Creativity Neurodynamics and Ageing: a microstate EEG pilot study.

Creativity Neurodynamics and Ageing: a microstate EEG pilot study. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Creativity Neurodynamics and Ageing: a microstate EEG pilot study. Martyna Olszewska, Ewa Katarzyna Ratajczak, Bartłomiej Kiljanek, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7857214/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The main aim of the presented pilot study was to investigate brain network dynamics during creative task performance in older adults. Creativity was operationalised as divergent thinking and assessed via the Alternative Uses Task (AUT) upon electroencephalography (EEG) recording. Thirty-eight healthy older adults participated in the study; 15 participants were excluded due to difficulties understanding or retaining task instructions, resulting in a final sample of 23 participants. We examined the neurodynamics of creativity in more and less creative older adults using EEG microstate analysis, which revealed eight microstate classes, interpreted in accordance with recent literature. Correlation analyses between microstate parameters and AUT scores (creativity, fluency, flexibility, and elaboration) revealed that more creative participants relied on executive control and default mode networks to generate original and novel ideas, while selectively drawing on autobiographical memory to produce a greater number of ideas across categories. Less creative participants showed stronger involvement of semantic and somatosensory/interoceptive networks, suggesting reliance on mental simulations and bodily awareness during idea generation. These findings provide preliminary evidence on neural mechanisms supporting creativity in late adulthood and offer insights for future research on age-related changes in cognition and brain network dynamics. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology ageing creativity divergent thinking EEG microstates AUT Figures Figure 1 Figure 2 BACKGROUND Creativity has long been studied across various age groups, from children and adolescents to younger and older adults, with the latter receiving a growing recognition nowadays. Prior research has established that creativity is a combination of cognitive, conative, and emotional factors interacting dynamically with the environment [1]. This perspective has led to an increasing interest in how creative skills can contribute to psychological well-being and cognitive functioning in late adulthood. Recent studies highlight several benefits of creative activities for older adults. For example, McQuade and O’Sullivan [2] found that participation in artistic and creative group activities has a positive impact on their physical, mental, and social health. Additionally, some studies have been conducted that link creativity to cognitive reserve - the ability to adapt, optimise, and maximise the brain's performance to cope with and minimise the effects of damage caused by brain pathology, such as neurodegenerative diseases [3]. For instance, Palmiero and colleagues [4] found that verbal creativity can serve as a predictor of cognitive reserve. There is also evidence that individuals working in creative professions, such as art gallery managers or musicians, achieve higher scores on measures of cognitive reserve [5]. Moreover, a recent study by Fusi and colleagues [6] not only supported that creativity predicts higher cognitive reserve but also pointed out that this relationship mediates a positive effect on psychological well-being. Despite growing evidence for the beneficial role of creativity in supporting mental and cognitive health in late adulthood, little is known about the underlying neural mechanisms. To address this gap, we present findings from a pilot study involving older adults, focusing on the neurodynamics of creativity operationalised as divergent thinking (DT). Measuring creativity To study cognitive aspects of creativity as well as its neural mechanisms, several tasks have been widely used, such as the Alternative Uses Task (AUT). This tool focuses specifically on DT, described by Guilford as the ability to generate many ideas in a broad context [7]. The AUT allows for the assessment of idea creativity as originality (defined as a combination of uncommonness, remoteness of association, and cleverness of each idea), as well as fluency (no. of valid ideas), flexibility (no. of categories spanning the ideas generated), and elaboration (no. of meaningful words used to describe each idea) [7, 8, 9]. Among older adults, the AUT has been employed, for instance, while designing cognitive training based on a semantic retrieval strategy to improve creative DT [10]. Additionally, based on the AUT performance, Cancer and colleagues [11] revealed that executive functioning and DT are related to efficient creative problem-solving by older adults. Neural Mechanisms of Creativity Various studies (see below) have extended the focus on creative cognition by utilising neuroimaging techniques, including functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, studies on the role of creative performance and expertise in healthy ageing are only getting traction. A recent study has reported that creative experience is associated with delayed brain ageing and increased neural connectivity in creativity-related areas, resulting in neuroplasticity-driven enhanced brain efficiency [12]. Yet, to our best knowledge, there is still little objective, neuroimaging evidence on brain activity during creative task performance in older adults. Among these, only a few studies have employed EEG neuroimaging based on the analysis of oscillatory frequency bands in brain waves. Compared to younger adults, the elderly exhibited smaller theta and alpha desynchronization in frontal and parieto-occipital areas, respectively, possibly related to different AUT problem-solving strategies employed [13]. Another study has shown differences between young and older adults in theta synchronisation and beta desynchronisation during AUT performance [14]. Further studies have focused on neuroplasticity-related post-AUT task neuronal traces detected in the delta–gamma rhythms, mostly in the parieto–occipital areas [15], as well as the interhemispheric asymmetry of AUT task-related alpha desynchronization in the elderly [14]. While these results provide valuable insights, an analysis of oscillatory frequency bands offers a relatively limited view of the complex neural dynamics involved in the creative process. A detailed review of the neural underpinnings of creativity has concluded that creative thinking does not rely on any particular mental process, such as previously hypothesised defocused attention, low arousal, or alpha synchronisation, and therefore it is not associated with any specific brain region [16]. Therefore, the creative process should be studied by monitoring large-scale brain network activity, which allows for examining concerted activity throughout the entire brain rather than changes in isolated frequency bands in specific brain regions [17]. Consequently, using fMRI neuroimaging, recent research has linked creative performance to brain network dynamics, indicating the cooperation of the Default Mode Network (DMN) and Central Executive Network (CEN) during creative cognition [18] Functional connectivity between these two networks has been shown to predict individual creative thinking skills [19]. These findings have strong support in several replication studies and meta-analyses [20]. Moreover, the Salience Network (SN) has been considered to contribute to the creative process by facilitating switches between the DMN and CEN, aiding their successive cooperation [21; 22]. Recent advances in EEG microstate methodology enable a similar visualisation of large network activity in EEG neuroimaging [23, 24]. Our recent research, employing EEG microstate analysis, has shown that compared to less creative individuals, the more creative group exhibits stronger activity of microstates associated with the DMN, CEN, and SN, while the activation of microstates linked to sensory networks is weaker [25]. The primary objective of the study was to investigate brain network dynamics during the performance of AUT in older adults. Based on the results of previously mentioned neuroimaging studies, we hypothesise that more creative older adults will display higher DMN, CEN and SN activity during the DT task performance. This pilot study aims at a preliminary analysis of neural mechanisms involved in creative thinking in late adulthood as a part of a larger ongoing project investigating various factors of cognitive performance in the ageing population. METHOD Participants and Study Design Thirty-eight elderly adults (86.6% female) aged 63 - 79 (70.82 ± .92) participated in the study carried out in the Electrophysiology and Neurotherapy Lab at the Institute of Psychology, Nicolaus Copernicus University in Toruń, Poland. The inclusion criteria required participants to be over the age of 60 and within the cognitive norm (min. 26 points on the Polish adaptation of Mini-Mental State Examination Scale, MMSE [26]). Exclusion criteria comprised uncorrected vision or hearing problems, neurological or psychiatric problems related to the presence and/or treatment of disorders selected from the literature (such as depression or epilepsy) or abuse of psychoactive substances such as drugs and alcohol. Fifteen participants were excluded due to incorrect performance on the creative task, likely because they did not understand, misunderstood, or forgot the instructions. The final sample consisted of twenty-three elderly adults (95.7% female) aged 63 - 79 (70.87 ± 4.40). Upon arrival at the lab, the volunteers gave written informed consent for participation in the study and were prepared for EEG registration. Brain activity was recorded during resting-state conditions (5 min. eyes open and 5 min. eyes closed), and cognitive tasks, including the dual-choice oddball task (6-7 min.), the picture 2-back task (6-7 min.), and the AUT paradigm adapted for EEG research (15 min.). For the purpose of this study, only the AUT results and related neurodynamics were analysed. Following EEG registration, the volunteers obtained remuneration for their participation in the form of gift vouchers worth 100 PLN (~23.3 EUR, ~25 USD). The study was approved by the Ethics Committee at the Institute of Psychology, Nicolaus Copernicus University in Toruń, Poland (no. 27/2023/FT). Research was performed in accordance with the Declaration of Helsinki. Creativity Assessment Creativity, operationalised as DT, was assessed with a computerised Polish AUT adapted for EEG studies. The participants obtained the following instruction: ’List as many unusual, original uses for an object of everyday use as you can think of.’ The following examples were provided: ‘hammer - can be used as a nutcracker,’ ‘hammer - a paperweight preventing papers from flying away with the wind.’ Three consecutive names of everyday objects (‘umbrella’, ‘soap’, and ‘pen’) were presented on the screen for five minutes each using Presentation® software (Version 24.0, Neurobehavioral Systems, Inc., Berkeley, CA, ⁦www.neurobs.com⁩). To mark the exact moment of idea generation and remove speech-related artefacts from the EEG signal, the participants were instructed to click a mouse button as soon as they thought of an alternative use for a given object, vocalise their idea, and click again once finished talking (Fig. 1). We assessed the creative performance of each participant based on four DT factors: idea creativity (idea originality), fluency, flexibility, and idea elaboration. First, unintelligible and nonsensical ideas were removed, resulting in a total of 676 responses included in the analysis. The remaining uses were coded (categorised) by two human raters, which aided creativity assessment and allowed for calculating flexibility. Next, four human judges (50% female) assessed the creativity and elaboration of each idea. Creativity was evaluated according to the guidelines derived from Guilford’s definition of creativity [8] based on three features: uncommonness (unusual, original), remoteness of association, and cleverness (useful, reasonable, functional), which may partially compensate for each other. Creativity of each alternative idea was rated on a scale from 1 to 5, while all typical uses were assigned a score of 0. Idea elaboration was evaluated based on the number of meaningful words [27], excluding general words such as prepositions, pronouns, conjunctions, etc. For each idea, creativity and elaboration ratings were first averaged across judges (idea-level scores) and then across all uses for a given object (object-level scores). Fluency and flexibility were calculated for each object (object-level scores), as the number of ideas and the number of different categories of ideas (codes) assigned, respectively. Finally, creativity, fluency, flexibility, and elaboration scores for each participant (participant-level scores) were calculated as the mean of object-level scores. EEG Microstates Analysis The EEG signal was recorded using the actiCAP system (Brain Products GmbH, Gilching, Germany) with 64 channels placed according to the international 5–10 system (FCz reference electrode, AFz ground electrode). The EEG recordings were processed using the EEGLAB toolbox (ver. 2019.1) [28] for MATLAB (ver. R2024b, Mathworks Inc., Natick, MA). Prior to any signal processing, for each participant, all EEG fragments corresponding to giving answers were removed (due to speech artefacts). Next, we subjected the signal to the following pre-processing steps: demeaning, filtering (high-pass > 1 Hz, low-pass < 40 Hz), downsampling (from 1 kHz to 256 Hz), re-referencing (to average), marking artefactual ICA components in an automated manner (using the ICLabel EEGLAB plug-in), and removing bad epochs (with amplitudes > 111 μV). Finally, for each participant, we selected a 30-second continuous (uncut) EEG fragment from the pre-processed EEG signal and performed microstate analysis using the Microstate Toolbox EEGLAB plug-in [29]. 1000 GFP maxima chosen from each EEG fragment (excluding peaks exceeding one standard deviation) were subjected to modified k-means clustering (3-15 clusters, 10 repetitions, max. 1000 iterations). Following GMD-based backfitting with 30 ms smoothing, microstate statistics were calculated for each class detected, including: occurrence (appearances per second), average duration, and coverage (percent of the signal assigned to a given class). Artificial intelligence tools were used exclusively for proofreading and language refinement, as well as for creating illustrative icons used in the procedure schema. RESULTS Microstate analysis revealed eight microstate (MS) classes. The prototypes were aligned using the Template Explorer MATLAB add-on [ 30 ] and visual inspection with maps described in previous EEG microstate studies (Fig. 2 ) focused on large brain networks [ 23 , 24 ], source localisation during resting state and Heart Rate Variability Biofeedback (HRV-BFB) training [ 31 , 32 ], and an AUT study with a younger population [ 25 ]. Based on the same studies, names and functions were assigned for each microstate class detected (Table 1 ). Table 1 Assigned names and functions of the microstate classes detected and their similarity to prototypes described in previous studies. No. Class SI SII SIII SIV Assigned function/large network MS1 E 92.0% 85.20% 95.0% 95.5% processing personally significant information, mental simulations, and theory of mind, associated with increased fluency in AUT MS2 C 89.6% 86.1% 92.7% 84.7% processing personally significant information, self-reflection, and self-referential internal mentation, associated with lower fluency in AUT MS3 B 90.7% 94.0% 95.9% 80.5% associated with visual processing related to self, self-visualisation, and autobiographical memory MS4 A 94.4% 87.3% 76.5% 77.7% associated with auditory and visual processing and arousal/arousability, associated with decreased fluency and originality in AUT G/F - - 76.5% - psychophysiological and emotional regulation during HRV-BFB MS5 D 85% 77.4% 86.9% 94.2% associated with executive functioning, associated with increased originality in AUT MS6 F 93.1% 60.4% 89.0% - processing interoceptive and emotional information, and the salience network MS7 E/F - - 73.5% 95.5% psychophysological and emotional control after HRV-BFB, associated with simultaneous high originality and high fluency in AUT E 67.1% 60.5% - - processing personally significant information, mental simulations, and the theory of mind F - 59.0% - - processing interoceptive and emotional information, and the salience network MS8 G 77.3% 51.5% 88.0% 95.8% the somatosensory network, interoception, somatosensory regulation during HRV-BFB, associated with increased fluency and simultaneous high originality and high fluency in AUT Note: SI - Custo et al., 2017; SII - Tarailis et al., 2021; SIII - Ratajczak, 2022; SIV - Ratajczak, 2024 Statistical Analysis Statistical analyses were conducted using IBM SPSS Statistics (ver. 29.0.2). Based on the median value of average creativity ( Med = 1.95), the participants were first divided into more (N = 11) and less (N = 12) creative. Table 2 displays average creativity, fluency, flexibility, and average elaboration in the whole sample and compares their values between the more and less creative groups using t-tests with 95% BCa bootstrap confidence intervals (2000 random samples). Table 2 Average creativity, fluency, flexibility, and average elaboration in the whole sample and compared between the more and less creative groups. Measure Whole sample M ( SD ) Less creative M ( SD ) More creative M ( SD ) t (21) 95% BCa CI Creativity 1.85(.46) 1.52(.32) 2.21(.27) -5.50*** − .91– − .48 Fluency 10.17(5.77) 9.06(6.61) 11.39(4.71) − .97 -6.31–2.16 Flexibility 7.13(3.39) 5.78(3.37) 8.61(2.86) -2.16* -5.07 – − .24 Elaboration 2.93(1.58) 2.39(.65) 3.51(2.07) -1.72 -2.58 – .07 * p < .05; *** p < .001 We performed an independent-samples t-test analysis to compare microstate class parameters between more and less creative groups, but found no significant differences. We then performed a Pearson’s correlation analysis between class parameters and creativity ratings for the entire sample, as well as for both participant groups. In addition to reporting statistical significance, we provide effect sizes (Pearson’s r) and 95% confidence intervals derived from bootstrap resampling (2000 random samples). We also report trends ( p < .10) as exploratory findings, in line with the hypothesis-generating character of pilot research. A full table of correlations is provided in the Supplementary Materials [see Additional file 1]. For the whole sample, we found a moderately significant positive correlation between fluency and the duration ( r = .45, p = .030) and coverage ( r = .45, p = .032) of MS1, with occurrence showing a tendency to significance ( r = .38, p = .078). We have also observed a tendency for a positive, moderate relationship between flexibility and measures of MS1, including duration (r = .38, p = .072), and coverage ( r = .40, p = .062). On the other hand, we found a negative, moderate tendency between the occurrence of MS8 and both fluency (r = − .37, p = .083) and flexibility ( r = − .36, p = .093). Moreover, further significant correlations emerged for both groups when analysed separately. Creativity. For more creative older adults, we found a strong, negative correlation between creativity ratings and all three MS3 parameters, including occurrence ( r = − .87, p < .001), duration ( r = − .66, p = .028) and coverage ( r = − .87, p < .001). There was also a significant, strong, positive correlation with the coverage of MS5 ( r = .63, p = .039), while occurrence ( r = .54, p = .086) and duration ( r = .59, p = .058) showed a trend toward significance. We’ve also observed a moderate, positive relationship with the coverage of MS7 ( r = .54, p = .083), approaching statistical significance. No significant correlations were found for the less creative group. Fluency. In the more creative group, fluency scores showed a strong positive relationship with the parameters of MS3, including occurrence ( r = .65, p = .032), duration ( r = .73, p = .011) and coverage ( r = .73, p = .011). For less creative participants, we’ve observed a strong positive correlation between fluency score and the parameters of MS1, including duration ( r = .71, p = .010) and coverage ( r = .65, p = .022), with occurrence approaching significance ( r = .54, p = .069). Moreover, we found a negative, significant correlation between fluency and duration of MS8 ( r = -61, p = .035) and a marginally significant correlation with MS8 occurrence ( r = − .56, p = .058) in this group. Flexibility. For the more creative individuals, we observed a strong, positive correlation between flexibility scores and occurrence ( r = .68, p = .020), duration ( r = .78, p = .004) and coverage ( r = .78, p = .005) of MS3. We found a strong, positive correlation for less creative participants with occurrence ( r = .59, p = .042); duration ( r = .74, p = .006) and coverage ( r = .70, p = .011) of MS1. There was also a strong negative correlation with the occurrence ( r = − .60, p = .039) and duration ( r = -70, p = .011) of MS8, while coverage showed a tendency for significance ( r = − .54, p = .073). Elaboration. No significant correlations were found for any of the study samples. However, for more creative participants, a moderate, negative relationship was found between elaboration and the occurrence of MS3 ( r = − .54, p = .084) and a moderate, positive connection with the occurrence of MS5 ( r = .58, p = .063). DISCUSSION The study explored brain network dynamics of divergent thinking during AUT performance in late adulthood. Using EEG microstate neuroimaging, we found that for more creative older adults, producing more creative ideas required higher activation of MS5 (class D) and MS7 (class E/F). The former is associated with executive functioning (CEN) [ 24 ], while the latter appears to be novel. MS7 topography resembles microstates class E, which is associated with processing personally significant information, mental simulations, and the theory of mind [ 24 ]. Jurewicz [ 33 ] linked these functions to the activity of the dorsomedial prefrontal subsystem of the DMN, which is responsible for semantic processing. However, the topography of MS7 is also highly similar to that of microstate class F, which is related to interoceptive and emotional information processing and SN activity [ 24 ]. At the same time, the MS7 prototype aligned closely with microstates described in our previous studies, linked to simultaneous high originality and high fluency in AUT [ 25 ], as well as psychophysiological and emotional control [ 32 ], further supporting the involvement of the SN. Therefore, MS7 seems to reflect the activity of the DMN and SN. Taken together, MS5 and MS7 relate to the high-creative ability network described by Beaty and colleagues [ 22 ], characterised by simultaneous engagement of DMN and CEN. The positive association of these two microstates’ activity with creativity observed in the more creative older adults (though it should be noted that the effect for MS7 emerged as a statistical trend and should therefore be treated with caution) agrees with recent literature, indicating that creative thinking requires efficient SN-driven switching between the CEN and the DMN [ 18 , 19 , 20 , 21 , 22 ], thereby confirming our hypothesis. Additionally, for more creative participants, creativity ratings were also higher with lower activation of MS3 (class B) associated with visual processing related to self, self-visualisation, and autobiographical memory [ 24 ]. A similar effect was detected at a tendency level for MS3 and elaboration. At the same time, higher activation of MS3 (class B) was associated with higher fluency and flexibility. This suggests that more creative older participants relied