{"publication_id":"b092a509-1835-4eb4-b3c1-854e808a1ed0","content_hash":"sha256:d594d5a107fd43f8a65fc7f3d86ad5496bdf3c9fba839084863cd586d82bfcfb","nodes":[{"id":"b092a509-1835-4eb4-b3c1-854e808a1ed0","type":"publication","title":"Hypothesis-Generating Brief: Brain age MRI — full paper"},{"id":"claim_1","type":"claim","text":"Evidence-honesty note: 63/65 retained sources are coded as null or no extracted directional signal; this corpus is non-supportive for clinical efficacy claims and hypothesis-generating only. Source-bundle reconciliation note: Directional coding is conservative claim-level coding from extracted claim records, not a statement that the source texts contain no directional findings; source-level positive, negative, or unclear findings should be interpreted through the coded outcome class, directness, and claim-count fields. 64/65 retained sources are indirect, review-level, adjacent, or mechanistic and are used only to bound interpretation. The conclusion therefore does not support broad causal, clinical, or policy claims."},{"id":"claim_2","type":"claim","text":"This paper synthesizes evidence on Brain age MRI across 65 accepted source papers and 1135 high-confidence extracted claims."},{"id":"claim_3","type":"claim","text":"The evidence profile contains 1 direct clinical source, 64 adjacent clinical sources, and no sources classified primarily as mechanistic or model-system evidence, with 66 cross-study disagreements across the evidence base."},{"id":"claim_4","type":"claim","text":"Positive study-level signals are summarized in the cardiometabolic outcome class, null signals in the contextual adjacent evidence, safety and comorbidity, cardiometabolic 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."},{"id":"claim_5","type":"claim","text":"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."},{"id":"claim_6","type":"claim","text":"Risk-of-bias appraisal summary: The public appraisal artifact reports 65 source-level rating row(s) using ROBINS-I, RoB-2, SYRCLE; overall ratings are some concerns=65. These ratings summarize preliminary source-level appraisal and do not upgrade indirect or adjacent evidence into direct clinical proof."},{"id":"claim_7","type":"claim","text":"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-2026-06-21T15-19-07Z-R2`."},{"id":"claim_8","type":"claim","text":"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."},{"id":"claim_9","type":"claim","text":"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."},{"id":"claim_10","type":"claim","text":"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."},{"id":"claim_11","type":"claim","text":"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."},{"id":"claim_12","type":"claim","text":"Directional coding note: Null or no extracted directional signal means no coded positive, negative, or mixed effect was extracted for that specific outcome class; it is not an absence-of-support finding. Positive, negative, mixed, unclear, and null are outcome-specific codes, so a bounded rationale can be supported by adjacent or different outcome evidence while another outcome remains null or unclear. Contextual claims contain bibliographic background, mechanism, methods, exposure definitions, or population context rather than effect-direction evidence. When an outcome-class summary uses no extracted directional signal, it should state the source proportion, such as X/Y sources, to avoid ambiguity."},{"id":"claim_13","type":"claim","text":"RCT-count reconciliation: Reviewer feedback indicates that at least one included source aggregates more than one randomized trial, so this manuscript treats any prior single-RCT wording as a source-coding count, not as a claim that the underlying trial evidence contains only one RCT."},{"id":"claim_14","type":"claim","text":"Substantive evidence synthesis: The manifest includes 65 retained sources, 1 direct-source row(s), and directional coding across null=63, positive=1, unclear=1. Representative source-level signals are: Levakov 2023: outcome=Cardiometabolic; direction=positive; directness=indirect; tier=B2; claims=56; Yilmaz 2025: outcome=Contextual Adjacent Evidence; direction=unclear; directness=indirect; tier=B2; claims=29; Huang 2025: outcome=Immune and Inflammation; direction=null; directness=indirect; tier=B2; claims=60; Ran 2022: outcome=Contextual Adjacent Evidence; direction=null; directness=indirect; tier=B2; claims=58; Tanner 2025: outcome=Safety and Comorbidity; direction=null; directness=indirect; tier=B2; claims=50; Selitser 2025: outcome=Contextual Adjacent Evidence; direction=null; directness=review; tier=B2; claims=43; Narula 2026: outcome=Contextual Adjacent Evidence; direction=null; directness=indirect; tier=B2; claims=43; Bao 2022: outcome=Contextual Adjacent Evidence; direction=null; directness=indirect; tier=B2; claims=43. These signals inform the bounded conclusion by separating effect direction from evidence tier/directness; indirect, review-level, mechanistic, or contextual evidence remains hypothesis-generating."