Beyond the Hippocampus: Objective Memory Stages Capture Widespread Brain Aging in a Cross-Sectional Analysis of Baseline RCT Data

Beyond the Hippocampus: Objective Memory Stages Capture Widespread Brain Aging in a Cross-Sectional Analysis of Baseline RCT Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Beyond the Hippocampus: Objective Memory Stages Capture Widespread Brain Aging in a Cross-Sectional Analysis of Baseline RCT Data Birthe Kristin Flo, Stavros Skouras, Anna Maria Matziorinis, Christian Gaser, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8530151/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Reliable staging of early memory decline is essential for identifying individuals at risk for Alzheimer’s disease. The Stages of Objective Memory Impairment (SOMI) framework provides a clinically scalable tool for characterizing episodic memory loss, yet its neurobiological validity remains underexplored. Recent advances in plasma biomarkers (e.g., p-tau217, p-tau181, Aβ42/40) offer emerging blood-based alternatives to CSF and PET, although their diagnostic implementation continues to evolve. Here, we examine whether SOMI stages reflect widespread brain aging, as indexed by BrainAGE, a structural MRI–based biomarker quantifying deviation from normative aging trajectories. While previous studies have linked SOMI to hippocampal atrophy and tau pathology, no study to date has examined its association with a global MRI-derived biomarker of systemic brain aging. Methods: In a well-characterized cohort of 119 older adults on the Alzheimer’s disease continuum, we evaluated whether higher SOMI stages were linked to elevated BrainAGE scores and examined whether this association remained significant after adjusting for age, sex, education, and hippocampal volume. Results: Higher SOMI stages were robustly associated with elevated BrainAGE scores, indicating accelerated neurobiological aging. This relationship remained significant after adjusting for covariates and was confirmed in sensitivity analyses. Notably, a marked discontinuity in BrainAGE emerged between SOMI stages 0–2 and 3–5, aligning with the theoretical transition from retrieval to storage impairment, long recognized as a turning point in prodromal Alzheimer’s disease. Conclusions: These findings validate SOMI as a low-cost, non-invasive behavioral marker of systemic brain health. By linking cognitive staging to global neuroimaging biomarkers, our study supports SOMI’s translational utility for large-scale screening, clinical trial stratification, and early intervention planning in Alzheimer’s disease. Health sciences/Biomarkers Health sciences/Neurology Biological sciences/Neuroscience Alzheimer's Disease BrainAGE Stages of Objective Memory Impairment MRI Figures Figure 1 Introduction Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide. 1 Despite substantial advances in biomarker discovery, a critical implementation gap persists: although cerebrospinal fluid (CSF) analysis and amyloid-PET imaging reliably indicate AD pathology 2 , 3 , their cost, invasiveness, and limited accessibility render them impractical for large-scale, population-based screening. 4 , 5 Recent advances in plasma biomarkers such as p-tau217, p-tau181, and Aβ42/40 provide less invasive methods for detecting AD pathology, though their implementation and validation continue to evolve. 6 , 7 This constraint hampers efforts to identify at-risk individuals during the early, preclinical window, which is precisely the stage when lifestyle modifications and emerging disease-modifying interventions may be most effective. There is therefore an urgent need for alternative tools that are both scalable and biologically informative, that is, tools capable of bridging the gap between advanced biomarker science and practical clinical detection. In this context, two complementary approaches have gained increasing attention. The Stages of Objective Memory Impairment (SOMI) framework offers a clinically grounded method to stage episodic memory decline (the hallmark cognitive symptom of prodromal AD) based on performance in the Free and Cued Selective Reminding Test (FCSRT). 8 The FCSRT combines controlled encoding with semantic cue–based retrieval, allowing a clear distinction between retrieval deficits (where cues normalize recall) and storage deficits (where recall remains impaired even with cueing). SOMI stages (0–5) map these qualitative differences, with SOMI-5 later added to capture more severe impairment. Prior studies have linked SOMI stages to key AD-related pathologies, including amyloid and tau deposition as well as structural atrophy in the hippocampus, entorhinal cortex, and inferior temporal regions (e.g., 8,9,10,11,12 ). Stage-dependent volume reductions of 5–7% in medial temporal lobe structures have been observed, and SOMI has demonstrated predictive utility in longitudinal studies. 11 Notably, the SOMI framework is designed to capture a qualitative transition in memory impairment, from retrieval difficulties in the earlier stages (SOMI 0–2) to actual storage failure at SOMI-3 and beyond. This retrieval-to-storage shift is clinically meaningful, as it marks the onset of the amnestic syndrome characteristic of Alzheimer’s disease and reflects a turning point in underlying neuropathological burden. Consistent with this, prior studies report steeper tau pathology and medial temporal atrophy emerging at SOMI-3. In parallel, Brain Age Gap Estimation (BrainAGE) has emerged as a powerful neuroimaging biomarker of individual brain health. Derived from structural MRI using voxel-based morphometry (VBM) and machine learning, BrainAGE quantifies the deviation between a person’s chronological and brain-predicted age, yielding a summary metric of whole-brain atrophy. 13 , 14 Elevated BrainAGE has been associated with increased risk of conversion from MCI to AD and has, in some studies, outperformed classical region-based volumetric measures. 15 , 16 Unlike traditional single-region indices, BrainAGE reflects distributed neurodegenerative processes, aligning with the multifocal pathology of early-stage AD. While SOMI provides a clinically intuitive staging of memory dysfunction and BrainAGE offers a biologically sensitive index of systemic brain aging, it remains unknown whether the two converge. Does behavioral evidence of early episodic memory impairment (as captured by SOMI) correspond to an accelerated BrainAGE signature? Addressing this question fills a critical gap in the current literature: it would help establish SOMI not only as a cognitive staging tool, but also as a proxy for global neurobiological aging in preclinical AD. While previous studies have linked SOMI to hippocampal atrophy and tau pathology, no study to date has examined its association with a global MRI-derived biomarker of systemic brain aging. In the present study, we test whether SOMI stages predict individual differences in BrainAGE. Such a finding would have immediate translational implications: it would position SOMI as a low-cost, non-invasive behavioral indicator of neurodegenerative burden, and strengthen its role in screening, risk stratification, and clinical trial design in the context of Alzheimer’s disease. Materials and methods Participants Participants were drawn from the ALMUTH study ( Alzheimer & Music Therapy ; for protocol details, see Flo et al. 17 ), a multicenter randomized controlled trial designed to assess music therapy interventions in individuals with or at risk for Alzheimer’s disease. The present cross-sectional analysis focuses exclusively on baseline data, collected prior to the onset of any intervention. Participants were recruited through outpatient memory clinics and community outreach in Bergen, Norway. Eligibility criteria for the parent study included the presence of subjective cognitive complaints, a Mini-Mental State Examination (MMSE) score above 17, and general health sufficient to permit participation. Exclusion criteria comprised non-AD dementia, significant cardiovascular or neurological disease, history of traumatic brain injury, major psychiatric conditions, sensory deficits that could interfere with testing, and any contraindications to MRI. In addition, individuals with atypical SOMI profiles (severe retrieval deficits without storage impairment) were excluded from the current analysis. A total of 152 participants were initially screened. Of these, 119 individuals completed high-quality structural MRI and were included in the final analysis sample. All participants provided written informed consent in accordance with the Declaration of Helsinki. The study was approved by the Regional Committees for Medical and Health Research Ethics (REC West; ref. 2018/206). SOMI Classification Episodic memory performance was staged using the Stages of Objective Memory Impairment (SOMI) framework, based on the picture version of the Free and Cued Selective Reminding Test with immediate recall (pFCSRT + IR). The SOMI system defines six sequential stages (SOMI-0 through SOMI-5) according to thresholds in free and total recall scores (see Table 1 ), and is designed to capture the gradual progression of memory impairment typical of prodromal Alzheimer’s disease. While SOMI-0 to SOMI-4 are part of the original framework 8 , SOMI-5 was later introduced to accommodate individuals with more pronounced memory deficits. 18 Prior research has demonstrated strong associations between SOMI stages and established AD biomarkers, including amyloid, tau, and medial temporal lobe atrophy. 9 , 11 For 16 early-enrolled participants who had a clinical diagnosis of Alzheimer’s disease and were assessed before the implementation of the pFCSRT + IR, SOMI stages were estimated using MMSE scores. Although SOMI was originally developed for preclinical and prodromal AD, SOMI-5 was subsequently introduced to accommodate more severe memory impairment and has been applied in neuropathology-validated studies. Because the aim of the present study was to examine brain correlates across the full spectrum of memory impairment, rather than to diagnose dementia, these participants were included to preserve the continuity of the SOMI staging distribution. Based on the empirical distribution of MMSE scores across SOMI stages, a cut-off of MMSE < 22 was used to distinguish SOMI-5 from SOMI-4. Importantly, we will show in the Results that their inclusion did not bias the results, as demonstrated by a sensitivity analysis excluding these 16 participants, which yielded virtually identical findings (see Results). Table 1 SOMI classification criteria SOMI Free Recall Scores Total Recall Scores Years to diagnosis: Mean (SD) Class of Memory Impairment 0 No Memory Impairment > 30 > 46 7.05 (2.80) None detected by pFCSRT + IR 1 Subtle Retrieval Impairment 25–30 > 46 4.89 (2.48) Free recall declines at a constant rate. Storage is preserved. 2 Moderate Retrieval Impairment 20–24 > 46 4.03 (2.62) Rate of free recall decline doubles. Executive dysfunction accelerates. Storage is preserved. 3 Subtle Storage Impairment any 45–46 2.09 (1.91) Cuing fails to normalize total recall. 4 Significant Storage Impairment compatible with dementia any 33–44 0.86 (1.30) Intellectual decline accelerates heralding ADL impairment. 5 Moderate Episodic Memory Impairment any ≤ 32 SOMI 5 was added to accommodate participants with moderate episodic memory impairment. Note. Criteria and years to diagnosis is derived from Grober et al., 2021b( 18 ) Neuropsychological Assessments Participants completed a standardized battery of cognitive, functional, and psychosocial assessments. The battery included: the Subjective Cognitive Decline Questionnaire (SCD-Q 19 ), consisting of a subscale answered by the participant (MyCog) and an informant-answered subscale (TheirCog), the picture version of the Free and Cued Selective Reminding Test with Immediate Recall (pFCSRT + IR 20 ), the Mini Mental State Examination (MMSE 21 , 22 ), Instrumental and Physical Activities of Daily Living (I-ADL, P-ADL 23 ), Geriatric Depression Scale (GDS 24 ), and a Short Physical Performance Battery (SPPB 25 ). MRI Acquisition and Preprocessing All participants underwent structural magnetic resonance imaging (MRI) using a 3T GE Discovery MR750 scanner (General Electric Medical Systems, Milwaukee, WI, USA) equipped with a 32-channel head coil. High-resolution T1-weighted anatomical images were acquired with a sagittal 3D fast spoiled gradient-echo (FSPGR) sequence. Acquisition parameters were as follows: repetition time (TR) = 6.9 ms, echo time (TE) = 3.0 ms, inversion time = 450 ms, flip angle = 12°, slice thickness = 1.0 mm, in-plane field of view (FOV) = 25.6 × 25.6 cm², matrix size = 256 × 256. The scan duration was approximately 9 minutes. Preprocessing was performed using the Computational Anatomy Toolbox (CAT12) within SPM12 (Wellcome Centre for Human Neuroimaging, London, UK), running under MATLAB R2021a. Standard steps included bias field correction, skull stripping, spatial normalization to MNI template space, and segmentation into gray matter, white matter, and cerebrospinal fluid compartments. Gray matter maps were registered using an affine registration and smoothed with a 4 mm and 8 mm full-width-at-half-maximum kernel and further resampled to a spatial resolution of 4 mm and 8 mm. To reduce data dimensionality and prevent collinearity, principal component analysis (PCA) was performed using the “Matlab toolbox for Dimensionality Reduction” ( https://lvdmaaten.github.io/drtoolbox/ ). This step yielded orthogonal components as inputs to the BrainAGE algorithm and helped mitigate risks of overfitting, in line with prior recommendations 26 . BrainAGE Estimation Biological brain age was estimated using a pretrained BrainAGE model implemented in CAT12. This model was developed using a relevance vector regression algorithm trained on a large reference sample of healthy individuals across the adult lifespan. 