Aging modulates frontal lobes involvement in emotion regulation processing

Emotion regulation (ER) is the process by which individuals can modulate the intensity of their emotional experience and it plays a crucial role in daily life. So far, behavioral analyses seem to suggest that ER ability remains stable throughout the lifespan. However, imaging studies evaluating the neural correlates of ER performance during the aging process have shown mixed results. In this study, we used the “Cambridge Centre for Ageing and Neuroscience cohort sample” to investigate: (1) ER behavioral performance and (2) the differential association between brain measures (based on both structural and functional connectivity data) and ER performance, in a group of younger/middle‐aged participants (N = 159; age range: 18y < x < 58y) relative to a group of older healthy subjects (N = 136; age range: 58y < =x < 89y). Whereas we found no group‐related differences either in ER behavioral data or the association between ER performance and structural data, we did observe that ER performance was differentially correlated in our two study groups to functional connectivity measures in the fronto‐insular‐temporal network, which has been shown to be involved in emotional processing. Group‐related differences were specifically localized in a cluster of voxels within the anterior cingulate areas which revealed a reverse pattern between our study groups: in younger/middle‐aged participants better ER performance was associated with increase connectivity, whereas among older participants better ER performance was related to reduced connectivity. Based on our results, we suggest that a de‐differentiation mechanism, known to affect the frontal lobes brain activity and connectivity in older subjects, might explain our findings.

homeostatic changes), emotion regulation (ER), emotion recognition (i.e., the interpretation of others' emotional states), emotional memory [i.e., a specific class of memory that involves implicit learning and storage of the information with salient emotional meaning; (LeDoux, 1993)] and emotional attention [i.e., the mechanism by which, during perceptual processing, some aspects of selective attention are influenced by sensory events that have strong affective significance; (Kremer & den Uijl, 2016;Vuilleumier, 2005)].While some of these factors are more prone to decline during the aging process, particularly from the late fifties onwards, [i.e., emotion recognition (Ruffman et al., 2008), emotional memory (Kensinger et al., 2002)], potentially reflecting the age-related degeneration and altered activity of frontal and limbic areas (Almaguer et al., 2002;Tessitore et al., 2005), others, such as ER, seem to remain stable and preserved (Charles & Carstensen, 2010;Scheibe & Blanchard-Fields, 2009).
ER is defined as a process by which the intensity and value of one's emotional experience can be remodulated.Researchers have reported different ER strategies employed to successfully achieve this task.Some are based on regulating the expression of responses to emotional stimuli, such as suppression or distraction (Kim et al., 2019).Others, such as cognitive reappraisal (CR), are considered antecedent-focused ER strategies, as they involve reformulating the emotional meaning of the situation (Gross, 1998).
Although suppression and distraction provide immediate relief from negative emotions, CR seems to guarantee long-term adaptation (Wilson & Gilbert, 2008).Indeed, CR connects the context in which the emotional events arise to the evoked emotions themselves.After processing the early emotional responses through attentional resources (i.e., appraisal), CR can become an adaptive functional process, which is recalled, when necessary, by individuals in order to modify the intensity or the values of the arousing emotional experience (Kim et al., 2019).Therefore, CR is one of the most studied and effective regulatory strategies for decreasing the emotional reaction to a stimulus.Due to the re-thinking processing of the emotional stimulus meaning and the attributed valence, CR has been suggested to rely on mechanisms associated with cognitive control and executive functioning (King Johnson et al., 2023).This view seems to be supported by functional magnetic resonance imaging (fMRI) studies, which have shown that, in healthy adults, CR processing is associated with increased activation of the executive control system and decreased activation of the emotional evaluation and response systems (Buhle et al., 2013;Ochsner et al., 2002).This suggests that CR may require prefrontal cortex (PFC) involvement in order to downregulate brain regions (i.e., amygdala) responses occurring in the presence of an emotional stimulus.Interestingly, fMRI studies have shown that CR-induced brain activations tend to be modulated by the aging process.Winecoff et al., found that older adults showed less reappraisal-related activation in the lateral PFC, specifically in the inferior frontal gyrus (Winecoff et al., 2011).Similarly, Opitz et al., reported age-related reduction in lateral and medial PFC activation during a CR task (Opitz et al., 2012).However, Allard et al., challenged these results.Indeed, they observed greater CRrelated recruitment in the lateral and medial PFC in older relative to younger healthy adults (Allard & Kensinger, 2014).A more recent study by Halfmann et al., performed in a group of older adults, found increased PFC activity and, unexpectedly, greater activation in the amygdala and insular cortex during CR processing (Halfmann et al., 2021).Taken together, these results seem to suggest that, although functional-related differences in ER processing between younger and older adults have been reported (particularly in frontal regions), the direction of the effect emerging from fMRI data is not yet clear.This may be due to different aspects: (1) most studies have been performed on small groups, which could explain the variability of the results; (2) up to date fMRI studies have mostly investigated age-related differences in ER processing using a taskbased approach.Differences in the stimuli employed, the timing of presentation, and the instructions may make the results difficult to compare.Moreover, little attention has been posed on the actual association between brain's functional response and ER performance.Indeed, whether the behavioral performance may be similar in younger and older subjects, the brain correlates involved in ER processing seem to be distinct.Further investigation on this aspect may help to elucidate the reported age-related differences in brain activation studies during ER processing.A recent review by Isaacowitz has highlighted the need, among other aspects, to explore the relationship between age-related trajectory changes and strategy efficacy, in order to identify a potential mechanism underlying ER processing in older people (Isaacowitz, 2022).
Here, we investigated age-related differences in ER performance and its association with imaging measures on data collected for the "The Cambridge Centre for Ageing and Neuroscience (Cam-CAN)" project (Shafto et al., 2014;Taylor et al., 2017).Our aims were twofold.First, we assessed, on large groups, age-related behavioral differences in ER performance and its relationship with other emotional-derived measures.Second, we evaluated the role played by age on the association between ER scores and structural/

