The Effect of Age on the Architecture of Psychological and Cognitive Dimensions: A Network Perspective

The Effect of Age on the Architecture of Psychological and Cognitive Dimensions: A Network Perspective | 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 Research Article The Effect of Age on the Architecture of Psychological and Cognitive Dimensions: A Network Perspective Stefano Vicentin, Umberto Granziol, Daniele Romano, Michele Scandola, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6523434/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in European Journal of Ageing → Version 1 posted 13 You are reading this latest preprint version Abstract Ageing refers to a series of changes occurring throughout the lifespan in cognitive abilities, physical and mental health, and personality traits. While these dimensions have traditionally been studied as separate compartments, recent findings highlight their interdependence and dynamic interplay over time. To investigate their relationships, we analysed data from the Human Connectome Project using a psychometric network approach. Participants were grouped into three age categories: Young (22–35), Middle-aged (36–59), and Older (60–100) adults. We examined the interrelationships among 31 cognitive, psychological, and personality variables using Exploratory Graph Analysis (EGA) to estimate one network per age group and explore how these variables cluster into communities across the lifespan. Networks were then compared using the Network Comparison Test (NCT) to identify age-related differences in both global and local network properties. We observed substantial age-related changes: variables clustered into six communities in the Young Adults group but only into four in both the Middle-aged and Older Adults, suggesting dedifferentiation and reduced domain specificity in the older age groups. The NCT revealed distinct network architectures for each age group, with the most pronounced differences between Young Adults and the two older groups. Additionally, global strength—a measure of overall network connectivity—was significantly lower in Older Adults, indicating that associations among variables were on average weaker. Overall, these findings support the view that ageing is associated with structural transformations in the relationships among cognitive, psychological, and personality domains, following a dedifferentiation trajectory and highlighting the reorganization of behavioural functioning with age. Human Connectome Project Network Ageing Exploratory graph analysis Network Comparison Test Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The concept of ageing encompasses a set of complex and multifaceted processes involving physiological, cognitive, and psychosocial changes that occur throughout the lifespan and impact an individual’s health, independence, and overall well-being (Pathy et al., 2006 ). Historically, ageing has often been associated with a negative connotation, framed as a gradual and irreversible decline in physical and mental capacities (Diehl et al., 2020 ). Contemporary research, however, increasingly challenges this perspective, suggesting that ageing involves a dynamic reorganization of cognitive and psychological resources (Park & McDonough, 2013 ), marked not only by decline but also by compensatory adaptations, preserved abilities, and new forms of resilience (Carstensen & DeLiema 2018 ; Salthouse 2019 ). Cognitive functions, for instance, have long been thought to undergo a generalized decline with age. While some abilities such as executive functions and mathematical reasoning do decline with age (Salthouse 2004 ), others—like implicit and semantic memory—remain stable or even improve (Fleischman et al. 2004 ; Nyberg et al. 2012 ). Furthermore, behavioural and neuroimaging findings indicate that older adults often compensate for structural and functional decline by recruiting preserved cognitive resources or additional brain regions, enabling comparable performance to that of younger adults—a phenomenon conceptualized by the Scaffolding Theory of Aging and Cognition (STAC; Park & Reuter-Lorentz 2009). The concept of ageing as an interplay between losses and compensation extends beyond cognition. The Selection, Optimization, and Compensation (SOC) model (Baltes & Baltes, 1990 ) offers a framework emphasizing how individuals adapt to age-related changes by selectively focusing on valued goals, optimizing remaining resources, and compensating for losses—thus underscoring the proactive and adaptive nature of aging. In line with this, research on ageing and psychological variables such as personality, mental health, and emotional well-being has reported multifaceted trajectories in these factors. For instance, older adults often exhibit better emotional regulation (Doerwald et al. 2016 ; Gross et al. 1997 ) and lower levels of negative emotions than younger adults (Carstensen et al. 2000 ; Charles & Carstensen 2010 ). However, ageing is also associated with greater vulnerability to anxiety and depression, partly due to increased risk factors such as social isolation, declining physical health, and loss of loved ones (Fiske et al. 2009 ). Regarding personality, this construct was mostly investigated analysing the five factors identified by Costa and McCrae ( 1999 ): Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness to experience. While core aspects of personality tend to remain stable across adulthood (Debast et al. 2014 ; Soto et al. 2011 ), meaningful mean-level changes have been observed (Roberts & Mroczek 2008 ). For example, Neuroticism—reflecting emotional instability and negative emotions—typically declines with age, fostering greater emotional stability and resilience (Roberts et al. 2006 ; Terracciano et al. 2005 ). Longitudinal studies of older adults, however, identified a shift in this trend after the age of 70, possibly due to the increasing occurrence of negative experiences and self-efficacy declines (Kandler et al. 2015 ; Wagner et al. 2016 ). In contrast, Agreeableness, encompassing traits like kindness, cooperation, and empathy, has been observed to increase steadily with age, likely as older adults prioritize nurturing relationships and enhancing others' well-being (Costa & McCrae 1997 ; Soto et al. 2011 ). Openness to experience—creativity, curiosity, willingness to engage in new ideas and experiences—has been found to either increase or remain stable depending on life circumstances and personal interests, reflecting enduring intellectual curiosity and engagement with new ideas (McCrae & Costa 2004 ). Altogether, these findings indicate that psychological and cognitive factors follow multiple, distinct trajectories throughout the lifespan. Crucially, emerging evidence suggests that their interrelationships may play a pivotal role in shaping these developmental pathways, highlighting the necessity for integrated and cross-domain approaches to investigate their evolution across the lifespan. For instance, higher levels of Openness to experience have been linked to better-preserved cognitive abilities (Curtis et al. 2015 ; Bastelica et al. 2023 ), while chronic stress is associated with accelerated cognitive decline and poorer health outcomes (Lupien et al. 2009 ). Nonetheless, existing cross-domain studies often limit their focus to specific dyadic associations (e.g., depression and working memory), rarely capturing the broader, reciprocal influences among multiple psychological and cognitive variables (Soubelet & Salthouse 2011 ; Wolf & Ackerman 2005 ). These limitations also depend on the challenge of collecting large datasets including cognitive, psychological, and behavioural measures, as well as on the choice of the statistical methodology selected to analyse them. A more comprehensive understanding of ageing may emerge from multivariate approaches that examine how cognitive, personality, and psychological factors interact as a system. Psychometric network analysis is one such method, enabling the concurrent examination of multiple variables and their interrelations. In this framework, variables (nodes) are embedded within a broader structure (network), with edges representing associations and the overall architecture reflecting systemic properties (Epskamp et al. 2012 ). This approach allows for the analysis of both local features (e.g., centrality of nodes) and global features (e.g., community structures), and can be extended to comparisons between groups or conditions (Chandrasekaran et al. 2010 ; Golino & Demetriou 2017 ). Originally developed in neuroscience and psychopathology, network analysis is now increasingly applied to psychological and cognitive sciences, offering new insights into the complex interdependencies that characterize ageing (Borsboom et al. 2021 ; Siew et al. 2019 ). The present study This project extends the investigation conducted by Granziol & Cona in a previous study (2024), which applied network analysis to investigate the architecture of relationships among cognitive, personality, and psychological variables focusing on young adults (22-36 years). In the present study, we applied the same methodology to investigate these relationships across different age groups, hypothesizing that ageing may lead to modifications in the network structure and in the associations among the variables. Specifically, we analysed data from three groups: a group of participants between 22 and 36 years of age, selected from the Human Connectome Project-Young Adults dataset (HCP-YA) and two populations extracted from the Human Connectome Project-Aging dataset (HCP-A), one composed of participants ranging between 36 and 60 years of age (Middle-aged Adults) and one of participants between 60 and 100 years of age (Older Adults). First, a network was estimated for each group, examining their architectures and the emerging communities. Subsequently, the networks’ global and local properties were compared across groups to identify structural and connectivity changes associated with ageing. Guided by the dedifferentiation hypothesis (Anstey et al. 2003; Balinsky 1941), which posits that as individuals age, their processes and abilities—particularly cognitive ones—become less distinct and more integrated, we hypothesize that variables in the Older Adults group will cluster into a smaller number of communities, especially within the cognitive domain (Baltes et al. 1980). For Middle-aged Adults, we expect network properties to reflect an intermediate structure, positioned between those of the Young and Older Adults groups. In terms of overall network architecture, we anticipate that ageing will result in significant structural differences between age groups, accompanied by a decrease in network complexity. This reduced complexity is expected to manifest as more unified but less specialized interactions among cognition, personality, and psychological factors in older adults compared to younger individuals. Additionally, we predict that the older adult network will exhibit a reduction in the strength or in the number of associations among its variables, consistent with previous research reporting a decline in connectivity between psychological and cognitive factors with age (Payne & Lohani 2020; Wettstein et al. 2020), reflecting the tendency of these dimensions to become less tightly coupled and to follow different trajectories in later stages of life. Ultimately, this study aims to elucidate how the interplay among cognitive, personality, and psychological variables transforms with age, proposing psychometric network analysis as a powerful tool to understand structural changes in behavioural functioning across the lifespan. Methods Participants Data for this study were obtained from two Human Connectome Project (HCP) databases (http://www.humanconnectomeproject.org/): The HCP-Young Adult dataset, consisting of 1,206 participants aged 22 to 36 years (Van