14 , 26 BrainAGE was defined as the difference between predicted brain age and chronological age (BrainAGE = predicted age – chronological age), with positive values indicating accelerated neurobiological aging. The BrainAGE algorithm is trained on voxelwise gray- and white-matter tissue maps derived from MRI, enabling the model to capture complex multivariate relationships across the whole brain rather than relying on single-region measures such as hippocampal volume. After segmentation, dimensionality reduction is performed using Principal Component Analysis (PCA), which reduces computational demands, increases robustness, and minimizes overfitting. RVR is then used to learn age-typical structural patterns, and these learned weights are applied to new MRI data to estimate individual brain age. 27 Our model was calibrated on healthy adults, who, on average, show a BrainAGE close to zero (reported in Matziorinis et al. 28 ). Therefore, deviations from zero in the independent test samples of the present study reflect genuine divergence from normative whole-brain aging trajectories rather than model artifact. In other words, the BrainAGE score quantifies the degree of accelerated or decelerated brain aging in biologically interpretable units (years). Statistical Analyses All statistical analyses were conducted using IBM SPSS Statistics (Version 29). To examine the association between episodic memory staging and biological aging, we first computed a Spearman rank-order correlation between SOMI stage and BrainAGE scores. Next, we constructed a general linear model (GLM) to test whether SOMI stage significantly predicted BrainAGE, while controlling for chronological age, gender, and years of education. A second model additionally included bilateral hippocampal volume (adjusted for intracranial volume) to examine whether the SOMI–BrainAGE association persisted beyond medial temporal lobe atrophy. All assumptions for linear modeling were assessed and met. To confirm that our results were not driven by participants with approximated SOMI classifications, we conducted a sensitivity analysis excluding the 16 individuals whose stages were derived from MMSE scores. Results remained consistent with the primary analyses (see Results). Additionally, we explored whether the association between SOMI and BrainAGE reflects a continuous trend or a categorical shift at the onset of storage impairment. For this purpose, we conducted an independent-samples t-test comparing BrainAGE scores between a lower SOMI group (stages 0–2) and a higher SOMI group (stages 3–5). Results Sample Characteristics Participant characteristics stratified by SOMI stage are summarized in Table 2 . Mean age increased progressively across stages, ranging from 66.08 years (SD = 11.64) in SOMI 0 to 75.14 years (SD = 4.18) in SOMI 5. The proportion of female participants varied across groups, with the highest in SOMI 0 (61.3%) and the lowest in SOMI 2 (31.6%). Years of education were relatively stable across stages, with group means between 13.00 and 15.04 years. As expected, BrainAGE scores increased with SOMI stage, reflecting a widening gap between brain-predicted and chronological age in more advanced stages. Neuropsychological performance declined across stages, with lower scores on the FCSRT and MMSE in individuals with higher SOMI stages. Subjective cognitive complaints, particularly informant-based ratings (TheirCog), also increased with advancing impairment. Functional ability, assessed via IADL and PADL, remained relatively high across the sample, though minor declines were observed in later stages. Depressive symptoms (GDS) varied without a consistent trend, and physical performance (SPPB) remained largely preserved. Formal comparisons across groups indicated significant differences in variables such as age, MMSE, and FCSRT performance, whereas other characteristics (such as education) did not differ significantly across SOMI stages (see Table 2 for details). Table 2 Sample Characteristics by SOMI Stage Variable SOMI 0 (n = 30) SOMI 1 (n = 27) SOMI 2 (n = 19) SOMI 3 (n = 9) SOMI 4 (n = 16) SOMI 5 (n = 18) Group Comparison Age (years) 65.60 (10.78) 70.41 (11.06) 72.05 (10.55) 76.89 (6.09) 73.94 (8.54) 74.78 (4.93) H(5) = 15.26, p = .009 % Female 63.3% 41.4% 31.6% 66.7% 50.0% 38.9% χ²(5) = 7.33, p = .197 Years of Education 14.77 (3.16) 14.06 (2.45) 14.37 (3.08) 11.75 (4.77) 14.06 (3.42) 14.83 (3.37) F(5, 112) = 1.29, p = .272 BrainAGE 2.97 (4.05) 3.00 (4.06) 4.59 (3.54) 7.37 (3.98) 8.32 (4.00) 9.04 (3.13) F(5, 113) = 10.22, p < .001 FCSRT FR 33.80 (2.59) 27.41 (1.60) 22.32 (1.49) 17.33 (5.32) 14.44 (6.48) 2.89 (3.26) H(5) = 94.02, p < .001 FCSRT TR 47.90 (0.31) 47.74 (0.45) 47.84 (0.38) 45.78 (0.44) 40.11 (3.89) 16.33 (10.37) H(5) = 79.88, p < .001 FCSRT DR 12.53 (1.53) 11.33 (1.62) 9.16 (2.17) 7.11 (1.45) 5.67 (3.71) 0.56 (0.73) H(5) = 67.78, p < .001 MMSE 28.43 (1.57) 28.00 (2.04) 27.63 (2.27) 25.56 (2.60) 24.69 (2.44) 18.83 (2.75) H(5) = 64.31, p < .001 SCD (MyCog) 12.43 (4.68) 11.96 (4.93) 12.68 (4.12) 10.78 (5.40) 8.33 (5.36) 9.00 (3.27) F(5, 95) = 1.77, p = .126 SCD (TheirCog) 7.48 (4.72) 10.11 (5.88) 9.94 (5.20) 14.17 (6.05) 13.44 (5.43) 17.57 (3.60) F(5, 76) = 5.50, p < .001 IADL 8.00 (0.95) 8.74 (1.40) 8.68 (1.57) 8.22 (1.56) 11.25 (5.34) 13.39 (4.73) H(5) = 39.55, p < .001 PADL 6.20 (0.55) 6.12 (0.33) 6.26 (0.81) 6.33 (0.71) 6.81 (1.60) 6.89 (1.28) H(5) = 11.49, p = .043 GDS 6.63 (4.99) 7.70 (6.04) 6.37 (4.63) 7.00 (5.00) 6.88 (5.14) 5.44 (4.36) H(5) = 1.84, p = .871 SPPB 10.53 (1.78) 10.56 (1.74) 10.37 (1.95) 9.67 (2.12) 10.31 (2.58) 9.44 (2.26) H(5) = 6.87, p = .231 Notes. Values represent Mean (Standard Deviation). Gender values reflect the proportion of female participants in each group. Group comparisons were conducted using one-way ANOVA, Kruskal–Wallis H tests, or Chi-squared tests as appropriate. Abbreviations: BrainAGE = Brain Age Gap Estimate; FCSRT FR = Free and Cued Selective Reminding Test – Free Recall; FCSRT TR = Free and Cued Selective Reminding Test – Total Recall; FCSRT DR = Free and Cued Selective Reminding Test – Delayed Recall; MMSE = Mini-Mental State Examination; SCD (MyCog) = Subjective Cognitive Decline – self-report; SCD (TheirCog) = Subjective Cognitive Decline – informant report; IADL = Instrumental Activities of Daily Living; PADL = Physical Activities of Daily Living; GDS = Geriatric Depression Scale; SPPB = Short Physical Performance Battery. Association Between SOMI Stage and BrainAGE A Spearman rank-order correlation revealed a significant positive association between SOMI stage and BrainAGE, ρ(117) = 0.53, p < .001. This suggests that more advanced stages of objective memory impairment are associated with greater deviations from normative brain aging trajectories, i.e., accelerated neurobiological aging. This pattern is illustrated in Fig. 1 . To assess the robustness of this relationship while adjusting for relevant covariates, a general linear model (GLM) was constructed. The overall model was statistically significant, F (13, 104) = 8.05, p < .001, explaining 50.2% of the variance in BrainAGE ( R² = .502, adjusted R² = .439). SOMI stage remained a significant positive predictor of BrainAGE, F (5, 104) = 7.81, p < .001, partial η ² = .273. Chronological age also emerged as a significant predictor, F (1, 104) = 29.50, p < .001, partial η ² = .221 (see Table S1 for full GLM statistics). In contrast, gender ( p = .95) and years of education ( p = .75) were not significant predictors. To verify that this effect was not driven by SOMI approximations based on MMSE, a sensitivity analysis was conducted excluding the 16 participants with estimated SOMI scores. The association between SOMI and BrainAGE remained statistically significant and comparable in magnitude in this reduced sample ( n = 103), Spearman’s ρ = 0.459, p < .001; multiple regression: β = .34, p < .001, Adjusted R² = .417. These findings confirm the robustness of the primary results. Discussion This study demonstrates a robust association between the Stages of Objective Memory Impairment (SOMI) and BrainAGE, a validated neuroimaging marker of biological brain aging. Individuals classified at more advanced SOMI stages showed significantly elevated BrainAGE scores, indicating that their brains appeared older than expected given their chronological age. Importantly, this association remained significant after adjusting for chronological age, gender, education, and even hippocampal volume, and was confirmed through sensitivity analyses that excluded participants with MMSE-based SOMI approximations. Taken together, these findings suggest that SOMI, although based on a clinically accessible and standardized memory test, reflects meaningful neurobiological degeneration across the brain. SOMI Reflects Systemic Neurodegeneration The strong correlation between SOMI stage and BrainAGE supports the validity of SOMI as an index of neurobiological aging. Prior studies have demonstrated that SOMI correlates with hallmark Alzheimer’s disease (AD) pathologies, including amyloid and tau deposition, and with structural changes in medial temporal regions. The present findings extend this literature by linking SOMI to a machine-learning-based metric of global brain aging, derived from whole-brain MRI patterns. Unlike traditional region-specific volumetric analyses, BrainAGE captures distributed cortical and subcortical atrophy, offering a comprehensive index of systemic neurodegeneration. That SOMI tracks with BrainAGE suggests that the behavioral stages of episodic memory decline are grounded in widespread structural brain changes, not merely focal hippocampal damage. A further interesting observation concerns the BrainAGE values in SOMI-0 and SOMI-1. Participants in these stages performed fully within the normal range on the FCSRT, yet nevertheless showed a modest but consistent elevation in brain age of approximately three years. Because the BrainAGE model used in this study was calibrated so that healthy adults across a wide age range show, on average, a deviation close to zero (demonstrated in Matziorinis et al. 28 ), this elevation is unlikely to reflect a calibration artifact or statistical fluctuation. Instead, it likely reflects a genuine biological characteristic of the sample. Importantly, all participants in the present study reported subjective memory complaints, and the group means for both SOMI-0 and SOMI-1 exceeded the formal criteria for SCD (≥7 on both SCD-Q MyCog and TheirCog). Although the individuals grouped into SOMI-0 showed no measurable impairment on standard memory tests, they nevertheless exhibited a detectable increase in brain age relative to their chronological age. This pattern indiciates that SCD is accompanied by subtle but meaningful deviations from normative brain aging. Our findings are consistent with recent results from the REMEMBER study 29 , which observed significantly elevated brain age values in SCD individuals, reinforcing the view that neurobiological alterations may precede measurable deficits on episodic memory tasks. Together, these converging observations support the notion that BrainAGE is sensitive to very early brain changes that arise even before the onset of objective memory impairment. Beyond the Hippocampus: SOMI as a Marker of Widespread Network Breakdown A particularly striking finding is that the SOMI–BrainAGE association persisted even after controlling for hippocampal volume. This result carries both conceptual and clinical significance. Conceptually, it implies that advancing SOMI stages capture a broader pathological footprint than medial temporal lobe degeneration alone. While the FCSRT (on which SOMI is based) is sensitive to hippocampal dysfunction, the current data show that it also reflects damage across distributed neural systems. In this sense, SOMI may serve as a proxy for network-level breakdown, reflecting both the multifocal pathology of preclinical AD and the differential vulnerability of neural systems to AD-related degeneration 30 . Clinically, this positions SOMI not just as a staging tool for memory decline, but as a potential surrogate marker of global brain health, capable of indexing systems-level changes detectable by high-dimensional neuroimaging. Notably, the sharp increase in BrainAGE at SOMI-3 provides new, independent neurobiological support for the functional distinction between retrieval deficits and true storage impairment, reinforcing the theoretical inflection point embedded in the SOMI framework. Clinical Implications and Future Directions The findings presented here have immediate translational implications. SOMI is rapid, cost-effective, and readily deployable in clinical and research settings. Its significant association with BrainAGE strengthens the rationale for its use as a first-line screening tool for individuals at elevated risk of AD-related neurodegeneration. In clinical trials, SOMI could aid in participant stratification, identifying individuals with high likelihood of relevant brain structural changes, without the need for expensive or invasive biomarker assessments. Future longitudinal studies are needed to examine whether SOMI predicts the pace of neurodegeneration and cognitive decline over time. If so, SOMI may become an important behavioral anchor for tracking disease progression in preclinical and prodromal AD. Strengths and Limitations Key strengths of this study include a well-characterized cohort covering the full spectrum of memory performance, the use of a validated cognitive staging system (SOMI), and the application of a sophisticated neuroimaging biomarker (BrainAGE) that summarizes brain-wide atrophy patterns. The inclusion of both covariate-adjusted and sensitivity analyses further enhances the robustness of our conclusions. Nonetheless, several limitations warrant mention. First, the cross-sectional design precludes causal inference about the temporal relationship between memory decline and brain