Too much stress is harmful to the brain and overall health, as it can lead to chronically high levels of the stress hormone cortisol. The part of the brain that regulates memory and emotions, called the hippocampus, is especially sensitive to stress because it has a high number of receptors that bind to cortisol. This connection may explain why stress can lead to memory lapses or strong emotions.

Studies have shown that mental training exercises, such as mindfulness and meditation, may change the structure of the brain and reduce stress. However, the results from these experiments have been mixed due to the variation in mental practices used by different programs. Here, Valk, Engert et al. set out to find how distinct types of mental training affect the brain, focusing on the hippocampus and cortisol levels.

The team used various magnetic resonance imaging techniques to study the hippocampus of 322 healthy adult volunteers who had undergone three months of mental training. The relationship between mental training, hippocampus size, and stress levels was complex when studying the results of each individual. However, when the results were grouped together, it revealed that volunteers who underwent training to increase empathy and compassion experienced expansion in parts of their hippocampus.

As these areas of the brain increased in size, these individuals experienced corresponding reductions in cortisol levels. But volunteers who underwent mental training focused on attention or developing perspective did not experience such increases.

These findings suggest that mental training to increase empathy and compassion alters brain structure and lowers cortisol levels. Future studies may explain how this happens, which could lead to improved mental training programs for mitigating stress.

Stress-related disorders rank among the leading causes for disease burden world-wide (GBD 2015 Disease and Injury Incidence and Prevalence Collaborators, 2016), and the global stress load has increased even more dramatically in recent years (Brooks et al., 2020; Robillard et al., 2020). It is therefore essential to find ways to efficiently prevent or reduce stress (Jorm et al., 2017). In recent years, research has shown that contemplative mental training programs can be efficient in stress reduction (Puhlmann et al., 2021b; Engert et al., 2023; Engert et al., 2017; for a meta-analysis see Khoury et al., 2015), while simultaneously inducing brain plasticity (Valk et al., 2017; Pernet et al., 2021; Davidson, 2016). It is, however, still unclear which types of mental practices are most efficient in reducing stress and inducing stress-related brain plasticity. Furthermore, stress is a multi-layered construct (Engert et al., 2018), and most studies focused on stress-related self-reports and questionnaires (Khoury et al., 2015). A less investigated marker in the stress reduction context through contemplative mental training is diurnal cortisol, from which summary indices such as the cortisol awakening response (CAR), the total diurnal output and the diurnal cortisol slope are frequently investigated (Ross et al., 2014). The steroid hormone and glucocorticoid cortisol is the end-product of the hypothalamic-pituitary-adrenal (HPA) axis and a key player in stress regulation (for reviews see e.g., Chrousos, 2009; Sapolsky, 2000a). Moreover, cortisol is considered an important mediator of the relation between chronic stress and stress-related disease (Adam et al., 2006; Miller et al., 1995). Previous research suggests an association between hippocampal structural integrity and stress-related cortisol activity (McEwen et al., 2016; McEwen, 1999), although findings are inconclusive. To close these gaps, we here investigate the differential efficiency of three types of mental training (attention-based, socio-affective and socio-cognitive) on their ability to induce structural as well as functional plasticity of hippocampal subfields and reduce diurnal cortisol levels.

The hippocampus has a high glucocorticoid receptor density (Herman and Cullinan, 1997; Herman et al., 1995; Jacobson and Sapolsky, 1991; McEwen, 2013), making this region a target of investigations into stress-related brain changes. Having a three layered allocortex, the hippocampal formation consists of multiple subfields, or zones, starting at the subiculum (SUB) and moving inward to the hippocampus proper, the cornu ammonis (CA1-3), and dentate gyrus (CA4/DG) (Palomero-Gallagher et al., 2020; Wisse et al., 2017; Yushkevich et al., 2015; DeKraker et al., 2018). These subfields have unique microstructure (Palomero-Gallagher et al., 2020; Wisse et al., 2017; Yushkevich et al., 2015; DeKraker et al., 2018; Paquola et al., 2020) and participate differently in the hippocampal circuitry (de Flores et al., 2017), likely implicating different functional contributions (Hodgetts et al., 2017; Berron et al., 2016; Plachti et al., 2019; Genon et al., 2021; Olsen et al., 2012; Vos de Wael et al., 2018). Indeed, intrinsic functional MRI analyses have shown that the hippocampal subfields show functional correlation with a broad range of cortical regions, part of visual, control, and default functional networks (Paquola et al., 2020; Vos de Wael et al., 2018; Bayrak et al., 2022; Zhong et al., 2019; Przeździk et al., 2019). Hippocampal subfield volumes and associated intrinsic functional connectivity have been shown to be heritable (Bayrak et al., 2022; Whelan et al., 2016), indicating that individual variation in subfield structure and function is, in part, under genetic control. Other lines of research have reported hippocampal structure and function to be highly sensitive to contextual factors, such as stress (McEwen, 2013). Mediated through its dense network of glucocorticoid receptors, the hippocampus transmits the negative feedback signals of a wide range of glucocorticoid levels on HPA axis activity (Jacobson and Sapolsky, 1991). Through this inhibitory role on HPA axis dynamics, it is linked to emotional reactivity (Phelps, 2004), stress sensitivity (McEwen, 1999; Bannerman et al., 2004; Franklin et al., 2012; Pruessner et al., 2010), and causally involved in a variety of stress-related disorders (O’Doherty et al., 2015).

Previous brain imaging research has examined the relationship between cortisol activity and hippocampal structure and function. Most of this research measured saliva cortisol levels to gauge the diurnal cortisol profile. Thus, a reduced cortisol awakening response, the response to the anticipated demands of the upcoming day (Fries et al., 2009), has been associated with smaller hippocampal volume in healthy individuals (Pruessner et al., 2007; Pruessner et al., 2005; Bruehl et al., 2009) and different psychiatric (Dedovic et al., 2010; Dedovic et al., 2009) and metabolic (Bruehl et al., 2009; Ursache et al., 2012) conditions. In fact, the examination of patients with temporal lobe damage suggested that hippocampal integrity may be a necessary condition for the proper mounting of the CAR (Buchanan et al., 2004; Wolf et al., 2005). Next, to changes in hippocampal structure, alterations in hippocampal functional connectivity have been reported to be associated with changes in cortisol levels (Hakamata et al., 2019; Chang and Yu, 2019). There is also contrary work showing associations between elevated awakening, evening, diurnal, or 24 hour cortisol levels in healthy elderly with age-related hippocampal atrophy (Knoops et al., 2010; Lupien et al., 1999; Lupien et al., 1998; Sudheimer et al., 2014) and, again, samples with psychiatric conditions (Gold et al., 2010; Mondelli et al., 2010). While such inconsistencies in previous neuroimaging work may reflect the fact that different indices of diurnal cortisol tap different facets of HPA axis regulation, the samples studied have been diverse in terms of health status, small in size, and largely cross-sectional. In addition, associations between stress and hippocampal structure and function over time are incompletely understood. Thus, longitudinal studies, such as mental training studies aimed at stress reduction that repeatedly assess both brain and cortisol release, may help to better understand the dynamic relationships between stress, cortisol, and hippocampal structure and function.

In recent years, contemplative mental training interventions, such as the mindfulness-based stress reduction (MBSR) program (Kabat-Zinn, 1982) or compassion-focused therapy (Gilbert, 2014), have gained in popularity as potential therapeutic tools to improve mental and physical health (Goldberg et al., 2018) and reducing stress (Khoury et al., 2015). These mental training interventions can have a positive impact on the practitioner’s stress sensitivity, making them a suitable model to investigate the interrelationship between training-related changes in hippocampal structure, function, and cortisol output. Next to reductions in reactive measures following acute psychosocial stress induction in the laboratory (Engert et al., 2017), reduced subjective-psychological stress load is the most widely reported outcome (for a review, see Khoury et al., 2015). Evidence for lower diurnal cortisol output stems mainly from mindfulness-based interventions, notably MBSR, for which reductions in CAR and afternoon/evening cortisol levels have been reported in healthy and diseased individuals (Brand et al., 2012; Carlson et al., 2007; Carlson et al., 2004). Moreover, other work in the current sample has shown that hair cortisol and cortisone are reduced through mental practice (Puhlmann et al., 2021b). Hair cortisol measurements have been suggested to provide a window into long-term impact of cortisol exposure (Stalder et al., 2017). These findings are contrasted by numerous null results (for meta-analyses see: Pascoe et al., 2017; Sanada et al., 2016), possibly due to modest samples sizes and mixed effects of different training contents on stress-related processes. Furthermore, 8 weeks mindfulness programs such as MBSR and others typically cultivate different types of mental practices, making it difficult to understand which type of mental practice is most efficient in reducing different types of outcomes, including various stress-markers (see also Puhlmann et al., 2021b; Engert et al., 2023; Engert et al., 2017).