more heavily on their personal memories to produce a greater number of ideas across multiple categories. In contrast, to produce original, novel and useful ideas and describe them in greater detail, they applied fewer autobiographical recollections. Moreover, the tendency-level, positive association between MS5 (class D) activation and elaboration in this group of participants indicates an increased involvement of executive control processes in describing and refining their ideas. We did not observe similar effects regarding creativity in less creative older adults. However, in this group, higher fluency and flexibility were connected to increased activation of MS1 (class E) and decreased activity of MS8 (class G). As previously mentioned, microstate E may resemble the activity of the dorsomedial prefrontal subsystem of the DMN, suggesting involvement in semantic processing [ 33 ]. These findings replicate the results of our previous study with younger adults [ 25 ], suggesting increased MS class E activity with higher fluency. Taken together, higher activity of MS1 for less creative adults may suggest a strategy based on semantic processing or mental simulations for generating more abundant alternative uses from a greater number of categories. MS8 may be associated with the somatosensory network and interoception [ 23 , 24 , 32 ], suggesting that less creative older adults may be distracted by processing bodily sensations, which hinders their ability to generate more numerous and varied ideas in the DT task. Interestingly, MS8 aligned very closely with an MS prototype associated with simultaneous high originality and high fluency in young adults [ 25 ]. This apparent discrepancy may suggest an age-related difference in how interoceptive processes interact with creative cognition. Specifically, it is possible that in older adults, heightened awareness of bodily sensations may be associated with a negative or discomfort-related valence that competes with cognitive resources required for creative idea generation. In contrast, young adults may experience interoceptive signals as more neutral or even positively valenced, allowing these bodily cues to facilitate creative thinking. Such an interpretation aligns with broader evidence indicating that interoception can either enhance or disrupt cognition, including perception, memory, attention, motor action and decision-making. Physiological signals can selectively enhance, interfere with, or suppress information processing across different psychological domains. Interoceptive signals can trigger or modulate cognitive activity, compete for attentional and representational resources, amplify or diminish other sensory inputs, and serve as targets of subjective or metacognitive evaluation [ 34 ]. Additionally, the study’s limitations included a long procedure during which the AUT was performed as the last task. Therefore, participants may have experienced greater cognitive fatigue or discomfort, which could have amplified bodily awareness and contributed to simultaneous higher MS8 activity and lower creative performance. Some of the associations observed in the group of less creative older adults also emerged in the whole sample of participants. This includes positive connections of MS1 and MS8 with both fluency and flexibility. However, these effects were not present in the more creative group. Therefore, their presence in the whole sample likely reflects a statistical phenomenon where a pattern observed within just one subgroup is strong enough to be visible as a main effect for the whole sample, rather than representing a uniform relationship across all participants. Limitations and future directions Certain limitations of the presented study should be acknowledged. The study sample was relatively small and mostly female, which restricts the statistical power to detect subtle effects and limits the generalizability of our findings. To avoid overfitting, we employed relatively simple statistical analyses. For more advanced statistical modeling (e.g., mixed-effects, multivariate, or Bayesian analyses), future studies should include larger samples. However, previous EEG studies investigating creative cognition in similar contexts have relied on comparably modest group sizes [ 35 , 36 , 37 ]. Additionally, recruiting older adults for EEG experiments is particularly challenging for several reasons. Generally, some individuals are reluctant to participate due to misconceptions about the procedure, which they misunderstand as an invasive or uncomfortable medical intervention. Moreover, women constitute the majority at universities for the third age or senior community clubs, where recruitment typically takes place [ 38 ], which explains the lack of gender balance among the participants. Additionally, based on our experience, women (regardless of their age) are more willing to volunteer for research. For all these reasons, the current project should be viewed as a pilot investigation, providing preliminary evidence and laying the groundwork for further research. Although all 38 participants scored within the high range on the MMSE, 15 of them found it challenging to understand or memorize the task instructions, which excluded them from the analysis. This problem could be attributed to physical and/or cognitive fatigue resulting from a lengthy study procedure, which included several cognitive tasks, with the AUT being the last. However, the effectiveness of divergent thinking may reflect general cognitive functioning. A previous study [ 39 ] reports a positive correlation between AUT scores and the results of the Montreal Cognitive Assessment (MoCA) test used to detect mild cognitive impairment (MCI) and early symptoms of dementia. Therefore, prospective studies should consider the AUT as a potential diagnostic tool sensitive to subtle cognitive decline, as observed, for example, in MCI. Last but not least, further investigation of brain network dynamics in older adults during the DT task performance may shed light on how ageing affects cognition, especially in terms of executive functions. A better understanding of this topic may inform therapeutic interventions aimed at preserving and/or enhancing creativity (and possibly general cognitive functioning) later in life. CONCLUSIONS In this pilot study, we examined the brain network dynamics supporting divergent thinking in late adulthood. Employing EEG microstate analysis, we investigated the contributions of large-scale brain networks to idea generation in the Alternative Uses Task (AUT) as a function of individual creativity levels. The findings indicate that more creative older adults predominantly engaged the executive control network (CEN) and the default mode network (DMN) to produce original and novel ideas, while recruiting autobiographical memory processes to generate a larger number of ideas across diverse categories. In contrast, less creative participants exhibited greater involvement of semantic and somatosensory/interoceptive networks, suggesting a stronger role of mental simulation and bodily awareness in their creative processes. Although preliminary, these results offer novel insights into the age-related modulation of neurodynamics underlying creative cognition and establish a foundation for future research on the cognitive and neural mechanisms of creativity across the lifespan. Abbreviations AUT - Alternative Uses Task DT - divergent thinking MMSE - Mini-Mental State Examination Scale HRV-BFB - Heart Rate Variability - Biofeedback EEG - electroencephalography fMRI - functional magnetic resonance imaging MS - microstate DMN - default mode network CEN - central executive network SN - salience network Declarations Ethics approval and consent to participate: The study was approved by the Ethics Committee at the Institute of Psychology, Nicolaus Copernicus University in Toruń, Poland (no. 27/2023/FT). All participants gave written informed consent for participation in the study. Funding: This research was funded by the Centre of Excellence “IMSErt – Interacting Minds, Societies, Environments” within the framework of the “Excellence Initiative – Research University” program at the Nicolaus Copernicus University in Toruń. Author Contribution MO: Conceptualization, Methodology, Data Curation, Software, Formal Analysis, Investigation, Writing – Original Draft, Writing – Review & Editing, Visualization, SupervisionER: Conceptualization, Methodology, Software, Formal Analysis, Writing – Original Draft, Writing – Review & Editing, Visualization, SupervisionBK: Conceptualization, Methodology, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, Supervision, Funding AcquisitionJS: Conceptualization, Methodology, Investigation, Data Curation, Writing – Review & Editing, Project Administration Data Availability Data available upon request. References Kanlı E. Assessment of Creativity: Theories and Methods. In: Creativity - A Force to Innovation. IntechOpen; 2021; doi:10.5772/intechopen.93971 McQuade L, O’Sullivan R. Examining arts and creativity in later life and its impact on older people’s health and wellbeing: a systematic review of the evidence. Perspectives in Public Health. 2023 Mar 11;144(6); doi:10.1177/17579139231157533 Stern Y, Arenaza‐Urquijo EM, Bartrés‐Faz D, Belleville S, Cantilon M, Chetelat G, et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer’s & Dementia. 2020 Jan 6;16(9); doi:10.1016/j.jalz.2018.07.219 Palmiero M, Di Giacomo D, Passafiume D. Can Creativity Predict Cognitive Reserve? The Journal of Creative Behavior. 2014 Apr 15;50(1):7–23; doi:10.1002/JOCB.62 Colombo B, Antonietti A, Daneau B. The Relationships Between Cognitive Reserve and Creativity. A Study on American Aging Population. Frontiers in Psychology. 2018 May 23; 356470; doi:10.3389/FPSYG.2018.00764/BIBTEX Fusi G, Giannì J, Virginia Maria Borsa, Colautti L, Crepaldi M, Palmiero M, et al. Can Creativity and Cognitive Reserve Predict Psychological Well-Being in Older Adults? The Role of Divergent Thinking in Healthy Aging. Healthcare. 2024 Jan 24;12(3):303–3; doi:10.3390/healthcare12030303 Guilford JP. (1967). Creativity: Yesterday, Today and Tomorrow. The Journal of Creative Behavior [Internet]. 1967 Jan;1(1):3–14; doi:10.1002/j.2162-6057.1967.tb00002.x Wilson RC, Guilford JP, Christensen PR. The measurement of individual differences in originality. Psychological Bulletin. 1953;50(5):362–70; doi:10.1037/h0060857 Silvia PJ, Winterstein BP, Willse JT, Barona CM, Cram JT, Hess KI, et al. Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods. Psychology of Aesthetics, Creativity, and the Arts. 2008 May;2(2):68–85; doi:10.1037/1931-3896.2.2.68 Dubec L, Gerver CR, Dennis NA, Beaty RE. Enhancing creative divergent thinking in older adults with a semantic retrieval strategy. Aging Neuropsychology and Cognition. 2024 Oct 17;1–10; doi:10.1080/13825585.2024.2414855 Cancer A, Iannello P, Salvi C, Antonietti A. Executive functioning and divergent thinking predict creative problem-solving in young adults and elderlies. Psychological Research. 2022 Apr 2;87; doi:10.1007/s00426-022-01678-8 Coronel-Oliveros C, Migeot J, Lehue F, Amoruso L, Kowalczyk-Grębska N, Jakubowska N, et al. Creative experiences and brain clocks. Nature Communications. 2025 Oct 3;16(1); doi:10.1038/s41467-025-64173-9 Privodnova EYu, Volf NV. Features of temporal dynamics of oscillatory brain activity during creative problem solving in young and elderly adults. Human Physiology. 2016 Sep;42(5):469–75; doi:10.1134/S0362119716050133 Privodnova EYu, Volf NV, Knyazev GG. The Evaluation of Creative Ideas in Older and Younger Adults. Journal of Psychophysiology. 2020 Jan 1;34(1):19–34; doi: 10.1027/0269-8803/a000232 Privodnova EYu, Volf NV. Figural Creative Task Sculpts the Baseline Resting-State EEG in Older Adults: A Pilot Study. Human Physiology. 2021 Sep;47(5):498–505; doi:10.1134/S0362119721020122 Dietrich A, Kanso R. A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin. 2010;136(5):822–48; doi:10.1037/a0019749 Jung RE, Haier RJ. Creativity and Intelligence: Brain Networks That Link and Differentiate the Expression of Genius. 