aging. Second, although 16 early-enrolled participants had SOMI stages estimated from MMSE scores rather than from the FCSRT, sensitivity analyses confirmed that all main findings persisted when these individuals were excluded. Their inclusion therefore did not materially influence the results and allowed us to preserve continuity across the full range of memory impairment. Third, the absence of direct amyloid or tau biomarkers prevents definitive attribution of observed changes to Alzheimer’s pathology. Future multimodal studies integrating SOMI, BrainAGE, and molecular markers will be essential to clarify the biological specificity of these associations. Stepwise Increase in BrainAGE Between SOMI Categories While the overall rank-order correlation between SOMI stage and BrainAGE was significant (ρ(117) = 0.53, p < .001), inspection of the group means suggested that the relationship may not be strictly linear. BrainAGE scores showed a modest increase across the lower SOMI stages (0–2), followed by a marked elevation from SOMI-3 onwards (see Table 2). To formally test this pattern, we categorized participants into a lower-impairment group (SOMI 0–2; n = 76) and a higher-impairment group (SOMI 3–5; n = 43). An independent-samples t -test revealed a significant difference in BrainAGE between the two groups with large effect size, t (117) = 6.90, p < .001, d = 1.32. Participants in the lower SOMI group had a mean BrainAGE of 3.38 years ( SD = 3.94), compared to 8.42 years ( SD = 3.62) in the higher group. In contrast, Spearman correlations within each group were not statistically significant: ρ = .14 ( p = .23) for SOMI 0–2, and ρ = .15 ( p = .34) for SOMI 3–5. These findings suggest that the significant SOMI–BrainAGE relationship is primarily driven by a categorical shift at the transition to storage impairment, rather than by a gradual, stage-wise progression across the full SOMI continuum. Association Between SOMI Stage and Hippocampal Volume Correlational analyses confirmed that SOMI stage was significantly associated with medial temporal atrophy. Specifically, SOMI stage negatively correlated with left hippocampal volume, ρ(117) = −0.47, p < .001, and with right hippocampal volume, ρ(117) = −0.45, p < .001. To examine whether the SOMI–BrainAGE association extended beyond hippocampal degeneration, we conducted a second GLM that included bilateral hippocampal volume (adjusted for intracranial volume) as an additional covariate. This model remained statistically significant, F (14, 103) = 9.77, p < .001, explaining 57.0% of the variance in BrainAGE (adjusted R ² = .512). Crucially, SOMI stage remained a significant predictor of BrainAGE even after accounting for hippocampal volume, F (5, 103) = 4.24, p = .002, partial η ² = .171. These results suggest that SOMI stages reflect widespread cortical atrophy and systemic brain aging not fully explained by hippocampal volume alone (see Table S2 for full GLM statistics). Conclusion Our findings are important for several reasons. First, they show that SOMI stages correspond closely to BrainAGE, a well-established marker of systemic brain aging, thereby anchoring a behavioral classification scheme in neurobiological reality. Importantly, the observed discontinuity in BrainAGE scores between SOMI stages 0–2 and 3–5 mirrors previously reported shifts in tau pathology and medial temporal atrophy 9,10, 31 , supporting the biological validity of this cognitive staging framework. This transition marks the theoretical shift from retrieval difficulties (SOMI 0–2) to genuine storage impairment (SOMI 3–5, a hallmark of Alzheimer’s-type memory decline) and our results demonstrate that this shift has a clear neurobiological correlate in terms of accelerated, brain-wide aging. Secondly, the association between SOMI and BrainAGE was confirmed through sensitivity analyses that excluded estimated SOMI cases and controlled for hippocampal volume. This indicates that SOMI captures widespread cortical atrophy beyond the medial temporal lobe. Thirdly, these findings validate SOMI as a simple, scalable, and biologically meaningful tool for identifying individuals at heightened risk for neurodegeneration. Fourthly, SOMI may serve as a useful stratification tool in clinical trials (especially those targeting preclinical stages of AD) where rapid, non-invasive screening methods are urgently needed 32 . Finally, the convergence between cognitive staging and machine learning–based neuroimaging biomarkers underscores SOMI’s translational potential, bridging the gap between clinical assessment and systems-level neuroscience in the early detection of Alzheimer’s disease. Abbreviations AD : Alzheimer’s disease A β 42/40 : Amyloid beta 42/40 BrainAGE : Brain Age Gap Estimation CAT12 : Computational Anatomy Toolbox CSF : Cerebrospinal fluid FCSRT : Free and Cued Selective Reminding Test FCSRT DR : Free and Cued Selective Reminding Test – Delayed Recall FCSRT FR: Free and Cued Selective Reminding Test – Free Recall FCSRT TR : Free and Cued Selective Reminding Test – Total Recall FOV : Field of view FSPGR : Fast Spoiled Gradient Echo GDS : Geriatric Depression Scale GLM : General Linear Model IADL : Instrumental Activities of Daily Living IR : Immediate Recall MCI : Mild Cognitive Impairment MMSE : Mini-Mental State Examination MRI : Magnetic Resonance Imaging MNI : Montreal Neurological Institute PADL: Physical Activities of Daily Living PCA: Principal Component Analysis PET: Positron Emission Tomography pFCSRT+IR : Picture version of the Free and Cued Selective Reminding Test with Immediate Recall p-tau18: Plasma phosphorylated tau at threonine 181 p-tau217 : Plasma phosphorylated tau at threonine 217 REC : Regional Committee for Medical and Health Research Ethics RVR : Relevance Vector Regression SCD : Subjective Cognitive Decline SCD-Q: Subjective Cognitive Decline Questionnaire SOMI : Stages of Objective Memory Impairment SPM : Statistical Parametric Mapping SPPB : Short Physical Performance Battery TE : Echo Time TR : Repetition Time VBM : Voxel-Based Morphometry Declarations Consent for publication Ethical approval was obtained from the Regional Committees for Medical and Health Research Ethics (REC West; ref. 2018/206), and all participants provided written informed consent in accordance with the Declaration of Helsinki. Data availability The data supporting the findings described can be obtained from the corresponding author upon request. Competing interests The authors declare no competing interests. Funding A grant by the The Research Council of Norway (RCN; https://www.forskningsradet.no/en/) [Norges Forskningsråd], reference number 260576 was awarded to S.K. The Project is further supported by: Trond-Mohn-Stiftelse (TMS; https://mohnfoundation.no/), Bergens Forskningsstifelse (BFS; https://www.uib.no/foransatte/75175/bergens-forskningsstiftelse), and the Institute of Biological and Medical Psychology (IBMP; https://www.uib.no/ibmp) at the University of Bergen (UiB) in Norway. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions S.K. designed the clinical trial. S.S. supervised the clinical trial. C.G. implemented the BrainAGE algorithm. B.K.F. and A.M.M. conducted participant recruitment and clinical assessments. B.K.F. carried out the analyses supporting the study's results. B.K.F and S.K. wrote the paper. All authors discussed the results and commented on the manuscript. Acknowledgements We thank the therapists from Bergen Municipality, the Grieg Academy, and the Western Norway University of Applied Sciences for their contribution to intervention delivery, as well as the research assistants involved in participant assessments. We also thank the participants for their trust and engagement. References Alzheimer's, A. Alzheimer's disease facts and figures. Alzheimers Dement. 19, 1598–1695 (2023). (2023). https://doi.org/10.1002/alz.13016 Jack, C. R. Jr. et al. NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14 , 535–562. https://doi.org/10.1016/j.jalz.2018.02.018 (2018). Jacobsen, J. H. et al. Why musical memory can be preserved in advanced Alzheimer’s disease. Brain 138 (8), 2438–2450. https://doi.org/10.1093/brain/awv135 (2015). Wimo, A. et al. Health economic evaluation of treatments for Alzheimer’s disease: Impact of new diagnostic criteria. J. Intern. Med. 275 , 304–316. https://doi.org/10.1111/joim.12167 (2014). Samson, S., Clément, S., Narme, P., Schiaratura, L. & Ehrlé, N. Efficacy of musical interventions in dementia: methodological requirements of nonpharmacological trials. Ann. N Y Acad. Sci. 1337 , 249–255. https://doi.org/10.1111/nyas.12621 (2015). Palmqvist, S., Warmenhoven, N., Anastasi, F., Pilotto, A., Janelidze, S., Tideman,P., … Hansson, O. (2025). Plasma phospho-tau217 for Alzheimer’s disease diagnosis in primary and secondary care using a fully automated platform. Nature Medicine, 1–8.https://doi.org/10.1038/s41591-025-03622-w. Schöll, M., Vrillon, A., Ikeuchi, T., Quevenco, F. C., Iaccarino, L., Vasileva-Metodiev,S. Z., … Palmqvist, S. (2025). Cutting through the noise: a narrative review of Alzheimer's disease plasma biomarkers for routine clinical use. The Journal of Prevention of Alzheimer's Disease, 100056.https://doi.org/10.1016/j.tjpad.2024.100056. Grober, E., Veroff, A. E. & Lipton, R. B. Temporal unfolding of declining episodic memory on the Free and Cued Selective Reminding Test in the predementia phase of Alzheimer’s disease: Implications for clinical trials. Alzheimers Dement. (Amst) . 10 , 161–171. https://doi.org/10.1016/j.dadm.2017.12.004 (2018). Grober, E. et al. Neuroimaging correlates of stages of objective memory impairment (SOMI) system. Alzheimers Dement. (Amst) . 13 , e12224. https://doi.org/10.1002/dad2.12224 (2021). Grober, E. et al. Associations of stages of objective memory impairment with amyloid PET and structural MRI: The A4 Study. Neurology 98 , e1327–e1336. https://doi.org/10.1212/WNL.0000000000200046 (2022). Matziorinis, A. M., Leemans, A., Skouras, S. & Koelsch, S. Navigating the stages of objective memory impairment (SOMI) through the Papez circuit: Hippocampal brain reserve, white matter microstructure, and structural network topology along the Alzheimer’s continuum. Hum. Brain Mapp. https://doi.org/10.21203/rs.3.rs-3412028/v1 (2023). Epelbaum, S. et al. Preclinical Alzheimer's disease: a systematic review of the cohorts underlying the concept. Alzheimers Dement. 13 , 454–467. https://doi.org/10.1016/j.jalz.2016.12.003 (2017). Franke, K. & Gaser, C. Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer’s disease. GeroPsych 25 , 235–247. https://doi.org/10.1024/1662-9647/a000074 (2012). Gaser, C. et al. BrainAGE in mild cognitive impaired patients: Predicting the conversion to Alzheimer’s disease. PLoS One . 8 , e67346. https://doi.org/10.1371/journal.pone.0067346 (2013). Wang, J. et al. Gray matter age prediction as a biomarker for risk of dementia. Proc. Natl Acad. Sci. USA 116, 21213–21218 (2019). https://doi.org/10.1073/pnas.1902376116 Bashyam, V. M. et al. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14,468 individuals worldwide. Brain 143 , 2312–2324. https://doi.org/10.1093/brain/awaa160 (2020). Flo, B. K. et al. Study protocol for the Alzheimer and music therapy study: An RCT to compare the efficacy of music therapy and physical activity on brain plasticity, depressive symptoms, and cognitive decline, in a population with and at risk for Alzheimer’s disease. PLoS One . 17 , e0270682. https://doi.org/10.1371/journal.pone.0270682 (2022). Grober, E. et al. Stages of objective memory impairment predict Alzheimer’s disease neuropathology: Comparison with the Clinical Dementia Rating Scale–Sum of Boxes. J. Alzheimers Dis. 80 , 185–195. https://doi.org/10.3233/JAD-200946 (2021). Rami, L. et al. The Subjective Cognitive Decline Questionnaire (SCD-Q): A validation study. J. Alzheimers Dis. 41 , 453–466. https://doi.org/10.3233/JAD-132027 (2014). Grober, E. & Buschke, H. Genuine memory deficits in dementia. Dev. Neuropsychol. 3 , 13–36. https://doi.org/10.1080/87565648709540361 (1987). Folstein, M. F., Folstein, S. E. & McHugh, P. R. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr Res. 12 , 189–198. https://doi.org/10.1016/0022-3956(75)90026-6 (1975). Strobel, C. & Engedal, K. MMSE-NR. Norwegian revised Mini Mental Status Evaluation. Revised and expanded manual (National Centre for Ageing and Health, 2008). Lawton, M. P. & Brody, E. M. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 9 , 179–186. https://doi.org/10.1093/geront/9.3_Part_1.179 (1969). Yesavage, J. A. et al. Development and validation of a geriatric depression screening scale: A preliminary report. J. Psychiatr Res. 17 , 37–49. https://doi.org/10.1016/0022-3956(82)90033-4 (1982). Pavasini, R. et al. Short physical performance battery and all-cause mortality: Systematic review and meta-analysis. BMC Med. 14 , 215. https://doi.org/10.1186/s12916-016-0763-7 (2016). Franke, K., Ziegler, G., Klöppel, S. & Gaser, C. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. Neuroimage 50 , 883–892. https://doi.org/10.1016/j.neuroimage.2010.01.005 (2010). Franke, K. & Gaser, C. Ten years of BrainAGE as a neuroimaging biomarker of brain aging: what insights have we gained? Front. Neurol. 10 , 789. https://doi.org/10.3389/fneur.2019.00789 (2019). Matziorinis, A. M., Gaser, C. & Koelsch, S. Is musical engagement enough to keep the brain young? Brain Struct. Function . 228 (2), 577–588. https://doi.org/10.1007/s00429-022-02602-x (2023). Wittens, M. M., Denissen, S., Sima, D. M., Fransen, E., Niemantsverdriet, E., Bastin,C., … Engelborghs, S. (2024). Brain age as a biomarker for pathological versus healthy ageing–a REMEMBER study. Alzheimer's research & therapy, 16(1), 128. https://doi.org/10.1186/s13195-024-01491-y Leggieri, M. et al. Music intervention approaches for Alzheimer’s disease: A review of the literature. Front. Neurosci. 13 , 132. 