Regional Patch-Based MRI Brain Age Modeling
with an Interpretable Cognitive Reserve Proxy
Samuel Maddox^c,1, Lemuel Puglisi^a,1, Fatemeh Darabifard^b, for the Alzheimer’s Disease Neuroimaging Initiative,
the Australian Imaging Biomarkers and Lifestyle flagship study of aging, Saber Sami^c,2, Daniele Ravi^b,2
^a University of Catania, Catania, Italy
^b University of Messina, Messina, Italy
^c University of East Anglia, Norwich, United Kingdom
Abstract
Accurate brain age prediction from MRI is a promising biomarker for brain health and neurodegenerative disease risk, but current deep learning models often lack anatomical specificity and clinical insight. We present a regional patch-based ensemble framework that uses 3D Convolutional Neural Networks (CNNs) trained on bilateral patches from ten subcortical structures, enhancing anatomical sensitivity. Ensemble predictions are combined with cognitive assessments to derive a cognitively informed proxy for cognitive reserve (CR-Proxy), quantifying resilience to age- related brain changes. We train our framework on a large, multi-cohort dataset of healthy controls and test it on independent samples that include individuals with Alzheimer’s disease and mild cognitive impairment. The results demonstrate that our method achieves robust brain age prediction and provides a practical, interpretable CR-Proxy capable of distinguishing diagnostic groups and identifying individuals with high or low cognitive reserve. This pipeline offers a scalable, clinically accessible tool for early risk assessment and personalized brain health monitoring.
Keywords: Brain Age Prediction, Cognitive Reserve, Brain-PAD, Magnetic Resonance Imaging

1.		Introduction
As individuals age, the brain undergoes progressive structural changes that manifest in complex, measurable patterns. These changes often vary across demographic and clinical groups [4]. Accurately capturing these subtle and heterogeneous patterns remains a major challenge. Brain age prediction based on neuroimaging data has emerged as a promising biomarker for tracking both healthy and pathological aging. This technique estimates the biological age of an individual’s brain and compares it to their chronological age. The difference between these two values, referred to as the Brain-Predicted Age Difference (Brain-PAD), can provide clinically meaningful insights into brain aging and overall neurological health [10]. Brain-PAD has shown strong potential as a marker of accelerated neurobio- logical aging and cognitive decline [5]. Early brain
1		Samuel Maddox and Lemuel Puglisi contributed equally as first authors.
2		Daniele Ravi and Saber Sami contributed equally as senior authors.
age prediction models [11] relied on manual feature engineering, which may obscure relevant anatomical information. Furthermore, such approaches often fail to fully leverage the richness of high-resolution structural MRI data. In recent years, deep learning, particularly Convolutional Neural Networks (CNNs), has become the state-of-the-art approach for brain age prediction. CNNs are well-suited for analyzing structural MRI scans because they can automatically learn hierarchical spatial features, eliminating the need for handcrafted inputs. Nonetheless, challenges remain, especially regarding the interpretability of these models and their generalizability across diverse populations. Moreover, brain age predictions alone cannot fully explain the variability in cognitive function observed among individuals. This gap has spurred interest in reserve mechanisms, which help explain why some people maintain cognitive performance despite age-related brain changes or pathology [21]. Among these, cognitive reserve has gained prominence as a concept describing the brain’s resilience to damage. Individuals with higher cognitive reserve tend to maintain function longer, whereas those with lower reserve often exhibit earlier cognitive decline [14].