2013, 233-54; doi:10.7551/mitpress/9780262019583.003.0011 Beaty RE, Benedek M, Silvia PJ, Schacter DL. Creative Cognition and Brain Network Dynamics. Trends in Cognitive Sciences. 2016 Feb;20(2):87–95; doi:10.1016/j.tics.2015.10.004 Beaty RE, Seli P, Schacter DL. Network neuroscience of creative cognition: mapping cognitive mechanisms and individual differences in the creative brain. Current Opinion in Behavioral Sciences. 2019 Jun;27:22–30; doi:10.1016/j.cobeha.2018.08.013 Chen Q, Kenett YN, Cui Z, Takeuchi H, Fink A, Benedek M, et al. Dynamic switching between brain networks predicts creative ability. Communications Biology. 2025 Jan 15;8(1); doi:10.1038/s42003-025-07470-9 Cocchi L, Zalesky A, Fornito A, Mattingley JB. Dynamic cooperation and competition between brain systems during cognitive control. Trends in Cognitive Sciences. 2013 Oct;17(10):493–50; doi:10.1016/j.tics.2013.08.006 Beaty RE, Kenett YN, Christensen AP, Rosenberg MD, Benedek M, Chen Q, et al. Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences. 2018 Jan 16;115(5):1087–92; doi:10.1073/pnas.1713532115 Tarailis P, Šimkutė D, Koenig T, Griškova-Bulanova I. Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach. Journal of Personalized Medicine. 2021 Nov 17;11(11):1216; doi:10.3390/jpm11111216 Povilas Tarailis, Koenig T, Michel CM, Griskova-Bulanova I. The Functional Aspects of Resting EEG Microstates: A Systematic Review. Brain Topography. 2023 May 10; doi:10.1007/s10548-023-00958-9 Ratajczak EK. EEG microstate neurodynamics of divergent thinking. Conference poster; Visual Science of Art Conference; 2024, August 22-24; Aberdeen, Scotland; doi:10.13140/RG.2.2.34622.91207 Folstein MF, Folstein SE, Fanjiang G. Mini-Mental State Examination (MMSE). Polish adaptation: Stańczak J, editor. Warsaw: Psychological Test Laboratory of the Polish Psychological Association; 2013. Alhashim AG, Marshall M, Hartog T, Jonczyk R, Dickson D, van Hell J et al. WIP: Assessing creativity of alternative uses task responses: A detailed procedure. ASEE Annual Conference and Exposition, Conference Proceedings. 2020 Jun 22;2020-June:1656; doi:10.18260/1-2--35612 Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods. 2004 Mar;134(1):9–21; doi:10.1016/j.jneumeth.2003.10.009 Poulsen AT, Pedroni A, Langer N, Hansen LD. Microstate EEGlab toolbox: An introductory guide. bioRxiv (Cold Spring Harbor Laboratory). 2018 Mar 27; doi:10.1101/289850 Koenig T, Diezig S, Sahana Nagabhushan Kalburgi, Antonova E, Fiorenzo Artoni, Bréchet L, et al. EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies. Brain Topography. 2023 Jul 29; doi:10.1007/s10548-023-00993-6 Custo A, Van De Ville D, Wells WM, Tomescu MI, Brunet D, Michel CM. Electroencephalographic Resting-State Networks: Source Localization of Microstates. Brain Connectivity. 2017 Dec;7(10):671–82; doi:10.1089/brain.2016.0476. Ratajczak EK. Microstate neurodynamics in HRV biofeedback [Doctoral dissertation, Nicolaus Copernicus University in Toruń; 2022 Jurewicz K. Sieci spoczynkowe i ich rola w zrozumieniu organizacji funkcjonalnej mózgu. Kosmos. 2020 Apr 11;69(1):105–21; doi:10.36921/kos.2020_2629 Critchley HD. Garfinkel SN. The influence of physiological signals on cognition. Current Opinion in Behavioral Science. 2018 Feb;19:13–8.; doi:10.1016/j.cobeha.2017.08.014 Benedek M, Bergner S, Könen T, Fink A, Neubauer AC. EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia. 2011 Oct;49(12):3505–11; doi:10.1016/j.neuropsychologia.2011.09.004 Grabner RH, Fink A, Neubauer AC. Brain correlates of self-rated originality of ideas: Evidence from event-related power and phase-locking changes in the EEG. Behavioral Neuroscience. 2007;121(1):224–30; doi:10.1037/0735-7044.121.1.224 Lustenberger C, Boyle MR, Foulser AA, Mellin JM, Fröhlich F. Functional role of frontal alpha oscillations in creativity. Cortex. 2015 Jun;67:74–82;doi:10.1016/j.cortex.2015.03.012 Gierszewski D, Kluzowicz J. The role of the University of the Third Age in meeting the needs of older adult learners in Poland. Gerontology & Geriatrics Education. 2021 Jan 11;1–15; doi:10.1080/02701960.2021.1871904 Ross SD, Lachmann T, Saskia Jaarsveld, Riedel-Heller SG, Rodriguez FS. Creativity across the lifespan: changes with age and with dementia. BMC Geriatrics. 2023 Mar 22;23(1); doi:10.1186/s12877-023-03825-1 Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Jan, 2026 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 31 Oct, 2025 Editor invited by journal 20 Oct, 2025 Submission checks completed at journal 17 Oct, 2025 First submitted to journal 17 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7857214","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542851343,"identity":"88c57057-2155-4b29-ade2-1e91b4d26d50","order_by":0,"name":"Martyna Olszewska","email":"data:image/png;base64,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","orcid":"","institution":"Nicolaus Copernicus University","correspondingAuthor":true,"prefix":"","firstName":"Martyna","middleName":"","lastName":"Olszewska","suffix":""},{"id":542851344,"identity":"658e3f03-0c96-442d-9393-32d454b9a308","order_by":1,"name":"Ewa Katarzyna Ratajczak","email":"","orcid":"","institution":"Nicolaus Copernicus University","correspondingAuthor":false,"prefix":"","firstName":"Ewa","middleName":"Katarzyna","lastName":"Ratajczak","suffix":""},{"id":542851345,"identity":"b386dfe1-ac53-46a9-8c93-b6d083ebefb1","order_by":2,"name":"Bartłomiej Kiljanek","email":"","orcid":"","institution":"Nicolaus Copernicus University","correspondingAuthor":false,"prefix":"","firstName":"Bartłomiej","middleName":"","lastName":"Kiljanek","suffix":""},{"id":542851346,"identity":"4489eaf7-d76f-4c81-b873-4ff439aa4a64","order_by":3,"name":"Jakub Słupczewski","email":"","orcid":"","institution":"Nicolaus Copernicus University","correspondingAuthor":false,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Słupczewski","suffix":""}],"badges":[],"createdAt":"2025-10-14 10:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7857214/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7857214/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96049168,"identity":"8f6d83e8-a072-4cd9-941d-d7494d28b59a","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":301388,"visible":true,"origin":"","legend":"","description":"","filename":"CreativityNeurodynamicsandAgeingamicrostateEEGpilotstudy.3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/48b3ec699c0a3b3172acc4d2.docx"},{"id":96247829,"identity":"145e8a18-dbbd-4364-bfa4-d92e2071f24c","added_by":"auto","created_at":"2025-11-19 07:27:45","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6015,"visible":true,"origin":"","legend":"","description":"","filename":"06a9dfdefaa24b17b66bdfe6b33f149b.json","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/010df05de5dbb0aa2bf2b9c9.json"},{"id":96246001,"identity":"a35bc7cc-f58b-4a04-9973-3488a83ac279","added_by":"auto","created_at":"2025-11-19 07:24:07","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186772,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/b24252cc149e36da2e6e7b20.pdf"},{"id":96049172,"identity":"b03840c1-4f6d-4bc6-8161-a65f381eef8a","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107383,"visible":true,"origin":"","legend":"","description":"","filename":"06a9dfdefaa24b17b66bdfe6b33f149b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/9171ce95b226a9849b2790b6.xml"},{"id":96049170,"identity":"d44047dc-b84d-4286-bc43-eaaa96a5ea90","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133648,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/0622b26899135a1dfee4f0cf.jpeg"},{"id":96049173,"identity":"c3b13ef5-a460-4703-a4c2-26432dcaa0d1","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203839,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/2ec200e6243184a921fec1c0.jpeg"},{"id":96049176,"identity":"34b5d683-8725-424b-931f-8a47f17cda28","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79856,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/865951b3e4cb928fa1e7903a.png"},{"id":96049177,"identity":"9013c59f-9389-4bae-abe0-62991e5e08f2","added_by":"auto","created_at":"2025-11-17 06:30:47","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164275,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/0aaf588e040c482e769b3b5a.png"},{"id":96049174,"identity":"3ab6c693-f0bc-4e17-8196-4be1c8369a2c","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104500,"visible":true,"origin":"","legend":"","description":"","filename":"06a9dfdefaa24b17b66bdfe6b33f149b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/8d26d0886816bce6e3c8823c.xml"},{"id":96049175,"identity":"7e18cfd2-cd04-4dc3-a1af-eab743d0932d","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116774,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/fdfea732fbbc65eae973ae86.html"},{"id":96049164,"identity":"4e44bb5f-8fed-44d2-b700-6c7470966b94","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":199510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic representation of the computerised AUT adapted for EEG studies.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/960ed62e4d3f4737cff51933.jpeg"},{"id":96049162,"identity":"44346b0f-9028-4715-9967-78f8c10e6315","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAlignment of the eight microstate prototypes detected in the EEG signal corresponding to AUT idea generation to maps from previous studies.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/4d48a00ab48887c5acaea1c1.jpeg"},{"id":96452931,"identity":"79960f77-1509-4cf6-b172-e60586183fdc","added_by":"auto","created_at":"2025-11-21 09:55:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1026198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/bcaf9906-9bc2-4f84-a300-d3a0935010a9.pdf"},{"id":96049166,"identity":"11136547-a25c-4dc7-aa1c-81b5e2793f67","added_by":"auto","created_at":"2025-11-17 06:30:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":186772,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7857214/v1/22ad7b4416aa956d210dead2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Creativity Neurodynamics and Ageing: a microstate EEG pilot study.","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCreativity has long been studied across various age groups, from children and adolescents to younger and older adults, with the latter receiving a growing recognition nowadays. Prior research has established that creativity is a combination of cognitive, conative, and emotional factors interacting dynamically with the environment [1]. This perspective has led to an increasing interest in how creative skills can contribute to psychological well-being and cognitive functioning in late adulthood. Recent studies highlight several benefits of creative activities for older adults. For example,\u0026nbsp;McQuade and O\u0026rsquo;Sullivan [2] found that participation in artistic and creative group activities has a positive impact on their physical, mental, and social health.\u0026nbsp;Additionally, some studies have been conducted that link creativity to cognitive reserve - the ability to adapt, optimise, and maximise the brain\u0026apos;s performance to cope with and minimise the effects of damage caused by brain pathology, such as neurodegenerative diseases [3]. For instance, Palmiero and colleagues [4] found that\u0026nbsp;verbal creativity can serve as a predictor of cognitive reserve. There is also evidence that individuals working in creative professions, such as art gallery managers or musicians, achieve higher scores on measures of cognitive reserve [5]. Moreover, a recent study by Fusi and colleagues [6] not only supported that creativity predicts higher cognitive reserve but also pointed out that this relationship mediates a positive effect on psychological well-being. Despite growing evidence for the beneficial role of creativity in supporting mental and cognitive health in late adulthood, little is known about the underlying neural mechanisms.