10.3389/fnins.2019.00132 (2019). Petersen, K. K. et al. Associations of stages of objective memory impairment with cerebrospinal fluid and neuroimaging biomarkers of Alzheimer’s disease. J. Prev. Alzheimers Dis. 10 , 112–119. https://doi.org/10.14283/jpad.2022.98 (2023). James, C. E. et al. Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living—A 23-year scoping review. Heliyon 10 (9), e26674. 10.1016/j.heliyon.2024.e26674 (2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 19 Jan, 2026 Editor invited by journal 09 Jan, 2026 Editor assigned by journal 06 Jan, 2026 Submission checks completed at journal 06 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8530151","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":577566785,"identity":"c2ae6692-df87-47ed-a0eb-555acf5fc129","order_by":0,"name":"Birthe Kristin Flo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACA2YGBiBi4OdjYD7AkNhAnBbGZiAt2cbAlkCkFga4Fh4DBkZitJiz8z5/XFDBIMHG3vNN4uEOhjx+Qlosm9kNm2ecAWrhObtNIvEMQ7EkIZsMDrMxNvO2MdSxSeQCtbQxJG44QJSWf0BbJHKekaKlAayFjTgtls1sjLN5jkkA/XLM2CLxjETiTEJ+Mec/xvCZp8ZGgp+9+eHNnztsEvsJ6IABCQzGKBgFo2AUjAJKAACswDXuOU3LQwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Bergen","correspondingAuthor":true,"prefix":"","firstName":"Birthe","middleName":"Kristin","lastName":"Flo","suffix":""},{"id":577566786,"identity":"520d8b6a-cc6a-45d9-92ba-4e34369081f5","order_by":1,"name":"Stavros Skouras","email":"","orcid":"","institution":"University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Stavros","middleName":"","lastName":"Skouras","suffix":""},{"id":577566793,"identity":"1804ba84-85b6-49d0-9e9b-11c67097f8fe","order_by":2,"name":"Anna Maria Matziorinis","email":"","orcid":"","institution":"University of Bergen","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"Maria","lastName":"Matziorinis","suffix":""},{"id":577566794,"identity":"06df5f4b-5b57-4fb8-846b-fd1c405fba72","order_by":3,"name":"Christian Gaser","email":"","orcid":"","institution":"Friedrich Schiller University Jena","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Gaser","suffix":""},{"id":577566800,"identity":"210afa5c-e724-4f2c-ba5f-c0838458f71b","order_by":4,"name":"Stefan Koelsch","email":"","orcid":"","institution":"University of Bergen","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Koelsch","suffix":""}],"badges":[],"createdAt":"2026-01-06 10:38:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8530151/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8530151/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-41282-z","type":"published","date":"2026-04-09T15:58:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100876652,"identity":"b25039c6-182c-4134-bb23-03bd3889ae33","added_by":"auto","created_at":"2026-01-22 10:26:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200381,"visible":true,"origin":"","legend":"","description":"","filename":"09.12BeyondtheHippocampus.docx","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/2edbacdf7a545afaceeed4a0.docx"},{"id":100876574,"identity":"290bc235-71d6-4e87-b185-c98e2bb81c13","added_by":"auto","created_at":"2026-01-22 10:26:03","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7505,"visible":true,"origin":"","legend":"","description":"","filename":"53a78eed9e7e4e4b8ccdc83da1a9fa7e.json","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/cbf45f965eaeb42f5f2dc294.json"},{"id":100876576,"identity":"d9d88119-3721-446b-a5f3-af4d72e50689","added_by":"auto","created_at":"2026-01-22 10:26:06","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120429,"visible":true,"origin":"","legend":"","description":"","filename":"53a78eed9e7e4e4b8ccdc83da1a9fa7e1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/58ccd895832ff37bd1efa3d2.xml"},{"id":100876653,"identity":"18e226cb-7c3d-4658-8cb7-3781365f0b79","added_by":"auto","created_at":"2026-01-22 10:26:25","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":222908,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/94c99c45392aa17d821a1d3b.jpeg"},{"id":100876568,"identity":"efe6ca2f-54b3-4073-b2f7-2e119c21b1a5","added_by":"auto","created_at":"2026-01-22 10:26:03","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119970,"visible":true,"origin":"","legend":"","description":"","filename":"53a78eed9e7e4e4b8ccdc83da1a9fa7e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/a2d69cea4e18490c14b38a0b.xml"},{"id":100876573,"identity":"785fee9b-7b2c-4cba-b840-8d9259ab1e7b","added_by":"auto","created_at":"2026-01-22 10:26:03","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134931,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/a7860f223960ffcaa29f63cf.html"},{"id":100876572,"identity":"65d08871-db75-4b76-aea9-5a5550f87cc2","added_by":"auto","created_at":"2026-01-22 10:26:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrainAGE distributions across SOMI stages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eViolin plots show the distribution of BrainAGE values across SOMI stages 0 to 5. White circles denote group means, with vertical lines indicating 95% confidence intervals. Black dots represent individual participants. The red dashed line indicates BrainAGE = 0, where predicted and chronological age align. The figure illustrates a progressive increase in BrainAGE with advancing SOMI stage. Notably, BrainAGE scores rise sharply at the transition from SOMI-2 to SOMI-3, consistent with the onset of storage impairment and supporting the biological validity of this cognitive staging threshold.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/062d360fb19f9762f8d8cf65.png"},{"id":106809321,"identity":"e45d8ec3-72f8-421d-b087-5f89cc2b381b","added_by":"auto","created_at":"2026-04-13 16:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1064892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/a9bc1932-9e9d-4a73-a493-08f56dedae8f.pdf"},{"id":100876566,"identity":"5ce76123-d758-4536-9ddb-1801afa1e160","added_by":"auto","created_at":"2026-01-22 10:26:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16005,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8530151/v1/1270511589e2d415be6973b3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond the Hippocampus: Objective Memory Stages Capture Widespread Brain Aging in a Cross-Sectional Analysis of Baseline RCT Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Despite substantial advances in biomarker discovery, a critical implementation gap persists: although cerebrospinal fluid (CSF) analysis and amyloid-PET imaging reliably indicate AD pathology\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, their cost, invasiveness, and limited accessibility render them impractical for large-scale, population-based screening.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Recent advances in plasma biomarkers such as p-tau217, p-tau181, and Aβ42/40 provide less invasive methods for detecting AD pathology, though their implementation and validation continue to evolve.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e This constraint hampers efforts to identify at-risk individuals during the early, preclinical window, which is precisely the stage when lifestyle modifications and emerging disease-modifying interventions may be most effective. There is therefore an urgent need for alternative tools that are both scalable and biologically informative, that is, tools capable of bridging the gap between advanced biomarker science and practical clinical detection.\u003c/p\u003e \u003cp\u003eIn this context, two complementary approaches have gained increasing attention. The Stages of Objective Memory Impairment (SOMI) framework offers a clinically grounded method to stage episodic memory decline (the hallmark cognitive symptom of prodromal AD) based on performance in the Free and Cued Selective Reminding Test (FCSRT).\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The FCSRT combines controlled encoding with semantic cue\u0026ndash;based retrieval, allowing a clear distinction between retrieval deficits (where cues normalize recall) and storage deficits (where recall remains impaired even with cueing). SOMI stages (0\u0026ndash;5) map these qualitative differences, with SOMI-5 later added to capture more severe impairment.\u003c/p\u003e \u003cp\u003ePrior studies have linked SOMI stages to key AD-related pathologies, including amyloid and tau deposition as well as structural atrophy in the hippocampus, entorhinal cortex, and inferior temporal regions (e.g., \u003csup\u003e8,9,10,11,12\u003c/sup\u003e). Stage-dependent volume reductions of 5\u0026ndash;7% in medial temporal lobe structures have been observed, and SOMI has demonstrated predictive utility in longitudinal studies.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Notably, the SOMI framework is designed to capture a qualitative transition in memory impairment, from retrieval difficulties in the earlier stages (SOMI 0\u0026ndash;2) to actual storage failure at SOMI-3 and beyond. This retrieval-to-storage shift is clinically meaningful, as it marks the onset of the amnestic syndrome characteristic of Alzheimer\u0026rsquo;s disease and reflects a turning point in underlying neuropathological burden. Consistent with this, prior studies report steeper tau pathology and medial temporal atrophy emerging at SOMI-3.\u003c/p\u003e \u003cp\u003eIn parallel, Brain Age Gap Estimation (BrainAGE) has emerged as a powerful neuroimaging biomarker of individual brain health. Derived from structural MRI using voxel-based morphometry (VBM) and machine learning, BrainAGE quantifies the deviation between a person\u0026rsquo;s chronological and brain-predicted age, yielding a summary metric of whole-brain atrophy.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Elevated BrainAGE has been associated with increased risk of conversion from MCI to AD and has, in some studies, outperformed classical region-based volumetric measures.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Unlike traditional single-region indices, BrainAGE reflects distributed neurodegenerative processes, aligning with the multifocal pathology of early-stage AD.\u003c/p\u003e \u003cp\u003eWhile SOMI provides a clinically intuitive staging of memory dysfunction and BrainAGE offers a biologically sensitive index of systemic brain aging, it remains unknown whether the two converge. Does behavioral evidence of early episodic memory impairment (as captured by SOMI) correspond to an accelerated BrainAGE signature? Addressing this question fills a critical gap in the current literature: it would help establish SOMI not only as a cognitive staging tool, but also as a proxy for global neurobiological aging in preclinical AD. While previous studies have linked SOMI to hippocampal atrophy and tau pathology, no study to date has examined its association with a global MRI-derived biomarker of systemic brain aging.\u003c/p\u003e \u003cp\u003eIn the present study, we test whether SOMI stages predict individual differences in BrainAGE. Such a finding would have immediate translational implications: it would position SOMI as a low-cost, non-invasive behavioral indicator of neurodegenerative burden, and strengthen its role in screening, risk stratification, and clinical trial design in the context of Alzheimer\u0026rsquo;s disease.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants were drawn from the ALMUTH study (\u003cem\u003eAlzheimer \u0026amp; Music Therapy\u003c/em\u003e; for protocol details, see Flo et al.\u003csup\u003e17\u003c/sup\u003e), a multicenter randomized controlled trial designed to assess music therapy interventions in individuals with or at risk for Alzheimer\u0026rsquo;s disease. The present cross-sectional analysis focuses exclusively on baseline data, collected prior to the onset of any intervention.\u003c/p\u003e \u003cp\u003eParticipants were recruited through outpatient memory clinics and community outreach in Bergen, Norway. Eligibility criteria for the parent study included the presence of subjective cognitive complaints, a Mini-Mental State Examination (MMSE) score above 17, and general health sufficient to permit participation. Exclusion criteria comprised non-AD dementia, significant cardiovascular or neurological disease, history of traumatic brain injury, major psychiatric conditions, sensory deficits that could interfere with testing, and any contraindications to MRI. In addition, individuals with atypical SOMI profiles (severe retrieval deficits without storage impairment) were excluded from the current analysis.\u003c/p\u003e \u003cp\u003eA total of 152 participants were initially screened. Of these, 119 individuals completed high-quality structural MRI and were included in the final analysis sample. All participants provided written informed consent in accordance with the Declaration of Helsinki. The study was approved by the Regional Committees for Medical and Health Research Ethics (REC West; ref. 2018/206).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSOMI Classification\u003c/h3\u003e\n\u003cp\u003eEpisodic memory performance was staged using the \u003cem\u003eStages of Objective Memory Impairment\u003c/em\u003e (SOMI) framework, based on the picture version of the Free and Cued Selective Reminding Test with immediate recall (pFCSRT\u0026thinsp;+\u0026thinsp;IR). The SOMI system defines six sequential stages (SOMI-0 through SOMI-5) according to thresholds in free and total recall scores (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and is designed to capture the gradual progression of memory impairment typical of prodromal Alzheimer\u0026rsquo;s disease. While SOMI-0 to SOMI-4 are part of the original framework\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, SOMI-5 was later introduced to accommodate individuals with more pronounced memory deficits.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Prior research has demonstrated strong associations between SOMI stages and established AD biomarkers, including amyloid, tau, and medial temporal lobe atrophy.