Significance
Our results offer new insights into the neurobiological mechanisms of emotion regulation (ER) in the older population.Using MRI techniques, we were able to shed light on two opposite trajectories of ER performance and the functionality of the underlying brain areas (particularly involving frontal brain regions), occurring during the aging process.This aspect is particularly relevant because it would seem to corroborate the age-related de-differentiation process.Moreover, these different pathways suggest that the increased resting frontal connectivity in the elderly, could reflect a reduction in the distinctiveness of neural representations potentially affecting the successful ER processing.
functional measures.For this study, we have used resting-state fMRI sequences as they allow investigating the intrinsic association of functionally related brain regions.One of the main advantages of this approach is that it is data-driven, therefore it does not rely on any a priori hypothesis, any explicit temporal modeling, or the choice of a specific task, which may be difficult to match between subject groups.Moreover, recent studies have consistently shown that resting-state fMRI (rs-fMRI) data can accurately predict task-based results (Cole et al., 2016;Tavor et al., 2016), suggesting that resting-state networks (RSNs) represent functionally critical neuronal networks that reflect fundamental properties of the functional brain organization.Specifically, we were interested in targeting two RSNs, such as the fronto-insular-temporal network (FITN) and the salience network (SN), which include several frontal and limbic regions that have been seen to be highly involved during emotional processing (Ince et al., 2023;Seeley, 2019).Indeed, the FITN encompasses the insular cortex, which has a key node in the recognition and experience of basic emotions (Couto et al., 2013), but also plays a fundamental role in integrating the contextual social features and processing social feelings such as empathy and moral judgment (Decety et al., 2012).On the other hand, the SN is involved in the detection of salient information, and it drives attentional resources and autonomic processes in order to obtain environmentally coherent cognitive and homeostatic responses (Ince et al., 2023).Therefore, the SN is particularly relevant in the detection of emotional stimuli, which, due to their high-salience nature, capture attention more readily than other non-emotional events (Dolcos et al., 2020).
Understanding the role played by age on the association between ER performance and neuronal correlates, may help to increase our understanding of the mechanisms associated with a successful aging process.

