207219. PMID: 37015818. - **Gemein 2024.** _Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning._ Imaging Neuroscience, 2024. DOI: 10.1162/imag_a_00210. PMID: 40800431. - **Sun 2026.** _Machine Learning–Based Sleep Electroencephalographic Brain Age Index and Dementia Risk._ JAMA Network Open, 2026. DOI: 10.1001/jamanetworkopen.2026.1521. PMID: 41854616. - **Lu 2024.** _MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters._ Journal of Central Nervous System Disease, 2024. DOI: 10.1177/11795735241266556. PMID: 39049837. - **Millar 2023.** _Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study._ eLife, 2023. DOI: 10.7554/eLife.81869. PMID: 36607335. - **Navarro-Gonzalez 2023.** _Increased MRI-based Brain Age in chronic migraine patients._ The Journal of Headache and Pain, 2023. DOI: 10.1186/s10194-023-01670-6. PMID: 37798720. - **Yilmaz 2025.** _Brain age gap reduction following exercise mirrors clinical improvements in schizophrenia spectrum disorders._ NeuroImage : Clinical, 2025. DOI: 10.1016/j.nicl.2025.103881. PMID: 41067091. - **Teipel 2024.** _Brain age in genetic and idiopathic Parkinson's disease._ Brain Communications, 2024. DOI: 10.1093/braincomms/fcae382. PMID: 39713239. - **Jawinski 2025.** _Genome-wide analysis of brain age identifies 59 associated loci and unveils relationships with mental and physical health._ Nature Aging, 2025. DOI: 10.1038/s43587-025-00962-7. PMID: 41044200. - **Dartora 2024.** _A deep learning model for brain age prediction using minimally preprocessed T1w images as input._ Frontiers in Aging Neuroscience, 2024. DOI: 10.3389/fnagi.2023.1303036. PMID: 38259636. - **Ull 2025.** _Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets._ Journal of Magnetic Resonance Imaging, 2025. DOI: 10.1002/jmri.70180. PMID: 41414873. - **Park 2026.** _Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study._ The Lancet. Digital health, 2026. DOI: 10.1016/j.landig.2025.100942. PMID: 41577565. - **Heffernan 2025.** _Association between low‐frequency oscillations in blood pressure variability and brain age derived from neuroimaging._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.70833. PMID: 41126772. - **Stefaniak 2024.** _Brain age gap, dementia risk factors and cognition in middle age._ Brain Communications, 2024. DOI: 10.1093/braincomms/fcae392. PMID: 39605972. - **Haudry 2025.** _Impact of meditation on brain age derived from multimodal neuroimaging in experts and older adults from a randomized trial._ Scientific Reports, 2025. DOI: 10.1038/s41598-025-21490-9. PMID: 41152396. - **Derboghossian 2024.** _ASSOCIATIONS BETWEEN CARDIORESPIRATORY FITNESS, BRAIN AGE, AND NEURODEGENERATION AMONG OLDER ADULTS._ Innovation in Aging, 2024. DOI: 10.1093/geroni/igae098.2304. - **Coetzee 2025.** _Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis._ Frontiers in Aging Neuroscience, 2025. DOI: 10.3389/fnagi.2025.1472207. PMID: 40443792. - **Li 2024.** _Decoding MRI-informed brain age using mutual information._ Insights into Imaging, 2024. DOI: 10.1186/s13244-024-01791-9. PMID: 39186199. - **Ly 2024.** _Longitudinal accelerated brain age in mild cognitive impairment and Alzheimer’s disease._ Frontiers in Aging Neuroscience, 2024. DOI: 10.3389/fnagi.2024.1433426. PMID: 39503045. - **Plini 2025.** _Meditation Linked to Enhanced MRI Signal Intensity in the Pineal Gland and Reduced Predicted Brain Age._ Journal of Pineal Research, 2025. DOI: 10.1111/jpi.70033. PMID: 39940075. - **Hendrikse 2025.** _Increased Brain Age Among Psychiatrically Healthy Adults Exposed to Childhood Trauma._ Brain and Behavior, 2025. DOI: 10.1002/brb3.70450. PMID: 40170519. - **Hu 2025.** _Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso._ Frontiers in Aging Neuroscience, 2025. DOI: 10.3389/fnagi.2025.1559067. PMID: 40766176. - **Claros-Olivares 2024.** _MRI-based whole-brain elastography and volumetric measurements to predict brain age._ Biology Methods & Protocols, 2024. DOI: 10.1093/biomethods/bpae086. PMID: 39902188. - **Motaghi 2025.** _Investigating the Association of Frailty Score and Diabetes with Relative Brain Age : Insights from the UK Biobank._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70856_103010. - **Cavailles 2025.** _Sleep Patterns in Midlife and Brain Age._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.085643. - **Wang 2025.** _Plasma‐based Brain Age as a Biomarker for Cognitive Health and Risk of Brain‐Related Diseases._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70856_103849. - **Hanson 2024.** _Examining the reliability of brain age algorithms under varying degrees of participant motion._ Brain Informatics, 2024. DOI: 10.1186/s40708-024-00223-0. PMID: 38573551. - **Aghaei 2024.