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFor 16 early-enrolled participants who had a clinical diagnosis of Alzheimer\u0026rsquo;s disease and were assessed before the implementation of the pFCSRT\u0026thinsp;+\u0026thinsp;IR, SOMI stages were estimated using MMSE scores. Although SOMI was originally developed for preclinical and prodromal AD, SOMI-5 was subsequently introduced to accommodate more severe memory impairment and has been applied in neuropathology-validated studies. Because the aim of the present study was to examine brain correlates across the full spectrum of memory impairment, rather than to diagnose dementia, these participants were included to preserve the continuity of the SOMI staging distribution. Based on the empirical distribution of MMSE scores across SOMI stages, a cut-off of MMSE\u0026thinsp;\u0026lt;\u0026thinsp;22 was used to distinguish SOMI-5 from SOMI-4. Importantly, we will show in the Results that their inclusion did not bias the results, as demonstrated by a sensitivity analysis excluding these 16 participants, which yielded virtually identical findings (see Results).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSOMI classification criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree Recall Scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Recall Scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYears to diagnosis: Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClass of Memory Impairment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 No Memory Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.05 (2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone detected by pFCSRT + IR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 Subtle Retrieval Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.89 (2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree recall declines at a constant rate. Storage is preserved.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 Moderate Retrieval Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt; 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.03 (2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRate of free recall decline doubles. Executive dysfunction\u003c/p\u003e \u003cp\u003eaccelerates. Storage is preserved.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 Subtle Storage Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u0026ndash;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.09 (1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCuing fails to normalize total recall.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 Significant Storage Impairment compatible with dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntellectual decline accelerates heralding ADL impairment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 Moderate Episodic Memory Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOMI 5 was added to accommodate participants with\u003c/p\u003e \u003cp\u003emoderate episodic memory impairment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. Criteria and years to diagnosis is derived from Grober et al., 2021b(\u003csup\u003e18\u003c/sup\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eNeuropsychological Assessments\u003c/h3\u003e\n\u003cp\u003eParticipants completed a standardized battery of cognitive, functional, and psychosocial assessments. The battery included: the Subjective Cognitive Decline Questionnaire (SCD-Q\u003csup\u003e19\u003c/sup\u003e), consisting of a subscale answered by the participant (MyCog) and an informant-answered subscale (TheirCog), the picture version of the Free and Cued Selective Reminding Test with Immediate Recall (pFCSRT\u0026thinsp;+\u0026thinsp;IR\u003csup\u003e20\u003c/sup\u003e), the Mini Mental State Examination (MMSE\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e), Instrumental and Physical Activities of Daily Living (I-ADL, P-ADL\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e), Geriatric Depression Scale (GDS\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e), and a Short Physical Performance Battery (SPPB\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\n\u003ch3\u003eMRI Acquisition and Preprocessing\u003c/h3\u003e\n\u003cp\u003eAll participants underwent structural magnetic resonance imaging (MRI) using a 3T GE Discovery MR750 scanner (General Electric Medical Systems, Milwaukee, WI, USA) equipped with a 32-channel head coil. High-resolution T1-weighted anatomical images were acquired with a sagittal 3D fast spoiled gradient-echo (FSPGR) sequence. Acquisition parameters were as follows: repetition time (TR)\u0026thinsp;=\u0026thinsp;6.9 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;3.0 ms, inversion time\u0026thinsp;=\u0026thinsp;450 ms, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm, in-plane field of view (FOV)\u0026thinsp;=\u0026thinsp;25.6 \u0026times; 25.6 cm\u0026sup2;, matrix size\u0026thinsp;=\u0026thinsp;256 \u0026times; 256. The scan duration was approximately 9 minutes.\u003c/p\u003e \u003cp\u003ePreprocessing was performed using the Computational Anatomy Toolbox (CAT12) within SPM12 (Wellcome Centre for Human Neuroimaging, London, UK), running under MATLAB R2021a. Standard steps included bias field correction, skull stripping, spatial normalization to MNI template space, and segmentation into gray matter, white matter, and cerebrospinal fluid compartments. Gray matter maps were registered using an affine registration and smoothed with a 4 mm and 8 mm full-width-at-half-maximum kernel and further resampled to a spatial resolution of 4 mm and 8 mm.\u003c/p\u003e \u003cp\u003eTo reduce data dimensionality and prevent collinearity, principal component analysis (PCA) was performed using the \u0026ldquo;Matlab toolbox for Dimensionality Reduction\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lvdmaaten.github.io/drtoolbox/\u003c/span\u003e\u003cspan address=\"https://lvdmaaten.github.io/drtoolbox/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This step yielded orthogonal components as inputs to the BrainAGE algorithm and helped mitigate risks of overfitting, in line with prior recommendations\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eBrainAGE Estimation\u003c/h3\u003e\n\u003cp\u003eBiological brain age was estimated using a pretrained BrainAGE model implemented in CAT12. This model was developed using a relevance vector regression algorithm trained on a large reference sample of healthy individuals across the adult lifespan.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e BrainAGE was defined as the difference between predicted brain age and chronological age (BrainAGE\u0026thinsp;=\u0026thinsp;predicted age \u0026ndash; chronological age), with positive values indicating accelerated neurobiological aging.\u003c/p\u003e \u003cp\u003eThe BrainAGE algorithm is trained on voxelwise gray- and white-matter tissue maps derived from MRI, enabling the model to capture complex multivariate relationships across the whole brain rather than relying on single-region measures such as hippocampal volume. After segmentation, dimensionality reduction is performed using Principal Component Analysis (PCA), which reduces computational demands, increases robustness, and minimizes overfitting. RVR is then used to learn age-typical structural patterns, and these learned weights are applied to new MRI data to estimate individual brain age.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur model was calibrated on healthy adults, who, on average, show a BrainAGE close to zero (reported in Matziorinis et al. \u003csup\u003e28\u003c/sup\u003e). Therefore, deviations from zero in the independent test samples of the present study reflect genuine divergence from normative whole-brain aging trajectories rather than model artifact. In other words, the BrainAGE score quantifies the degree of accelerated or decelerated brain aging in biologically interpretable units (years).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics (Version 29). To examine the association between episodic memory staging and biological aging, we first computed a Spearman rank-order correlation between SOMI stage and BrainAGE scores.\u003c/p\u003e \u003cp\u003eNext, we constructed a general linear model (GLM) to test whether SOMI stage significantly predicted BrainAGE, while controlling for chronological age, gender, and years of education. A second model additionally included bilateral hippocampal volume (adjusted for intracranial volume) to examine whether the SOMI\u0026ndash;BrainAGE association persisted beyond medial temporal lobe atrophy. All assumptions for linear modeling were assessed and met.\u003c/p\u003e \u003cp\u003eTo confirm that our results were not driven by participants with approximated SOMI classifications, we conducted a sensitivity analysis excluding the 16 individuals whose stages were derived from MMSE scores. Results remained consistent with the primary analyses (see Results). Additionally, we explored whether the association between SOMI and BrainAGE reflects a continuous trend or a categorical shift at the onset of storage impairment. For this purpose, we conducted an independent-samples t-test comparing BrainAGE scores between a lower SOMI group (stages 0\u0026ndash;2) and a higher SOMI group (stages 3\u0026ndash;5).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eSample Characteristics\u003c/h2\u003e\n \u003cp\u003eParticipant characteristics stratified by SOMI stage are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Mean age increased progressively across stages, ranging from 66.08 years (SD\u0026thinsp;=\u0026thinsp;11.64) in SOMI 0 to 75.14 years (SD\u0026thinsp;=\u0026thinsp;4.18) in SOMI 5. The proportion of female participants varied across groups, with the highest in SOMI 0 (61.3%) and the lowest in SOMI 2 (31.6%). Years of education were relatively stable across stages, with group means between 13.00 and 15.04 years. As expected, BrainAGE scores increased with SOMI stage, reflecting a widening gap between brain-predicted and chronological age in more advanced stages. Neuropsychological performance declined across stages, with lower scores on the FCSRT and MMSE in individuals with higher SOMI stages. Subjective cognitive complaints, particularly informant-based ratings (TheirCog), also increased with advancing impairment. Functional ability, assessed via IADL and PADL, remained relatively high across the sample, though minor declines were observed in later stages. Depressive symptoms (GDS) varied without a consistent trend, and physical performance (SPPB) remained largely preserved. Formal comparisons across groups indicated significant differences in variables such as age, MMSE, and FCSRT performance, whereas other characteristics (such as education) did not differ significantly across SOMI stages (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for details).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample Characteristics by SOMI Stage\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOMI 0 (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOMI 1 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOMI 2 (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOMI 3 (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOMI 4 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOMI 5 (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup Comparison\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.60 (10.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.41 (11.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.05 (10.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.89 (6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.94 (8.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.78 (4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;15.26, p\u0026thinsp;=\u0026thinsp;.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;(5)\u0026thinsp;=\u0026thinsp;7.33, p\u0026thinsp;=\u0026thinsp;.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.77 (3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.06 (2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.37 (3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.75 (4.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.06 (3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.83 (3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(5, 112)\u0026thinsp;=\u0026thinsp;1.29, p\u0026thinsp;=\u0026thinsp;.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrainAGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.97 (4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 (4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.59 (3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.37 (3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.32 (4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.04 (3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(5, 113)\u0026thinsp;=\u0026thinsp;10.22, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFCSRT FR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.80 (2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.41 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.32 (1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.33 (5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.44 (6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.89 (3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;94.02, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFCSRT TR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.90 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.74 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.84 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.78 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.11 (3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.33 (10.