Essen et al. 2012) and the HCP-Aging dataset, which includes data from 726 participants aged 36 to 100 years (Bookheimer et al. 2019). To better examine age-related differences, the HCP-Aging dataset was divided into two subgroups: Middle-aged Adults (36–59 years) and Older Adults (60–100 years). This division was based on both methodological and practical considerations. First, we aimed to create subgroups with sufficient sample sizes (approximately 300 participants each), which is essential for network analysis. In fact, while no formal power analysis exists for this method, a general rule suggests having at least 10 participants per variable. Second, some of the measures considered in this study (e.g., the Achenbach Adult Self Report Scale) proposed different versions depending on age, using 60 years as the threshold. Exclusion criteria involved the presence of neurological or psychiatric conditions, severe cognitive or sensory impairments, missing data on any of the selected variables (outlined in the Measures section) and the presence of a twin/sibling in the same dataset, in which case only one individual per pair was retained. These criteria yielded a final sample of 599 participants: 329 in the Middle-aged group and 270 in the Older Adult group. To ensure comparability, the same inclusion criteria were applied to the HCP-Young Adult dataset. From this dataset, 300 participants were pseudorandomly selected to form a third group (Young Adults), matched in size and gender distribution to the other two groups. Gender balance was maintained by selecting female participants to represent 60% of the sample, consistent with the proportions observed in the two older groups. Age and gender of each group are reported in Table 1. Table 1 Sample size, age, and percentage of female participants across the three age groups Population Sample Size Age Gender Young Adults 300 28.90 ± 3.75 60% F Middle-aged Adults 329 47.60 ± 6.978 58% F Older Adults 270 72.92 ± 8.956 63% F Measures The HCP datasets include a wide range of behavioural and demographic data, including participant information, a comprehensive battery of cognitive tests, and various personality and mental health questionnaires. The full list of measures included in the HCP is available at the following links: HCP-Young Adult: https://www.humanconnectome.org/study/hcp-young-adult/document/quick-reference-open-access-vs-restricted-data HCP-Aging: https://www.humanconnectome.org/study/hcp-lifespan-aging/document/hcp-aging-20-release Following the criteria established in previous studies (Cona et al. 2019; Granziol & Cona 2024), we selected measures capturing cognitive abilities evaluated by HCP experts (excluding self-report ones) and measures associated with mental health, mental and behavioural disorders, and personality traits. Specifically, we followed the same selection criteria used by Granziol and Cona (2024), who investigated the relationships between cognition, personality, and mental health in the young population using the HCP-Young Adult dataset. However, seven of the 38 variables used in their study were not available in the HCP-Aging dataset. These included three cognitive measures —Penn Progressive Matrices, the Line Orientation Test, and the Continuous Performance Test—as well as four measures related to substance use (tobacco, alcohol, marijuana, and illicit drugs). As a result, these variables were excluded from the present study. A detailed list of the behavioural measures used in our analysis is provided in Table 2. Details on the mean and standard deviations for each variable in each group are reported in the Supplementary Information (Tables S1, S2, and S3). Table 2 List of the measures included in the study, together with the scale/questionnaire/test used to measure each, and the variable name used in the further analyses Category Scale Measure Name Variable Personality Five Factor Inventory (NEO-FFI) NEOFAC_A Agreeableness NEOFAC_C Conscientiousness NEOFAC_E Extraversion NEOFAC_N Neuroticism NEOFAC_O Openness Cognition Picture Sequence Memory Test Picture Sequence Memory Pic_Seq Dimensional Change Card Sorting task Dimensional Change Card Sorting Card_Sort Pattern Comparison Processing Speed task Pattern Comparison Processing Speed Proc_Speed List Sorting Working Memory Task List Sorting Working Memory Task List_Sort Flanker Task Flanker Task Flanker Oral Reading Recognition Task Oral Reading Recognition Task Oral_Read Delay Discounting Task Delay Discounting (200 $) DD_200 Delay Discounting (40000 $) DD_40K Mental Health Penn Emotion Recognition Task Emotion Recognition (ER40) Emotion_Rec Achenbach Adult Self Report (ASR) WithDrawal WithDrawal Thought_Problems Thought_Prob Rule_Breaking Rule_Break Externalizing_Behaviors Externalizing Antisocial_Behaviors Antisocial Anxiety_Problems Anxiety Attention_Problems Hiperactivity Depression Depression Anger-Hostility Survey Aggressivity Aggressivity Hostility Hostility Perceived Rejection Scale Perceived Rejection Rejection Perceived Stress Scale Perceived Stress Stress Self-Efficacy Survey Self-Efficacy Self_Efficacy Well-being Toolbox Well-Being Well_Being Sensory Pittsburg Sleep Quality Inventory Sleep Quality Sleep_Quality Pain Intensity Survey Pain Interference Survey Intensity_Score Pain_Intensity Interference_Score Pain_Interference Network Analysis The network analysis employed in this study consisted of two separate procedures. First, each age group was analysed using the Exploratory Graph Analysis (EGA) approach (Hudson et al. 2024; Golino & Epskamp 2017). EGA is a methodological approach that examines a large set of variables from a network perspective, considering the relationships between each measure and estimating the best way to categorize them into separate clusters (or communities). Considered an innovative approach to investigate multivariate data, EGA has been found to perform as well as several traditional factor analytic techniques (Golino et al., 2020). EGA involves multiple steps: First, a Gaussian graphical model is computed to calculate the network structure that better represents the relationships among the variables. In the network computation, each variable represents a node and each association between two nodes constitutes an edge. The edges are estimated as partial correlation coefficients between each pair of nodes, controlling for all other variables. Given the large number of nodes (and edges), the network was regularized using the LASSO (Least Absolute Shrinkage and Selection Operation) method (Epskamp et al. 2018), which prevent overfitting. After regularization, a walktrap community detection algorithm is applied to identify the clusters in which variables can be grouped to better explain their relationships (Christensen & Golino 2019; 2021). Finally, the reliability and replicability of the identified networks and communities are tested using a nonparametric bootstrap approach with 5,000 replications to assess the robustness of the results. The second procedure involved the direct comparison between the so-obtained networks through the Network Comparison Test (NCT; van Borkulo 2022) approach. This method evaluates the networks for their level of invariance (the similarity between the structures of the two networks) and compares their global strength (the absolute sum of network edge weights). The networks from the three age groups were compared in pairs (Young vs. Middle-aged: Middle-aged vs. Older; Young vs. Older). Post-hoc analyses were conducted to identify specific edges with significant differences in strength between each pair of networks. For each comparison, the permutation seed was set to 123, and 1,000 permutations were performed. Given the large number of nodes and edges, the results were corrected for multiple comparisons using the False Discovery Rate (FDR) method (Benjamini & Hochberg 1995). Results EGA Network – Young Adults In the Young Adults group, the EGA analysis revealed six distinct clusters, each representing a unique community of variables. Cognitive measures divided into two separate communities: one reflecting basic functions such as processing speed and attention (Low_Cognition), and another encompassing more complex skills like reading ability and emotion recognition (High_Cognition). A third community (Delay_Discount) was centred on decision-making processes, represented by performance on the Delay Discounting Task. Psychological and psychopathological measures formed two additional communities. One captured externalising behaviours, including rule-breaking, aggression, and hostility (Externalizing). The other grouped together mental health-related factors, such as depression, anxiety, poor sleep quality, low self-efficacy, and thought problems (Mental_Health). Pain perception—measured through pain intensity and interference—formed a sixth, separate cluster (Pain). Personality traits did not form a standalone community. Instead, they were integrated into other domains: Neuroticism, Conscientiousness, and Extraversion were part of the Mental_Health community; Agreeableness clustered with Externalizing traits; and Openness aligned with the High_Cognition community. A full overview of edge weights and zero-order correlations among all variables can be found in the Supplementary Information (Table S1). EGA Network – Middle-aged Adults In the Middle-aged Adults group, the EGA revealed a reorganization of variable relationships, with the network structure shifting from six communities (in young adults) to four. Cognitive variables—previously split into separate clusters for lower- and higher-level functions—now formed a single, unified domain (Cognition), which also included Openness to experience. Two other communities mirrored those found in the Young Adults: one related to decision-making processes (Delay_Discount), and another encompassing externalizing behaviours such as rule-breaking and aggression, along with Agreeableness (Externalizing). A fourth community grouped together the variables related to mental health, well-being, pain perception, and the remaining personality traits (Mental_Health). For detailed edge weights and variable correlations, refer to Supplementary Information (Table S2). EGA Network – Older Adults The network structure in the Older Adults group also revealed four communities, paralleling those of the Middle-aged group: Cognition, Delay_Discount, Externalizing, and Mental_Health. However, subtle yet meaningful shifts in variable organization were observed, mostly involving the Externalizing community. Namely, Agreeableness and Aggressiveness, previously integrated in this cluster, showed to shift toward the Mental_Health community, increasing their associations (either positive or negative) with Neuroticism and other mental health-related factors, while reducing their associations with externalizing behaviour measures. Edge weights and correlations are detailed in Supplementary Information (Table S3). Network Comparison Test – Young vs. Middle-aged Adults The Network Comparison Test (NCT) indicated significant structural differences between the networks of the Young and Middle-aged Adults groups (M = 0.617, p < .001), in line with the different number of communities identified through the EGA approach (six vs. four). Despite this structural reorganization, global strength values were comparable (Young: 11.139; Middle-aged: 12.116), suggesting that the overall strength or number of associations among variables remained stable between these age groups. Nonetheless, post-hoc comparisons identified 29 edges with significantly different strengths, mainly involving connections within the Mental_Health community and between Mental_Health and Externalizing domains (see Figure 4). The complete list of these edges is provided in the Supplementary Information (Table S5). Network Comparison Test – Young vs. Older Adults The comparison between the Young and Older