The current study, therefore, investigated differential effects of distinct mental training practices onto the association between changes in hippocampal subfields and underlying stress-related diurnal cortisol profiles changes in the context of a large-scale 9month mental training study, the ReSource Project (Singer et al., 2016). We explored impact of long-term exposure to stress onto hippocampal subfields as a function of mental training in a subset of individuals (Puhlmann et al., 2021b). Healthy participants attended three 3 months Training Modules termed Presence (cultivating attention and interoceptive awareness), Affect (cultivating compassion, prosocial motivation and dealing with difficult emotions) and Perspective (cultivating metacognition and perspective-taking on self and others) (Figure 1). Presence resembles typical mindfulness-based interventions, but excludes socio-emotional or socio-cognitive practices (Kabat-Zinn, 1982; Segal et al., 2002). By contrast, Affect and Perspective target social skills through the training of either socio-emotional and motivational skills such as empathy, compassion and care (Affect) or socio-cognitive skills such as perspective taking on self and others (Perspective). In previous work, stemming from the same participant sample as examined here, we found a reduction in CAR specifically after the training of socio-affective capacities (Engert et al., 2023), and of acute stress reactivity after the training of socio-affective or socio-cognitive capacities (Engert et al., 2017). Alternatively, but also in the current sample, different types of mental practices equally reduced hair cortisol levels, a marker of long-term stress (Puhlmann et al., 2021b). This suggests that the content of mental training has a specific effect on daily cortisol changes but not on long-term stress levels. Our group could also show differentiable training-related changes in cortical structure and intrinsic functional organization following the three ReSource project Training Modules, illustrating the existence of training-related structural plasticity of the social brain (Valk et al., 2017; Valk et al., 2023b). Domain-specific changes in hippocampal subfield structure and intrinsic functional connectivity, and how these relate to mental training specific changes in stress-related diurnal cortisol output, have not yet been studied. We, therefore, examined whether module-specific changes in diurnal cortisol levels may relate to specific structural and intrinsic functional changes in different hippocampal subfields and functional resting state data.

Figure 1

Download asset Open asset

We evaluated the longitudinal relationship between hippocampal subfield volumetry, a quantitative index of hippocampal grey matter, and studied whether volumetric changes were paralleled by subfields’ resting-state functional connectivity in a large sample of healthy adults participating in the ReSource Project (Singer et al., 2016). This enabled us to evaluate training effects on hippocampal structure, function, and their associations with cortisol as a function of mental training targeting either attention-based mindfulness (Presence), socio-affective (Affect), or socio-cognitive (Perspective) skills. Hippocampal structure was quantified via a surface-based multi-template algorithm that has been shown to perform with excellent accuracy in healthy and diseased populations of a comparable age range as the currently evaluated cohort (Caldairou et al., 2016). Such a model is good to represent different subfields in vivo, which have a differentiable structure and function (Paquola et al., 2020; DeKraker et al., 2021), and thus may show differentiable changes as a function of mental training. We expect that assessment of hippocampal sub-regions may help to accurately map circuit plasticity as a result of potential stress reduction, and to observe that changes in hippocampal structure are paralleled by changes in functional connectivity of hippocampal subfield functional networks. To model the interplay between individual-level correspondence in hippocampal and stress markers, we assessed the association of changes in hippocampal structure and function with changes in several indices of diurnal cortisol release.

We analyzed structural MRI, resting-state functional MRI, as well as cortisol-based stress markers from the large-scale ReSource Project (Singer et al., 2016). For details, see http://resource-project.org and the preregistered trial https://clinicaltrials.gov/ct2/show/NCT01833104.

In the Resource study, participants were randomly assigned to two training cohorts (TC1, N=80; TC2, N=81) and underwent a 9 months training consisting of three sequential Training Modules (Presence, Affect, and Perspective) with weekly group sessions and daily exercises, completed via cell-phone and internet platforms (Figure 1, Tables 1 and 2, Table 3, Materials and Methods and Supplementary file 1 for more details). TC1 and TC2 started their training regimen with the Presence module, and then underwent the latter two modules in different orders (TC1: Affect-Perspective; TC2 Perspective-Affect) to serve as active control groups for each other (Figure 1C). Another active control group (TC3; N=81) completed three months of Affect training only. Additionally, a matched test-retest control cohort did not undergo any training (RCC, N=90). All participants were examined at the end of each 3 months module (T₁, T₂, T₃) using 3T MRI, behavioral and peripheral-physiological measures that were identical to the baseline (T₀) measures.

Increased functional connectivity of CA1-3 following socio-affective versus socio-cognitive mental training

Subsequently, we studied whether volumetric change in hippocampal CA1-3 would show corresponding changes in intrinsic function following the Affect mental training. To probe the CA1-3 functional connectivity networks per subfield, we mapped the top 10% of normalized functional connections at baseline. Functional connectivity was strongest to medial prefrontal regions, precuneus extending to posterior cingulate, anterior temporal regions and angular gyrus (CA1-3: Figure 2; see Supplementary file 1 for other subfields). Evaluating functional connectivity changes, we found that the right CA1-3 functional network showed differential changes when comparing Affect training to Perspective training (2.420, p=0.016, q=0.032, Cohens D=0.289), which could be attributed to subtle (p<0.05) decreases in right CA1-3 mean FC following Perspective (t=−2.012, p=0.045, q>0.1, M:–0.024, std: 0.081, CI [-0.041–0.006]), but not Affect training (t=1.691, p=0.092, q>0.1, M: 0.010, std: 0.098, CI [–0.01 0.031]); changes were not present when comparing Affect training versus retest control (Table 4 and Supplementary file 1k-q). Comparing Affect TC3 relative to Presence training, we did not observe changes (Table 4). No other subfield showed differential changes in main contrasts within its functional network.

Figure 2

Download asset Open asset

Table 4

Affect TC3 vs Presence	LCA1-3	RCA1-3
t-value	0.366	–0.411
p- and q-value	p>0.1, q>0.1	p>0.1, q>0.1
Cohens D	0.052	–0.058
Affect vs Perspective
t-value	0.137	2.420
p- and q-value	p=0.891, q>0.1	p=0.016, q=0.032
Cohens D	0.016	0.289

Exploring whether particular regions within the CA1-3 network showed alterations in intrinsic functional connectivity when comparing Affect to Perspective, we investigated connectivity changes within regions of the subfields’ functional networks. Left CA1-3 connectivity showed decreases in connectivity to left posterior insula when comparing Affect to Perspective training (FDRq <0.05; t=−3.097, p=0.003, Cohens D=−0.370). On the other hand, we observed connectivity increases between right CA1-3 to right mPFC for the same contrast (FDRq <0.05; t=3.262, p=0.002, Cohens D=0.389). No other subfield’s functional connectivity showed alterations when comparing Affect to Perspective or Presence to Affect TC3. These analyses indicate an overlap between volumetric increases and functional alterations when comparing changes following socio-affective mental training in CA1-3. In particular, the moderately consistent CA1-3 volume increases following Affect training were complemented with differential functional connectivity alterations of this subfield when comparing Affect to Perspective training.