** _Brain age gap estimation using attention-based ResNet method for Alzheimer’s disease detection._ Brain Informatics, 2024. DOI: 10.1186/s40708-024-00230-1. PMID: 38833039. - **Pang 2024.** _Predicting brain age using Tri-UNet and various MRI scale features._ Scientific Reports, 2024. DOI: 10.1038/s41598-024-63998-6. PMID: 38877107. - **Ahmadi 2025.** _Advanced brain age prediction using 3D convolutional neural network on structural MRI._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.089776. - **Casanova 2024.** _A PROTEOMICS-BASED MEASURE OF ACCELERATING AGING IS CORRELATED WITH THE BRAIN AGE GAP IN THE ARIC STUDY._ Innovation in Aging, 2024. DOI: 10.1093/geroni/igae098.2303. - **Dunk 2025.** _The association between a pro‐inflammatory diet and machine learning‐based brain age in middle‐aged and older adults: Findings from the UK Biobank._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.086979. - **Kim 2025.** _Association between shift work and brain age gap: a neuroimaging study using MRI-based brain age prediction algorithms._ Frontiers in Aging Neuroscience, 2025. DOI: 10.3389/fnagi.2025.1650497. PMID: 40951919. - **Tavakoli 2025.** _Evaluating the Impact of Cardiometabolic Risk Factors on Neuroimaging‐Based Brain Age: A Deep Learning Approach._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.095769. - **Pallapothu 2025.** _Association between cardiovascular disease risk, regional brain age gap, and cognition in healthy adults._ Frontiers in Aging Neuroscience, 2025. DOI: 10.3389/fnagi.2025.1611847. PMID: 41049536. - **Meysami 2025.** _White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70862_110308. - **Roman 2025.** _The Impact of Brain Age versus Chronological Age on Cognitive Fatigue: Novel Metrics and New Insights._ Innovation in Aging, 2025. DOI: 10.1093/geroni/igaf122.4213. - **Meysami 2026.** _White Matter Hyperintensities on Brain MRI are Related to Brain Atrophy and Accelerated Brain Age._ Alzheimer's & Dementia, 2026. DOI: 10.1002/alz70856_106425. - **Jonemo 2023.** _Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections._ Brain Sciences, 2023. DOI: 10.3390/brainsci13091329. PMID: 37759930. - **Kou 2024.** _PROTEOMIC BRAIN AGE GAP, DEMENTIA RISK, AND BRAIN VOLUME MEASUREMENTS._ Innovation in Aging, 2024. DOI: 10.1093/geroni/igae098.3470. - **Toraih 2025.** _Brain Age Acceleration on MRI Due to Poor Sleep: Associations, Mechanisms, and Clinical Implications._ Brain Sciences, 2025. DOI: 10.3390/brainsci15121325. PMID: 41440121. - **Yu 2025.** _Chronic Medical Conditions and Dementia Risk: Brain Age Models for Quantifying Impact and Understanding Mechanisms._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.093829. - **Yu 2025b.** _Chronic Medical Conditions and Dementia Risk: Brain Age Models for Quantifying Impact and Understanding Mechanisms._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.089382. - **Rajabli 2025.** _Sex Differences in Brain Age Gap Estimation Across Alzheimer's Disease Diagnostic Groups._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70862_110227. - **Satpathi 2025.** _Developing scanner change invariant brain age models for aging and dementia studies._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70856_097891. - **Rajabli 2026.** _Sex Differences in Brain Age Gap Estimation Across Alzheimer's Disease Diagnostic Groups._ Alzheimer's & Dementia, 2026. DOI: 10.1002/alz70856_107437. - **Kim 2023.** _REPRODUCIBILITY OF BRAIN AGE SALIENCIES ACROSS DEEP NEURAL NETWORK ARCHITECTURES._ Innovation in Aging, 2023. DOI: 10.1093/geroni/igad104.3572. - **Dorfel 2024.** _Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features._ GeroScience, 2024. DOI: 10.1007/s11357-024-01148-6. PMID: 38668887. - **Aithal 2025.** _Simple fully convolutional network to estimate Brain Age._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz.088019. - **Raji 2025.** _Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age._ Alzheimer's & Dementia, 2025. DOI: 10.1002/alz70862_110051. - **Raji 2026.** _Higher Muscle Volume is Inversely Related to Chronological and Brain Age While Increased Visceral to Muscle Fat Ratio is Positively Related to Chronological and Brain Age._ Alzheimer's & Dementia, 2026. DOI: 10.1002/alz70856_106692. ### Background References *Methodological references cited in prose. Each entry's `citation_token` appears at least once in the body of the paper, paired with its numeric per the background-literature gate (Fix #16).* - **Ioannidis 2005.** _Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124._ (methodological reference) DOI: 10.1371/journal.pmed.0020124. PMID: 16060722.
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