Adults networks revealed significant structural differences (M = 0.519, p < .001). Global strength was significantly lower in the Older Adults (8.781) compared to the Young Adults group (11.139; S = 2.358, p = .039), indicating an average reduction of associations, either in their number or in their strength. Post-hoc tests identified 25 edges with significantly differences between groups, mostly between nodes of the Mental_Health community and between its nodes and the ones of the Externalizing domain. A full list of these edges is provided in the Supplementary Information (Table S6). Network Comparison Test – Middle-aged vs. Older Adults The NCT revealed significant differences in both the structure and overall connectivity of the networks between Middle-aged and Older Adults. Specifically, structural differences were observed (M = 0.281, p = .006), alongside a significant reduction in global strength in the Older Adults group (S = 3.335, p = .013), which showed reduced connectivity (8.781) compared to the Middle-aged Adults group (12.116). A total of 19 edges differed significantly, most of which involved nodes of the Externalizing community. Detailed results are provided in Supplementary Information (Table S7). Discussion This study investigated how the structure and interrelations among personality, psychological, and cognitive variables change across the adult lifespan. Using network analysis, we compared these patterns across three age groups: young adults (22–36 years), middle-aged adults (36–60 years), and older adults (60–100 years). Our findings revealed marked age-related differences in the organization of these domains, highlighting both structural and functional transformations across the lifespan. First, we used Exploratory Graph Analysis (EGA: Golino & Epskamp 2017 ) to examine how variables clustered into communities within each age group. In the Young Adults group, six distinct communities emerged: two encompassing cognitive measures (Low_Cognition and High_Cognition), one grouping the measures of the Delay Discounting Task, a task associated with decision-making (Delay_Discount), two focused on personality and psychological measures (Externalizing and Mental_Health), and the last associated with pain perception (Pain). These findings replicate the ones of Granziol and Cona ( 2024 ), with each variable of interest clustered in the same communities identified by the authors. In contrast, the analysis performed on the Middle-aged and the Older Adults groups revealed the emergence of networks characterized by a lower number of communities (four), suggesting decreased segregation among variables. Specifically, cognitive variables (split into low- and high-level domains in young adults) were merged into a single community (Cognition). Similarly, the variables of the Mental_Health and Pain communities clustered into a single domain. These convergences may reflect the dedifferentiation of functional domains in older participants, reflecting reduced specialization and greater integration, findings that align with the dedifferentiation hypothesis (Anstey et al., 2003 ; Balinsky, 1941 ). The integration of low- and high-level cognitive domains into a single community in the older age groups also aligns with the Scaffolding Theory of Aging and Cognition (STAC; Park & Reuter-Lorenz, 2009 ), which posits that ageing is associated with a more generalized recruitment of cognitive resources to support complex task performance. Similarly, the merging of psychological variables—including mental health factors, personality traits, and self-reported well-being on one side, and indicators of pain perception on the other—may reflect a growing interdependence between emotional and somatic experiences with age. This pattern can be interpreted in light of the Selective Optimization with Compensation model (SOC; Baltes & Baltes, 1990 ), which posits that older adults adapt to functional declines by engaging additional psychological resources and employing compensatory strategies. Notably, the reduction in the number of communities was found already in middle adulthood (36–60 years), suggesting that such reorganizational processes may begin earlier than typically assumed. While cognitive and mental health variables consistently clustered together (either in one or two communities), personality traits followed a different trajectory. In fact, the traits of Extraversion, Neuroticism, and Conscientiousness were strongly associated and clustered within the same community (Mental_Health) across all age groups. Openness to experience, on the other hand, aligned more closely with cognitive variables— clustering within the High_Cognition community in young adults and within Cognition in middle-aged and older adults. This finding supports prior evidence of a strong link between Openness and cognitive functioning across the lifespan (Curtis et al. 2015 ; Soubelet & Salthouse 2011 ). Concerning Agreeableness, by contrast, clustered with Externalizing factors in the Young Adults network—consistent with its known inverse relationship to aggressivity and antisocial behaviour (Jiang et al. 2022 ; Jones et al. 2022)— but shifted to the Mental_Health community in the other two age groups. This transition may reflect both a weakening of the association between Agreeableness and externalizing behaviours, and the general increase in Agreeableness with age (Terracciano et al. 2005 ), possibly driven by heightened emphasis on emotional regulation and social connectedness in later years (Carstensen et al. 2000 ; 2018). Taken together, the distribution of personality traits across different communities supports the hypothesis that personality is not driven by a single latent factor, but rather emerges from the dynamic interplay among its constituent traits (Costantini et al. 2019 ; Kan et al. 2019 ). The Network Comparison Test (NCT) revealed significant structural differences across age groups. Pairwise network comparisons indicated that each of the three networks differed significantly in their overall architecture. Specifically, the largest differences were observed between the networks of young adults and middle-aged adults (M = .617), and between young and older adults (M = .519). Although the difference between middle-aged and older adults was smaller, it remained statistically significant (M = .282). These findings suggest that the most substantial structural reorganization may occur between early and mid-adulthood, with more gradual changes in older age. In contrast, comparing the networks for their global strength—the sum of all edge weights across nodes—showed that older adults exhibited significantly lower values (S = 8.781) compared to both young (S = 11.139) and middle-aged adults (S = 12.116). This finding indicates that older participants were characterized by weaker overall associations among variables. While this pattern may appear in contrast with the dedifferentiation hypothesis—which posits that aging leads to less distinct and more interrelated functional domains—it is important to distinguish between structural differentiation (i.e., the number and separation of communities) and connection strength. While dedifferentiation typically implies fewer and more integrated communities, it does not necessarily entail stronger connections among nodes (Bringmann et al. 2019 ; Fried et al 2017 ). In fact, the observed difference in global strength may reflect two non-mutually exclusive processes: either a reduction in connection strength—potentially indicative of diminished cognitive flexibility or psychological resilience—or a reduction in the number of connections, indicating a shift toward fewer but more functionally essential associations. The reduced number of communities in the older age groups supports the latter interpretation, suggesting a shift toward simplified and less modular networks, characterized by a more streamlined—and possibly compensatory—organization. This aligns with the notion that ageing is accompanied by dedifferentiation and compensation, processes enhancing efficiency to face functional decline, but reducing the system’s adaptability (Cabeza 2002 ; Park & Reuter-Lorenz 2009 ). Limitations This study presents several limitations that warrant consideration. First, the delineation of age groups was influenced by the structure of the available datasets (HCP-YA: 22–36; HCP-A: 36–100), the use of different versions of certain questionnaires across age ranges (ASR for younger participants versus ASR-O for those over 60), and the goal of achieving age groups with comparable sample sizes (~ 300 participants). While these thresholds supported internal consistency, they may have oversimplified the continuum of ageing and obscured more nuanced age-related changes, particularly during transitional periods. In line with this, the cross-sectional design of this study limits our ability to draw causal or developmental conclusions about changes in network structure over time. The observed age-related differences may reflect cohort effects or other unmeasured variables, rather than lifespan transformations in psychometric networks. Longitudinal studies tracking within-person changes would provide more definitive insights into the dynamics of psychological and cognitive aging. Finally, it must be pointed out that while Exploratory Graph Analysis (EGA) reflects the current best practice for data-driven identification of network community structure and has shown high replicability in prior work (Golino et al., 2020 ), it remains an exploratory approach. Future studies using confirmatory techniques and testing for measurement invariance across age groups would help validate the robustness of these findings. Conclusion This study provides novel evidence of lifespan-related reconfigurations in the organization of cognitive, personality, and psychological constructs, as revealed through network analysis. Younger adults exhibited a more differentiated and modular network structure, with variables clustering into six distinct communities. In contrast, middle-aged and older adults displayed a reduced number of communities, reflecting more integrated, less compartmentalized networks. This reorganization, with less specialized psychological and cognitive domains in the older age groups, appears consistent with theoretical models of ageing that emphasize compensation and dedifferentiation. Interestingly, this process appears to emerge during the middle stages of adulthood, highlighting the importance of considering midlife as a key transition point in cognitive and psychological development. In addition to this reorganization, older adults were also characterized by reduced levels of global strength compared to young and middle-aged adults, reflecting reduced overall network connectivity. Since only few studies examined the relationship between ageing and psychometric variables from a network perspective, further research is necessary to deeper understand these patterns, including studies involving pathological populations or comparisons between high- and low-performing older participants. Nevertheless, it is worth noting that similar trends have been observed in neuroimaging studies, which reported reduced connections and increased dedifferentiation in brain networks with ageing (Deery et al. 2023 ; Iordan et al. 2018 ). Altogether, our findings suggest the presence of structural changes in the relationships across psychometric variables in different age groups, supporting the notion that ageing is accompanied by functional reorganization. As the global population continues to age, understanding how the variables that constitute individuality interact and evolve across the lifespan is increasingly important. This study demonstrates the value of network analysis in capturing age-related differences, advancing beyond traditional compartmentalized views of cognitive, personality, and psychological domains, and offering new insights into the complex dynamics that characterize the ageing process. Declarations Author Contribution S.V.: Data curation, Formal analysis, Writing—original draftU.G.: Methodology, Supervision, Data curationD.R.: Conceptualization, Funding acquisition, Writing—editing and revisionM.S.: Conceptualization, Funding acquisition, Writing—editing and revisionG.C.: Conceptualization, Supervision, Funding acquisition, Writing—editing and revision Acknowledgements This research was supported by the Research Project of National Interest (PRIN), funded by the Italian Ministry of University and Research (MUR) Grant Number 2022BNMZJC. Data Availability The raw data used in this study were obtained from the Human Connectome Project (HCP) and are subject to access restrictions imposed by the National Institutes of Health (NIH). Due to licensing agreements, the authors cannot redistribute the raw data. 