Association between change in subfield volume, function, and stress markers

Last, we probed whether group-level changes in hippocampal subfield CA1-3 volume would correlate with individual-level changes in diurnal cortisol indices (Presence: n=86; Affect: n=92; Perspective: n=81), given that the hippocampal formation is a nexus of the HPA-axis (McEwen, 1999). We took a two-step approach. First, we studied univariate associations between cortisol and subfield change, particularly focusing on the Affect module and CA1-3 volume based on increases in CA1-3 volume identified in our group-level analysis. We observed that increases in bilateral CA1-3 following Affect showed a negative association with change in total diurnal cortisol output (operationalized as the area under the curve with respect to ground; AUC_g) (Table 5, left: t=–2.237, p=0.028, q=0.056; right: t=−2.283, p=0.025, q=0.05), indicating that with a reduction in stress-levels as measured by AUC_g, there were increases in CA1-3 volume. Post-hoc analyses indicated no other subfield showed an association with AUC_g, or with any of the other cortisol indices, below p<0.05 (Supplementary file 1r). Assessing the associations between cortisol indices and the right CA1-3 subfield functional networks in Affect (n=92), we could not observe individual level modulation of diurnal cortisol markers and group-level effects (right CA1-3 functional network change and cortisol markers or within the PI or mPFC ROI, Table 6 and Supplementary file 1s). Yet, we observed positive associations between mean functional network of left CA1-3 and diurnal slope (t=2.653, p=0.01, q=0.02) and AUC_g (t=2.261, p=0.026, q=0.052), Table 6 and Supplementary file 1t. When assessing whether particular regions within the CA1-3 network showed alterations in intrinsic functional connectivity, we observed that AUC_g modulated increases in connectivity between left CA1-3 and parietal occipital area (q<0.05). These analyses extend group-level observations regarding the relation between socio-affective mental training and CA1-3 structure to the individual-level. Again, we observed some consistency in structure and function in case of CA1-3. We did not observe alterations in CA1-3 volume in relation to change in cortisol markers in Presence or Perspective. Yet, for Presence we observed association between slope and LCA4/DG change (t=−2.89, p=0.005, q=0.03) (Supplementary file 1uv). In case of intrinsic function, we also did not observe alterations in CA1-3 in relation to change in cortisol markers in Presence or Perspective, nor in other subfields (Supplementary file 1wx). When evaluating overall associations between diurnal cortisol change in CA1-3 volume in all modules combined, (Presence, Affect, and Perspective), we observed comparable patterns as for Affect only, further underscoring the association between cortisol markers and CA1-3 (Figure 3B; Supplementary file 1y and z). Last, we explored whether associations of subfield volume were found with levels of hair cortisol, a long-term marker of systemic cortisol exposure, in a sub-sample of N=44 participants repeatedly tested across modules (Presence, Affect, and Perspective), based on previous observations of domain-general effects of mental training on cortisol and cortisone (Puhlmann et al., 2021b). We identified consistent associations between increases in LCA1-3 volume and intrinsic function and hair cortisol decreases (volume: t=−2.574, p=0.011, q=0.022, function: t=−2.700, p=0.008, q=0.016). Exploring effects in subfields other than CA1-3 we revealed associations between right CA4/DG volume and hair cortisol (t=−3.138, p=0.002, q=0.01) and left SUB function (t=−2.890, p=0.005, q=0.03; Figure 3B; Supplementary file 1za and zb).

Table 5

	LCA1-3	RCA1-3
CAR	–0,355, p>0.1	–1,543, p>0.1
Slope	–0,878, p>0.1	–1,245, p>0.1
AUCg	–2,237, p=0.028, q=0.056	–2,283, p=0.025, q=0.05

Table 6

	LCA1-3	RCA1-3
CAR	–0,476, p>0.1	–0,425, p>0.1
Slope	2,653, p=0.009, q=0.018	0,773, p>0.1
AUCg	2,261, p=0.026, q=0.052	0,024, p>0.1

Figure 3

Download asset Open asset

We employed a multivariate partial least squares method, with 1000 permutations to account for stability (McIntosh and Lobaugh, 2004; Kebets et al., 2019) and bootstrapping (100 times) with replacement, which aims to identify the directions in the predictor space that account for the most variance in changes observed, by creating latent variables. Initially, we investigated whether there was a general connection between CA1-3 subfields and cortisol changes, regardless of which Training Module produced these effects (Figure 4). This analysis was motivated by our observations that the bilateral CA1-3 showed increases in volume following Affect training and differential change between Affect and Perspective training in our resting state analyses. In a second model included structural and functional data of all subfields. Both models included all stress markers, and we regressed out age, sex and random effects of subject. We found that both models could identify significant associations between cortisol stress markers and hippocampal plasticity (FDRq <0.05), and that in particular Affect showed strongest associations with the latent markers for CA1-3 (Table 7). Both analyses showed inverse effects of subfield structure and function in relation to stress markers and both slope and AUC changes showed strongest associations with the latent factor.

Figure 4

Download asset Open asset

Table 7

	LC1	Overall	Presence	Affect	Perspective
CA1-3	p<0.01, 67%	r=0.20	r=0.17	r=0.27	r=0.16
all	p<0.01, 71%	r=0.24	r=0.16	r=0.30	r=0.26

The goal of the current work was to investigate the effects of different types of mental training regimens on stress-related changes in the human hippocampus. The hippocampal formation is a highly plastic allocortical structure implicated in stress and emotional reactivity (Chrousos, 2009; Sapolsky, 2000a; McEwen, 1999). In this study, we used automated segmentation to examine if the volumes of hippocampal subfields (SUB, CA1-3, CA4/DG) change in a large healthy sample over a 9-month longitudinal mental training study, the ReSource project (Singer et al., 2016). We investigated whether three different interoceptive and social mental Training Modules could lead to changes in hippocampal subfield volume. Additionally, we explored if these changes were associated with alterations in both intrinsic brain function and stress-related physiological changes, as indicated by shifts in diurnal and hair cortisol levels due to training.

When comparing the differential efficiency of the 3 months mental Training Modules Presence, Affect, and Perspective against each other and a retest-control group on hippocampal subfield structure, we observed consistent increases in bilateral CA1-3 volume following socio-emotional Affect training relative to socio-cognitive Perspective training and no training in retest controls. Moreover, alterations in structure were mirrored by changes in functional connectivity of right CA1-3 following Affect versus Perspective training. In particular, we observed relative increases of functional connectivity between right CA1-3 and mPFC, and decreases between left CA1-3 and posterior insula, mainly driven by changes in connectivity following Perspective training. Evaluating training-related changes in diurnal cortisol output (cortisol awakening response, total diurnal output and diurnal slope), we observed that bilateral CA1-3 volume increases correlated with decreases in total diurnal cortisol output (assessed as the area under the curve with respect to ground, AUC_g, sampled on 10 occasions over two consecutive days). Intrinsic connectivity of CA1-3 following Affect showed a positive association with left CA1-3 network change and diurnal slope and total diurnal cortisol output, where the latter associated with increased connectivity between left CA1-3 and parietal-occipital area. Interestingly, these associations were similar when combining Training Modules, suggesting the association between CA1-3 and diurnal cortisol markers is present irrespective of training content. Moreover, we additionally observed consistent associations between left CA1-3 and hair cortisol, a systemic marker of chronic stress, across trainings in a sub-sample of the current study. Finally, through conducting multivariate analysis, we once more noticed associations between changes in CA1-3 volume and functional adaptability and alterations in stress levels, particularly prominent within the Affect Module. Integrating all subfields into a unified model highlighted a distinct significance of CA1-3, although for the left hemisphere, we observed a more diverse range of contributions across subfields. In summary, we establish a connection between a socio-emotional behavioral intervention, shifts in hippocampal subfield structure and function, and decreases in cortisol levels among healthy adults.

Our longitudinal, multi-modal approach could thus show that compassion-based mental training alters CA1-3 structure. Second, training-based increases in CA1-3 volume related to decreases in total diurnal cortisol release, suggesting that mental training and CA1-3 volume changes are linked to cortisol release. The results regarding changes in functional connectivity profiles were not as straightforward, but they did reveal a distinction between Affect and Perspective mental training in the CA1-3 region. Moreover, CA1-3 intrinsic functional change was associated with changes in diurnal cortisol slope and release, and long-term cortisol exposure. While the experimental nature of our training study allows concluding that CA1-3 structure changed as a function of Affect training, and that individual differences in CA1-3 structural change corresponded to cortisol release change, we cannot make any claims about which training-induced change caused the other. Thus, it is possible that, owing to the Affect module, the activation of emotion/motivation-related functional processes is key to reducing the daily stress load and associated cortisol release (Klimecki et al., 2013; McCall and Singer, 2012). Such reduction in cortisol levels may then explain the observed downstream brain alterations. According to this interpretation, changes in CA1-3 volume may come secondary to stress reduction and consequently alterations in cortisol release following compassion training. Alternatively, emotion/compassion training may specifically targets CA volume and function, and, as per its role as the central break of the HPA axis, improves its capacity to inhibit cortisol release. This explanation could explain the lack of average diurnal cortisol (i.e., AUC_g) change following Affect training per se (Engert et al., 2023), as it may be relevant for individual variations in brain change and thus be more difficult to detect based only on average change per module. In sum, it is likely that observed alterations in hippocampal structure and function, as well as their associations with diurnal cortisol change, are not explained by a single mechanism, but rather result as a combination of different factors. This interpretation is also supported by our multivariate observations. For example, given the anatomical and functional complexity of the hippocampal formation (Yushkevich et al., 2015; Genon et al., 2021) as well as the multifaceted cognitive processes underlying stress, it seems plausible that our observations are an emergent effect of multiple, cognitively distinct, functional sub-processes. Thus, future studies may directly test the potential specificity of the interrelationship between stress on the hippocampus using further targeted measurements.