},{"id":"claim_15","type":"claim","text":"Key findings from source synthesis: First, the strongest positive or favorable signals are treated as narrow source-level signals, not broad clinical proof (Levakov 2023: outcome=Cardiometabolic; direction=positive; directness=indirect; tier=B2; claims=56; Yilmaz 2025: outcome=Contextual Adjacent Evidence; direction=unclear; directness=indirect; tier=B2; claims=29; Huang 2025: outcome=Immune and Inflammation; direction=null; directness=indirect; tier=B2; claims=60). Second, negative, mixed, unclear, or no-directional-signal rows are given equal interpretive weight (Ran 2022: outcome=Contextual Adjacent Evidence; direction=null; directness=indirect; tier=B2; claims=58; Tanner 2025: outcome=Safety and Comorbidity; direction=null; directness=indirect; tier=B2; claims=50; Selitser 2025: outcome=Contextual Adjacent Evidence; direction=null; directness=review; tier=B2; claims=43). Third, the bounded conclusion follows from the balance of source direction, outcome class, evidence tier, and directness rather than from source count alone."},{"id":"claim_16","type":"claim","text":"| Evidence domain | Corpus slice | Strongest signal | Directness | Main limitation |"},{"id":"claim_17","type":"claim","text":"| Contextual Adjacent Evidence | n=51; claims=842 | no extracted directional signal in 50/51 sources | 1 direct; 47 indirect; 3 review | limited corpus depth in this outcome class |"},{"id":"claim_18","type":"claim","text":"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."},{"id":"claim_19","type":"claim","text":"This evidence brief reports outcome packets as a map of retained evidence rather than as a full journal Results narrative or pooled effect estimate."},{"id":"claim_20","type":"claim","text":"51 included sources were assigned to this outcome class. Directional coding: null=50, unclear=1. Directness coding: direct=1, indirect=47, review=3."},{"id":"claim_21","type":"claim","text":"5 included sources were assigned to this outcome class. Directional coding: null=5. Directness coding: indirect=5."},{"id":"claim_22","type":"claim","text":"3 included sources were assigned to this outcome class. Directional coding: null=2, positive=1. Directness coding: indirect=3."},{"id":"claim_23","type":"claim","text":"2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2."},{"id":"claim_24","type":"claim","text":"2 included sources were assigned to this outcome class. Directional coding: null=2. Directness coding: indirect=2."},{"id":"claim_25","type":"claim","text":"1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1."},{"id":"claim_26","type":"claim","text":"1 included source were assigned to this outcome class. Directional coding: null=1. Directness coding: indirect=1."},{"id":"claim_27","type":"claim","text":"Verification note:** Reference-only or no-abstract records are treated as verification-limited context, not as equal-weight support for the main claim."},{"id":"claim_28","type":"claim","text":"The curated corpus on brain-age MRI is overwhelmingly observational, with a single randomized trial (Haudry 2025, an RCT with a mechanistic/biomarker endpoint) supplying direct interventional evidence in older adults; no long-term mortality or hard-outcome RCTs in non-diabetic or non-meditation populations are present, so causal claims about anti-aging benefit cannot be sustained. The cardiometabolic and immune-inflammation outcome classes are represented only by cohort designs (Levakov 2023, Motaghi 2025, Huang 2025, Mouches 2022, Derboghossian 2024, Selitser 2025, Tavakoli 2025), and even within those cohorts effect directions diverge — Levakov 2023 reports a positive weight-loss effect after 18 months of lifestyle intervention while Mouches 2022 and Derboghossian 2024 report null associations between cardiovascular risk factors and brain-age gap, leaving the cardiometabolic signal unresolved. The absence of replication-grade interventional evidence means the headline synthesis is constrained to biomarker associations rather than clinical benefit, and the headline-level null-vs-positive tension in cardiometabolic outcomes is not adjudicable from this corpus alone."},{"id":"claim_29","type":"claim","text":"Several outcome claims rest on a single source and therefore cannot be internally replicated within the corpus. The Tai-Chi/balance-exercise MRI analysis (Narula 2026) and the unilateral exercise-in-schizophrenia brain-age-gap finding (Yilmaz 2025, n=134) similarly stand alone, so their directional signals — including the null and unclear direction codes — cannot be triangulated, and the synthesis cannot promote any of them to a robust claim without external replication."