| Study sample
All participants included in this study were taken from the publicly available database "The Cambridge Centre for Ageing and Neuroscience (Cam-CAN)" (available at http:// www.mrccbu.cam.ac.uk/ datas ets/ camcan/ ) (Shafto et al., 2014;Taylor et al., 2017).Data collection was performed in accordance with the Declaration of Helsinki, approved by the local ethics committee and all participants provided written informed consent prior to data acquisition for the study.Here, we selected subjects included in Stage II ("CC700" phase) of the Cam-CAN dataset.Inclusion criteria comprised: (1) recorded information for socio-demographic (age, sex, education, handedness), cognitive (mini-mental-state-examination score), and mental health (anxiety and depression scores) characteristics, (2) performance at the ER task, (3) performance at the facial emotion recognition task, and (4) availability of good quality structural and resting functional MRI (fMRI) scans.

| ER task
During the task, participants viewed 30-s positive, neutral, and negative film clips.After viewing each clip, they were asked to rate their emotional reactions.However, for half of the negative stimuli, subjects were instructed to reappraise the content of the movies, in order to reduce the emotional impact.The experiment consisted of eight blocks, each containing four experimental conditions: (1) watch emotionally neutral film clips, (2) watch emotionally positive video clips, (3) watch video frames with a negative emotional impact, or (4) reappraise negative clips, which means attempting to reduce the emotional impact through a re-interpretation of the meaning and the content of the negative video.For each trial, participants were given a prompt about the valence, and how they should respond to the clip (e.g., "WATCH NEUTRAL" or "REAPPRAISE NEGATIVE").After the 30-s film clip, subjects provided rating on three scales using a Likert scale from 0 to 100.They rated: (1) how negative they felt during the clip, (2) how positive they felt during the clip, (3) how successfully they reappraised, indicating the degree to which they viewed and reappraised the movie.These rating scales were displayed for 10 s.After the four trials, before the next block began, the participants were shown a calming 30-s washout video.The task provided two types of measures: (1) "emotional regulation" (ER) score, which indicates the subjects' ability to regulate negative emotions during the negative reappraise trial.The total score was calculated by comparing "watch" and "reappraise" conditions; (2) "emotional reactivity" (valence-EV) score, which represents a measure of how the video affected the viewer during the watching condition.This score was calculated by comparing ratings during the positive and negative viewing to ratings during neutral stimuli.More details about the scoring are included in the "Behavioral analysis" paragraph (please, see Section 2.4).The task was performed outside the scanner.

| Facial emotion recognition task
In order to determine whether the ER scores were related to or independent from other emotional aspects of emotional processing, we performed a correlation between ER values and the global score for the facial emotion recognition test (Calder et al., 2003), which is another task evaluating emotional aspect available for the Can-CAM dataset.Briefly, participants were asked to select the right response among a list reporting, as potential choices, the six basic emotions (happy, sadness, anger, fear, disgust, and surprise).A brief practice test preceded the real experiment, which took ∼20 min.See (Calder et al., 1996) and (Shafto et al., 2014) for a more detailed description of the test.

| Behavioral analysis
The ER scores, following the CR process were determined for all study participants and represented our primary outcome.For completeness, we also evaluated the EV scores, attributed to negative stimuli seen for the first time.As the reappraisal process was performed only for the negative stimuli, here we focused only on the EV scores for the negative stimuli.
ER was calculated as the difference between the score attributed to the negative stimulus after the instruction to reappraise and the score attributed to the same stimulus seen the first time.
The ER scores had a range of scores including negative and positive values.The more negative the score, the more successful the CR process, whereas the more positive the score, the least successful the CR process.EV was calculated as the difference between the scores attributed to negative stimuli and the score attributed to neutral stimuli.
Participants were divided in two groups based on their age: younger/middle aged (18 < x < 58 years old) and older adults (58 < =x < 89 years old).The rationale for this was two-fold: (1) in a recently published manuscript, evaluating age-related changes in a facial emotion recognition test across the life span, we have observed reduced performance in the facial emotion recognition ability in participants older than 58 years old, whereas performance in participants younger than 58 years old was not altered (Orlando et al., 2023); (2) previous studies investigating ER ability used an age cut-off very close to ours to differentiate younger and older participants (Winecoff et al., 2011(Winecoff et al., , 2013)).run, participants were instructed to rest with their eyes shut.Further details about the MRI protocol can be found in (Shafto et al., 2014;Taylor et al., 2017).

| MRI data processing
Data analysis was carried out using FSL tools (Smith et al., 2004).