The observed increases in CA1-3 volumes following socio-affective mental training were small-sized effects. However, findings were consistent when independently assessing the left and right hippocampus subfields. In particular, we observed that increases in CA1-3 volume after Affect training corresponded to a decrease in total diurnal cortisol as well as hair cortisol output. These results can be interpreted in line with the mainly inhibitory role of the hippocampus in stress regulation (Herman and Cullinan, 1997; Herman et al., 1995; Jacobson and Sapolsky, 1991; Herman et al., 2005). Specifically, the hippocampus is involved in the negative feedback inhibition of the HPA axis. Mineral- and glucocorticoid receptors are present in abundance in hippocampal neurons, from where they transmit the negative feedback signals of a wide range of glucocorticoid levels on HPA axis activity (Jacobson and Sapolsky, 1991). The extremely high numbers of mineral- and glucocorticoid receptors make the hippocampus a prominent target for the neurotoxic effects of glucocorticoids (Sapolsky, 2000b; Sapolsky et al., 1985a; Sapolsky and Pulsinelli, 1985b). In particular the CA1 may be susceptive to stress-based environmental effects due to synaptogenesis associated with NR2B subunits of glutamate receptors (NMDAR) (Coultrap et al., 2005). Along these lines, sustained exposure to high glucocorticoid levels was shown to relate to calcium influx, and may produce CA3 pyramidal neuronal damage, which has been reported in rodents and tree shrews (McKittrick et al., 2000; Magariños et al., 1998; Magariños and McEwen, 1995). Next to demonstrating a consistent relationship between total daily cortisol output and hippocampal structure, the absence of univariate findings for cortisol awakening response (CAR), diurnal slope or hair cortisone levels may a divergence in the sensitivity of alternative cortisol-based stress markers to structural neuroimaging markers. It is of note that the current work relies on a segmentation approach of hippocampal subfields including projection to MNI template space, an implicit correction for total brain volume through the use of a stereotaxic reference frame. Some caution for this method may be warranted, as complex hippocampal anatomy can in some cases lead to over- as well as underestimation of subfield volumes, as well as subfield boundaries may not always be clearly demarcated (Wisse et al., 2021). Future work, studying the hippocampal surface at higher granularity, for example though unfolding the hippocampal sheet (Vos de Wael et al., 2018; DeKraker et al., 2021; DeKraker et al., 2022; Bernhardt et al., 2016), may further help with both alignment and identification of not only subfield-specific change but also alterations as a function of the hippocampal long axis, a key dimension of hippocampal structural and functional variation that was not assessed in the current work (Genon et al., 2021; Vogel et al., 2020).

Structural MRI findings were complemented by the separate assessment of task-free ("resting-state") functional connectivity networks. Identification of networks that show coupled spontaneous brain activity through resting-state fMRI is currently considered an effective approach to study whole-brain functional connectivity (Smith et al., 2011; Biswal et al., 1995; Greicius et al., 2003). In the current cohort, we could demonstrate widespread patterns of hippocampal functional connectivity to mesiotemporal, lateral temporal, together with anterior as well as posterior midline regions, lateral temporo-parietal, and dorsolateral prefrontal cortices - a pattern in excellent accordance to previous studies probing hippocampal functional connectivity at rest in healthy populations (Paquola et al., 2020; Vos de Wael et al., 2018; Przeździk et al., 2019; Buckner and Vincent, 2007; Vincent et al., 2006), and outlining ‘mesiotemporal’ components of default-mode networks (Andrews-Hanna et al., 2010; Yeo et al., 2011). Assessing modulations of connectivity by mental training, we could provide independent, yet weak, support for a specific relationship of the socio-affective Affect training, relative to socio-cognitive Perspective Training Modules, on hippocampal network embedding. In particular, we observed an increased functional integration of the right CA1-3 with medial prefrontal cortical regions (mPFC) in individuals following Affect relative to Perspective training. Studies in rats and non-human primates have demonstrated a high density of glucocorticoid receptors in the mPFC (Sánchez et al., 2000; Ahima and Harlan, 1990). Accordingly, the mPFC, like the hippocampus, was shown to play a key role in HPA-axis regulation (Dedovic et al., 2009; Herman et al., 2005; Radley and Sawchenko, 2011; Diorio et al., 1993). In a previous positron emission tomography study, glucose metabolism in the mPFC was negatively associated with acute stress-induced salivary cortisol increases; notably, the authors observed a negative metabolic coupling between mPFC areas and the mesiotemporal lobe (Kern et al., 2008). In related work on isocortical changes in structure and intrinsic function following the ReSource training, we have observed structural changes in insular, opercular and orbitofrontal regions following Affect training (Valk et al., 2017; Valk et al., 2023b). At the same time, we observed little change in large-scale functional organization, relative to changes observed following Presence and Perspective training. Previous work has implicated the hippocampal formation at the nexus of multiple large-scale networks and cortical organization (Paquola et al., 2020; Vogel et al., 2020). Indeed, it may be that particular changes in the CA1-3 are central in coordinating the signal flow within the hippocampal complex, coordinating the balance between large-scale association networks in the iso-cortex (Paquola et al., 2020). Integrating this with our empirical observation of Affect training taking up a regulatory or stabilizing functional role, relative to Perspective and Presence training, it is possible that such alterations are orchestrated by adaptive processes (McEwen et al., 2015). Future work may be able to further disentangle the causal relationship between iso- and allo-cortical structure and function, and the role of specific hippocampal subfields.

Using univariate approaches, we could observe that training-induced HC volume increases following socio-affective mental training overlapped with reductions in cumulative diurnal cortisol release. Additionally, we observed functional connectivity decrease between left CA1-3 and parietal-occipital area in individuals showing reduced diurnal cortisol release and overall connectivity decreases of left CA1-3 relating to reductions in diurnal cortisol slope. Importantly, these associations could be found also when including Presence and Perspective in our analysis, suggesting of a domain-general relationship between diurnal cortisol alterations and CA1-3 volume and function. In line with our observations in univariate analysis, we found multivariate associations between hippocampal subfield volume, intrinsic function and cortisol markers. Again, the contribution of volume and intrinsic function was inverse. This may possibly relate to the averaging procedure of the functional networks. Combined, outcomes of our univariate and multivariate analyses point to an association between change in hippocampal subfields and stress markers, and that these changes, at the level of the individual, ultimately reflect complex interactions within and across hippocampal subfields and may capture different aspects of diurnal stress. Future work may more comprehensively study the plasticity of the hippocampal structure, and link this to intrinsic functional change and cortisol to gain full insights in the specificity and system-level interplay across subfields, for example using more detailed hippocampal models (DeKraker et al., 2022). Incorporating further multivariate, computational, models is needed to further unpack and investigate the complex and nuanced association between hippocampal structure and function, in particular in relation to subfield plasticity and short and long-term stress markers. In line with our multivariate observations, in other work from the ReSource study we observed mixed specificity of stress-reducing effects as a function of mental training. For example, both social modules, that is the Affect and Perspective trainings, reduced acute cortisol reactivity to a psychosocial stressor (Kirschbaum et al., 1993), which is considered a dynamic state of HPA axis activity (Engert et al., 2017). Regarding the CAR, only Affect training was able to reduce this dynamic cortisol response to awakening, known to reflect anticipatory stress (Engert et al., 2023). Lastly, regarding hair cortisol, a long-term measure of systemic stress, all Training Modules were shown to be equally effective in stress reduction over a training period of three to nine months (Puhlmann et al., 2021b). In our work we observed a consistent association between left CA1-3 volume and functional increases and hair cortisol decreases, hinting at a potential relationship between CA1-3 and both short-term and long-term stress level changes.