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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-6523434","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457931349,"identity":"a2a2998c-5dbc-4888-8c1e-ebba689909af","order_by":0,"name":"Stefano Vicentin","email":"","orcid":"","institution":"University of Padua","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Vicentin","suffix":""},{"id":457931350,"identity":"9f0f8a3f-d57d-458f-833e-7d6a752b56dd","order_by":1,"name":"Umberto Granziol","email":"","orcid":"","institution":"University of Padua","correspondingAuthor":false,"prefix":"","firstName":"Umberto","middleName":"","lastName":"Granziol","suffix":""},{"id":457931351,"identity":"86160348-b88d-4b53-b9cd-a1e010f2a390","order_by":2,"name":"Daniele Romano","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"","lastName":"Romano","suffix":""},{"id":457931352,"identity":"257f5e9d-da31-4559-b68e-ae483c9f5ea2","order_by":3,"name":"Michele Scandola","email":"","orcid":"","institution":"University of Verona","correspondingAuthor":false,"prefix":"","firstName":"Michele","middleName":"","lastName":"Scandola","suffix":""},{"id":457931353,"identity":"e900be5d-06d7-4677-a6e6-383c68a16f59","order_by":4,"name":"Giorgia Cona","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZhBhAOV8YGDgYWBIYDhAtBbGGTAt+PUga+cBUwkMeK2Rd2c+9vFLgR0Df3vvwc+2bXdkzNkTGA9/wKPF8DBb8mwZg2QGiTPnkqVz257xWPY8wO8ww2YeY2YJA2YGA4kcA6CWwzwGNwj4BaqlHqTF+LclMVrkmXmMGT8YHAZpMZNmJEaLATNbMtBVx3kkzpwxs+w5dxjol4cNB87gs6X/8GHGH3+q5fjbe4xv/Cg7bG/Onnz4QwU+Ww5AooMHIcLA2IBHA9AWoDTjDxRD8KofBaNgFIyCkQgAdvVKkE2HoT4AAAAASUVORK5CYII=","orcid":"","institution":"University of Padua","correspondingAuthor":true,"prefix":"","firstName":"Giorgia","middleName":"","lastName":"Cona","suffix":""}],"badges":[],"createdAt":"2025-04-24 19:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6523434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6523434/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10433-025-00903-8","type":"published","date":"2026-02-02T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83128757,"identity":"4c6ab529-efb9-43d6-a8f0-f1ddc201ae57","added_by":"auto","created_at":"2025-05-20 09:57:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92967,"visible":true,"origin":"","legend":"\u003cp\u003eThe exploratory network estimated from the data of the Young Adults population.\u003c/p\u003e\n\u003cp\u003eThe legend (right) represents the six communities identified by the EGA analysis\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6523434/v1/9ddaf6760a764d87f704a775.png"},{"id":83126895,"identity":"9e74dcef-eb64-4a4d-ac36-dd1804b1bd19","added_by":"auto","created_at":"2025-05-20 09:49:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103813,"visible":true,"origin":"","legend":"\u003cp\u003eThe exploratory network estimated from the data of the Middle-aged Adults population. The legend (right) represents the four communities identified by the EGA analysis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6523434/v1/37130392f30a757fca0d6619.png"},{"id":83126894,"identity":"7e7a7cc2-e807-4cd5-b671-b46eb7fe40cc","added_by":"auto","created_at":"2025-05-20 09:49:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99765,"visible":true,"origin":"","legend":"\u003cp\u003eThe exploratory network estimated from the data of the Older Adults population.\u003c/p\u003e\n\u003cp\u003eThe legend (right) represents the four communities identified by the EGA analysis\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6523434/v1/159c123054a229819e675521.png"},{"id":83126898,"identity":"b96edbdd-3f9e-4aad-9376-fd7abe08b698","added_by":"auto","created_at":"2025-05-20 09:49:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75688,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of edges with significant differences in strength across age group networks. Each square represents a specific node in the network and is coloured according to the cluster identified by the EGA analysis for the specific population. The two rows correspond to the two populations compared: A) and B) display nodes from the Young Adult network in the upper row, while C) shows nodes from the Middle-Aged network. Different colours between rows indicates that the significant strength difference occurs between nodes belonging to different communities, whereas identical colours reflect significant differences in edge strength within the same cluster\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6523434/v1/b296036c36d1791ce02c2d45.png"},{"id":102234217,"identity":"69ee3ec0-5376-4f3a-9caf-e6af8f664986","added_by":"auto","created_at":"2026-02-09 16:07:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1001293,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6523434/v1/339d9b0e-5e36-4914-a774-7e1229f49fb3.pdf"},{"id":83126910,"identity":"f17f2768-7c03-440c-9e21-e199e270c900","added_by":"auto","created_at":"2025-05-20 09:49:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":862032,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6523434/v1/fe8dfcae467aba234dff5dd8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effect of Age on the Architecture of Psychological and Cognitive Dimensions: A Network Perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe concept of ageing encompasses a set of complex and multifaceted processes involving physiological, cognitive, and psychosocial changes that occur throughout the lifespan and impact an individual\u0026rsquo;s health, independence, and overall well-being (Pathy et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Historically, ageing has often been associated with a negative connotation, framed as a gradual and irreversible decline in physical and mental capacities (Diehl et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Contemporary research, however, increasingly challenges this perspective, suggesting that ageing involves a dynamic reorganization of cognitive and psychological resources (Park \u0026amp; McDonough, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), marked not only by decline but also by compensatory adaptations, preserved abilities, and new forms of resilience (Carstensen \u0026amp; DeLiema \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Salthouse \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCognitive functions, for instance, have long been thought to undergo a generalized decline with age. While some abilities such as executive functions and mathematical reasoning do decline with age (Salthouse \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), others\u0026mdash;like implicit and semantic memory\u0026mdash;remain stable or even improve (Fleischman et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Nyberg et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, behavioural and neuroimaging findings indicate that older adults often compensate for structural and functional decline by recruiting preserved cognitive resources or additional brain regions, enabling comparable performance to that of younger adults\u0026mdash;a phenomenon conceptualized by the Scaffolding Theory of Aging and Cognition (STAC; Park \u0026amp; Reuter-Lorentz 2009).\u003c/p\u003e \u003cp\u003eThe concept of ageing as an interplay between losses and compensation extends beyond cognition. The Selection, Optimization, and Compensation (SOC) model (Baltes \u0026amp; Baltes, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) offers a framework emphasizing how individuals adapt to age-related changes by selectively focusing on valued goals, optimizing remaining resources, and compensating for losses\u0026mdash;thus underscoring the proactive and adaptive nature of aging. In line with this, research on ageing and psychological variables such as personality, mental health, and emotional well-being has reported multifaceted trajectories in these factors. For instance, older adults often exhibit better emotional regulation (Doerwald et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gross et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and lower levels of negative emotions than younger adults (Carstensen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Charles \u0026amp; Carstensen \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, ageing is also associated with greater vulnerability to anxiety and depression, partly due to increased risk factors such as social isolation, declining physical health, and loss of loved ones (Fiske et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Regarding personality, this construct was mostly investigated analysing the five factors identified by Costa and McCrae (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1999\u003c/span\u003e): Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness to experience. While core aspects of personality tend to remain stable across adulthood (Debast et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Soto et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), meaningful mean-level changes have been observed (Roberts \u0026amp; Mroczek \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For example, Neuroticism\u0026mdash;reflecting emotional instability and negative emotions\u0026mdash;typically declines with age, fostering greater emotional stability and resilience (Roberts et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Terracciano et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Longitudinal studies of older adults, however, identified a shift in this trend after the age of 70, possibly due to the increasing occurrence of negative experiences and self-efficacy declines (Kandler et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wagner et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, Agreeableness, encompassing traits like kindness, cooperation, and empathy, has been observed to increase steadily with age, likely as older adults prioritize nurturing relationships and enhancing others' well-being (Costa \u0026amp; McCrae \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Soto et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Openness to experience\u0026mdash;creativity, curiosity, willingness to engage in new ideas and experiences\u0026mdash;has been found to either increase or remain stable depending on life circumstances and personal interests, reflecting enduring intellectual curiosity and engagement with new ideas (McCrae \u0026amp; Costa \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAltogether, these findings indicate that psychological and cognitive factors follow multiple, distinct trajectories throughout the lifespan. Crucially, emerging evidence suggests that their interrelationships may play a pivotal role in shaping these developmental pathways, highlighting the necessity for integrated and cross-domain approaches to investigate their evolution across the lifespan. For instance, higher levels of Openness to experience have been linked to better-preserved cognitive abilities (Curtis et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bastelica et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while chronic stress is associated with accelerated cognitive decline and poorer health outcomes (Lupien et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Nonetheless, existing cross-domain studies often limit their focus to specific dyadic associations (e.g., depression and working memory), rarely capturing the broader, reciprocal influences among multiple psychological and cognitive variables (Soubelet \u0026amp; Salthouse \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wolf \u0026amp; Ackerman \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These limitations also depend on the challenge of collecting large datasets including cognitive, psychological, and behavioural measures, as well as on the choice of the statistical methodology selected to analyse them.