In this study, we propose a novel brain age prediction pipeline featuring two key innovations: (i) a patchbased, multi-region ensemble framework that utilizes a combination of 3D CNNs to estimate brain age across selected subcortical structures, moving beyond conventional whole-brain approaches; and (ii) a cognitively informed proxy for cognitive reserve, derived from our brain age estimation pipeline. The first innovation enhances anatomical specificity by focusing on distinct subcortical regions, particularly those affected in the early stages of neurodegenerative processes that might be overlooked in whole-brain analyses. The second innovation increases the clinical applicability of brain age models by integrating regional brain age estimates with cognitive performance to derive a clinically interpretable Cognitive Reserve Proxy (CR-Proxy), which quantifies resilience to neurobiological decline. To the best of our knowledge, we are the first to propose this integration as a novel approach for quantifying cognitive reserve through the joint analysis of regional brain aging and cognitive performance. We envision our framework as a tool for early risk stratification, capable of detecting neurodegenerative processes at their earliest stages. By identifying individuals at elevated risk, our approach may facilitate timely interventions and support the development of precise, actionable biomarkers for clinical practice.
2.		Related Work
2.1.		Brain Age Prediction
Early brain age prediction models primarily relied on traditional machine learning algorithms applied to whole-brain MRI data. For example, Cole et al. [5] segmented the brain into gray and white matter and used these features as input to a Gaussian process regression model to estimate brain age. Their study introduced the Brain-PAD metric and demonstrated its association with increased mortality risk. Subsequent work shifted toward deep learning, which has since become the leading approach in brain age research. Peng et al. [15] introduced a lightweight deep neural network called Simple Fully Convolutional Network (SFCN), which achieved a Mean Absolute Error (MAE) of 2.14 years on the UK Biobank dataset (N = 14503) and 2.90 years on external test data. Building on this, Leonardsen et al. [13] compiled one of the largest and most diverse training datasets to date (N = 53542), further improving model robustness and generalizability using CNNs. To address the challenge of domain shift across imaging protocols and scanners, Puglisi et al. [19] proposed Syn- thBA, a brain age estimation model designed to generalize across varying MRI sequences and resolutions. Their approach leverages domain randomization techniques to enhance robustness, demonstrating the feasibility of deploying brain age models across heterogeneous clinical datasets.
While whole-brain models have achieved strong predictive performance, they often require substantial computational resources and may obscure region-specific contributions to aging. To overcome these limitations, recent methods have explored localized or region-based modeling strategies. For example, Pilli et al. [17] developed a model based on just three mid-sagittal slices, segmented into gray and white matter, using a multikernel regression network. This approach demonstrates that accurate brain age estimation can be achieved using limited yet informative inputs. Another alternative was proposed by Poloni and Ferrari [18], who developed an ensemble CNN model that uses 3D patches extracted from bilateral hippocampal regions. Their ensemble method outperformed individual networks. This patchbased strategy highlights the potential benefits of focusing on anatomically localized brain regions, such as the hippocampus and thalamus which are particularly sensitive to aging and neurodegenerative processes. Such approaches may offer both computational efficiency and improved biological interpretability.
2.2.		Cognitive Reserve and Brain Age
As mentioned earlier, the concept of cognitive reserve suggests that some individuals are more resilient to age- related brain changes and pathology, influenced by factors such as education, occupation, and lifestyle. Since cognitive reserve cannot be directly measured, many studies have proposed the use of proxy measures that correlate with it. Common proxies include education and lifestyle factors [21], while others have used composite indicators derived from structural or functional imaging data, such as white matter hyperintensities or functional MRI metrics [12]. However, simple behavioral or education-based proxies often fail to fully capture the complexity of cognitive reserve, while imagingbased markers such as white matter hyperintensities or functional MRI often require longitudinal data and complex processing pipelines, which typically involve substantial human effort. These factors can limit their scalability and broader clinical applicability [21]. Building on these insights, we propose a new proxy for cognitive reserve based on our brain age model, combined with a single cognitive assessment—the Mini-Mental State Examination (MMSE) [7]. The MMSE is a brief (5–10 minute), 30-point test that evaluates key cognitive domains such as orientation, memory, and attention. It focuses specifically on cognitive function and offers a practical, widely-used measure in clinical and research settings.