Overall, different types of mental training result in stress reduction (e.g. Puhlmann et al., 2021b; Engert et al., 2023; Engert et al., 2017). In a recent paper we argue that the variable pattern of mental training effects on different cortisol indices may be explained by the functional roles of these indices (Engert et al., 2023). Thus, indices reflecting dynamic HPA axis properties, such as acute stress reactivity and the CAR, were suggested to change with Affect and Perspective training (also see Engert et al., 2017). Hair cortisol as a marker of cumulative stress load likely reflecting the low-grade and continuous strain inherent to daily hassles (Almeida, 2005; DeLongis et al., 1982; Lazarus and Folkman, 1984), was contrarily suggested to change independent of training type (also see Puhlmann et al., 2021b). The current findings do not necessarily contradict this reasoning, due to differences in interpretation of group-level and individual-level changes. Although we observed that CA1-3 volume was selectively increased by socio-affective mental training at the group level, and that individual differences in CA1-3 volume increase within the Affect module correlated with reduced diurnal cortisol release, the pattern linking bilateral CA1-3 volume increases with reduced diurnal cortisol release was also present when all modules were combined. Similarly, in follow-up analysis on functional alterations of hippocampal subfields, we could observe group-level increases in connectivity to mPFC for right, but not left, CA1-3, when comparing socio-affective and socio-cognitive training. Though right CA1-3 group-level changes did not link to individual level change in cortisol markers following Affect training, individual level changes in left CA1-3 corresponded to changes in cortisol markers, again following Affect but also across all practices combined. Thus, we cannot at this point derive a consistent pattern of how mental training influences different indices of cortisol activity, yet we do find a consistent change in CA1-3 following Affect training, and observe domain-general patterns of change associations between CA1-3 and cortisol markers, indicating CA1-3 may play a central role within the context of Affect training and diurnal stress reduction based on univariate analysis. Although the univariate examination of changes specific to modules in volume and connections within the Affect Module presents how changes in cortisol align with group-level rises in CA1-3 volume, the multivariate analysis extended this observation through considering individual-level alterations not discernible at the group level through a data-driven method. These results generally corresponded with observations at the group level but offer additional insights into specificity, and hint at system-level alterations. Lastly, from a mechanistic viewpoint, we hypothesize that Affect training stimulates emotion-motivational (reward) systems associated with positive affect (Klimecki et al., 2013; McCall and Singer, 2012), and regulated by oxytocin and opiates (Depue and Morrone-Strupinsky, 2005; Nelson and Panksepp, 1998). Since these neuropeptides are also involved in stress regulation (Carter, 2014; Drolet et al., 2001), they could be considered to provide a double hit, and prime candidates to mediate hippocampal volume increase and stress reduction in particular following compassion-based practice, yet also present following other practices.

It is of note that non-adherence to saliva sampling in ambulatory settings has been shown to exert a significant impact on the resulting cortisol data (Kudielka et al., 2007; Kudielka et al., 2003) and that the present data does not fully conform to the recently provided consensus guidelines on the assessment of the CAR (Stalder et al., 2022; Stalder et al., 2016), which were published after the conception of our study. Most importantly, we did not employ objective measures for the verification of participants’ sampling times. Hence, diurnal cortisol data have to be treated with some caution since the possibility of non-adherence-related confounding cannot be excluded (Kudielka et al., 2007; Kudielka et al., 2003; Stalder et al., 2022; Stalder et al., 2016). We nevertheless addressed the issue of non-adherence through an experience sampling approach based on mobile phones handed out to our participants. As shown by the relatively low proportion of missing data, these devices may have boosted adherence by reminding participants of a forthcoming sampling time-point.

To conclude, using a longitudinal model, we investigated how different types of mental training differentially result in changes in hippocampal subfield volume, resting-state functional networks, and stress-related markers of diurnal cortisol and hair cortisol. We find that only the 3 months Affect training module cultivating compassion and care, rather than attention-based (Presence) or socio-cognitive (Perspective) training, related to an increase of hippocampal CA1-3 subfield volume, with corresponding alterations in functional connectivity and a reduction in total diurnal cortisol output. Across analyses we observed consistent alterations between cortisol change and CA1-3 volume and function, pinpointing this region as a potential target for further investigations on stress and the human brain. Lastly, our multivariate analyses also point to a circuit level understanding of latent diurnal stress scores. Our results may be informative for the development of targeted interventions to reduce stress, and inspire the update of models on the role of different hippocampal formations for human socio-emotional and stress-related processes.

The specifics on the experimental design are the similar to related works in the same sample (Valk et al., 2017; Trautwein et al., 2020). They are provided again here for completeness.

The present work is based on personal and sensitive biological data that could be matched to individuals. Participants did not consent to data-sharing with parties outside the MPI CBS, such that in line with the GDPR, data cannot be made publicly available. Data are available upon reasonable request (contact via valk@cbs.mpg.de). Together with the data officer at the Max Planck Institute for Human Cognitive and Brain Sciences each request will be assessed with respect to GDPR regulations. No commercial research can be performed on the data. Analysis scripts (Matlab) to reproduce primary analyses and figures are publicly available on GitHub (copy archived at Valk, 2024) and raw data-plots are provided whenever possible in the figures and supplementary figures.

1. 1. Adam EK
   2. Hawkley LC
   3. Kudielka BM
   4. Cacioppo JT

   (2006) Day-to-day dynamics of experience--cortisol associations in a population-based sample of older adults

   PNAS 103:17058–17063.

   https://doi.org/10.1073/pnas.0605053103

   • PubMed
   • Google Scholar
2. 1. Ahima RS
   2. Harlan RE

   (1990) Charting of type II glucocorticoid receptor-like immunoreactivity in the rat central nervous system

   Neuroscience 39:579–604.

   https://doi.org/10.1016/0306-4522(90)90244-x

   • PubMed
   • Google Scholar
3. 1. Almeida DM

   (2005) Resilience and vulnerability to daily stressors assessed via diary methods

   Current Directions in Psychological Science 14:64–68.

   https://doi.org/10.1111/j.0963-7214.2005.00336.x

   • Google Scholar
4. 1. Andrews-Hanna JR
   2. Reidler JS
   3. Sepulcre J
   4. Poulin R
   5. Buckner RL

   (2010) Functional-anatomic fractionation of the brain’s default network

   Neuron 65:550–562.

   https://doi.org/10.1016/j.neuron.2010.02.005

   • PubMed
   • Google Scholar
5. 1. Bannerman DM
   2. Rawlins JNP
   3. McHugh SB
   4. Deacon RMJ
   5. Yee BK
   6. Bast T
   7. Zhang WN
   8. Pothuizen HHJ
   9. Feldon J

   (2004) Regional dissociations within the hippocampus--memory and anxiety

   Neuroscience and Biobehavioral Reviews 28:273–283.

   https://doi.org/10.1016/j.neubiorev.2004.03.004

   • PubMed
   • Google Scholar
6. 1. Bayrak Ş
   2. de Wael RV
   3. Schaare HL
   4. Hettwer MD
   5. Caldairou B
   6. Bernasconi A
   7. Bernasconi N
   8. Bernhardt BC
   9. Valk SL

   (2022) Heritability of hippocampal functional and microstructural organisation

   NeuroImage 264:119656.

   https://doi.org/10.1016/j.neuroimage.2022.119656

   • PubMed
   • Google Scholar
7. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society Series B 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
8. 1. Bernhardt BC
   2. Bernasconi A
   3. Liu M
   4. Hong SJ
   5. Caldairou B
   6. Goubran M
   7. Guiot MC
   8. Hall J
   9. Bernasconi N

   (2016) The spectrum of structural and functional imaging abnormalities in temporal lobe epilepsy

   Annals of Neurology 80:142–153.

   https://doi.org/10.1002/ana.24691

   • PubMed
   • Google Scholar
9. 1. Berron D
   2. Schütze H
   3. Maass A
   4. Cardenas-Blanco A
   5. Kuijf HJ
   6. Kumaran D
   7. Düzel E