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;79.88, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFCSRT DR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.53 (1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.33 (1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.11 (1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.67 (3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;67.78, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.43 (1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.00 (2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.63 (2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.56 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.69 (2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.83 (2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;64.31, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCD (MyCog)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.43 (4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.96 (4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.68 (4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.78 (5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.33 (5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.00 (3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(5, 95)\u0026thinsp;=\u0026thinsp;1.77, p\u0026thinsp;=\u0026thinsp;.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCD (TheirCog)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.48 (4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.11 (5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.94 (5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.17 (6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.44 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.57 (3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(5, 76)\u0026thinsp;=\u0026thinsp;5.50, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIADL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.74 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.68 (1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.22 (1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.25 (5.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.39 (4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;39.55, p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePADL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.20 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.26 (0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.33 (0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.81 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.89 (1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;11.49, p\u0026thinsp;=\u0026thinsp;.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.63 (4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.70 (6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.37 (4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.00 (5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.88 (5.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.44 (4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;1.84, p\u0026thinsp;=\u0026thinsp;.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.53 (1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.56 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.37 (1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.67 (2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.31 (2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.44 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH(5)\u0026thinsp;=\u0026thinsp;6.87, p\u0026thinsp;=\u0026thinsp;.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNotes. Values represent Mean (Standard Deviation). Gender values reflect the proportion of female participants in each group. Group comparisons were conducted using one-way ANOVA, Kruskal\u0026ndash;Wallis H tests, or Chi-squared tests as appropriate. Abbreviations: BrainAGE\u0026thinsp;=\u0026thinsp;Brain Age Gap Estimate; FCSRT FR\u0026thinsp;=\u0026thinsp;Free and Cued Selective Reminding Test \u0026ndash; Free Recall; FCSRT TR\u0026thinsp;=\u0026thinsp;Free and Cued Selective Reminding Test \u0026ndash; Total Recall; FCSRT DR\u0026thinsp;=\u0026thinsp;Free and Cued Selective Reminding Test \u0026ndash; Delayed Recall; MMSE\u0026thinsp;=\u0026thinsp;Mini-Mental State Examination; SCD (MyCog)\u0026thinsp;=\u0026thinsp;Subjective Cognitive Decline \u0026ndash; self-report; SCD (TheirCog)\u0026thinsp;=\u0026thinsp;Subjective Cognitive Decline \u0026ndash; informant report; IADL\u0026thinsp;=\u0026thinsp;Instrumental Activities of Daily Living; PADL\u0026thinsp;=\u0026thinsp;Physical Activities of Daily Living; GDS\u0026thinsp;=\u0026thinsp;Geriatric Depression Scale; SPPB\u0026thinsp;=\u0026thinsp;Short Physical Performance Battery.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation Between SOMI Stage and BrainAGE\u003c/h2\u003e\n \u003cp\u003eA Spearman rank-order correlation revealed a significant positive association between SOMI stage and BrainAGE, \u0026rho;(117)\u0026thinsp;=\u0026thinsp;0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. This suggests that more advanced stages of objective memory impairment are associated with greater deviations from normative brain aging trajectories, i.e., accelerated neurobiological aging. This pattern is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTo assess the robustness of this relationship while adjusting for relevant covariates, a general linear model (GLM) was constructed. The overall model was statistically significant, \u003cem\u003eF\u003c/em\u003e(13, 104)\u0026thinsp;=\u0026thinsp;8.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, explaining 50.2% of the variance in BrainAGE (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = .502, adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .439). SOMI stage remained a significant positive predictor of BrainAGE, \u003cem\u003eF\u003c/em\u003e(5, 104)\u0026thinsp;=\u0026thinsp;7.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u0026sup2; = .273. Chronological age also emerged as a significant predictor, \u003cem\u003eF\u003c/em\u003e(1, 104)\u0026thinsp;=\u0026thinsp;29.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, partial \u003cem\u003e\u0026eta;\u003c/em\u003e\u0026sup2; = .221 (see Table S1 for full GLM statistics). In contrast, gender (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.95) and years of education (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.75) were not significant predictors.\u003c/p\u003e\n \u003cp\u003eTo verify that this effect was not driven by SOMI approximations based on MMSE, a sensitivity analysis was conducted excluding the 16 participants with estimated SOMI scores. The association between SOMI and BrainAGE remained statistically significant and comparable in magnitude in this reduced sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;103), Spearman\u0026rsquo;s \u0026rho;\u0026thinsp;=\u0026thinsp;0.459, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; multiple regression: \u0026beta;\u0026thinsp;=\u0026thinsp;.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, Adjusted R\u0026sup2; = .417. These findings confirm the robustness of the primary results.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates a robust association between the Stages of Objective Memory Impairment (SOMI) and BrainAGE, a validated neuroimaging marker of biological brain aging. Individuals classified at more advanced SOMI stages showed significantly elevated BrainAGE scores, indicating that their brains appeared older than expected given their chronological age. Importantly, this association remained significant after adjusting for chronological age, gender, education, and even hippocampal volume, and was confirmed through sensitivity analyses that excluded participants with MMSE-based SOMI approximations. Taken together, these findings suggest that SOMI, although based on a clinically accessible and standardized memory test, reflects meaningful neurobiological degeneration across the brain.\u003c/p\u003e\n\u003cp\u003eSOMI Reflects Systemic Neurodegeneration\u003c/p\u003e\n\u003cp\u003eThe strong correlation between SOMI stage and BrainAGE supports the validity of SOMI as an index of neurobiological aging. Prior studies have demonstrated that SOMI correlates with hallmark Alzheimer\u0026rsquo;s disease (AD) pathologies, including amyloid and tau deposition, and with structural changes in medial temporal regions. The present findings extend this literature by linking SOMI to a machine-learning-based metric of global brain aging, derived from whole-brain MRI patterns. Unlike traditional region-specific volumetric analyses, BrainAGE captures distributed cortical and subcortical atrophy, offering a comprehensive index of systemic neurodegeneration. That SOMI tracks with BrainAGE suggests that the behavioral stages of episodic memory decline are grounded in widespread structural brain changes, not merely focal hippocampal damage. A further interesting observation concerns the BrainAGE values in SOMI-0 and SOMI-1. Participants in these stages performed fully within the normal range on the FCSRT, yet nevertheless showed a modest but consistent elevation in brain age of approximately three years. Because the BrainAGE model used in this study was calibrated so that healthy adults across a wide age range show, on average, a deviation close to zero (demonstrated in Matziorinis et al.\u003csup\u003e28\u003c/sup\u003e), this elevation is unlikely to reflect a calibration artifact or statistical fluctuation. Instead, it likely reflects a genuine biological characteristic of the sample. Importantly, all participants in the present study reported subjective memory complaints, and the group means for both SOMI-0 and SOMI-1 exceeded the formal criteria for SCD (\u0026ge;7 on both SCD-Q MyCog and TheirCog). Although the individuals grouped into SOMI-0 showed no measurable impairment on standard memory tests, they nevertheless exhibited a detectable increase in brain age relative to their chronological age. This pattern indiciates that SCD is accompanied by subtle but meaningful deviations from normative brain aging. Our findings are consistent with recent results from the REMEMBER study\u003csup\u003e29\u003c/sup\u003e, which observed significantly elevated brain age values in SCD individuals, reinforcing the view that neurobiological alterations may precede measurable deficits on episodic memory tasks. Together, these converging observations support the notion that BrainAGE is sensitive to very early brain changes that arise even before the onset of objective memory impairment.\u003c/p\u003e\n\u003cp\u003eBeyond the Hippocampus: SOMI as a Marker of Widespread Network Breakdown\u003c/p\u003e\n\u003cp\u003eA particularly striking finding is that the SOMI\u0026ndash;BrainAGE association persisted even after controlling for hippocampal volume. This result carries both conceptual and clinical significance. Conceptually, it implies that advancing SOMI stages capture a broader pathological footprint than medial temporal lobe degeneration alone. While the FCSRT (on which SOMI is based) is sensitive to hippocampal dysfunction, the current data show that it also reflects damage across distributed neural systems. In this sense, SOMI may serve as a proxy for network-level breakdown, reflecting both the multifocal pathology of preclinical AD and the differential vulnerability of neural systems to AD-related degeneration\u003csup\u003e30\u003c/sup\u003e. Clinically, this positions SOMI not just as a staging tool for memory decline, but as a potential surrogate marker of global brain health, capable of indexing systems-level changes detectable by high-dimensional neuroimaging. Notably, the sharp increase in BrainAGE at SOMI-3 provides new, independent neurobiological support for the functional distinction between retrieval deficits and true storage impairment, reinforcing the theoretical inflection point embedded in the SOMI framework.\u003c/p\u003e\n\u003cp\u003eClinical Implications and Future Directions\u003c/p\u003e\n\u003cp\u003eThe findings presented here have immediate translational implications. SOMI is rapid, cost-effective, and readily deployable in clinical and research settings. Its significant association with BrainAGE strengthens the rationale for its use as a first-line screening tool for individuals at elevated risk of AD-related neurodegeneration. In clinical trials, SOMI could aid in participant stratification, identifying individuals with high likelihood of relevant brain structural changes, without the need for expensive or invasive biomarker assessments. Future longitudinal studies are needed to examine whether SOMI predicts the pace of neurodegeneration and cognitive decline over time. If so, SOMI may become an important behavioral anchor for tracking disease progression in preclinical and prodromal AD.\u003c/p\u003e\n\u003cp\u003eStrengths and Limitations\u003c/p\u003e\n\u003cp\u003eKey strengths of this study include a well-characterized cohort covering the full spectrum of memory performance, the use of a validated cognitive staging system (SOMI), and the application of a sophisticated neuroimaging biomarker (BrainAGE) that summarizes brain-wide atrophy patterns. The inclusion of both covariate-adjusted and sensitivity analyses further enhances the robustness of our conclusions. Nonetheless, several limitations warrant mention. First, the cross-sectional design precludes causal inference about the temporal relationship between memory decline and brain aging. Second, although 16 early-enrolled participants had SOMI stages estimated from MMSE scores rather than from the FCSRT, sensitivity analyses confirmed that all main findings persisted when these individuals were excluded. Their inclusion therefore did not materially influence the results and allowed us to preserve continuity across the full range of memory impairment. Third, the absence of direct amyloid or tau biomarkers prevents definitive attribution of observed changes to Alzheimer\u0026rsquo;s pathology. Future multimodal studies integrating SOMI, BrainAGE, and molecular markers will be essential to clarify the biological specificity of these associations.