\u0026nbsp;To address this gap, we present findings from a pilot study involving older adults, focusing on the neurodynamics of creativity operationalised as divergent thinking (DT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasuring creativity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study cognitive aspects of creativity as well as its neural mechanisms, several tasks have been widely used, such as the Alternative Uses Task\u0026nbsp;(AUT). This tool focuses specifically on DT, described by Guilford as the ability to generate many ideas in a broad context [7]. The AUT allows for the assessment of idea creativity as originality (defined as a combination of uncommonness, remoteness of association, and cleverness of each idea), as well as fluency (no. of valid ideas), flexibility (no. of categories spanning the ideas generated), and elaboration (no. of meaningful words used to describe each idea) [7, 8, 9]. Among older adults, the AUT has been employed, for instance, while designing cognitive training based on a semantic retrieval strategy to improve creative DT [10]. Additionally, based on the AUT performance, Cancer and colleagues [11] revealed that executive functioning and DT are related to efficient creative problem-solving by older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeural Mechanisms of Creativity\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVarious studies (see below) have extended the focus on creative cognition by utilising neuroimaging techniques, including functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, studies on the role of creative performance and expertise in healthy ageing are only getting traction. A recent study has reported that creative experience is associated with delayed brain ageing and increased neural connectivity in creativity-related areas, resulting in neuroplasticity-driven enhanced brain efficiency [12]. Yet, to our best knowledge, there is still little objective, neuroimaging evidence on brain activity during creative task performance in older adults. Among these, only a few studies have employed EEG neuroimaging based on the analysis of oscillatory frequency bands in brain waves. Compared to younger adults, the elderly exhibited smaller theta and alpha desynchronization in frontal and parieto-occipital areas, respectively, possibly related to different AUT problem-solving strategies employed [13]. Another study has shown differences between young and older adults in theta synchronisation and beta desynchronisation during AUT performance [14]. Further studies have focused on neuroplasticity-related post-AUT task neuronal traces detected in the delta\u0026ndash;gamma rhythms, mostly in the parieto\u0026ndash;occipital areas [15], as well as the interhemispheric asymmetry of AUT task-related alpha desynchronization in the elderly [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile these results provide valuable insights, an analysis of oscillatory frequency bands offers a relatively limited view of the complex neural dynamics involved in the creative process. A detailed review of the neural underpinnings of creativity has concluded that creative thinking does not rely on any particular mental process, such as previously hypothesised defocused attention, low arousal, or alpha synchronisation, and therefore it is not associated with any specific brain region [16]. Therefore, the creative process should be studied\u0026nbsp;by monitoring large-scale brain network activity, which allows for examining concerted activity throughout the entire brain rather than changes in isolated frequency bands in specific brain regions [17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsequently, using fMRI neuroimaging, recent research has linked creative performance to brain network dynamics, indicating the cooperation of the Default Mode Network (DMN) and Central Executive Network (CEN) during creative cognition [18] Functional connectivity between these two networks has been shown to predict individual creative thinking skills [19].\u0026nbsp;These findings have strong support in several replication studies and meta-analyses [20]. Moreover, the Salience Network (SN) has been considered to contribute to the creative process by facilitating switches between the DMN and CEN, aiding their successive cooperation\u0026nbsp;[21; 22].\u003c/p\u003e\n\u003cp\u003eRecent advances in EEG microstate methodology enable a similar visualisation of large network activity in EEG neuroimaging [23, 24]. Our recent research, employing EEG microstate analysis, has shown that compared to less creative individuals, the more creative group exhibits stronger activity of microstates associated with the DMN, CEN, and SN, while the activation of microstates linked to sensory networks is weaker [25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary objective of the study was to investigate brain network dynamics during the performance of AUT in older adults. Based on the results of previously mentioned neuroimaging studies, we hypothesise that more creative older adults will display higher DMN, CEN and SN activity during the DT task performance. This pilot study aims at a preliminary analysis of neural mechanisms involved in creative thinking in late adulthood as a part of a larger ongoing project investigating various factors of cognitive performance in the ageing population.\u0026nbsp;\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003e\u003cstrong\u003eParticipants and Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThirty-eight elderly adults \u0026nbsp;(86.6% female) aged 63 - 79 (70.82 \u0026plusmn; .92) participated in the study carried out in the Electrophysiology and Neurotherapy Lab at the Institute of Psychology, Nicolaus Copernicus University in Toruń, Poland. The inclusion criteria required participants to be over the age of 60 and within the cognitive norm (min. 26 points on the Polish adaptation of Mini-Mental State Examination Scale, MMSE [26]). Exclusion criteria comprised uncorrected vision or hearing problems, neurological or psychiatric problems related to the presence and/or treatment of disorders selected from the literature (such as depression or epilepsy) or abuse of psychoactive substances such as drugs and alcohol. Fifteen participants were excluded due to incorrect performance on the creative task, likely because they did not understand, misunderstood, or forgot the instructions. The final sample consisted of twenty-three elderly adults (95.7% female) aged 63 - 79 (70.87 \u0026plusmn; 4.40).\u003c/p\u003e\n\u003cp\u003eUpon arrival at the lab, the volunteers gave written informed consent for participation in the study and were prepared for EEG registration. Brain activity was recorded during resting-state conditions (5 min. eyes open and 5 min. eyes closed), and cognitive tasks, including the dual-choice oddball task (6-7 min.), the picture 2-back task (6-7 min.), and the AUT paradigm adapted for EEG research (15 min.). For the purpose of this study, only the AUT results and related neurodynamics were analysed. Following EEG registration, the volunteers obtained remuneration for their participation in the form of gift vouchers worth 100 PLN (~23.3 EUR, ~25 USD). The study was approved by the Ethics Committee at the Institute of Psychology, Nicolaus Copernicus University in Toruń, Poland (no. 27/2023/FT). Research was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCreativity Assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreativity, operationalised as DT, was assessed with a computerised Polish AUT adapted for EEG studies. The participants obtained the following instruction: \u0026rsquo;List as many unusual, original uses for an object of everyday use as you can think of.\u0026rsquo; The following examples were provided: \u0026lsquo;hammer - can be used as a nutcracker,\u0026rsquo; \u0026lsquo;hammer - a paperweight preventing papers from flying away with the wind.\u0026rsquo; Three consecutive names of everyday objects (\u0026lsquo;umbrella\u0026rsquo;, \u0026lsquo;soap\u0026rsquo;, and \u0026lsquo;pen\u0026rsquo;) were presented on the screen for five minutes each using Presentation\u0026reg; software (Version 24.0, Neurobehavioral Systems, Inc., Berkeley, CA, ⁦www.neurobs.com⁩). To mark the exact moment of idea generation and remove speech-related artefacts from the EEG signal, the participants were instructed to click a mouse button as soon as they thought of an alternative use for a given object, vocalise their idea, and click again once finished talking (Fig. 1).\u003c/p\u003e\n\u003cp\u003eWe assessed the creative performance of each participant based on four DT factors: idea creativity (idea originality), fluency, flexibility, and idea elaboration. First, unintelligible and nonsensical ideas were removed, resulting in a total of 676 responses included in the analysis. The remaining uses were coded (categorised) by two human raters, which aided creativity assessment and allowed for calculating flexibility. Next, four human judges (50% female) assessed the creativity and elaboration of each idea. Creativity was evaluated according to the guidelines derived from Guilford\u0026rsquo;s definition of creativity [8] based on three features: uncommonness (unusual, original), remoteness of association, and cleverness (useful, reasonable, functional), which may partially compensate for each other. Creativity of each alternative idea was rated on a scale from 1 to 5, while all typical uses were assigned a score of 0. Idea elaboration was evaluated based on the number of meaningful words [27], excluding general words such as prepositions, pronouns, conjunctions, etc. For each idea, creativity and elaboration ratings were first averaged across judges (idea-level scores) and then across all uses for a given object (object-level scores). Fluency and flexibility were calculated for each object (object-level scores), as the number of ideas and the number of different categories of ideas (codes) assigned, respectively. Finally, creativity, fluency, flexibility, and elaboration scores for each participant (participant-level scores) were calculated as the mean of object-level scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEEG Microstates Analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe EEG signal was recorded using the actiCAP system (Brain Products GmbH, Gilching, Germany) with 64 channels placed according to the international 5\u0026ndash;10 system (FCz reference electrode, AFz ground electrode). The EEG recordings were processed using the EEGLAB toolbox (ver. 2019.1) [28] for MATLAB (ver. R2024b, Mathworks Inc., Natick, MA). Prior to any signal processing, for each participant, all EEG fragments corresponding to giving answers were removed (due to speech artefacts). Next, we subjected the signal to the following pre-processing steps: demeaning, filtering (high-pass \u0026gt; 1 Hz, low-pass \u0026lt; 40 Hz), downsampling (from 1 kHz to 256 Hz), re-referencing (to average), marking artefactual ICA components in an automated manner (using the ICLabel EEGLAB plug-in), and removing bad epochs (with amplitudes \u0026gt; 111 \u0026mu;V). Finally, for each participant, we selected a 30-second continuous (uncut) EEG fragment from the pre-processed EEG signal and performed microstate analysis using the Microstate Toolbox EEGLAB plug-in [29]. 1000 GFP maxima chosen from each EEG fragment (excluding peaks exceeding one standard deviation) were subjected to modified k-means clustering (3-15 clusters, 10 repetitions, max. 1000 iterations). Following GMD-based backfitting with 30 ms smoothing, microstate statistics were calculated for each class detected, including: occurrence (appearances per second), average duration, and coverage (percent of the signal assigned to a given class).\u003c/p\u003e\n\u003cp\u003eArtificial intelligence tools were used exclusively for proofreading and language refinement, as well as for creating illustrative icons used in the procedure schema.