   (2016) Strong evidence for pattern separation in human dentate gyrus

   The Journal of Neuroscience 36:7569–7579.

   https://doi.org/10.1523/JNEUROSCI.0518-16.2016

   • PubMed
   • Google Scholar
10. 1. Biswal B
    2. Yetkin FZ
    3. Haughton VM
    4. Hyde JS

    (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI

    Magnetic Resonance in Medicine 34:537–541.

    https://doi.org/10.1002/mrm.1910340409

    • PubMed
    • Google Scholar
11. 1. Brand S
    2. Holsboer-Trachsler E
    3. Naranjo JR
    4. Schmidt S

    (2012) Influence of mindfulness practice on cortisol and sleep in long-term and short-term meditators

    Neuropsychobiology 65:109–118.

    https://doi.org/10.1159/000330362

    • PubMed
    • Google Scholar
12. 1. Brooks SK
    2. Webster RK
    3. Smith LE
    4. Woodland L
    5. Wessely S
    6. Greenberg N
    7. Rubin GJ

    (2020) The psychological impact of quarantine and how to reduce it: rapid review of the evidence

    Lancet 395:912–920.

    https://doi.org/10.1016/S0140-6736(20)30460-8

    • PubMed
    • Google Scholar
13. 1. Bruehl H
    2. Wolf OT
    3. Convit A

    (2009) A blunted cortisol awakening response and hippocampal atrophy in type 2 diabetes mellitus

    Psychoneuroendocrinology 34:815–821.

    https://doi.org/10.1016/j.psyneuen.2008.12.010

    • PubMed
    • Google Scholar
14. 1. Buchanan TW
    2. Kern S
    3. Allen JS
    4. Tranel D
    5. Kirschbaum C

    (2004) Circadian regulation of cortisol after hippocampal damage in humans

    Biological Psychiatry 56:651–656.

    https://doi.org/10.1016/j.biopsych.2004.08.014

    • PubMed
    • Google Scholar
15. 1. Buckner RL
    2. Vincent JL

    (2007) Unrest at rest: default activity and spontaneous network correlations

    NeuroImage 37:1091–1096.

    https://doi.org/10.1016/j.neuroimage.2007.01.010

    • PubMed
    • Google Scholar
16. Book

    1. Caldairou B
    2. Bernhardt B
    3. Kulaga-Yoskivitz J
    4. Kim H
    5. Bernasconi N
    6. Bernasconi A

    (2016)

    A Surface Patch-Based Segmentation Method for Hippocampal Subfields

    Springer.

    • Google Scholar
17. 1. Carlson LE
    2. Speca M
    3. Patel KD
    4. Goodey E

    (2004) Mindfulness-based stress reduction in relation to quality of life, mood, symptoms of stress and levels of cortisol, dehydroepiandrosterone sulfate (DHEAS) and melatonin in breast and prostate cancer outpatients

    Psychoneuroendocrinology 29:448–474.

    https://doi.org/10.1016/s0306-4530(03)00054-4

    • PubMed
    • Google Scholar
18. 1. Carlson LE
    2. Campbell TS
    3. Garland SN
    4. Grossman P

    (2007) Associations among salivary cortisol, melatonin, catecholamines, sleep quality and stress in women with breast cancer and healthy controls

    Journal of Behavioral Medicine 30:45–58.

    https://doi.org/10.1007/s10865-006-9082-3

    • PubMed
    • Google Scholar
19. 1. Carter CS

    (2014) Oxytocin pathways and the evolution of human behavior

    Annual Review of Psychology 65:17–39.

    https://doi.org/10.1146/annurev-psych-010213-115110

    • PubMed
    • Google Scholar
20. 1. Chang J
    2. Yu R

    (2019) Hippocampal connectivity in the aftermath of acute social stress

    Neurobiology of Stress 11:100195.

    https://doi.org/10.1016/j.ynstr.2019.100195

    • PubMed
    • Google Scholar
21. 1. Chao-Gan Y
    2. Yu-Feng Z

    (2010) DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI

    Frontiers in Systems Neuroscience 4:13.

    https://doi.org/10.3389/fnsys.2010.00013

    • PubMed
    • Google Scholar
22. 1. Chrousos GP

    (2009) Stress and disorders of the stress system

    Nature Reviews. Endocrinology 5:374–381.

    https://doi.org/10.1038/nrendo.2009.106

    • PubMed
    • Google Scholar
23. 1. Coultrap SJ
    2. Nixon KM
    3. Alvestad RM
    4. Valenzuela CF
    5. Browning MD

    (2005) Differential expression of NMDA receptor subunits and splice variants among the CA1, CA3 and dentate gyrus of the adult rat

    Brain Research. Molecular Brain Research 135:104–111.

    https://doi.org/10.1016/j.molbrainres.2004.12.005

    • PubMed
    • Google Scholar
24. 1. Davidson RJ

    (2016) Mindfulness-based cognitive therapy and the prevention of depressive relapse: Measures, mechanisms, and mediators

    JAMA Psychiatry 73:547–548.

    https://doi.org/10.1001/jamapsychiatry.2016.0135

    • PubMed
    • Google Scholar
25. 1. Dedovic K
    2. Duchesne A
    3. Andrews J
    4. Engert V
    5. Pruessner JC

    (2009) The brain and the stress axis: the neural correlates of cortisol regulation in response to stress

    NeuroImage 47:864–871.

    https://doi.org/10.1016/j.neuroimage.2009.05.074

    • PubMed
    • Google Scholar
26. 1. Dedovic K
    2. Engert V
    3. Duchesne A
    4. Lue SD
    5. Andrews J
    6. Efanov SI
    7. Beaudry T
    8. Pruessner JC

    (2010) Cortisol awakening response and hippocampal volume: vulnerability for major depressive disorder?

    Biological Psychiatry 68:847–853.

    https://doi.org/10.1016/j.biopsych.2010.07.025

    • PubMed
    • Google Scholar
27. 1. de Flores R
    2. Mutlu J
    3. Bejanin A
    4. Gonneaud J
    5. Landeau B
    6. Tomadesso C
    7. Mézenge F
    8. de La Sayette V
    9. Eustache F
    10. Chételat G

    (2017) Intrinsic connectivity of hippocampal subfields in normal elderly and mild cognitive impairment patients

    Human Brain Mapping 38:4922–4932.

    https://doi.org/10.1002/hbm.23704

    • PubMed
    • Google Scholar
28. 1. DeKraker J
    2. Ferko KM
    3. Lau JC
    4. Köhler S
    5. Khan AR

    (2018) Unfolding the hippocampus: An intrinsic coordinate system for subfield segmentations and quantitative mapping

    NeuroImage 167:408–418.

    https://doi.org/10.1016/j.neuroimage.2017.11.054

    • PubMed
    • Google Scholar
29. 1. DeKraker J
    2. Köhler S
    3. Khan AR

    (2021) Surface-based hippocampal subfield segmentation

    Trends in Neurosciences 44:856–863.

    https://doi.org/10.1016/j.tins.2021.06.005

    • PubMed
    • Google Scholar
30. 1. DeKraker J
    2. Haast RAM
    3. Yousif MD
    4. Karat B
    5. Lau JC
    6. Köhler S
    7. Khan AR

    (2022) Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold

    eLife 11:e77945.

    https://doi.org/10.7554/eLife.77945

    • PubMed
    • Google Scholar
31. 1. DeLongis A
    2. Coyne JC
    3. Dakof G
    4. Folkman S
    5. Lazarus RS

    (1982) Relationship of daily hassles, uplifts, and major life events to health status

    Health Psychology 1:119–136.

    https://doi.org/10.1037/0278-6133.1.2.119

    • Google Scholar
32. 1. Depue RA
    2. Morrone-Strupinsky JV

    (2005) A neurobehavioral model of affiliative bonding: implications for conceptualizing A human trait of affiliation

    The Behavioral and Brain Sciences 28:313–350.

    https://doi.org/10.1017/S0140525X05000063

    • PubMed
    • Google Scholar
33. 1. Diorio D
    2. Viau V
    3. Meaney MJ

    (1993) The role of the medial prefrontal cortex (cingulate gyrus) in the regulation of hypothalamic-pituitary-adrenal responses to stress

    The Journal of Neuroscience 13:3839–3847.