Resting fMRI (rs-fMRI) scans
Data pre-processing consisted of motion correction, brain extraction, Gaussian kernel smoothing of FWHM of 5 mm, high-pass temporal filtering with a cut-off of 100 s (.01 Hz), and it was carried out using first level fMRI Expert Analysis Tool (FEAT) v. 6.00 (Woolrich et al., 2001).
FMRI volumes were registered to the individual's structural scan and standard space images using both FLIRT and FNIRT registration tools, then optimized using boundary-based-registration approach (Greve & Fischl, 2009).In order to denoise functional images from the spurious signal and increase the possibility of identifying markers of effective connectivity, FMRIB's ICA-based X-noiseifier (FIX) was applied (Griffanti et al., 2014) and a training dataset specifically developed on the Cam-CAN dataset.Pre-processing results of each of the 295 study participants were visually inspected by a trained neuroscientist (NF) to ensure registration accuracy.The optimal threshold for the use of FIX, to denoise functional images, was identified as 10 with a mean (median) true positive rate (TPR) and true negative rate (TNR) of 97.2% (100%) and 85.5% (87%), respectively.This is in line or above the suggested thresholding cut-offs (TPR > 95%-TNR > 70%).Pre-processed and denoised functional data for each subject were temporally concatenated across all subjects in order to create a single 4D dataset and to derive the population-based RSNs using multivariate exploratory linear optimized decomposition into independent components (MELODIC) (Beckmann et al., 2005).The number of components was fixed to 25 based on an initial analysis of the population using model order estimation, which suggested that only 25 components were significantly non-zero on average.

| Behavioral and MRI data statistical analysis
Group comparison of the behavioral variables [emotion regulation (ER) and emotion valence (EV) scores] derived from the ER task was carried out using SPSS software (SPSS, Inc., Chicago IL).
Regarding the imaging data, the ER and the EV values were included as separate covariates of interest for two general linear model (GLM) analyses on MRI data.For each of the GLMs other variables were included in the model as covariates of no interest (nuisance variables) in order to account for potential confounding effects influencing our results.Nuisance variables included: age, sex, handedness (assessed by the Edinburgh Handedness Inventory, with a score ranging from −100 to 100)) (Oldfield, 1971), mini mental state examination (MMSE) score (Folstein et al., 1975), anxiety and depression scores (assessed by the hospital anxiety and depression scale-HADS) (Zigmond & Snaith, 1983), education level (expressed as a categorical ≤50 indicated left-handedness (Dragovic, 2004).Given the heterogeneity of the measures, variable of interest and nuisances data were normalized before being included in the model.The GLM model including the ER scores for the two study groups as covariate of interest represented our primary research outcome.The objective was to identify those brain regions reflecting age-related group differences (i.e., differences in "correlation slopes") associated with successful ER processing.The GLM model including EV scores as covariate of interest was run for study completeness.The objective of this second model was to identify those brain areas reproducing age-related group differences (i.e., differences in "correlation slopes") associated with the scores attributed to negative stimuli.
Whole brain analysis for anatomical scans was carried out using a voxel-based morphometry-style analysis (FSL-VBM) (Douaud et al., 2007).Brain extraction and tissue-type segmentation were performed and resulting GM partial volume images were aligned to the MNI standard space using first linear (FLIRT) and then non-linear (FNIRT) registration tools.A study-specific GM template was created.
Images were averaged, modulated, and smoothed with an isotropic Gaussian kernel of 5 mm full-width at half max (FWHM) and the GM images were reregistered to the study-specific GM template, including modulation by the warp field Jacobian.The between-subject analysis of the resting data was carried out using the "dual regression" approach, which allows for voxel-wise comparisons of resting functional connectivity maps (Filippini et al., 2009).Voxel-wise GLM was applied on structural and RSN maps using randomize, a permutation-based nonparametric testing (5000 permutations) (Nichols & Holmes, 2002), and threshold-free-cluster-enhancement (TFCE) for clusters identification (Smith & Nichols, 2009).FWE corrected cluster significance threshold of p < .05 was applied to the suprathreshold clusters.
Non-parametric tests were used to safeguard against the possibility that the between-subjects effects were non-Gaussian, and because such nonparametric inference has much greater robustness against spatial non-stationarity than commonly used parametric methods (Hayasaka & Nichols, 2004).
To determine whether functional differences between the two groups were influenced by morphological variability, even at a subthreshold level, structural images were used as additional covariates on a voxel-by-voxel basis to interrogate their effect on the RSNs association with the variables of interest (Oakes et al., 2007).In detail, GM images of each subject were registered to standard space, smoothed to match the intrinsic smoothness of the fMRI data, demeaned within each group and added as confound regressors (nuisance) to each of the two GLM design matrices.