\u003c/p\u003e\u003ch2\u003e\u003cstrong\u003eStepwise Increase in BrainAGE Between SOMI Categories\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWhile the overall rank-order correlation between SOMI stage and BrainAGE was significant (ρ(117) = 0.53, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), inspection of the group means suggested that the relationship may not be strictly linear. BrainAGE scores showed a modest increase across the lower SOMI stages (0–2), followed by a marked elevation from SOMI-3 onwards (see Table 2). To formally test this pattern, we categorized participants into a lower-impairment group (SOMI 0–2; n = 76) and a higher-impairment group (SOMI 3–5; n = 43).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn independent-samples \u003cem\u003et\u003c/em\u003e-test revealed a significant difference in BrainAGE between the two groups with large effect size, \u003cem\u003et\u003c/em\u003e(117) = 6.90, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 1.32. Participants in the lower SOMI group had a mean BrainAGE of 3.38 years (\u003cem\u003eSD\u003c/em\u003e = 3.94), compared to 8.42 years (\u003cem\u003eSD\u003c/em\u003e = 3.62) in the higher group. In contrast, Spearman correlations within each group were not statistically significant: \u003cem\u003eρ\u003c/em\u003e = .14 (\u003cem\u003ep\u003c/em\u003e = .23) for SOMI 0–2, and \u003cem\u003eρ\u003c/em\u003e = .15 (\u003cem\u003ep\u003c/em\u003e = .34) for SOMI 3–5. These findings suggest that the significant SOMI–BrainAGE relationship is primarily driven by a categorical shift at the transition to storage impairment, rather than by a gradual, stage-wise progression across the full SOMI continuum.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAssociation Between SOMI Stage and Hippocampal Volume\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCorrelational analyses confirmed that SOMI stage was significantly associated with medial temporal atrophy. Specifically, SOMI stage negatively correlated with left hippocampal volume, ρ(117) = −0.47, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, and with right hippocampal volume, ρ(117) = −0.45, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eTo examine whether the SOMI–BrainAGE association extended beyond hippocampal degeneration, we conducted a second GLM that included bilateral hippocampal volume (adjusted for intracranial volume) as an additional covariate. This model remained statistically significant, \u003cem\u003eF\u003c/em\u003e(14, 103) = 9.77, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, explaining 57.0% of the variance in BrainAGE (adjusted \u003cem\u003eR\u003c/em\u003e² = .512). Crucially, SOMI stage remained a significant predictor of BrainAGE even after accounting for hippocampal volume, \u003cem\u003eF\u003c/em\u003e(5, 103) = 4.24, \u003cem\u003ep\u003c/em\u003e = .002, partial \u003cem\u003eη\u003c/em\u003e² = .171. These results suggest that SOMI stages reflect widespread cortical atrophy and systemic brain aging not fully explained by hippocampal volume alone (see Table S2 for full GLM statistics).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings are important for several reasons. First, they show that SOMI stages correspond closely to BrainAGE, a well-established marker of systemic brain aging, thereby anchoring a behavioral classification scheme in neurobiological reality. Importantly, the observed discontinuity in BrainAGE scores between SOMI stages 0\u0026ndash;2 and 3\u0026ndash;5 mirrors previously reported shifts in tau pathology and medial temporal atrophy\u003csup\u003e9,10, 31\u003c/sup\u003e, supporting the biological validity of this cognitive staging framework. This transition marks the theoretical shift from retrieval difficulties (SOMI 0\u0026ndash;2) to genuine storage impairment (SOMI 3\u0026ndash;5, a hallmark of Alzheimer\u0026rsquo;s-type memory decline) and our results demonstrate that this shift has a clear neurobiological correlate in terms of accelerated, brain-wide aging. Secondly, the association between SOMI and BrainAGE was confirmed through sensitivity analyses that excluded estimated SOMI cases and controlled for hippocampal volume. This indicates that SOMI captures widespread cortical atrophy beyond the medial temporal lobe. Thirdly, these findings validate SOMI as a simple, scalable, and biologically meaningful tool for identifying individuals at heightened risk for neurodegeneration. Fourthly, SOMI may serve as a useful stratification tool in clinical trials (especially those targeting preclinical stages of AD) where rapid, non-invasive screening methods are urgently needed\u003csup\u003e32\u003c/sup\u003e. Finally, the convergence between cognitive staging and machine learning\u0026ndash;based neuroimaging biomarkers underscores SOMI\u0026rsquo;s translational potential, bridging the gap between clinical assessment and systems-level neuroscience in the early detection of Alzheimer\u0026rsquo;s disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAD\u003c/strong\u003e: Alzheimer\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e42/40\u003c/strong\u003e: Amyloid beta 42/40\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrainAGE\u003c/strong\u003e: Brain Age Gap Estimation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAT12\u003c/strong\u003e: Computational Anatomy Toolbox\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCSF\u003c/strong\u003e: Cerebrospinal fluid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFCSRT\u003c/strong\u003e: Free and Cued Selective Reminding Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFCSRT DR\u003c/strong\u003e: Free and Cued Selective Reminding Test \u0026ndash; Delayed Recall\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFCSRT FR:\u003c/strong\u003e Free and Cued Selective Reminding Test \u0026ndash; Free Recall\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFCSRT TR\u003c/strong\u003e: Free and Cued Selective Reminding Test \u0026ndash; Total Recall\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFOV\u003c/strong\u003e: Field of view\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFSPGR\u003c/strong\u003e: Fast Spoiled Gradient Echo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGDS\u003c/strong\u003e: Geriatric Depression Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGLM\u003c/strong\u003e: General Linear Model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIADL\u003c/strong\u003e: Instrumental Activities of Daily Living\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e: Immediate Recall\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMCI\u003c/strong\u003e: Mild Cognitive Impairment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMMSE\u003c/strong\u003e: Mini-Mental State Examination\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI\u003c/strong\u003e: Magnetic Resonance Imaging\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMNI\u003c/strong\u003e: Montreal Neurological Institute\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePADL:\u003c/strong\u003e Physical Activities of Daily Living\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA:\u003c/strong\u003e Principal Component Analysis\u003cbr\u003e\u003cstrong\u003ePET:\u003c/strong\u003e Positron Emission Tomography\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003epFCSRT+IR\u003c/strong\u003e: Picture version of the Free and Cued Selective Reminding Test with Immediate Recall\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep-tau18:\u0026nbsp;\u003c/strong\u003ePlasma phosphorylated tau at threonine 181\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep-tau217\u003c/strong\u003e: Plasma phosphorylated tau at threonine 217\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eREC\u003c/strong\u003e: Regional Committee for Medical and Health Research Ethics\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRVR\u003c/strong\u003e: Relevance Vector Regression\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCD\u003c/strong\u003e: Subjective Cognitive Decline\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCD-Q:\u003c/strong\u003e Subjective Cognitive Decline Questionnaire\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSOMI\u003c/strong\u003e: Stages of Objective Memory Impairment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSPM\u003c/strong\u003e: Statistical Parametric Mapping\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSPPB\u003c/strong\u003e: Short Physical Performance Battery\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTE\u003c/strong\u003e: Echo Time\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTR\u003c/strong\u003e: Repetition Time\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVBM\u003c/strong\u003e: Voxel-Based Morphometry\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eEthical approval was obtained from the Regional Committees for Medical and Health Research Ethics (REC West; ref. 2018/206), and all participants provided written informed consent in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe data supporting the findings described can be obtained from the corresponding author upon request.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA grant by the The Research Council of Norway (RCN; https://www.forskningsradet.no/en/) [Norges Forskningsr\u0026aring;d], reference number 260576 was awarded to S.K. The Project is further supported by: Trond-Mohn-Stiftelse (TMS; https://mohnfoundation.no/), Bergens Forskningsstifelse (BFS; https://www.uib.no/foransatte/75175/bergens-forskningsstiftelse), and the Institute of Biological and Medical Psychology (IBMP; https://www.uib.no/ibmp) at the University of Bergen (UiB) in Norway. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eS.K. designed the clinical trial. S.S. supervised the clinical trial. C.G. implemented the BrainAGE algorithm. B.K.F. and A.M.M. conducted participant recruitment and clinical assessments. B.K.F. carried out the analyses supporting the study\u0026apos;s results. B.K.F and S.K. wrote the paper. All authors discussed the results and commented on the manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe thank the therapists from Bergen Municipality, the Grieg Academy, and the Western Norway University of Applied Sciences for their contribution to intervention delivery, as well as the research assistants involved in participant assessments. We also thank the participants for their trust and engagement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlzheimer's, A. Alzheimer's disease facts and figures. \u003cem\u003eAlzheimers Dement.\u003c/em\u003e 19, 1598\u0026ndash;1695 (2023). (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/alz.13016\u003c/span\u003e\u003cspan address=\"10.1002/alz.13016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJack, C. R. Jr. et al. NIA-AA research framework: Toward a biological definition of Alzheimer\u0026rsquo;s disease. \u003cem\u003eAlzheimers Dement.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 535\u0026ndash;562. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jalz.2018.02.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jalz.2018.02.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobsen, J. H. et al. Why musical memory can be preserved in advanced Alzheimer\u0026rsquo;s disease. \u003cem\u003eBrain\u003c/em\u003e \u003cb\u003e138\u003c/b\u003e (8), 2438\u0026ndash;2450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/brain/awv135\u003c/span\u003e\u003cspan address=\"10.1093/brain/awv135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWimo, A. et al. Health economic evaluation of treatments for Alzheimer\u0026rsquo;s disease: Impact of new diagnostic criteria. \u003cem\u003eJ. Intern. Med.\u003c/em\u003e \u003cb\u003e275\u003c/b\u003e, 304\u0026ndash;316. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/joim.12167\u003c/span\u003e\u003cspan address=\"10.1111/joim.12167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamson, S., Cl\u0026eacute;ment, S., Narme, P., Schiaratura, L. \u0026amp; Ehrl\u0026eacute;, N. Efficacy of musical interventions in dementia: methodological requirements of nonpharmacological trials. \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e \u003cb\u003e1337\u003c/b\u003e, 249\u0026ndash;255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nyas.12621\u003c/span\u003e\u003cspan address=\"10.1111/nyas.12621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmqvist, S., Warmenhoven, N., Anastasi, F., Pilotto, A., Janelidze, S., Tideman,P., \u0026hellip; Hansson, O. (2025). Plasma phospho-tau217 for Alzheimer\u0026rsquo;s disease diagnosis in primary and secondary care using a fully automated platform. Nature Medicine, 1\u0026ndash;8.https://doi.org/10.1038/s41591-025-03622-w.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSch\u0026ouml;ll, M., Vrillon, A., Ikeuchi, T., Quevenco, F. C., Iaccarino, L., Vasileva-Metodiev,S. Z., \u0026hellip; Palmqvist, S. (2025). Cutting through the noise: a narrative review of Alzheimer's disease plasma biomarkers for routine clinical use. The Journal of Prevention of Alzheimer's Disease, 100056.https://doi.org/10.1016/j.tjpad.2024.100056.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrober, E., Veroff, A. E. \u0026amp; Lipton, R. B. Temporal unfolding of declining episodic memory on the Free and Cued Selective Reminding Test in the predementia phase of Alzheimer\u0026rsquo;s disease: Implications for clinical trials. \u003cem\u003eAlzheimers Dement. (Amst)\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, 161\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dadm.2017.