\u003c/p\u003e \u003cp\u003eA more comprehensive understanding of ageing may emerge from multivariate approaches that examine how cognitive, personality, and psychological factors interact as a system. Psychometric network analysis is one such method, enabling the concurrent examination of multiple variables and their interrelations. In this framework, variables (nodes) are embedded within a broader structure (network), with edges representing associations and the overall architecture reflecting systemic properties (Epskamp et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This approach allows for the analysis of both local features (e.g., centrality of nodes) and global features (e.g., community structures), and can be extended to comparisons between groups or conditions (Chandrasekaran et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Golino \u0026amp; Demetriou \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Originally developed in neuroscience and psychopathology, network analysis is now increasingly applied to psychological and cognitive sciences, offering new insights into the complex interdependencies that characterize ageing (Borsboom et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Siew et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"The present study","content":"\u003cp\u003eThis project extends the investigation conducted by Granziol \u0026amp; Cona in a previous study (2024), which applied network analysis to investigate the architecture of relationships among cognitive, personality, and psychological variables focusing on young adults (22-36 years).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, we applied the same methodology to investigate these relationships across different age groups, hypothesizing that ageing may lead to modifications in the network structure and in the associations among the variables. Specifically, we analysed data from three groups: a group of participants between 22 and 36 years of age, selected from the Human Connectome Project-Young Adults dataset (HCP-YA) and two populations extracted from the Human Connectome Project-Aging dataset (HCP-A), one composed of participants ranging between 36 and 60 years of age (Middle-aged Adults) and one of participants between 60 and 100 years of age (Older Adults). First, a network was estimated for each group,\u0026nbsp;examining their architectures and the emerging communities. Subsequently, the networks\u0026rsquo; global and local properties were compared across groups to identify structural and connectivity changes associated with ageing.\u003c/p\u003e\n\u003cp\u003eGuided by the dedifferentiation hypothesis (Anstey et al. 2003; Balinsky 1941), which posits that as individuals age, their processes and abilities\u0026mdash;particularly cognitive ones\u0026mdash;become less distinct and more integrated, we hypothesize that variables in the Older Adults group will cluster into a smaller number of communities, especially within the cognitive domain (Baltes et al. 1980). For Middle-aged Adults, we expect network properties to reflect an intermediate structure, positioned between those of the Young and Older Adults groups. In terms of overall network architecture, we anticipate that ageing will result in significant structural differences between age groups, accompanied by a decrease in network complexity.\u0026nbsp;This reduced complexity is expected to manifest as more unified but less specialized interactions among cognition, personality, and psychological factors in older adults compared to younger individuals. Additionally, we predict that the older adult network will exhibit a reduction in the strength or in the number of associations among its variables, consistent with previous research reporting a decline in connectivity between psychological and cognitive factors with age\u0026nbsp;(Payne \u0026amp; Lohani 2020; Wettstein et al. 2020), reflecting the tendency of these dimensions to become less tightly coupled and to follow different trajectories in later stages of life.\u003c/p\u003e\n\u003cp\u003eUltimately, this study aims to elucidate how the interplay among cognitive, personality, and psychological variables transforms with age, proposing psychometric network analysis as a powerful tool to understand structural changes in behavioural functioning across the lifespan.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were obtained from two Human Connectome Project (HCP) databases (http://www.humanconnectomeproject.org/): The HCP-Young Adult dataset, consisting of 1,206 participants aged 22 to 36 years (Van Essen et al. 2012) and the HCP-Aging dataset, which includes data from 726 participants aged 36 to 100 years (Bookheimer et al. 2019). To better examine age-related differences, the HCP-Aging dataset was divided into two subgroups: Middle-aged Adults (36\u0026ndash;59 years) and Older Adults (60\u0026ndash;100 years). This division was based on both methodological and practical considerations. First, we aimed to create subgroups with sufficient sample sizes (approximately 300 participants each), which is essential for network analysis. In fact, while no formal power analysis exists for this method, a general rule suggests having at least 10 participants per variable. Second, some of the measures considered in this study (e.g., the Achenbach Adult Self Report Scale) proposed different versions depending on age, using 60 years as the threshold. Exclusion criteria involved the presence of neurological or psychiatric conditions, severe cognitive or sensory impairments, missing data on any of the selected variables (outlined in the Measures section) and the presence of a twin/sibling in the same dataset, in which case only one individual per pair was retained. These criteria yielded a final sample of 599 participants: 329 in the Middle-aged group and 270 in the Older Adult group. To ensure comparability, the same inclusion criteria were applied to the HCP-Young Adult dataset. From this dataset, 300 participants were pseudorandomly selected to form a third group (Young Adults), matched in size and gender distribution to the other two groups. Gender balance was maintained by selecting female participants to represent 60% of the sample, consistent with the proportions observed in the two older groups. Age and gender of each group are reported in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eSample size, age, and percentage of female participants across the three age groups\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eYoung Adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e28.90 \u0026plusmn; 3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e60% F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eMiddle-aged Adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e47.60 \u0026plusmn; 6.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e58% F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003eOlder Adults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e72.92 \u0026plusmn; 8.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e63% F\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe HCP datasets include a wide range of behavioural and demographic data, including participant information, a comprehensive battery of cognitive tests, and various personality and mental health questionnaires. The full list of measures included in the HCP is available at the following links:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eHCP-Young Adult: https://www.humanconnectome.org/study/hcp-young-adult/document/quick-reference-open-access-vs-restricted-data\u003c/li\u003e\n \u003cli\u003eHCP-Aging: https://www.humanconnectome.org/study/hcp-lifespan-aging/document/hcp-aging-20-release\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFollowing the criteria established in previous studies (Cona et al. 2019; Granziol \u0026amp; Cona 2024), we selected measures capturing cognitive abilities evaluated by HCP experts (excluding self-report ones) and measures associated with mental health, mental and behavioural disorders, and personality traits. Specifically, we followed the same selection criteria used by Granziol and Cona (2024), who investigated the relationships between cognition, personality, and mental health in the young population using the HCP-Young Adult dataset. However, seven of the 38 variables used in their study were not available in the HCP-Aging dataset. These included three cognitive measures\u0026nbsp;\u0026mdash;Penn Progressive Matrices, the Line Orientation Test, and the Continuous Performance Test\u0026mdash;as well as four measures related to substance use (tobacco, alcohol, marijuana, and illicit drugs). As a result, these variables were excluded from the present study. A detailed list of the behavioural measures used in our analysis is provided in Table 2. Details on the mean and standard deviations for each variable in each group are reported in the Supplementary Information (Tables S1, S2, and S3).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eList of the measures included in the study, together with the scale/questionnaire/test used to measure each, and the variable name used in the further analyses\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePersonality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFive Factor Inventory\u003c/p\u003e\n \u003cp\u003e(NEO-FFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNEOFAC_A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eAgreeableness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNEOFAC_C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eConscientiousness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNEOFAC_E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eExtraversion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNEOFAC_N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNeuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNEOFAC_O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eOpenness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCognition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePicture Sequence Memory Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePicture Sequence Memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePic_Seq\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eDimensional Change Card Sorting task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDimensional Change Card Sorting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCard_Sort\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePattern Comparison Processing Speed task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePattern Comparison Processing Speed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProc_Speed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eList Sorting Working Memory Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eList Sorting Working