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eMicrostate analysis revealed eight microstate (MS) classes. The prototypes were aligned using the Template Explorer MATLAB add-on [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and visual inspection with maps described in previous EEG microstate studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) focused on large brain networks [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], source localisation during resting state and Heart Rate Variability Biofeedback (HRV-BFB) training [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and an AUT study with a younger population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Based on the same studies, names and functions were assigned for each microstate class detected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eAssigned names and functions of the microstate classes detected and their similarity to prototypes described in previous studies.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSIII\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSIV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAssigned function/large network\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eprocessing personally significant information, mental simulations, and theory of mind, associated with increased fluency in AUT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eprocessing personally significant information, self-reflection, and self-referential internal mentation, associated with lower fluency in AUT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eassociated with visual processing related to self, self-visualisation, and autobiographical memory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eassociated with auditory and visual processing and arousal/arousability, associated with decreased fluency and originality in AUT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG/F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003epsychophysiological and emotional regulation during HRV-BFB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eassociated with executive functioning, associated with increased originality in AUT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eprocessing interoceptive and emotional information, and the salience network\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMS7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE/F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003epsychophysological and emotional control after HRV-BFB, associated with simultaneous high originality and high fluency in AUT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eprocessing personally significant information, mental simulations, and the theory of mind\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eprocessing interoceptive and emotional information, and the salience network\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ethe somatosensory network, interoception, somatosensory regulation during HRV-BFB, associated with increased fluency and simultaneous high originality and high fluency in AUT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: SI - Custo et al., 2017; SII - Tarailis et al., 2021; SIII - Ratajczak, 2022; SIV - Ratajczak, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using IBM SPSS Statistics (ver. 29.0.2). Based on the median value of average creativity (\u003cem\u003eMed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.95), the participants were first divided into more (N\u0026thinsp;=\u0026thinsp;11) and less (N\u0026thinsp;=\u0026thinsp;12) creative. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays average creativity, fluency, flexibility, and average elaboration in the whole sample and compares their values between the more and less creative groups using t-tests with 95% BCa bootstrap confidence intervals (2000 random samples).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eAverage creativity, fluency, flexibility, and average elaboration in the whole sample and compared between the more and less creative groups.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhole sample \u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLess creative \u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMore creative\u003c/p\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(21)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% BCa CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreativity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85(.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.52(.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.21(.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.50***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.91\u0026ndash; \u0026minus;\u0026thinsp;.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFluency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.17(5.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.06(6.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.39(4.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-6.31\u0026ndash;2.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlexibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.13(3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.78(3.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.61(2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.16*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-5.07 \u0026ndash; \u0026minus;\u0026thinsp;.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElaboration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.93(1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.39(.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.51(2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.58 \u0026ndash; .07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e* \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe performed an independent-samples t-test analysis to compare microstate class parameters between more and less creative groups, but found no significant differences. We then performed a Pearson\u0026rsquo;s correlation analysis between class parameters and creativity ratings for the entire sample, as well as for both participant groups. In addition to reporting statistical significance, we provide effect sizes (Pearson\u0026rsquo;s r) and 95% confidence intervals derived from bootstrap resampling (2000 random samples). We also report trends (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.10) as exploratory findings, in line with the hypothesis-generating character of pilot research. A full table of correlations is provided in the Supplementary Materials [see Additional file 1].\u003c/p\u003e\u003cp\u003eFor the whole sample, we found a moderately significant positive correlation between fluency and the duration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.45, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.030) and coverage (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.032) of MS1, with occurrence showing a tendency to significance (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.078). We have also observed a tendency for a positive, moderate relationship between flexibility and measures of MS1, including duration (r\u0026thinsp;=\u0026thinsp;.38, p\u0026thinsp;=\u0026thinsp;.072), and coverage (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.062). On the other hand, we found a negative, moderate tendency between the occurrence of MS8 and both fluency (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.37, p\u0026thinsp;=\u0026thinsp;.083) and flexibility (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.093). Moreover, further significant correlations emerged for both groups when analysed separately.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCreativity.\u003c/em\u003e For more creative older adults, we found a strong, negative correlation between creativity ratings and all three MS3 parameters, including occurrence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.87, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), duration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.028) and coverage (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). There was also a significant, strong, positive correlation with the coverage of MS5 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.039), while occurrence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.086) and duration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.058) showed a trend toward significance. We\u0026rsquo;ve also observed a moderate, positive relationship with the coverage of MS7 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.083), approaching statistical significance. No significant correlations were found for the less creative group.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFluency.\u003c/em\u003e In the more creative group, fluency scores showed a strong positive relationship with the parameters of MS3, including occurrence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.032), duration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011) and coverage (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011). For less creative participants, we\u0026rsquo;ve observed a strong positive correlation between fluency score and the parameters of MS1, including duration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.010) and coverage (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022), with occurrence approaching significance (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.069). Moreover, we found a negative, significant correlation between fluency and duration of MS8 (\u003cem\u003er\u003c/em\u003e = -61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.035) and a marginally significant correlation with MS8 occurrence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.058) in this group.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFlexibility.\u003c/em\u003e For the more creative individuals, we observed a strong, positive correlation between flexibility scores and occurrence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020), duration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004) and coverage (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005) of MS3. We found a strong, positive correlation for less creative participants with occurrence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.042); duration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006) and coverage (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011) of MS1. There was also a strong negative correlation with the occurrence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.039) and duration (\u003cem\u003er\u003c/em\u003e = -70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011) of MS8, while coverage showed a tendency for significance (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.073).\u003c/p\u003e\u003cp\u003e\u003cem\u003eElaboration.\u003c/em\u003e No significant correlations were found for any of the study samples. However, for more creative participants, a moderate, negative relationship was found between elaboration and the occurrence of MS3 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.084) and a moderate, positive connection with the occurrence of MS5 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.063).