3.		Method
In this section, we describe our proposed pipeline, illustrated in Fig. 1, which is composed of six main stages: (i) the input T1-weighted MRI is first preprocessed through intensity and spatial normalization; (ii) the normalized image is then segmented using Synth- Seg [2] to obtain a semantic map of brain structures; (iii) based on this segmentation, 3D patches corresponding to gray matter regions are extracted; (iv) each patch is independently classified using a 3D CNN, inspired by the method proposed in [18]; (v) predictions from the individual 3D CNNs are aggregated through an ensemble strategy to generate a global brain age estimate; (vi) this estimate is then combined with the subject’s MMSE score to compute a final cognitive reserve proxy. Let each subject’s T1-weighted MRI scan be denoted as I ∈ R^H×W×D, where H, W, and D represent the height, width, and depth of the image, respectively.
3.1.		MRI Preprocessing and SynthSeg Segmentation
The input MRI scans undergo a series of standardized preprocessing steps, including bias field correction, skull stripping, spatial normalization to the MNI space at 1 mm^3 resolution, and intensity normalization. The preprocessed image is represented as a 3D volume I ∈ R^H×W×D, where H, W, and D denote the height, width, and depth of the volume, respectively. Anatomical segmentation is performed using SynthSeg [2], yielding a corresponding label map S ∈ L^H×W×D , where L denotes the set of anatomical labels.
3.2.		Patch Extraction
Using the segmentation masks, we identify five subcortical structures in each hemisphere (hippocampus, amygdala, thalamus, putamen, and pallidum), resulting in ten Regions Of Interest (ROIs). We select these regions based on their established involvement in Alzheimer’s disease (AD) [20], as well as in other neurodegenerative conditions [24], with the aim to enable the model to differentiate disease-related patterns from healthy aging.For each ROI, we compute the medoid location, defined as the center of mass of the region, by calculating the median coordinates of all voxels assigned to that structure. Around each medoid, we extract a cubic patch from the preprocessed T1-weighted image. The patch size is adaptively determined based on the spatial extent of the ROI, with a small additional offset of 6 voxels. This margin ensures the inclusion of contextual information from surrounding brain tissues, enabling the model to learn local interactions from surrounding structures. Each extracted patch is then resampled to a fixed dimension of 64×64×64 voxels to ensure uniform input size across ROIs.
3.3.		Patch-Based Brain Age Models Training
For each ROI, we train a dedicated brain age prediction model Mi, based on a 3D convolutional EfficientNet-B0 architecture, inspired by the work proposed in [22]. Training is performed by minimizing the L1 loss between the predicted brain age and the subject’s chronological age. We employ the AdamW optimizer with a batch size of 64 and a dropout rate of 0.6. The learning rate follows a cyclic schedule over 2000 epochs, with minimum and maximum values set as 10^-5 to 10^-2. Dropout rate is selected via grid search on the validation set. To improve generalization and reduce overfitting, we apply online data augmentation during training, including random rotations, translations, and intensity variations constrained within anatomically plausible ranges. Each training batch is constructed by uniformly sampling subjects across age bins spanning 20 to 100 years, ensuring balanced representation across the age distribution. All model training and evaluation are conducted using the MONAI framework [3].
3.4.		Ensemble Brain Age Model
After training, the validation MAE scores from the five regional models in the left and right hemispheres (denoted respectively as MAEL = {MAEL,i }_i^5_=1 and MAE_R = {MAE_R,i}_i^5_=1)were used to compute hemisphere-specific ensemble weights. To prioritize more accurate models (i.e., those with lower MAE), we used a Softmax function with temperature scaling. We selected τ = 0.1 as the optimal temperature for all subsequent experiments, based on its superior performance on the validation dataset. We then applied this each hemisphere:
e-MAEL,i/τ	e-MAER,i/τ
w^L,i = P5 e-MAEL,j/τ,	w^R,i = P5 e-MAER,j/τ (1)
These weights were then used to compute weighted average predictions within each hemisphere:
55
yˆL =	wL,i · yˆL,i , yˆR =	wR,i · yˆR,i	(2)
i=1	i=1
Where yˆL,i and yˆR,i denote the brain age predictions from the i-th region in the left and right hemispheres, respectively. Finally, the two hemisphere-level predictions were averaged to produce the final brain age estimate:
1
yˆ	= (yˆ + yˆ )	(3) final 2 L R
This ensemble strategy ensures that more accurate regional models contribute more significantly within each hemisphere while maintaining equal overall contributions from the left and right hemispheres.