Memory Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eList_Sort\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eFlanker Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFlanker Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFlanker\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eOral Reading Recognition Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOral Reading Recognition Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOral_Read\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eDelay Discounting Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDelay Discounting (200 $)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDD_200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDelay Discounting (40000 $)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDD_40K\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"15\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMental Health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePenn Emotion Recognition Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eEmotion Recognition\u003c/p\u003e\n \u003cp\u003e(ER40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEmotion_Rec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAchenbach Adult Self Report (ASR)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eWithDrawal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWithDrawal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eThought_Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eThought_Prob\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRule_Breaking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRule_Break\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eExternalizing_Behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eExternalizing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAntisocial_Behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAntisocial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAnxiety_Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAttention_Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHiperactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eAnger-Hostility Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAggressivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAggressivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eHostility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHostility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePerceived Rejection Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePerceived Rejection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRejection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePerceived Stress Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePerceived Stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eSelf-Efficacy Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSelf-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSelf_Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eWell-being Toolbox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eWell-Being\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWell_Being\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSensory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePittsburg Sleep Quality Inventory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSleep_Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePain Intensity Survey\u003c/p\u003e\n \u003cp\u003ePain Interference Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eIntensity_Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePain_Intensity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eInterference_Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePain_Interference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe network analysis employed in this study consisted of two separate procedures. First, each age group was analysed using the Exploratory Graph Analysis (EGA) approach (Hudson et al. 2024; Golino \u0026amp; Epskamp 2017). EGA is a methodological approach that examines a large set of variables from a network perspective, considering the relationships between each measure and estimating the best way to categorize them into separate clusters (or communities). Considered an innovative approach to investigate multivariate data, EGA has been found to perform as well as several traditional factor analytic techniques (Golino et al., 2020). EGA involves multiple steps: First, a Gaussian graphical model is computed to calculate the network structure that better represents the relationships among the variables. In the network computation, each variable represents a node and each association between two nodes constitutes an edge.\u0026nbsp;The edges are estimated as partial correlation coefficients between each pair of nodes, controlling for all other variables. Given the large number of nodes (and edges), the network was regularized using the LASSO (Least Absolute Shrinkage and Selection Operation) method (Epskamp et al. 2018), which prevent overfitting. After regularization, a walktrap community detection algorithm is applied to identify the clusters in which variables can be grouped to better explain their relationships (Christensen \u0026amp; Golino 2019; 2021). Finally, the reliability and replicability of the identified networks and communities are tested using a nonparametric bootstrap approach with 5,000 replications to assess the robustness of the results.\u003c/p\u003e\n\u003cp\u003eThe second procedure involved the direct comparison between the so-obtained networks through the Network Comparison Test (NCT; van Borkulo 2022) approach. This method evaluates the networks for their level of invariance (the similarity between the structures of the two networks) and compares their global strength (the absolute sum of network edge weights). The networks from the three age groups were compared in pairs (Young vs. Middle-aged: Middle-aged vs. Older; Young vs. Older). Post-hoc analyses were conducted to identify specific edges with significant differences in strength between each pair of networks. For each comparison, the permutation seed was set to 123, and 1,000 permutations were performed. Given the large number of nodes and edges, the results were corrected for multiple comparisons using the False Discovery Rate (FDR) method (Benjamini \u0026amp; Hochberg 1995).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEGA Network \u0026ndash; Young Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the Young Adults group, the EGA analysis revealed six distinct clusters, each representing a unique community of variables. Cognitive measures divided into two separate communities: one reflecting basic functions such as processing speed and attention (Low_Cognition), and another encompassing more complex skills like reading ability and emotion recognition (High_Cognition). A third community (Delay_Discount) was centred on decision-making processes, represented by performance on the Delay Discounting Task. Psychological and psychopathological measures formed two additional communities. One captured externalising behaviours, including rule-breaking, aggression, and hostility (Externalizing). The other grouped together mental health-related factors, such as depression, anxiety, poor sleep quality, low self-efficacy, and thought problems (Mental_Health). Pain perception\u0026mdash;measured through pain intensity and interference\u0026mdash;formed a sixth, separate cluster (Pain). Personality traits did not form a standalone community. Instead, they were integrated into other domains: Neuroticism, Conscientiousness, and Extraversion were part of the Mental_Health community; Agreeableness clustered with Externalizing traits; and Openness aligned with the High_Cognition community. A full overview of edge weights and zero-order correlations among all variables can be found in the Supplementary Information (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEGA Network \u0026ndash; Middle-aged Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the Middle-aged Adults group, the EGA revealed a reorganization of variable relationships, with the network structure shifting from six communities (in young adults) to four. Cognitive variables\u0026mdash;previously split into separate clusters for lower- and higher-level functions\u0026mdash;now formed a single, unified domain (Cognition), which also included Openness to experience. Two other communities mirrored those found in the Young Adults: one related to decision-making processes (Delay_Discount), and another encompassing externalizing behaviours such as rule-breaking and aggression, along with Agreeableness (Externalizing).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA fourth community grouped together the variables related to mental health, well-being, pain perception, and the remaining personality traits (Mental_Health). For detailed edge weights and variable correlations, refer to Supplementary Information (Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEGA Network \u0026ndash; Older Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe network structure in the Older Adults group also revealed four communities, paralleling those of the Middle-aged group: Cognition, Delay_Discount, Externalizing, and Mental_Health. However, subtle yet meaningful shifts in variable organization were observed, mostly involving the Externalizing community. Namely, Agreeableness and Aggressiveness, previously integrated in this cluster, showed to shift toward the Mental_Health community, increasing their associations (either positive or negative) with Neuroticism and other mental health-related factors, while reducing their associations with externalizing behaviour measures. Edge weights and correlations are detailed in Supplementary Information (Table S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork Comparison Test \u0026ndash; Young vs. Middle-aged Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Network Comparison Test (NCT) indicated significant structural differences between the networks of the Young and Middle-aged Adults groups (M = 0.617, p \u0026lt; .001), in line with the different number of communities identified through the EGA approach (six vs. four). Despite this structural reorganization, global strength values were comparable (Young: 11.139; Middle-aged: 12.116), suggesting that the overall strength or number of associations among variables remained stable between these age groups. Nonetheless, post-hoc comparisons identified 29 edges with significantly different strengths, mainly involving connections within the Mental_Health community and between Mental_Health and Externalizing domains (see Figure 4). The complete list of these edges is provided in the Supplementary Information (Table S5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork Comparison Test \u0026ndash; Young vs. Older Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison between the Young and Older Adults networks revealed significant structural differences (M = 0.519, p \u0026lt; .001). Global strength was significantly lower in the Older Adults (8.781) compared to the Young Adults group (11.139; S = 2.358, p = .039), indicating an average reduction of associations, either in their number or in their strength. Post-hoc tests identified 25 edges with significantly differences between groups, mostly between nodes of the Mental_Health community and between its nodes and the ones of the Externalizing domain. A full list of these edges is provided in the Supplementary Information (Table S6). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork Comparison Test \u0026ndash; Middle-aged vs. Older Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NCT revealed significant differences in both the structure and overall connectivity of the networks between Middle-aged and Older Adults. Specifically, structural differences were observed (M = 0.281, p = .006), alongside a significant reduction in global strength in the Older Adults group (S = 3.335, p = .013), which showed reduced connectivity (8.781) compared to the Middle-aged Adults group (12.116). A total of 19 edges differed significantly, most of which involved nodes of the Externalizing community. Detailed results are provided in Supplementary Information (Table S7).