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study explored brain network dynamics of divergent thinking during AUT performance in late adulthood. Using EEG microstate neuroimaging, we found that for more creative older adults, producing more creative ideas required higher activation of MS5 (class D) and MS7 (class E/F). The former is associated with executive functioning (CEN) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while the latter appears to be novel. MS7 topography resembles microstates class E, which is associated with processing personally significant information, mental simulations, and the theory of mind [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Jurewicz [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] linked these functions to the activity of the dorsomedial prefrontal subsystem of the DMN, which is responsible for semantic processing. However, the topography of MS7 is also highly similar to that of microstate class F, which is related to interoceptive and emotional information processing and SN activity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. At the same time, the MS7 prototype aligned closely with microstates described in our previous studies, linked to simultaneous high originality and high fluency in AUT [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], as well as psychophysiological and emotional control [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], further supporting the involvement of the SN. Therefore, MS7 seems to reflect the activity of the DMN and SN. Taken together, MS5 and MS7 relate to the high-creative ability network described by Beaty and colleagues [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], characterised by simultaneous engagement of DMN and CEN. The positive association of these two microstates\u0026rsquo; activity with creativity observed in the more creative older adults (though it should be noted that the effect for MS7 emerged as a statistical trend and should therefore be treated with caution) agrees with recent literature, indicating that creative thinking requires efficient SN-driven switching between the CEN and the DMN [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], thereby confirming our hypothesis.\u003c/p\u003e\u003cp\u003eAdditionally, for more creative participants, creativity ratings were also higher with lower activation of MS3 (class B) associated with visual processing related to self, self-visualisation, and autobiographical memory [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A similar effect was detected at a tendency level for MS3 and elaboration. At the same time, higher activation of MS3 (class B) was associated with higher fluency and flexibility. This suggests that more creative older participants relied more heavily on their personal memories to produce a greater number of ideas across multiple categories. In contrast, to produce original, novel and useful ideas and describe them in greater detail, they applied fewer autobiographical recollections. Moreover, the tendency-level, positive association between MS5 (class D) activation and elaboration in this group of participants indicates an increased involvement of executive control processes in describing and refining their ideas.\u003c/p\u003e\u003cp\u003eWe did not observe similar effects regarding creativity in less creative older adults. However, in this group, higher fluency and flexibility were connected to increased activation of MS1 (class E) and decreased activity of MS8 (class G). As previously mentioned, microstate E may resemble the activity of the dorsomedial prefrontal subsystem of the DMN, suggesting involvement in semantic processing [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These findings replicate the results of our previous study with younger adults [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], suggesting increased MS class E activity with higher fluency. Taken together, higher activity of MS1 for less creative adults may suggest a strategy based on semantic processing or mental simulations for generating more abundant alternative uses from a greater number of categories.\u003c/p\u003e\u003cp\u003eMS8 may be associated with the somatosensory network and interoception [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], suggesting that less creative older adults may be distracted by processing bodily sensations, which hinders their ability to generate more numerous and varied ideas in the DT task. Interestingly, MS8 aligned very closely with an MS prototype associated with simultaneous high originality and high fluency in young adults [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This apparent discrepancy may suggest an age-related difference in how interoceptive processes interact with creative cognition. Specifically, it is possible that in older adults, heightened awareness of bodily sensations may be associated with a negative or discomfort-related valence that competes with cognitive resources required for creative idea generation. In contrast, young adults may experience interoceptive signals as more neutral or even positively valenced, allowing these bodily cues to facilitate creative thinking. Such an interpretation aligns with broader evidence indicating that interoception can either enhance or disrupt cognition, including perception, memory, attention, motor action and decision-making. Physiological signals can selectively enhance, interfere with, or suppress information processing across different psychological domains. Interoceptive signals can trigger or modulate cognitive activity, compete for attentional and representational resources, amplify or diminish other sensory inputs, and serve as targets of subjective or metacognitive evaluation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, the study\u0026rsquo;s limitations included a long procedure during which the AUT was performed as the last task. Therefore, participants may have experienced greater cognitive fatigue or discomfort, which could have amplified bodily awareness and contributed to simultaneous higher MS8 activity and lower creative performance.\u003c/p\u003e\u003cp\u003eSome of the associations observed in the group of less creative older adults also emerged in the whole sample of participants. This includes positive connections of MS1 and MS8 with both fluency and flexibility. However, these effects were not present in the more creative group. Therefore, their presence in the whole sample likely reflects a statistical phenomenon where a pattern observed within just one subgroup is strong enough to be visible as a main effect for the whole sample, rather than representing a uniform relationship across all participants.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future directions\u003c/h2\u003e\u003cp\u003eCertain limitations of the presented study should be acknowledged. The study sample was relatively small and mostly female, which restricts the statistical power to detect subtle effects and limits the generalizability of our findings. To avoid overfitting, we employed relatively simple statistical analyses. For more advanced statistical modeling (e.g., mixed-effects, multivariate, or Bayesian analyses), future studies should include larger samples. However, previous EEG studies investigating creative cognition in similar contexts have relied on comparably modest group sizes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, recruiting older adults for EEG experiments is particularly challenging for several reasons. Generally, some individuals are reluctant to participate due to misconceptions about the procedure, which they misunderstand as an invasive or uncomfortable medical intervention. Moreover, women constitute the majority at universities for the third age or senior community clubs, where recruitment typically takes place [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which explains the lack of gender balance among the participants. Additionally, based on our experience, women (regardless of their age) are more willing to volunteer for research. For all these reasons, the current project should be viewed as a pilot investigation, providing preliminary evidence and laying the groundwork for further research.\u003c/p\u003e\u003cp\u003eAlthough all 38 participants scored within the high range on the MMSE, 15 of them found it challenging to understand or memorize the task instructions, which excluded them from the analysis. This problem could be attributed to physical and/or cognitive fatigue resulting from a lengthy study procedure, which included several cognitive tasks, with the AUT being the last. However, the effectiveness of divergent thinking may reflect general cognitive functioning. A previous study [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] reports a positive correlation between AUT scores and the results of the Montreal Cognitive Assessment (MoCA) test used to detect mild cognitive impairment (MCI) and early symptoms of dementia. Therefore, prospective studies should consider the AUT as a potential diagnostic tool sensitive to subtle cognitive decline, as observed, for example, in MCI. Last but not least, further investigation of brain network dynamics in older adults during the DT task performance may shed light on how ageing affects cognition, especially in terms of executive functions. A better understanding of this topic may inform therapeutic interventions aimed at preserving and/or enhancing creativity (and possibly general cognitive functioning) later in life.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn this pilot study, we examined the brain network dynamics supporting divergent thinking in late adulthood. Employing EEG microstate analysis, we investigated the contributions of large-scale brain networks to idea generation in the Alternative Uses Task (AUT) as a function of individual creativity levels. The findings indicate that more creative older adults predominantly engaged the executive control network (CEN) and the default mode network (DMN) to produce original and novel ideas, while recruiting autobiographical memory processes to generate a larger number of ideas across diverse categories. In contrast, less creative participants exhibited greater involvement of semantic and somatosensory/interoceptive networks, suggesting a stronger role of mental simulation and bodily awareness in their creative processes. Although preliminary, these results offer novel insights into the age-related modulation of neurodynamics underlying creative cognition and establish a foundation for future research on the cognitive and neural mechanisms of creativity across the lifespan.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUT - Alternative Uses Task\u003c/p\u003e\n\u003cp\u003eDT - divergent thinking\u003c/p\u003e\n\u003cp\u003eMMSE - Mini-Mental State Examination Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHRV-BFB - Heart Rate Variability - Biofeedback\u003c/p\u003e\n\u003cp\u003eEEG - electroencephalography\u003c/p\u003e\n\u003cp\u003efMRI - functional magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eMS - microstate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDMN - default mode network\u003c/p\u003e\n\u003cp\u003eCEN - central executive network\u003c/p\u003e\n\u003cp\u003eSN - salience network\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e\u003cp\u003eThe study was approved by the Ethics Committee at the Institute of Psychology, Nicolaus Copernicus University in Toruń, Poland (no. 27/2023/FT). All participants gave written informed consent for participation in the study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was funded by the Centre of Excellence \u0026ldquo;IMSErt \u0026ndash; Interacting Minds, Societies, Environments\u0026rdquo; within the framework of the \u0026ldquo;Excellence Initiative \u0026ndash; Research University\u0026rdquo; program at the Nicolaus Copernicus University in Toruń.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMO: Conceptualization, Methodology, Data Curation, Software, Formal Analysis, Investigation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization, SupervisionER: Conceptualization, Methodology, Software, Formal Analysis, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization, SupervisionBK: Conceptualization, Methodology, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Supervision, Funding AcquisitionJS: Conceptualization, Methodology, Investigation, Data Curation, Writing \u0026ndash; Review \u0026amp; Editing, Project Administration\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eKanlı E. Assessment of Creativity: Theories and Methods. In: Creativity - A Force to Innovation. IntechOpen; 2021; doi:10.5772/intechopen.93971\u003c/li\u003e\n \u003cli\u003eMcQuade L, O\u0026rsquo;Sullivan R. Examining arts and creativity in later life and its impact on older people\u0026rsquo;s health and wellbeing: a systematic review of the evidence. Perspectives in Public Health. 