3.5.		Interpretable Cognitive Reserve Proxy
We introduce a novel Cognitive Reserve Proxy (CR- Proxy), a metric that combines the concept of brain age with a well-established cognitive assessment to provide an interpretable measure of cognitive reserve. Both Brain-PAD and MMSE scores are normalized to [0, 1] using population-based minimum and maximum values, enabling standardized comparison across subjects. The Brain-PAD score was inverted prior to this so that higher values consistently represent more favorable outcomes (i.e., a younger-appearing brain). The CR-Proxy is then computed as the addition of these two metrics, with all scores being standardized by converting them to Z-scores relative to the control (CN) group; this was achieved by subtracting the mean of the CN group’s scores and dividing by their standard deviation. The resulting standardized CR-proxy provides an intuitive metric where higher values indicate a more robust cognitive reserve compared to the healthy control cohort. Finally, pairwise comparisons between diagnostic groups were conducted on the CR-Proxy and BrainPAD scores using Welch’s two-sample t-tests, with Bon- ferroni correction applied for multiple comparisons.
3.6.		Datasets
To train and evaluate our brain age prediction model, we used a dataset of 7,095 T1w MRI scans from healthy individuals, sourced from five publicly available repositories. The dataset includes 3,345 scans from ADNI [16], 596 from AIBL [6], 1,461 from CoRR [25], 1,105 from HCP [23], and 588 from IXI [1]. We refer to this multi-cohort dataset as the internal dataset. We partitioned the internal dataset into training (85%), validation (5%), and test (10%) sets, ensuring strict subjectlevel separation to prevent data leakage. For subsequent analyses involving the proposed CR-Proxy metric, we evaluated diagnostic group data from the ADNI dataset, with 585 T1w scans from individuals with Mild Cognitive Impairment (MCI), 258 diagnosed with MCI who later developed AD (MCI-to-AD), 246 AD, and 365 CN participants. For external validation of the proposed brain age model and associated CR-Proxy, we applied our framework to 551 T1w scans from the National Alzheimer’s Coordinating Centre (NACC) dataset. The cohort included 404 cognitively normal (NACC-CN), 107 with mild cognitive impairment (NACC-MCI), and 40 with Alzheimer’s disease (NACC-AD). All scans were processed following the same pipeline, with models generating predictions across 10 regional patches.
4.		Results
In this section, we (i) evaluate the accuracy of individual regional models as well as the ensemble model, (ii) compare our approach with existing brain age models, (iii) assess the Brain-PAD and CR-Proxy metrics across diagnostic groups in the ADNI dataset, and (iv) replicate the experiment in the external NACC cohort.
4.1.		Regional and Ensemble Models Performance
Validation MAE for regional brain age models varied across subcortical structures and hemispheres, ranging from 2.97 to 3.34 years. Left hemisphere models generally showed lower errors. The left amygdala achieved the best overall performance (MAE=2.97 years), followed by the left thalamus (MAE=3.15 years) and right thalamus (MAE=3.16 years). Bilateral regions such as the hippocampus and putamen also performed well, with MAEs between 3.01 and 3.26 years. The pallidum models exhibited the highest MAEs (3.15–3.17 years) but remained within a robust range for singleregion models. These validation scores determined the SoftMax weighting in the ensemble, prioritising models with lower MAE. The largest contributions were from the left amygdala (37.4%), right thalamus (33.5%), and right pallidum (30.3%), with smaller inputs from other regions. Table 1 summarises model performance on the test set. The ensemble achieved the best overall accuracy (MAE = 2.92 years; Pearson’s r = 0.9834), outperforming all individual regional models. These findings demonstrate that performance-weighted aggregation of regional models enhances predictive accuracy and robustness. Figure 2 illustrates the strong correspondence between predicted and chronological age for the ensemble model.