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated how the structure and interrelations among personality, psychological, and cognitive variables change across the adult lifespan. Using network analysis, we compared these patterns across three age groups: young adults (22\u0026ndash;36 years), middle-aged adults (36\u0026ndash;60 years), and older adults (60\u0026ndash;100 years). Our findings revealed marked age-related differences in the organization of these domains, highlighting both structural and functional transformations across the lifespan.\u003c/p\u003e \u003cp\u003eFirst, we used Exploratory Graph Analysis (EGA: Golino \u0026amp; Epskamp \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to examine how variables clustered into communities within each age group. In the Young Adults group, six distinct communities emerged: two encompassing cognitive measures (Low_Cognition and High_Cognition), one grouping the measures of the Delay Discounting Task, a task associated with decision-making (Delay_Discount), two focused on personality and psychological measures (Externalizing and Mental_Health), and the last associated with pain perception (Pain). These findings replicate the ones of Granziol and Cona (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with each variable of interest clustered in the same communities identified by the authors. In contrast, the analysis performed on the Middle-aged and the Older Adults groups revealed the emergence of networks characterized by a lower number of communities (four), suggesting decreased segregation among variables. Specifically, cognitive variables (split into low- and high-level domains in young adults) were merged into a single community (Cognition). Similarly, the variables of the Mental_Health and Pain communities clustered into a single domain. These convergences may reflect the dedifferentiation of functional domains in older participants, reflecting reduced specialization and greater integration, findings that align with the dedifferentiation hypothesis (Anstey et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Balinsky, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1941\u003c/span\u003e). The integration of low- and high-level cognitive domains into a single community in the older age groups also aligns with the Scaffolding Theory of Aging and Cognition (STAC; Park \u0026amp; Reuter-Lorenz, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which posits that ageing is associated with a more generalized recruitment of cognitive resources to support complex task performance. Similarly, the merging of psychological variables\u0026mdash;including mental health factors, personality traits, and self-reported well-being on one side, and indicators of pain perception on the other\u0026mdash;may reflect a growing interdependence between emotional and somatic experiences with age. This pattern can be interpreted in light of the Selective Optimization with Compensation model (SOC; Baltes \u0026amp; Baltes, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), which posits that older adults adapt to functional declines by engaging additional psychological resources and employing compensatory strategies. Notably, the reduction in the number of communities was found already in middle adulthood (36\u0026ndash;60 years), suggesting that such reorganizational processes may begin earlier than typically assumed.\u003c/p\u003e \u003cp\u003eWhile cognitive and mental health variables consistently clustered together (either in one or two communities), personality traits followed a different trajectory. In fact, the traits of Extraversion, Neuroticism, and Conscientiousness were strongly associated and clustered within the same community (Mental_Health) across all age groups. Openness to experience, on the other hand, aligned more closely with cognitive variables\u0026mdash; clustering within the High_Cognition community in young adults and within Cognition in middle-aged and older adults. This finding supports prior evidence of a strong link between Openness and cognitive functioning across the lifespan (Curtis et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Soubelet \u0026amp; Salthouse \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Concerning Agreeableness, by contrast, clustered with Externalizing factors in the Young Adults network\u0026mdash;consistent with its known inverse relationship to aggressivity and antisocial behaviour (Jiang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jones et al. 2022)\u0026mdash; but shifted to the Mental_Health community in the other two age groups. This transition may reflect both a weakening of the association between Agreeableness and externalizing behaviours, and the general increase in Agreeableness with age (Terracciano et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), possibly driven by heightened emphasis on emotional regulation and social connectedness in later years (Carstensen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; 2018). Taken together, the distribution of personality traits across different communities supports the hypothesis that personality is not driven by a single latent factor, but rather emerges from the dynamic interplay among its constituent traits (Costantini et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kan et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Network Comparison Test (NCT) revealed significant structural differences across age groups. Pairwise network comparisons indicated that each of the three networks differed significantly in their overall architecture. Specifically, the largest differences were observed between the networks of young adults and middle-aged adults (M\u0026thinsp;=\u0026thinsp;.617), and between young and older adults (M\u0026thinsp;=\u0026thinsp;.519). Although the difference between middle-aged and older adults was smaller, it remained statistically significant (M\u0026thinsp;=\u0026thinsp;.282). These findings suggest that the most substantial structural reorganization may occur between early and mid-adulthood, with more gradual changes in older age. In contrast, comparing the networks for their global strength\u0026mdash;the sum of all edge weights across nodes\u0026mdash;showed that older adults exhibited significantly lower values (S\u0026thinsp;=\u0026thinsp;8.781) compared to both young (S\u0026thinsp;=\u0026thinsp;11.139) and middle-aged adults (S\u0026thinsp;=\u0026thinsp;12.116). This finding indicates that older participants were characterized by weaker overall associations among variables. While this pattern may appear in contrast with the dedifferentiation hypothesis\u0026mdash;which posits that aging leads to less distinct and more interrelated functional domains\u0026mdash;it is important to distinguish between structural differentiation (i.e., the number and separation of communities) and connection strength. While dedifferentiation typically implies fewer and more integrated communities, it does not necessarily entail stronger connections among nodes (Bringmann et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fried et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In fact, the observed difference in global strength may reflect two non-mutually exclusive processes: either a reduction in connection strength\u0026mdash;potentially indicative of diminished cognitive flexibility or psychological resilience\u0026mdash;or a reduction in the number of connections, indicating a shift toward fewer but more functionally essential associations. The reduced number of communities in the older age groups supports the latter interpretation, suggesting a shift toward simplified and less modular networks, characterized by a more streamlined\u0026mdash;and possibly compensatory\u0026mdash;organization. This aligns with the notion that ageing is accompanied by dedifferentiation and compensation, processes enhancing efficiency to face functional decline, but reducing the system\u0026rsquo;s adaptability (Cabeza \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Park \u0026amp; Reuter-Lorenz \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study presents several limitations that warrant consideration. First, the delineation of age groups was influenced by the structure of the available datasets (HCP-YA: 22\u0026ndash;36; HCP-A: 36\u0026ndash;100), the use of different versions of certain questionnaires across age ranges (ASR for younger participants versus ASR-O for those over 60), and the goal of achieving age groups with comparable sample sizes (~\u0026thinsp;300 participants). While these thresholds supported internal consistency, they may have oversimplified the continuum of ageing and obscured more nuanced age-related changes, particularly during transitional periods. In line with this, the cross-sectional design of this study limits our ability to draw causal or developmental conclusions about changes in network structure over time. The observed age-related differences may reflect cohort effects or other unmeasured variables, rather than lifespan transformations in psychometric networks. Longitudinal studies tracking within-person changes would provide more definitive insights into the dynamics of psychological and cognitive aging. Finally, it must be pointed out that while Exploratory Graph Analysis (EGA) reflects the current best practice for data-driven identification of network community structure and has shown high replicability in prior work (Golino et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it remains an exploratory approach. Future studies using confirmatory techniques and testing for measurement invariance across age groups would help validate the robustness of these findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides novel evidence of lifespan-related reconfigurations in the organization of cognitive, personality, and psychological constructs, as revealed through network analysis. Younger adults exhibited a more differentiated and modular network structure, with variables clustering into six distinct communities. In contrast, middle-aged and older adults displayed a reduced number of communities, reflecting more integrated, less compartmentalized networks. This reorganization, with less specialized psychological and cognitive domains in the older age groups, appears consistent with theoretical models of ageing that emphasize compensation and dedifferentiation. Interestingly, this process appears to emerge during the middle stages of adulthood, highlighting the importance of considering midlife as a key transition point in cognitive and psychological development. In addition to this reorganization, older adults were also characterized by reduced levels of global strength compared to young and middle-aged adults, reflecting reduced overall network connectivity. Since only few studies examined the relationship between ageing and psychometric variables from a network perspective, further research is necessary to deeper understand these patterns, including studies involving pathological populations or comparisons between high- and low-performing older participants. Nevertheless, it is worth noting that similar trends have been observed in neuroimaging studies, which reported reduced connections and increased dedifferentiation in brain networks with ageing (Deery et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Iordan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAltogether, our findings suggest the presence of structural changes in the relationships across psychometric variables in different age groups, supporting the notion that ageing is accompanied by functional reorganization. As the global population continues to age, understanding how the variables that constitute individuality interact and evolve across the lifespan is increasingly important. This study demonstrates the value of network analysis in capturing age-related differences, advancing beyond traditional compartmentalized views of cognitive, personality, and psychological domains, and offering new insights into the complex dynamics that characterize the ageing process.