2023 Mar 11;144(6); doi:10.1177/17579139231157533\u003c/li\u003e\n \u003cli\u003eStern Y, Arenaza‐Urquijo EM, Bartr\u0026eacute;s‐Faz D, Belleville S, Cantilon M, Chetelat G, et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimer\u0026rsquo;s \u0026amp; Dementia. 2020 Jan 6;16(9); doi:10.1016/j.jalz.2018.07.219\u003c/li\u003e\n \u003cli\u003ePalmiero M, Di Giacomo D, Passafiume D. Can Creativity Predict Cognitive Reserve? The Journal of Creative Behavior. 2014 Apr 15;50(1):7\u0026ndash;23; doi:10.1002/JOCB.62\u003c/li\u003e\n \u003cli\u003eColombo B, Antonietti A, Daneau B. The Relationships Between Cognitive Reserve and Creativity. A Study on American Aging Population. Frontiers in Psychology. 2018 May 23; 356470; doi:10.3389/FPSYG.2018.00764/BIBTEX\u003c/li\u003e\n \u003cli\u003eFusi G, Giann\u0026igrave; J, Virginia Maria Borsa, Colautti L, Crepaldi M, Palmiero M, et al. Can Creativity and Cognitive Reserve Predict Psychological Well-Being in Older Adults? The Role of Divergent Thinking in Healthy Aging. Healthcare. 2024 Jan 24;12(3):303\u0026ndash;3; doi:10.3390/healthcare12030303\u003c/li\u003e\n \u003cli\u003eGuilford JP. (1967). Creativity: Yesterday, Today and Tomorrow. The Journal of Creative Behavior [Internet]. 1967 Jan;1(1):3\u0026ndash;14; doi:10.1002/j.2162-6057.1967.tb00002.x\u003c/li\u003e\n \u003cli\u003eWilson RC, Guilford JP, Christensen PR. The measurement of individual differences in originality. Psychological Bulletin. 1953;50(5):362\u0026ndash;70; doi:10.1037/h0060857\u003c/li\u003e\n \u003cli\u003eSilvia PJ, Winterstein BP, Willse JT, Barona CM, Cram JT, Hess KI, et al. Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods. Psychology of Aesthetics, Creativity, and the Arts. 2008 May;2(2):68\u0026ndash;85; doi:10.1037/1931-3896.2.2.68\u003c/li\u003e\n \u003cli\u003eDubec L, Gerver CR, Dennis NA, Beaty RE. Enhancing creative divergent thinking in older adults with a semantic retrieval strategy. Aging Neuropsychology and Cognition. 2024 Oct 17;1\u0026ndash;10; doi:10.1080/13825585.2024.2414855\u003c/li\u003e\n \u003cli\u003eCancer A, Iannello P, Salvi C, Antonietti A. Executive functioning and divergent thinking predict creative problem-solving in young adults and elderlies. Psychological Research. 2022 Apr 2;87; doi:10.1007/s00426-022-01678-8\u003c/li\u003e\n \u003cli\u003eCoronel-Oliveros C, Migeot J, Lehue F, Amoruso L, Kowalczyk-Grębska N, Jakubowska N, et al. Creative experiences and brain clocks. Nature Communications. 2025 Oct 3;16(1); doi:10.1038/s41467-025-64173-9\u003c/li\u003e\n \u003cli\u003ePrivodnova EYu, Volf NV. Features of temporal dynamics of oscillatory brain activity during creative problem solving in young and elderly adults. Human Physiology. 2016 Sep;42(5):469\u0026ndash;75; doi:10.1134/S0362119716050133\u003c/li\u003e\n \u003cli\u003ePrivodnova EYu, Volf NV, Knyazev GG. The Evaluation of Creative Ideas in Older and Younger Adults. Journal of Psychophysiology. 2020 Jan 1;34(1):19\u0026ndash;34; doi: 10.1027/0269-8803/a000232\u003c/li\u003e\n \u003cli\u003ePrivodnova EYu, Volf NV. Figural Creative Task Sculpts the Baseline Resting-State EEG in Older Adults: A Pilot Study. Human Physiology. 2021 Sep;47(5):498\u0026ndash;505; doi:10.1134/S0362119721020122\u003c/li\u003e\n \u003cli\u003eDietrich A, Kanso R. A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin. 2010;136(5):822\u0026ndash;48; doi:10.1037/a0019749\u003c/li\u003e\n \u003cli\u003eJung RE, Haier RJ. Creativity and Intelligence: Brain Networks That Link and Differentiate the Expression of Genius. 2013, 233-54; doi:10.7551/mitpress/9780262019583.003.0011\u003c/li\u003e\n \u003cli\u003eBeaty RE, Benedek M, Silvia PJ, Schacter DL. Creative Cognition and Brain Network Dynamics. Trends in Cognitive Sciences. 2016 Feb;20(2):87\u0026ndash;95; doi:10.1016/j.tics.2015.10.004\u003c/li\u003e\n \u003cli\u003eBeaty RE, Seli P, Schacter DL. Network neuroscience of creative cognition: mapping cognitive mechanisms and individual differences in the creative brain. Current Opinion in Behavioral Sciences. 2019 Jun;27:22\u0026ndash;30; doi:10.1016/j.cobeha.2018.08.013\u003c/li\u003e\n \u003cli\u003eChen Q, Kenett YN, Cui Z, Takeuchi H, Fink A, Benedek M, et al. Dynamic switching between brain networks predicts creative ability. Communications Biology. 2025 Jan 15;8(1); doi:10.1038/s42003-025-07470-9\u003c/li\u003e\n \u003cli\u003eCocchi L, Zalesky A, Fornito A, Mattingley JB. Dynamic cooperation and competition between brain systems during cognitive control. Trends in Cognitive Sciences. 2013 Oct;17(10):493\u0026ndash;50; doi:10.1016/j.tics.2013.08.006\u003c/li\u003e\n \u003cli\u003eBeaty RE, Kenett YN, Christensen AP, Rosenberg MD, Benedek M, Chen Q, et al. Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences. 2018 Jan 16;115(5):1087\u0026ndash;92; doi:10.1073/pnas.1713532115\u003c/li\u003e\n \u003cli\u003eTarailis P, \u0026Scaron;imkutė D, Koenig T, Gri\u0026scaron;kova-Bulanova I. Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach. Journal of Personalized Medicine. 2021 Nov 17;11(11):1216; doi:10.3390/jpm11111216\u003c/li\u003e\n \u003cli\u003ePovilas Tarailis, Koenig T, Michel CM, Griskova-Bulanova I. The Functional Aspects of Resting EEG Microstates: A Systematic Review. Brain Topography. 2023 May 10; doi:10.1007/s10548-023-00958-9\u003c/li\u003e\n \u003cli\u003eRatajczak EK. EEG microstate neurodynamics of divergent thinking. Conference poster; Visual Science of Art Conference; \u0026nbsp;2024, August 22-24; Aberdeen, Scotland; doi:10.13140/RG.2.2.34622.91207\u003c/li\u003e\n \u003cli\u003eFolstein MF, Folstein SE, Fanjiang G. Mini-Mental State Examination (MMSE). Polish adaptation: Stańczak J, editor. Warsaw: Psychological Test Laboratory of the Polish Psychological Association; 2013.\u003c/li\u003e\n \u003cli\u003eAlhashim AG, Marshall M, Hartog T, Jonczyk R, Dickson D, van Hell J et al. WIP: Assessing creativity of alternative uses task responses: A detailed procedure. ASEE Annual Conference and Exposition, Conference Proceedings. 2020 Jun 22;2020-June:1656; doi:10.18260/1-2--35612\u003c/li\u003e\n \u003cli\u003eDelorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods. 2004 Mar;134(1):9\u0026ndash;21; doi:10.1016/j.jneumeth.2003.10.009\u003c/li\u003e\n \u003cli\u003ePoulsen AT, Pedroni A, Langer N, Hansen LD. Microstate EEGlab toolbox: An introductory guide. bioRxiv (Cold Spring Harbor Laboratory). 2018 Mar 27; doi:10.1101/289850\u003c/li\u003e\n \u003cli\u003eKoenig T, Diezig S, Sahana Nagabhushan Kalburgi, Antonova E, Fiorenzo Artoni, Br\u0026eacute;chet L, et al. EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies. Brain Topography. 2023 Jul 29; doi:10.1007/s10548-023-00993-6\u003c/li\u003e\n \u003cli\u003eCusto A, Van De Ville D, Wells WM, Tomescu MI, Brunet D, Michel CM. Electroencephalographic Resting-State Networks: Source Localization of Microstates. Brain Connectivity. 2017 Dec;7(10):671\u0026ndash;82; doi:10.1089/brain.2016.0476.\u003c/li\u003e\n \u003cli\u003eRatajczak EK. Microstate neurodynamics in HRV biofeedback [Doctoral dissertation, Nicolaus Copernicus University in Toruń; 2022\u003c/li\u003e\n \u003cli\u003eJurewicz K. Sieci spoczynkowe i ich rola w zrozumieniu organizacji funkcjonalnej m\u0026oacute;zgu. Kosmos. 2020 Apr 11;69(1):105\u0026ndash;21; doi:10.36921/kos.2020_2629\u003c/li\u003e\n \u003cli\u003eCritchley HD. Garfinkel SN. The influence of physiological signals on cognition. Current Opinion in Behavioral Science. 2018 Feb;19:13\u0026ndash;8.; doi:10.1016/j.cobeha.2017.08.014\u003c/li\u003e\n \u003cli\u003eBenedek M, Bergner S, K\u0026ouml;nen T, Fink A, Neubauer AC. EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia. 2011 Oct;49(12):3505\u0026ndash;11; doi:10.1016/j.neuropsychologia.2011.09.004\u003c/li\u003e\n \u003cli\u003eGrabner RH, Fink A, Neubauer AC. Brain correlates of self-rated originality of ideas: Evidence from event-related power and phase-locking changes in the EEG. Behavioral Neuroscience. 2007;121(1):224\u0026ndash;30; doi:10.1037/0735-7044.121.1.224\u003c/li\u003e\n \u003cli\u003eLustenberger C, Boyle MR, Foulser AA, Mellin JM, Fr\u0026ouml;hlich F. Functional role of frontal alpha oscillations in creativity. Cortex. 2015 Jun;67:74\u0026ndash;82;doi:10.1016/j.cortex.2015.03.012\u003c/li\u003e\n \u003cli\u003eGierszewski D, Kluzowicz J. The role of the University of the Third Age in meeting the needs of older adult learners in Poland. Gerontology \u0026amp; Geriatrics Education. 2021 Jan 11;1\u0026ndash;15; doi:10.1080/02701960.2021.1871904\u003c/li\u003e\n \u003cli\u003eRoss SD, Lachmann T, Saskia Jaarsveld, Riedel-Heller SG, Rodriguez FS. Creativity across the lifespan: changes with age and with dementia. BMC Geriatrics. 2023 Mar 22;23(1); doi:10.1186/s12877-023-03825-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ageing, creativity, divergent thinking, EEG microstates, AUT","lastPublishedDoi":"10.21203/rs.3.rs-7857214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7857214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe main aim of the presented pilot study was to investigate brain network dynamics during creative task performance in older adults. Creativity was operationalised as divergent thinking and assessed via the Alternative Uses Task (AUT) upon electroencephalography (EEG) recording. Thirty-eight healthy older adults participated in the study; 15 participants were excluded due to difficulties understanding or retaining task instructions, resulting in a final sample of 23 participants. We examined the neurodynamics of creativity in more and less creative older adults using EEG microstate analysis, which revealed eight microstate classes, interpreted in accordance with recent literature. Correlation analyses between microstate parameters and AUT scores (creativity, fluency, flexibility, and elaboration) revealed that more creative participants relied on executive control and default mode networks to generate original and novel ideas, while selectively drawing on autobiographical memory to produce a greater number of ideas across categories. Less creative participants showed stronger involvement of semantic and somatosensory/interoceptive networks, suggesting reliance on mental simulations and bodily awareness during idea generation. These findings provide preliminary evidence on neural mechanisms supporting creativity in late adulthood and offer insights for future research on age-related changes in cognition and brain network dynamics.\u003c/p\u003e","manuscriptTitle":"Creativity Neurodynamics and Ageing: a microstate EEG pilot study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 06:30:40","doi":"10.21203/rs.3.rs-7857214/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T17:16:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T13:15:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43980968497166676146062891974456590855","date":"2025-11-14T11:04:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T17:22:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T07:22:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-20T12:35:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-17T11:30:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-17T11:27:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b821ca9c-250d-4196-ab3e-14e5e477c187","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57749061,"name":"Biological sciences/Neuroscience"},{"id":57749062,"name":"Biological sciences/Psychology"},{"id":57749063,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-16T11:51:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 06:30:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7857214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7857214","identity":"rs-7857214","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}