| Participants
We identified 295 participants across the entire lifespan (18-89 years old) that met the inclusion criteria.All participants had a normal MMSE score (>=25).The socio-demographic characteristics of the two study groups are summarized in Table 1.

| Behavioral analysis for emotional-related stimuli
Group comparison, using an independent samples T-test, for the ER scores, after the CR process, of negative stimuli and the EV scores TA B L E 1 Overview of the two study groups' socio-demographic characteristics.(X 2 = .13-p= .13).
Statistical analyses of derived variables were carried out using SPSS software (SPSS, Inc., Chicago IL).

| Structural MRI
After controlling for confounding factors, voxel-wise GLM analysis revealed no group-related differential association between morphological measures and the variables of interest (either ER or EV).

| Resting-state fMRI
Among the 25 derived RSNs, we selected for our analyses two RSNs of "interest", namely the FITN and the SN, and two other RSNs, which we investigated as "control" RSNs, namely the medial visual (MV) and the Sensorimotor (SM) RSNs.The FITN encompasses brain regions such as the ventral anterior cingulate and the posterior insular areas, the superior temporal gyrus, and the thalami bilaterally (van den Heuvel & Hulshoff Pol, 2010).The SN includes the dorsal anterior cingulate, the frontal pole bilaterally, and the ventral insular cortices (Seeley et al., 2007).Both these networks and their included brain regions, such as frontal and insular areas, were previously shown to be involved in the processing of emotional stimuli (Ince et al., 2023;Seeley, 2019), and to be critical during the integration process of the internal and external milieus (insulae) with adaptive emotional representations and the social context (Amoruso et al., 2011;Bonaz et al., 2021;Ibañez & Manes, 2012).The MV and SM networks include the medial portion of the occipital lobe and motor-related areas, respectively.
These networks are mostly involved in visual processing (MV) and motor control/planning (SM), but do not directly guide the processing of emotional stimuli.
GLM analysis revealed group-related differential association between functional connectivity measures and the variables of interest for the FITN and the SN, but not for the MV or the SM networks.In 68-73-40) (Figure 1b).
It is important to notice that group differences in functional connectivity measures were not influenced by underlying structural differences.Indeed, by adding GM maps as covariates (nuisance variables) to the RSN fMRI analysis models, the previously reported group differences survived largely unchanged.

| DISCUSS ION
In the present study, we compared ER behavioral scores and their association with imaging-derived measures, in a group of young/ middle-aged healthy subjects (younger than 58 years old) relative to a group of healthy older participants (equal or older than 58 years old).The two groups together covered almost entirely the adult lifespan (18-89 years old) and all the subjects were cognitively healthy (MMSE score greater than 25).
Because of its involvement in daily life, effective ER is an important contributor to long-term mental health (Gross & John, 2003;Preston et al., 2022;Sachs-Ericsson et al., 2021).
On one hand, successful ER seems to be associated and predict problem-solving skills, relationship quality, and physical health (Repetti et al., 2002).On the other hand, poor ER performance is related to different neuropsychiatric conditions, namely anxiety (Baker et al., 2004) and depression (Rude & McCarthy, 2003).Our results add further evidence on the role played by age in modulating ER processing.