Figure 1: Proposed pipeline for cognitive reserve proxy estimation. The process begins with an input T1w MRI, which undergoes preprocessing (intensity and spatial normalization) and segmentation using SynthSeg to identify key brain structures. From the resulting segmentation mask, 3D patches are extracted from ten ROIs (hippocampus, amygdala, thalamus, putamen, and pallidum in each hemisphere). Each patch is then independently fed into a dedicated 3D EfficientNet-B0 (M1 -M10) for regional brain age prediction. The predictions from these individual models are aggregated using a weighted ensemble strategy to generate a global brain age estimate. Finally, this brain age estimate is combined with the individual’s MMSE score to compute the cognitive reserve proxy.

Table 1: Performance evaluation of individual 3D CNN models trained on subcortical MRI patches, reporting test set MAE, 95% CI, and Pearson’s correlation, alongside the SoftMax-weighted ensemble model for comparison.
Model | MAE (years) | Pearson’s R
Pallidum Right | 3.19, [3.01, 3.36] | 0.9792
Pallidum Left | 3.86, [3.64, 4.07] | 0.9719
Amygdala Right | 3.62, [3.44, 3.81] | 0.9766
Amygdala Left | 3.25, [3.06, 3.44] | 0.9781
Thalamus Right | 3.49, [3.31, 3.67] | 0.9767
Thalamus Left | 3.28, [3.10, 3.46] | 0.9798
Hippocampus Right | 3.44, [3.25, 3.62] | 0.9791
Hippocampus Left | 3.32, [3.13, 3.50] | 0.9773
Putamen Right | 3.43, [3.24, 3.62] | 0.9759
Putamen Left | 3.50, [3.30, 3.70] | 0.9759
Proposed Ensemble | 2.92, [2.77, 3.08] | 0.9834
Table 2: Comparison of our proposed method with existing brain age models in terms of MAE and Pearson’s R on internal and external datasets.
Method | Int. Data | NACC-CN
| MAE | R | MAE | R
Leonardsen et al. [13] | 2.47 | - | 15.1 | 0.88
Puglisi et al. [19] | 3.8 | 0.71 | 8.1 | 0.84
Poloni and Ferrari [18] | 3.3 | 0.95 | 5.0 | 0.87
Proposed approach | 2.9 | 0.98 | 4.0 | 0.90

4.2.		Comparison with State-of-the-Art
We compared our results with state-of-the-art approaches on both the internal test set and the external NACC-CN dataset, as summarised in Table 2. For [13], we report the original paper’s internal results, as we could not replicate their performance, and evaluated their pretrained model on our external NACC-CN test set. Although Leonardsen et al. [13] reported higher internal scores, our model outperformed all baselines on the external NACC-CN dataset, demonstrating the best

Figure 2: A scatter plot comparing actual age (x-axis) to predicted age (y-axis). Each dot represents an individual sample. The dashed black line is the line of identity, indicating perfect prediction. The plot demonstrates a very strong linear relationship between actual and predicted age obtained by our pipeline, as quantified by a high Pearson correlation coefficient (r=0.983), and a MAE of 2.92 years.

overall generalisability with only a 1.1-year increase in MAE.