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.dadm.2017.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrober, E. et al. Neuroimaging correlates of stages of objective memory impairment (SOMI) system. \u003cem\u003eAlzheimers Dement. (Amst)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, e12224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/dad2.12224\u003c/span\u003e\u003cspan address=\"10.1002/dad2.12224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrober, E. et al. Associations of stages of objective memory impairment with amyloid PET and structural MRI: The A4 Study. \u003cem\u003eNeurology\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, e1327\u0026ndash;e1336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000200046\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000200046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatziorinis, A. M., Leemans, A., Skouras, S. \u0026amp; Koelsch, S. Navigating the stages of objective memory impairment (SOMI) through the Papez circuit: Hippocampal brain reserve, white matter microstructure, and structural network topology along the Alzheimer\u0026rsquo;s continuum. \u003cem\u003eHum. Brain Mapp.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-3412028/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-3412028/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpelbaum, S. et al. Preclinical Alzheimer's disease: a systematic review of the cohorts underlying the concept. \u003cem\u003eAlzheimers Dement.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 454\u0026ndash;467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jalz.2016.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jalz.2016.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranke, K. \u0026amp; Gaser, C. Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer\u0026rsquo;s disease. \u003cem\u003eGeroPsych\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 235\u0026ndash;247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1024/1662-9647/a000074\u003c/span\u003e\u003cspan address=\"10.1024/1662-9647/a000074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaser, C. et al. BrainAGE in mild cognitive impaired patients: Predicting the conversion to Alzheimer\u0026rsquo;s disease. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, e67346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0067346\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0067346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J. et al. Gray matter age prediction as a biomarker for risk of dementia. \u003cem\u003eProc. Natl Acad. Sci. USA\u003c/em\u003e 116, 21213\u0026ndash;21218 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1902376116\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1902376116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBashyam, V. M. et al. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14,468 individuals worldwide. \u003cem\u003eBrain\u003c/em\u003e \u003cb\u003e143\u003c/b\u003e, 2312\u0026ndash;2324. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/brain/awaa160\u003c/span\u003e\u003cspan address=\"10.1093/brain/awaa160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlo, B. K. et al. Study protocol for the Alzheimer and music therapy study: An RCT to compare the efficacy of music therapy and physical activity on brain plasticity, depressive symptoms, and cognitive decline, in a population with and at risk for Alzheimer\u0026rsquo;s disease. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, e0270682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0270682\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0270682\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrober, E. et al. Stages of objective memory impairment predict Alzheimer\u0026rsquo;s disease neuropathology: Comparison with the Clinical Dementia Rating Scale\u0026ndash;Sum of Boxes. \u003cem\u003eJ. Alzheimers Dis.\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e, 185\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/JAD-200946\u003c/span\u003e\u003cspan address=\"10.3233/JAD-200946\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRami, L. et al. The Subjective Cognitive Decline Questionnaire (SCD-Q): A validation study. \u003cem\u003eJ. Alzheimers Dis.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 453\u0026ndash;466. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/JAD-132027\u003c/span\u003e\u003cspan address=\"10.3233/JAD-132027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrober, E. \u0026amp; Buschke, H. Genuine memory deficits in dementia. \u003cem\u003eDev. Neuropsychol.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 13\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/87565648709540361\u003c/span\u003e\u003cspan address=\"10.1080/87565648709540361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1987).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFolstein, M. F., Folstein, S. E. \u0026amp; McHugh, P. R. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. \u003cem\u003eJ. Psychiatr Res.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 189\u0026ndash;198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0022-3956(75)90026-6\u003c/span\u003e\u003cspan address=\"10.1016/0022-3956(75)90026-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1975).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrobel, C. \u0026amp; Engedal, K. \u003cem\u003eMMSE-NR. Norwegian revised Mini Mental Status Evaluation. Revised and expanded manual\u003c/em\u003e (National Centre for Ageing and Health, 2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawton, M. P. \u0026amp; Brody, E. M. Assessment of older people: Self-maintaining and instrumental activities of daily living. \u003cem\u003eGerontologist\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 179\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/geront/9.3_Part_1.179\u003c/span\u003e\u003cspan address=\"10.1093/geront/9.3_Part_1.179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1969).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYesavage, J. A. et al. Development and validation of a geriatric depression screening scale: A preliminary report. \u003cem\u003eJ. Psychiatr Res.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 37\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0022-3956(82)90033-4\u003c/span\u003e\u003cspan address=\"10.1016/0022-3956(82)90033-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1982).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavasini, R. et al. Short physical performance battery and all-cause mortality: Systematic review and meta-analysis. \u003cem\u003eBMC Med.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12916-016-0763-7\u003c/span\u003e\u003cspan address=\"10.1186/s12916-016-0763-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranke, K., Ziegler, G., Kl\u0026ouml;ppel, S. \u0026amp; Gaser, C. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 883\u0026ndash;892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2010.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2010.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranke, K. \u0026amp; Gaser, C. Ten years of BrainAGE as a neuroimaging biomarker of brain aging: what insights have we gained? \u003cem\u003eFront. Neurol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2019.00789\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2019.00789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatziorinis, A. M., Gaser, C. \u0026amp; Koelsch, S. Is musical engagement enough to keep the brain young? \u003cem\u003eBrain Struct. Function\u003c/em\u003e. \u003cb\u003e228\u003c/b\u003e (2), 577\u0026ndash;588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00429-022-02602-x\u003c/span\u003e\u003cspan address=\"10.1007/s00429-022-02602-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWittens, M. M., Denissen, S., Sima, D. M., Fransen, E., Niemantsverdriet, E., Bastin,C., \u0026hellip; Engelborghs, S. (2024). Brain age as a biomarker for pathological versus healthy ageing\u0026ndash;a REMEMBER study. Alzheimer's research \u0026amp; therapy, 16(1), 128. https://doi.org/10.1186/s13195-024-01491-y\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeggieri, M. et al. Music intervention approaches for Alzheimer\u0026rsquo;s disease: A review of the literature. \u003cem\u003eFront. Neurosci.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2019.00132\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2019.00132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen, K. K. et al. Associations of stages of objective memory impairment with cerebrospinal fluid and neuroimaging biomarkers of Alzheimer\u0026rsquo;s disease. \u003cem\u003eJ. Prev. Alzheimers Dis.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 112\u0026ndash;119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14283/jpad.2022.98\u003c/span\u003e\u003cspan address=\"10.14283/jpad.2022.98\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames, C. E. et al. Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living\u0026mdash;A 23-year scoping review. \u003cem\u003eHeliyon\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (9), e26674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2024.e26674\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e26674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer's Disease, BrainAGE, Stages of Objective Memory Impairment, MRI","lastPublishedDoi":"10.21203/rs.3.rs-8530151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8530151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eReliable staging of early memory decline is essential for identifying individuals at risk for Alzheimer\u0026rsquo;s disease. The Stages of Objective Memory Impairment (SOMI) framework provides a clinically scalable tool for characterizing episodic memory loss, yet its neurobiological validity remains underexplored. Recent advances in plasma biomarkers (e.g., p-tau217, p-tau181, Aβ42/40) offer emerging blood-based alternatives to CSF and PET, although their diagnostic implementation continues to evolve. Here, we examine whether SOMI stages reflect widespread brain aging, as indexed by BrainAGE, a structural MRI\u0026ndash;based biomarker quantifying deviation from normative aging trajectories. While previous studies have linked SOMI to hippocampal atrophy and tau pathology, no study to date has examined its association with a global MRI-derived biomarker of systemic brain aging.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eIn a well-characterized cohort of 119 older adults on the Alzheimer\u0026rsquo;s disease continuum, we evaluated whether higher SOMI stages were linked to elevated BrainAGE scores and examined whether this association remained significant after adjusting for age, sex, education, and hippocampal volume.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eHigher SOMI stages were robustly associated with elevated BrainAGE scores, indicating accelerated neurobiological aging. This relationship remained significant after adjusting for covariates and was confirmed in sensitivity analyses. Notably, a marked discontinuity in BrainAGE emerged between SOMI stages 0\u0026ndash;2 and 3\u0026ndash;5, aligning with the theoretical transition from retrieval to storage impairment, long recognized as a turning point in prodromal Alzheimer\u0026rsquo;s disease.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThese findings validate SOMI as a low-cost, non-invasive behavioral marker of systemic brain health. By linking cognitive staging to global neuroimaging biomarkers, our study supports SOMI\u0026rsquo;s translational utility for large-scale screening, clinical trial stratification, and early intervention planning in Alzheimer\u0026rsquo;s disease.\u003c/p\u003e","manuscriptTitle":"Beyond the Hippocampus: Objective Memory Stages Capture Widespread Brain Aging in a Cross-Sectional Analysis of Baseline RCT Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 10:25:56","doi":"10.21203/rs.3.rs-8530151/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T06:49:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"97247531930317136538959538632386613965","date":"2026-01-26T08:08:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T17:33:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T17:10:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95140706958495605549698497135752459008","date":"2026-01-21T14:14:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8732953101949051021423271850709429540","date":"2026-01-20T14:56:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4204640667923246477231650275089571415","date":"2026-01-20T13:01:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T02:09:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-09T11:08:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T04:35:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T04:34:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-06T10:28:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78a165f1-b76f-4776-8c4f-8329ed478909","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61447657,"name":"Health sciences/Biomarkers"},{"id":61447658,"name":"Health sciences/Neurology"},{"id":61447659,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-13T16:05:28+00:00","versionOfRecord":{"articleIdentity":"rs-8530151","link":"https://doi.org/10.1038/s41598-026-41282-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-09 15:58:40","publishedOnDateReadable":"April 9th, 2026"},"versionCreatedAt":"2026-01-22 10:25:56","video":"","vorDoi":"10.1038/s41598-026-41282-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41282-z","workflowStages":[]},"version":"v1","identity":"rs-8530151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8530151","identity":"rs-8530151","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}