| Behavioral analysis
Behavioral analysis revealed no group-related differences in ER performance.Our results are in line with previous studies (Livingstone & Isaacowitz, 2019;Saeidi et al., 2021)

| Imaging analysis
Here, we expanded on these initial behavioral results and, by using a multimodal imaging approach, we assessed the differential association between ER scores and neural correlates (based on both structural and functional measures) in our two groups of subjects.
Voxel-wise whole brain analysis revealed no differential association for the two study groups between gray matter measures and our (MV, and SM) showed that only the networks previously reported to be involved with emotional processing (FITN and SN) held grouprelated differences.
We observed a differential association between ER scores (our primary outcome) and functional connectivity strength within the FITN network the younger/middle-aged and the older group.The significant cluster of voxels was localized in the ACC, a region shown to be involved in successful ER processing (Dörfel et al., 2020;Uchida et al., 2015).In detail, our results show that in the younger/middle-aged group of adults, the greater the connectivity in the ACC, the higher the ER performance score, whereas in the older group the greater the connectivity within the ACC, the worse the ER performance.Similarly, analysis investigating the association between EV scores and functional connectivity measures revealed group-related differences within the SN network in a region located in the inferior frontal gyrus (IFG).Specifically, we found reduced co-

| Results interpretation
Up to date, the correlation between ER scores and functional measures and the role played by age in modulating this association have not yet been fully investigated.Previous studies performed in younger/middle-aged subjects using resting-state fMRI data, have shown that successful reappraisal was correlated with increased functional connectivity in brain areas located in the cingulum and in prefrontal areas (Dörfel et al., 2020;Uchida et al., 2015).Our results in the young/middle-aged group are in line with these findings.However, we observed the opposite pattern in the older group.
Indeed, we found an increased recruitment of frontal areas negatively correlated with ER scores.So far, studies investigating the association between resting functional measures and behavioral scores (almost exclusively related to cognitive domains) throughout the adult lifespan, have reported an overall age-related reduction in intra-network specificity associated with reduced performance (Deery et al., 2023).However, a recent large study (N > 700 subjects) focusing exclusively on older participants, in a range of age overlapping with our older group, found that lower performance in working memory score negatively correlated with measures of intra-network functional connectivity (Jockwitz et al., 2017).The authors suggest that their findings might reflect de-differentiation mechanisms taking place during the aging process.Our results would seem to corroborate the de-differentiation process (Goh, 2011), at least with regard to the emotional processing.Indeed, the increased resting functional connectivity of frontal brain areas, crucial for emotional processing, in older people may reflect an overall reduction in the distinctiveness of neural representations affecting the correct performance of ER processing.

| Study limitations
Despite the large number of participants involved in our study, different limitations have to be considered in interpreting our results.
First, we did not analyze the entire array of ER strategies (emotions suppression, upregulation of emotions, positive reappraisal), but limited the focus to successful ER subsequent CR.Future studies could expand on this by examining other forms of reappraisalrelated mechanisms throughout the lifespan, in order to study whether some strategies are more effective for the elderly, while others may be more efficient for younger people.This could offer a deeper understanding on the trajectories of ER during the aging process.Second, we limited our analysis to negative stimuli, as they were the only investigated and reported in the dataset for both ER and reactivity.Future studies should assess potential age effects on regulation of positive stimuli to more fully evaluate the ER process.Third, we used ER scores based on videos.Different modalities of stimuli presentations should also be addressed, in order to assess whether the sensory way of presenting the stimuli may have an impact on the role played by age on the ER performance.Finally, we considered all our participants healthy.However, we cannot exclude that some subjects with mild cognitive impairment (MCI) might have been included in the older group.