    https://doi.org/10.1523/JNEUROSCI.13-09-03839.1993

    • PubMed
    • Google Scholar
34. 1. Dressendörfer RA
    2. Kirschbaum C
    3. Rohde W
    4. Stahl F
    5. Strasburger CJ

    (1992) Synthesis of a cortisol-biotin conjugate and evaluation as a tracer in an immunoassay for salivary cortisol measurement

    The Journal of Steroid Biochemistry and Molecular Biology 43:683–692.

    https://doi.org/10.1016/0960-0760(92)90294-s

    • PubMed
    • Google Scholar
35. 1. Drolet G
    2. Dumont EC
    3. Gosselin I
    4. Kinkead R
    5. Laforest S
    6. Trottier JF

    (2001) Role of endogenous opioid system in the regulation of the stress response

    Progress in Neuro-Psychopharmacology & Biological Psychiatry 25:729–741.

    https://doi.org/10.1016/s0278-5846(01)00161-0

    • PubMed
    • Google Scholar
36. 1. Engert V
    2. Kok BE
    3. Papassotiriou I
    4. Chrousos GP
    5. Singer T

    (2017) Specific reduction in cortisol stress reactivity after social but not attention-based mental training

    Science Advances 3:e1700495.

    https://doi.org/10.1126/sciadv.1700495

    • PubMed
    • Google Scholar
37. 1. Engert V
    2. Kok BE
    3. Puhlmann LMC
    4. Stalder T
    5. Kirschbaum C
    6. Apostolakou F
    7. Papanastasopoulou C
    8. Papassotiriou I
    9. Pervanidou P
    10. Chrousos GP
    11. Singer T

    (2018) Exploring the multidimensional complex systems structure of the stress response and its relation to health and sleep outcomes

    Brain, Behavior, and Immunity 73:390–402.

    https://doi.org/10.1016/j.bbi.2018.05.023

    • PubMed
    • Google Scholar
38. 1. Engert V
    2. Hoehne K
    3. Singer T

    (2023) Specific reduction in the cortisol awakening response after socio-affective mental training

    Mindfulness 14:681–694.

    https://doi.org/10.1007/s12671-023-02074-y

    • Google Scholar
39. Book

    1. First MB
    2. Spitzer RL
    3. Gibbon M
    4. Williams JB

    (2012)

    Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), Clinician Version, Administration Booklet

    American Psychiatric Pub.

    • Google Scholar
40. 1. Franklin TB
    2. Saab BJ
    3. Mansuy IM

    (2012) Neural mechanisms of stress resilience and vulnerability

    Neuron 75:747–761.

    https://doi.org/10.1016/j.neuron.2012.08.016

    • PubMed
    • Google Scholar
41. 1. Fries E
    2. Dettenborn L
    3. Kirschbaum C

    (2009) The cortisol awakening response (CAR): facts and future directions

    International Journal of Psychophysiology 72:67–73.

    https://doi.org/10.1016/j.ijpsycho.2008.03.014

    • PubMed
    • Google Scholar
42. 1. Gao W
    2. Kirschbaum C
    3. Grass J
    4. Stalder T

    (2016) LC-MS based analysis of endogenous steroid hormones in human hair

    The Journal of Steroid Biochemistry and Molecular Biology 162:92–99.

    https://doi.org/10.1016/j.jsbmb.2015.12.022

    • PubMed
    • Google Scholar
43. 1. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators

    (2016)

    Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

    Lancet 388:1545–1602.

    • Google Scholar
44. 1. Genon S
    2. Bernhardt BC
    3. La Joie R
    4. Amunts K
    5. Eickhoff SB

    (2021) The many dimensions of human hippocampal organization and (dys)function

    Trends in Neurosciences 44:977–989.

    https://doi.org/10.1016/j.tins.2021.10.003

    • PubMed
    • Google Scholar
45. 1. Gilbert P

    (2014) The origins and nature of compassion focused therapy

    The British Journal of Clinical Psychology 53:6–41.

    https://doi.org/10.1111/bjc.12043

    • PubMed
    • Google Scholar
46. 1. Gold SM
    2. Kern KC
    3. O’Connor M-F
    4. Montag MJ
    5. Kim A
    6. Yoo YS
    7. Giesser BS
    8. Sicotte NL

    (2010) Smaller cornu ammonis 2-3/dentate gyrus volumes and elevated cortisol in multiple sclerosis patients with depressive symptoms

    Biological Psychiatry 68:553–559.

    https://doi.org/10.1016/j.biopsych.2010.04.025

    • PubMed
    • Google Scholar
47. 1. Goldberg SB
    2. Tucker RP
    3. Greene PA
    4. Davidson RJ
    5. Wampold BE
    6. Kearney DJ
    7. Simpson TL

    (2018) Mindfulness-based interventions for psychiatric disorders: A systematic review and meta-analysis

    Clinical Psychology Review 59:52–60.

    https://doi.org/10.1016/j.cpr.2017.10.011

    • PubMed
    • Google Scholar
48. 1. Greicius MD
    2. Krasnow B
    3. Reiss AL
    4. Menon V

    (2003) Functional connectivity in the resting brain: A network analysis of the default mode hypothesis

    PNAS 100:253–258.

    https://doi.org/10.1073/pnas.0135058100

    • PubMed
    • Google Scholar
49. 1. Hakamata Y
    2. Komi S
    3. Sato E
    4. Izawa S
    5. Mizukami S
    6. Moriguchi Y
    7. Motomura Y
    8. Matsui M
    9. Kim Y
    10. Hanakawa T
    11. Inoue Y
    12. Tagaya H

    (2019) Cortisol-related hippocampal-extrastriate functional connectivity explains the adverse effect of cortisol on visuospatial retrieval

    Psychoneuroendocrinology 109:104310.

    https://doi.org/10.1016/j.psyneuen.2019.04.013

    • PubMed
    • Google Scholar
50. 1. Herman JP
    2. Cullinan WE
    3. Morano MI
    4. Akil H
    5. Watson SJ

    (1995) Contribution of the ventral subiculum to inhibitory regulation of the hypothalamo-pituitary-adrenocortical axis

    Journal of Neuroendocrinology 7:475–482.

    https://doi.org/10.1111/j.1365-2826.1995.tb00784.x

    • PubMed
    • Google Scholar
51. 1. Herman JP
    2. Cullinan WE

    (1997) Neurocircuitry of stress: central control of the hypothalamo-pituitary-adrenocortical axis

    Trends in Neurosciences 20:78–84.

    https://doi.org/10.1016/s0166-2236(96)10069-2

    • PubMed
    • Google Scholar
52. 1. Herman JP
    2. Ostrander MM
    3. Mueller NK
    4. Figueiredo H

    (2005) Limbic system mechanisms of stress regulation: hypothalamo-pituitary-adrenocortical axis

    Progress in Neuro-Psychopharmacology & Biological Psychiatry 29:1201–1213.

    https://doi.org/10.1016/j.pnpbp.2005.08.006

    • PubMed
    • Google Scholar
53. 1. Hodgetts CJ
    2. Voets NL
    3. Thomas AG
    4. Clare S
    5. Lawrence AD
    6. Graham KS

    (2017) Ultra-High-Field fMRI Reveals a Role for the Subiculum in Scene Perceptual Discrimination

    The Journal of Neuroscience 37:3150–3159.

    https://doi.org/10.1523/JNEUROSCI.3225-16.2017

    • PubMed
    • Google Scholar
54. 1. Jacobson L
    2. Sapolsky R

    (1991) The role of the hippocampus in feedback regulation of the hypothalamic-pituitary-adrenocortical axis

    Endocrine Reviews 12:118–134.

    https://doi.org/10.1210/edrv-12-2-118

    • PubMed
    • Google Scholar
55. 1. Jorm AF
    2. Patten SB
    3. Brugha TS
    4. Mojtabai R

    (2017) Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries

    World Psychiatry 16:90–99.

    https://doi.org/10.1002/wps.20388

    • PubMed
    • Google Scholar
56. 1. Kabat-Zinn J

    (1982) An outpatient program in behavioral medicine for chronic pain patients based on the practice of mindfulness meditation: theoretical considerations and preliminary results

    General Hospital Psychiatry 4:33–47.