},{"id":"claim_30","type":"claim","text":"Hard clinical endpoints are not measured in the included evidence. Falls, hospitalization, disability, and mortality are similarly absent; the only survival-related signal is Casanova 2024's elastic-net Cox model against all-cause mortality using SOMAscan proteins. As a result, the brain-age-MRI case is built entirely on surrogate associations, which carry the well-documented risk that biomarker movement does not translate into clinical benefit, and the corpus cannot adjudicate whether observed brain-age gap reductions (e. For example, Yilmaz 2025 in schizophrenia, Levakov 2023 with weight loss, Haudry 2025 with meditation) would yield fewer events if scaled."},{"id":"source_1","type":"source","study":"Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods","year":2025,"doi":"10.1097/JS9.0000000000002746","url":"https://doi.org/10.1097/JS9.0000000000002746","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_2","type":"source","study":"Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity","year":2022,"doi":"10.1002/hbm.26066","url":"https://doi.org/10.1002/hbm.26066","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_3","type":"source","study":"The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity","year":2023,"doi":"10.7554/eLife.83604","url":"https://doi.org/10.7554/eLife.83604","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_4","type":"source","study":"More than chronic pain: behavioural and psychosocial protective factors predict lower brain age in adults with/at risk of knee osteoarthritis over two years","year":2025,"doi":"10.1093/braincomms/fcaf344","url":"https://doi.org/10.1093/braincomms/fcaf344","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_5","type":"source","study":"The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis","year":2026,"doi":"10.1007/s40520-026-03322-6","url":"https://doi.org/10.1007/s40520-026-03322-6","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_6","type":"source","study":"Cardiometabolic risk factors and brain age: a meta-analysis to quantify brain structural differences related to diabetes, hypertension, and obesity","year":2025,"doi":"10.1503/jpn.240105","url":"https://doi.org/10.1503/jpn.240105","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_7","type":"source","study":"Prediction of brain age using quantitative parameters of synthetic magnetic resonance imaging","year":2022,"doi":"10.3389/fnagi.2022.963668","url":"https://doi.org/10.3389/fnagi.2022.963668","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_8","type":"source","study":"Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial","year":2024,"doi":"10.1080/19585969.2024.2373075","url":"https://doi.org/10.1080/19585969.2024.2373075","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_9","type":"source","study":"Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke","year":2023,"doi":"10.1212/WNL.0000000000207219","url":"https://doi.org/10.1212/WNL.0000000000207219","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_10","type":"source","study":"Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning","year":2024,"doi":"10.1162/imag_a_00210","url":"https://doi.org/10.1162/imag_a_00210","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_11","type":"source","study":"Machine Learning–Based Sleep Electroencephalographic Brain Age Index and Dementia Risk","year":2026,"doi":"10.1001/jamanetworkopen.2026.1521","url":"https://doi.org/10.1001/jamanetworkopen.2026.1521","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_12","type":"source","study":"MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters","year":2024,"doi":"10.1177/11795735241266556","url":"https://doi.org/10.1177/11795735241266556","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_13","type":"source","study":"Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study","year":2023,"doi":"10.7554/eLife.81869","url":"https://doi.org/10.7554/eLife.81869","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_14","type":"source","study":"Increased MRI-based Brain Age in chronic migraine patients","year":2023,"doi":"10.1186/s10194-023-01670-6","url":"https://doi.org/10.1186/s10194-023-01670-6","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_15","type":"source","study":"Novel Volumetric and Surface-Based Magnetic Resonance Indices of the Aging Brain – Does Male and Female Brain Age in the Same Way?","year":2021,"doi":"10.3389/fneur.2021.645729","url":"https://doi.org/10.3389/fneur.2021.645729","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_16","type":"source","study":"Brain age gap reduction following exercise mirrors clinical improvements in schizophrenia spectrum disorders","year":2025,"doi":"10.1016/j.nicl.2025.103881","url":"https://doi.org/10.1016/j.nicl.2025.103881","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_17","type":"source","study":"Brain age in genetic and idiopathic Parkinson's disease","year":2024,"doi":"10.1093/braincomms/fcae382","url":"https://doi.org/10.1093/braincomms/fcae382","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_18","type":"source","study":"Genome-wide analysis of brain age identifies 59 associated loci and unveils relationships with mental and physical