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.V.: Data curation, Formal analysis, Writing\u0026mdash;original draftU.G.: Methodology, Supervision, Data curationD.R.: Conceptualization, Funding acquisition, Writing\u0026mdash;editing and revisionM.S.: Conceptualization, Funding acquisition, Writing\u0026mdash;editing and revisionG.C.: Conceptualization, Supervision, Funding acquisition, Writing\u0026mdash;editing and revision\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis research was supported by the Research Project of National Interest (PRIN), funded by the Italian Ministry of University and Research (MUR) Grant Number 2022BNMZJC.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe raw data used in this study were obtained from the Human Connectome Project (HCP) and are subject to access restrictions imposed by the National Institutes of Health (NIH). Due to licensing agreements, the authors cannot redistribute the raw data. However, all R scripts and code used for analysis, and visualization are publicly available on the Open Science Framework (OSF) at https://osf.io/9q74d/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnstey, K. J., Hofer, S. M., \u0026amp; Luszcz, M. A. (2003). Cross-sectional and longitudinal patterns of dedifferentiation in late-life cognitive and sensory function: the effects of age, ability, attrition, and occasion of measurement. \u003cem\u003eJournal of Experimental Psychology: General, 132\u003c/em\u003e(3), 470. https://psycnet.apa.org/doi/10.1037/0096-3445.132.3.470\u003c/li\u003e\n \u003cli\u003eBalinsky, B. (1941). An analysis of the mental factors of various age groups from nine to sixty. \u003cem\u003eGenetic Psychology Monographs, 23,\u0026nbsp;\u003c/em\u003e191\u0026ndash; 234.\u003c/li\u003e\n \u003cli\u003eBaltes, P. B., Cornelius, S. 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Memory aging and brain maintenance. \u003cem\u003eTrends in Cognitive Sciences, 16\u003c/em\u003e(5), 292-305. https://doi.org/10.1016/j.tics.2012.04.005\u003c/li\u003e\n \u003cli\u003ePark, D. C., \u0026amp; Reuter-Lorenz, P. (2009).\u0026nbsp;The adaptive brain: Aging and neurocognitive scaffolding. \u003cem\u003eAnnual Review of Psychology, 60,\u003c/em\u003e 173-196. https://doi.org/10.1146/annurev.psych.59.103006.093656\u003c/li\u003e\n \u003cli\u003ePark, D. and McDonough, I. (2013). The dynamic aging mind. \u003cem\u003ePerspectives on Psychological Science,\u0026nbsp;\u003c/em\u003e8(1), 62-67. https://doi.org/10.1177/1745691612469034\u003c/li\u003e\n \u003cli\u003ePathy, M. J., Sinclair, A. J., \u0026amp; Morley, J. E. (Eds.). (2006). \u003cem\u003ePrinciples and practice of geriatric medicine\u003c/em\u003e. John Wiley \u0026amp; Sons.\u003c/li\u003e\n \u003cli\u003ePayne, B. R., \u0026amp; Lohani, M. (2020). Personality and cognitive health in aging. \u003cem\u003ePersonality and healthy aging in adulthood: New directions and techniques\u003c/em\u003e, 173-190. https://doi.org/10.1007/978-3-030-32053-9_11\u003c/li\u003e\n \u003cli\u003eReuter-Lorenz, P. A., \u0026amp; Park, D. C. (2014).\u0026nbsp;How does it STAC up? Revisiting the scaffolding theory of aging and cognition. \u003cem\u003eNeuropsychology Review, 24\u003c/em\u003e(3), 355-370. https://doi.org/10.1007/s11065-014-9270-9\u003c/li\u003e\n \u003cli\u003eRoberts, B. W., Walton, K. E., \u0026amp; Viechtbauer, W. (2006).\u0026nbsp;Personality traits change in adulthood. \u003cem\u003eCurrent Directions in Psychological Science, 15\u003c/em\u003e(1), 31-35. https://doi.org/10.1037/0033-2909.132.1.29\u003c/li\u003e\n \u003cli\u003eRoberts, B. W., \u0026amp; Mroczek, D. (2008). 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Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. \u003cem\u003eComplexity\u003c/em\u003e, \u003cem\u003e2019\u003c/em\u003e(1), 2108423. https://doi.org/10.1155/2019/2108423\u003c/li\u003e\n \u003cli\u003eSoto, C. J., John, O. P., Gosling, S. D., \u0026amp; Potter, J. (2011). Age differences in personality traits from 10 to 65: Big Five domains and facets in a large cross-sectional sample. \u003cem\u003eJournal of personality and social psychology, 100\u003c/em\u003e(2), 330. https://doi.org/10.1037/a0021717\u003c/li\u003e\n \u003cli\u003eSoubelet, A., \u0026amp; Salthouse, T. A. (2011). Personality\u0026ndash;cognition relations across adulthood. \u003cem\u003eDevelopmental psychology\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(2), 303.\u0026nbsp;https://doi.org/10.1037/a0021816\u003c/li\u003e\n \u003cli\u003eTerracciano, A., McCrae, R. R., Brant, L. J., \u0026amp; Costa, P. T., Jr. (2005). Hierarchical linear modeling analyses of the NEO-PI-R Scales in the Baltimore Longitudinal Study of Aging.\u0026nbsp;\u003cem\u003ePsychology and Aging, 20\u003c/em\u003e(3), 493\u0026ndash;506.\u0026nbsp;https://doi.org/10.1037/0882-7974.20.3.493\u003c/li\u003e\n \u003cli\u003eVan Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., \u0026amp; Waldorp, L. J. (2022).\u0026nbsp;Comparing network structures on three aspects: A permutation test. \u003cem\u003ePsychological methods, 28\u003c/em\u003e(6), 1273\u0026ndash;1285. https://doi.org/10.1037/met0000476\u003c/li\u003e\n \u003cli\u003eVan Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., \u0026amp; Yacoub, E. (2012). The human Connectome Project: A data acquisition perspective. \u003cem\u003eNeuroimage, 62\u003c/em\u003e(4), 2222\u0026ndash;2231. https://doi.org/10.1016/j.neuroimage.2012.02.018\u003c/li\u003e\n \u003cli\u003eWagner, J., Ram, N., Smith, J., \u0026amp; Gerstorf, D. (2016). Personality trait development at the end of life: Antecedents and correlates of mean-level trajectories. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(3), 411. https://doi.org/10.1037/pspp0000071\u003c/li\u003e\n \u003cli\u003eWettstein, M., Tauber, B., \u0026amp; Wahl, H. W. (2020). Associations between cognitive abilities and 20-year personality changes in older adults in the ILSE study: Does health matter?. \u003cem\u003eThe Journals of Gerontology: Series B\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(6), 1206-1218. https://doi.org/10.1093/geronb/gby155\u003c/li\u003e\n \u003cli\u003eWolf, M. B., \u0026amp; Ackerman, P. L. (2005). Extraversion and intelligence: A meta-analytic investigation. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(3), 531\u0026ndash;542. https://doi.org/10.1016/j.paid.2005.02.020\u003c/li\u003e\n\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":"european-journal-of-ageing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejoa","sideBox":"Learn more about [European Journal of Ageing](http://link.springer.com/journal/10433)","snPcode":"10433","submissionUrl":"https://submission.nature.com/new-submission/10433/3","title":"European Journal of Ageing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Human Connectome Project, Network, Ageing, Exploratory graph analysis, Network Comparison Test","lastPublishedDoi":"10.21203/rs.3.rs-6523434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6523434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgeing refers to a series of changes occurring throughout the lifespan in cognitive abilities, physical and mental health, and personality traits. While these dimensions have traditionally been studied as separate compartments, recent findings highlight their interdependence and dynamic interplay over time. To investigate their relationships, we analysed data from the Human Connectome Project using a psychometric network approach. Participants were grouped into three age categories: Young (22\u0026ndash;35), Middle-aged (36\u0026ndash;59), and Older (60\u0026ndash;100) adults. We examined the interrelationships among 31 cognitive, psychological, and personality variables using Exploratory Graph Analysis (EGA) to estimate one network per age group and explore how these variables cluster into communities across the lifespan. Networks were then compared using the Network Comparison Test (NCT) to identify age-related differences in both global and local network properties. We observed substantial age-related changes: variables clustered into six communities in the Young Adults group but only into four in both the Middle-aged and Older Adults, suggesting dedifferentiation and reduced domain specificity in the older age groups. The NCT revealed distinct network architectures for each age group, with the most pronounced differences between Young Adults and the two older groups. Additionally, global strength\u0026mdash;a measure of overall network connectivity\u0026mdash;was significantly lower in Older Adults, indicating that associations among variables were on average weaker. Overall, these findings support the view that ageing is associated with structural transformations in the relationships among cognitive, psychological, and personality domains, following a dedifferentiation trajectory and highlighting the reorganization of behavioural functioning with age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"The Effect of Age on the Architecture of Psychological and Cognitive Dimensions: A Network Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 09:49:53","doi":"10.21203/rs.3.rs-6523434/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-02T10:01:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T18:04:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T05:16:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-22T12:30:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T10:32:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211727005211508654620071709700500306111","date":"2025-08-04T14:42:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108876471065841769891572848611975900137","date":"2025-07-30T06:19:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32208620943614950556222505460657132304","date":"2025-07-27T08:07:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278700540911156274479559567593063822967","date":"2025-07-26T15:18:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-15T07:57:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-14T15:27:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T00:44:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Ageing","date":"2025-04-24T19:48:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-ageing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejoa","sideBox":"Learn more about [European Journal of Ageing](http://link.springer.com/journal/10433)","snPcode":"10433","submissionUrl":"https://submission.nature.com/new-submission/10433/3","title":"European Journal of Ageing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"54145836-3ee8-47bb-ada4-f66fee063e5f","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:03:56+00:00","versionOfRecord":{"articleIdentity":"rs-6523434","link":"https://doi.org/10.1007/s10433-025-00903-8","journal":{"identity":"european-journal-of-ageing","isVorOnly":false,"title":"European Journal of Ageing"},"publishedOn":"2026-02-02 15:57:27","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-05-20 09:49:53","video":"","vorDoi":"10.1007/s10433-025-00903-8","vorDoiUrl":"https://doi.org/10.1007/s10433-025-00903-8","workflowStages":[]},"version":"v1","identity":"rs-6523434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6523434","identity":"rs-6523434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}