4.3.		Diagnostic Group Analysis in ADNI
Further analysis was conducted using the ADNI diagnostic test set to evaluate the derived Brain-PAD and CR-Proxy scores. We first examined Brain-PAD across clinical groups. The lowest Brain-PAD was observed in the CN group (1.13 years, 95% CI=[0.77, 1.49]), followed by the MCI group (2.11 years, 95% CI=[1.77, 2.45]), the MCI-to-AD group (4.16 years, 95% CI=[3.60, 4.71]), and finally the AD group, which showed the highest Brain-PAD (5.27 years, 95% CI=[4.57, 5.95]). A similar trend was observed in CR-Proxy scores, which decreased along the diagnostic spectrum. The AD group exhibited the lowest score (CR-Proxy=-4.26, 95% CI=[-4.46, -4.06]), followed by the MCI-to-AD group (CR-Proxy=-1.67, 95% CI=[-1.83, -1.50]), the MCI group (CR-Proxy=- 0.80, 95% CI=[-0.90, -0.69]), and the CN group, which had the highest CR-Proxy score (0, 95% CI=[- 0.10, 0.10]). Table 3 summarizes the pairwise comparisons for both Brain-PAD and CR-Proxy, reporting Welch’s t-statistics and corresponding Bonferroni- corrected p-values. Overall, CR-Proxy exhibited consistently stronger statistical separation between clinical groups than Brain-PAD, as reflected in lower corrected p-values across all comparisons.
For example, when distinguishing individuals with MCI from those with CN, CR-Proxy showed a highly significant difference (p = 8.14 × 10^-24, Cohen’s d = 0.68), compared to Brain-PAD (p = 6.93 × 10^-4, d = -0.25). Notably, Brain-PAD did not significantly distinguish between the AD and MCI-to-AD groups ( p = 0.077, d = -0.22), while CR-Proxy achieved a highly significant separation (p = 1.59 × 10^-63, d = 1.75), indicating greater sensitivity to subtle clinical differences.
Table 3: ADNI Bonferroni-corrected T-test Comparisons for Performance Metrics. MCI+ refers to individuals with MCI who later converted to AD. P-value thresholds are set as follows: ns = p > 0.05, * = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001.
| Comparison | Cohen’s d | T-statistic
| CN vs MCI | -0.25 | -3.87 | ***
| CN vs MCI+ | -0.75 | -9.02 | ****
| CN vs AD | -0.89 | -10.55 | ****
| MCI vs MCI+ | -0.47 | -6.21 | ****
| MCI vs AD | -0.65 | -8.16 | ****
| MCI+ vs AD | -0.22 | -2.50 | ns
| CN vs MCI | 0.68 | 10.54 | ****
| CN vs MCI+ | 1.41 | 16.90 | ****
| CN vs AD | 3.18 | 37.43 | ****
| MCI vs MCI+ | 0.66 | 8.73 | ****
| MCI vs AD | 2.36 | 30.16 | ****
| MCI+ vs AD | 1.75 | 19.75 | ****

4.4.		External Validation on NACC Dataset
Validation tests were extended to the external NACC cohort, where model MAE performance and CR-Proxy scores were compared across the three stratified groups (NACC-CN, NACC-MCI, and NACC-AD). Group level ensemble predictions highlighted an increasing MAE across groups, with NACC-CN predicting brain ages closer to chronological ages (MAE=4.04 years, 95% CI=[3.72, 4.36]). Both NACC-MCI, and NACC-AD participant groups performed worse as expected, and reached MAE of 5.24 years (95% CI=[4.49, 6.00]), and 5.80 years (95% CI=[4.31, 7.29]) respectively. respectively. For Brain-PAD, a trend was observed, with scores showing a significant increase (after Bonferroni corrections) in the NACC-CN group compared to the NACC-AD group (p = 0.005, Cohen’s d = -0.69) and the NACC-MCI group, against the NACC-AD group (p = 0.020, Cohen’s d = -0.52). However, this metric failed to significantly distinguish between the NACC- CN and NACC-MCI groups (p = 1.00), with a negligible effect size (Cohen’s d = -0.04). In contrast, the CR-Proxy demonstrated a clear decrease across the groups, showing a highly significant difference in all pairwise comparisons. Crucially, where Brain-PAD detected no effect, the CR-Proxy robustly distinguished between the NACC-CN and NACC-MCI groups, showing a strong effect size (p = 1.72 × 10^-6, Cohen’s d = 0.72). The discriminative power of the CR- Proxy was most pronounced when comparing NACC- CN and NACC-AD, yielding a highly significant difference (p = 7.06 × 10^-11, Cohen’s d = 2.68). CR-Proxy also differentiated NACC-MCI from NACC-AD participants (p = 2.57 × 10^-7, Cohen’s d = 1.32). These results confirm that the CR-Proxy offers greater statistical power and sensitivity for detecting group differences in an independent dataset.