health","year":2025,"doi":"10.1038/s43587-025-00962-7","url":"https://doi.org/10.1038/s43587-025-00962-7","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_19","type":"source","study":"Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study","year":2026,"doi":"10.1016/j.landig.2025.100942","url":"https://doi.org/10.1016/j.landig.2025.100942","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_20","type":"source","study":"Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets","year":2025,"doi":"10.1002/jmri.70180","url":"https://doi.org/10.1002/jmri.70180","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_21","type":"source","study":"A deep learning model for brain age prediction using minimally preprocessed T1w images as input","year":2024,"doi":"10.3389/fnagi.2023.1303036","url":"https://doi.org/10.3389/fnagi.2023.1303036","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_22","type":"source","study":"Association between low‐frequency oscillations in blood pressure variability and brain age derived from neuroimaging","year":2025,"doi":"10.1002/alz.70833","url":"https://doi.org/10.1002/alz.70833","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_23","type":"source","study":"Brain age gap, dementia risk factors and cognition in middle age","year":2024,"doi":"10.1093/braincomms/fcae392","url":"https://doi.org/10.1093/braincomms/fcae392","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_24","type":"source","study":"The value of arterial spin labelling perfusion MRI in brain age prediction","year":2023,"doi":"10.1002/hbm.26242","url":"https://doi.org/10.1002/hbm.26242","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_25","type":"source","study":"Impact of meditation on brain age derived from multimodal neuroimaging in experts and older adults from a randomized trial","year":2025,"doi":"10.1038/s41598-025-21490-9","url":"https://doi.org/10.1038/s41598-025-21490-9","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_26","type":"source","study":"Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis","year":2025,"doi":"10.3389/fnagi.2025.1472207","url":"https://doi.org/10.3389/fnagi.2025.1472207","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_27","type":"source","study":"Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations","year":2024,"doi":"10.3389/fninf.2024.1496143","url":"https://doi.org/10.3389/fninf.2024.1496143","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"review-level"},{"id":"source_28","type":"source","study":"ASSOCIATIONS BETWEEN CARDIORESPIRATORY FITNESS, BRAIN AGE, AND NEURODEGENERATION AMONG OLDER ADULTS","year":2024,"doi":"10.1093/geroni/igae098.2304","url":"https://doi.org/10.1093/geroni/igae098.2304","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_29","type":"source","study":"Toward MR protocol-agnostic, unbiased brain age predicted from clinical-grade MRIs","year":2023,"doi":"10.1038/s41598-023-47021-y","url":"https://doi.org/10.1038/s41598-023-47021-y","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_30","type":"source","study":"Decoding MRI-informed brain age using mutual information","year":2024,"doi":"10.1186/s13244-024-01791-9","url":"https://doi.org/10.1186/s13244-024-01791-9","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_31","type":"source","study":"Longitudinal accelerated brain age in mild cognitive impairment and Alzheimer’s disease","year":2024,"doi":"10.3389/fnagi.2024.1433426","url":"https://doi.org/10.3389/fnagi.2024.1433426","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_32","type":"source","study":"An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors","year":2022,"doi":"10.3389/fnagi.2022.941864","url":"https://doi.org/10.3389/fnagi.2022.941864","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_33","type":"source","study":"Meditation Linked to Enhanced MRI Signal Intensity in the Pineal Gland and Reduced Predicted Brain Age","year":2025,"doi":"10.1111/jpi.70033","url":"https://doi.org/10.1111/jpi.70033","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_34","type":"source","study":"Increased Brain Age Among Psychiatrically Healthy Adults Exposed to Childhood Trauma","year":2025,"doi":"10.1002/brb3.70450","url":"https://doi.org/10.1002/brb3.70450","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_35","type":"source","study":"Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso","year":2025,"doi":"10.3389/fnagi.2025.1559067","url":"https://doi.org/10.3389/fnagi.2025.1559067","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_36","type":"source","study":"MRI-based whole-brain elastography and volumetric measurements to predict brain age","year":2024,"doi":"10.1093/biomethods/bpae086","url":"https://doi.org/10.1093/biomethods/bpae086","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_37","type":"source","study":"Investigating the Association of Frailty Score and Diabetes with Relative Brain Age : Insights from the UK 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