| CON CLUS IONS
Overall, two insights emerge from our behavioral and imaging- 2.5.1 | MRI data acquisitionMRI scans were performed at the MRC-CBSU on a Siemens 3 Tesla (3T) TIM trio System (Siemens Healthcare GmbH, Erlangen, Germany) equipped with a 32-channel receiver head coil.Briefly, the data used in this study included structural and resting-state fMRI (rs-fMRI) scans.The anatomical scans were acquired using a 3D T1-weighted (T1w) magnetization prepared rapid gradient echo (MPRAGE) sequence with the following parameters: Repetition time (TR) = 2250 ms; echo time (TE) = 2.99 ms; inversion time (TI) = 900 ms; flip angle = 9°; field of view (FOV) = 256 mm × 240 mm × 192 mm; voxel size = 1 mm isotropic; GRAPPA acceleration factor = 2; acquisition time of 4 min and 32 s.The rs-fMRI images were acquired using a Gradient-Echo echo-planar imaging (EPI) sequence.MRI parameters: TR = 1970 ms; TE = 30 ms; flip angle = 78°; FOV = 192 mm × 192 mm; voxel-size = 3 mm × 3 mm × 4.44 mm.Acquisition time of 8 min and 40 s, for a total number of 261 volumes acquired.During the rs-fMRI for structural images included the following steps: (a) re-orienting images to the standard (MNI) template, (b) bias field correction, (c) brain extraction, and (d) brain tissues segmentation using FMRIB's automated segmentation tool (FAST) that allows generating maps and derive measures of total GM, white matter (WM), and cerebro-spinal fluid (CSF) for each individual subject.
variable based on qualification level, 0 = no degree, 1 = O/GCSE levels or equivalent, 2 = A levels or equivalent, 3 = NVQ, HND, HNC or other professional qualification, 4 = CSE or University and entered in the model using dummy coding) and type of gradient coil used during MRI acquisition, as the coil failed just before the first 100 scans.For the Edinburgh handedness inventory, a cut-off score ≥50 indicated righthandedness; <50 to >50 indicated ambidextrous handedness; detail, for the FITN, we observed increased connectivity associated with ER scores in older relative to younger/middle-aged participants in a cluster located in the anterior cingulate cortex (ACC) [reporting here and below for each significant difference (1) the cluster size, expressed in number of voxels, (2) the T-max value for the peak significant area within the reported cluster and (3) the coordinates of the peak in MNI space, in voxels-(24 voxels, T-max: 4.61, MNI coordinates, x-y-z: 49-84-43)] (Figure1a).Similarly, for the SN we found increased connectivity associated with EV scores in older relative to younger/middle-aged participants in a cluster located in the right inferior frontal gyrus(26 voxels, MNI coordinates,

F I G U R E 1
Images depict group-related differences in voxel-wise correlations between emotion regulation (ER) and emotional valence (EV) scores and functional MRI images.(a) Top panel displays voxels showing differential correlation between ER scores and measures of strength of connectivity within the FITN, located in the anterior cingulate region [MNI coordinates peak effect, x-y-z: 49-84-43].Bottom panel reports the same cluster of voxels (in green) overlaid on the FITN.(b) Top panel displays voxels showing differential correlation between EV scores and measures of strength of connectivity within the SN, located in the right inferior frontal gyrus [MNI coordinates peak effect, x-y-z: 68-73-40].Bottom panel reports the same cluster of voxels (in green) overlaid on the SN.For illustrative purposes, for the two significant clusters reported here, scatterplots of values extracted from significant brain regions against ER and EV scores for the two study groups are shown.Red dots define younger/middle-aged participants, whereas blue dots define older participants.All images displayed here report results with p < .05,corrected for multiple comparisons (red-to-yellow color bars).R, right hemisphere; L, left hemisphere.variables of interest, either ER or EV scores.Conversely, analyses of four selected RSNs, 2 of interest (FITN and SN) and 2 used as control based analyses.With regard to behavioral data, we have shown that ER does seem to remain rather stable throughout the adult lifespan.As for the imaging data, a malfunctioning age-related adaptation of the brain's functional architecture, particularly in frontal regions, has been shown.This finding seems to point toward two trajectories occurring at different stages across the lifespan, suggesting that an increased involvement of a specific area (in our case the ACC) results in opposite performance as we age.Future studies evaluating the association between age-related behavioral changes in emotional processing and imaging data, should investigate whether observed results reflect either compensatory or maladaptive de-differentiation processes by directly exploring the association between behavioral performance and imaging outcomes.Indeed, a greater understanding of ER strategies and their neuronal correlates may lead to identifying markers of age-related brain changes and target regions for interventions to improve the quality of life of the elderly population and offer support toward a successful aging process.
Emotional regulation (ER) and emotional valence (EV) scores for the two study groups.