5.		Discussion and Future Work
In this study, we developed a novel brain age prediction pipeline featuring two key innovations. First, we implemented a patch-based, multi-region ensemble framework that leverages 3D CNNs to estimate brain age across subcortical structures, enhancing anatomical specificity by targeting regions particularly vulnerable in the early stages of neurodegeneration. Second, we introduced the Cognitive Reserve Proxy (CR-Proxy), which integrates regional brain age estimates with cognitive performance to quantify resilience to neu- robiological decline. The CR-Proxy captures both biological brain aging and cognitive function of individual’s, providing an interpretable, data-driven marker of cognitive reserve. Individuals showing preserved cognition and brain health exhibit higher CR-Proxy scores, whereas those with disproportionately low cognitive performance and older brain age predictions show lower CR-Proxy scores, indicating reduced reserve. The CR-Proxy is an interpretable metric by design; while delivering distinct group discrimination, its direct composite nature allows clinicians to evaluate the CR-Proxy in the context of its individual components of brain integrity and cognitive function, thereby enhancing its translational utility in a clinical setting.
Our regional patch-based ensemble achieved robust predictive accuracy, outperforming all individual regional models and existing state-of-the-art approaches, with a mean absolute error of 2.92 years and a strong correlation with chronological age (r = 0.98). The left amygdala and thalamus emerged as key contributors to brain age estimation, aligning with their known vulnerability to early neurodegenerative changes [8]. When applied to clinical cohorts, both Brain-PAD and the CR-Proxy differentiated diagnostic groups, showing increasing brain aging and decreasing reserve from cognitively normal individuals to those with MCI and AD. However, the CR-Proxy demonstrated markedly greater sensitivity, achieving significant separation across all pairwise comparisons, including cases where Brain-PAD failed to detect group differences. These findings highlight the CR-Proxy’s added value as a more precise and informative marker of cognitive resilience. External validation in the independent NACC dataset confirmed these trends, underscoring the generalizability and clinical potential of the framework as a robust marker of brain aging and cognitive resilience.
Despite these strengths, there are limitations that should be acknowledged. First, the validity of the CR-Proxy is coupled to the accuracy of the underlying brain age prediction model, with error in the brain age estimates potentially affecting downstream accuracy of the CR-Proxy score. Second, while the use of the MMSE in this model is practical given its widespread application, it has well-known limitations. The MMSE is subject to ceiling effects, which can reduce its sensitivity to subtle cognitive deficits in the early stages of decline [9]. Incorporating more comprehensive cognitive batteries in future work could therefore further enhance the CR-Proxy measure. Third, although the model was validated on a large dataset, most analyses were conducted within dementia-specific cohorts (CN, MCI, AD). This focus may limit the generalizability of our findings to other neurological and psychiatric conditions.
Future research could aim to compare the CR-Proxy with established proxies of cognitive reserve to better contextualize its validity within the broader literature, while also investigating the impact of potential confounding factors (such as education level, comorbidities, and other demographic variables) to clarify sources of variability in the derived scores. Assessing test–retest reliability would further establish the measure’s stability and robustness, supporting its potential adoption in clinical practice. Applying the CR-Proxy to longitudinal datasets could provide valuable insights into how cognitive reserve evolves over time. Finally, systematically exploring alternative ensemble strategies may yield additional improvements in predictive performance and overall model optimization.
Acknowledgements
This work was supported by the “SEcurity and RIghts in the CyberSpace (SERICS)” partnership (PE00000014), under the MUR National Recovery and Resilience Plan funded by the European Union – NextGenerationEU. In particular, it has been supported within the SERICS partnership through the FF4ALL project (CUP D43C22003050001).
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