    https://doi.org/10.1016/0163-8343(82)90026-3

    • PubMed
    • Google Scholar
57. 1. Kebets V
    2. Holmes AJ
    3. Orban C
    4. Tang S
    5. Li J
    6. Sun N
    7. Kong R
    8. Poldrack RA
    9. Yeo BTT

    (2019) Somatosensory-motor dysconnectivity spans multiple transdiagnostic dimensions of psychopathology

    Biological Psychiatry 86:779–791.

    https://doi.org/10.1016/j.biopsych.2019.06.013

    • PubMed
    • Google Scholar
58. 1. Kern S
    2. Oakes TR
    3. Stone CK
    4. McAuliff EM
    5. Kirschbaum C
    6. Davidson RJ

    (2008) Glucose metabolic changes in the prefrontal cortex are associated with HPA axis response to a psychosocial stressor

    Psychoneuroendocrinology 33:517–529.

    https://doi.org/10.1016/j.psyneuen.2008.01.010

    • PubMed
    • Google Scholar
59. 1. Khoury B
    2. Sharma M
    3. Rush SE
    4. Fournier C

    (2015) Mindfulness-based stress reduction for healthy individuals: A meta-analysis

    Journal of Psychosomatic Research 78:519–528.

    https://doi.org/10.1016/j.jpsychores.2015.03.009

    • PubMed
    • Google Scholar
60. 1. Kirschbaum C
    2. Pirke KM
    3. Hellhammer DH

    (1993) The ’Trier Social Stress Test’--A tool for investigating psychobiological stress responses in A laboratory setting

    Neuropsychobiology 28:76–81.

    https://doi.org/10.1159/000119004

    • PubMed
    • Google Scholar
61. 1. Klimecki OM
    2. Leiberg S
    3. Lamm C
    4. Singer T

    (2013) Functional neural plasticity and associated changes in positive affect after compassion training

    Cerebral Cortex 23:1552–1561.

    https://doi.org/10.1093/cercor/bhs142

    • PubMed
    • Google Scholar
62. 1. Knoops AJG
    2. Gerritsen L
    3. van der Graaf Y
    4. Mali W
    5. Geerlings MI

    (2010) Basal hypothalamic pituitary adrenal axis activity and hippocampal volumes: the SMART-Medea study

    Biological Psychiatry 67:1191–1198.

    https://doi.org/10.1016/j.biopsych.2010.01.025

    • PubMed
    • Google Scholar
63. 1. Kok BE
    2. Singer T

    (2017) Effects of contemplative dyads on engagement and perceived social connectedness over 9 months of mental training: A randomized clinical trial

    JAMA Psychiatry 74:126–134.

    https://doi.org/10.1001/jamapsychiatry.2016.3360

    • PubMed
    • Google Scholar
64. 1. Kudielka BM
    2. Broderick JE
    3. Kirschbaum C

    (2003) Compliance with saliva sampling protocols: electronic monitoring reveals invalid cortisol daytime profiles in noncompliant subjects

    Psychosomatic Medicine 65:313–319.

    https://doi.org/10.1097/01.psy.0000058374.50240.bf

    • PubMed
    • Google Scholar
65. 1. Kudielka BM
    2. Hawkley LC
    3. Adam EK
    4. Cacioppo JT

    (2007) Compliance with ambulatory saliva sampling in the chicago health, aging, and social relations study and associations with social support

    Annals of Behavioral Medicine 34:209–216.

    https://doi.org/10.1007/BF02872675

    • PubMed
    • Google Scholar
66. 1. Kulaga-Yoskovitz J
    2. Bernhardt BC
    3. Hong SJ
    4. Mansi T
    5. Liang KE
    6. van der Kouwe AJW
    7. Smallwood J
    8. Bernasconi A
    9. Bernasconi N

    (2015) Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset

    Scientific Data 2:150059.

    https://doi.org/10.1038/sdata.2015.59

    • PubMed
    • Google Scholar
67. 1. Larivière S
    2. Bayrak Ş
    3. Vos de Wael R
    4. Benkarim O
    5. Herholz P
    6. Rodriguez-Cruces R
    7. Paquola C
    8. Hong SJ
    9. Misic B
    10. Evans AC
    11. Valk SL
    12. Bernhardt BC

    (2023) BrainStat: A toolbox for brain-wide statistics and multimodal feature associations

    NeuroImage 266:119807.

    https://doi.org/10.1016/j.neuroimage.2022.119807

    • PubMed
    • Google Scholar
68. Book

    1. Lazarus RS
    2. Folkman S

    (1984)

    Stress, Appraisal, and Coping

    New York: Springer publishing company.

    • Google Scholar
69. 1. Lupien SJ
    2. de Leon M
    3. de Santi S
    4. Convit A
    5. Tarshish C
    6. Nair NPV
    7. Thakur M
    8. McEwen BS
    9. Hauger RL
    10. Meaney MJ

    (1998) Cortisol levels during human aging predict hippocampal atrophy and memory deficits

    Nature Neuroscience 1:69–73.

    https://doi.org/10.1038/271

    • PubMed
    • Google Scholar
70. 1. Lupien SJ
    2. Nair NP
    3. Brière S
    4. Maheu F
    5. Tu MT
    6. Lemay M
    7. McEwen BS
    8. Meaney MJ

    (1999) Increased cortisol levels and impaired cognition in human aging: implication for depression and dementia in later life

    Reviews in the Neurosciences 10:117–139.

    https://doi.org/10.1515/revneuro.1999.10.2.117

    • PubMed
    • Google Scholar
71. 1. Magariños AM
    2. McEwen BS

    (1995) Stress-induced atrophy of apical dendrites of hippocampal CA3c neurons: involvement of glucocorticoid secretion and excitatory amino acid receptors

    Neuroscience 69:89–98.

    https://doi.org/10.1016/0306-4522(95)00259-l

    • PubMed
    • Google Scholar
72. 1. Magariños AM
    2. Orchinik M
    3. McEwen BS

    (1998) Morphological changes in the hippocampal CA3 region induced by non-invasive glucocorticoid administration: a paradox

    Brain Research 809:314–318.

    https://doi.org/10.1016/s0006-8993(98)00882-8

    • PubMed
    • Google Scholar
73. 1. McCall C
    2. Singer T

    (2012) The animal and human neuroendocrinology of social cognition, motivation and behavior

    Nature Neuroscience 15:681–688.

    https://doi.org/10.1038/nn.3084

    • PubMed
    • Google Scholar
74. 1. McEwen BS

    (1999) Stress and hippocampal plasticity

    Annual Review of Neuroscience 22:105–122.

    https://doi.org/10.1146/annurev.neuro.22.1.105

    • PubMed
    • Google Scholar
75. 1. McEwen BS

    (2013) The brain on stress: Toward an integrative approach to brain, body, and behavior

    Perspectives on Psychological Science 8:673–675.

    https://doi.org/10.1177/1745691613506907

    • PubMed
    • Google Scholar
76. 1. McEwen BS
    2. Bowles NP
    3. Gray JD
    4. Hill MN
    5. Hunter RG
    6. Karatsoreos IN
    7. Nasca C

    (2015) Mechanisms of stress in the brain

    Nature Neuroscience 18:1353–1363.

    https://doi.org/10.1038/nn.4086

    • PubMed
    • Google Scholar
77. 1. McEwen BS
    2. Nasca C
    3. Gray JD

    (2016) Stress effects on neuronal structure: Hippocampus, amygdala, and prefrontal cortex

    Neuropsychopharmacology 41:3–23.

    https://doi.org/10.1038/npp.2015.171

    • PubMed
    • Google Scholar
78. 1. McIntosh AR
    2. Lobaugh NJ

    (2004) Partial least squares analysis of neuroimaging data: applications and advances

    NeuroImage 23 Suppl 1:S250–S263.

    https://doi.org/10.1016/j.neuroimage.2004.07.020

    • PubMed
    • Google Scholar
79. 1. McKittrick CR
    2. Magariños AM
    3. Blanchard DC
    4. Blanchard RJ
    5. McEwen BS
    6. Sakai RR

    (2000) Chronic social stress reduces dendritic arbors in CA3 of hippocampus and decreases binding to serotonin transporter sites

    Synapse 36:85–94.

    https://doi.org/10.1002/(SICI)1098-2396(200005)36:2<85::AID-SYN1>3.0.CO;2-Y

    • PubMed
    • Google Scholar
80. 1. Miller JJ
    2. Fletcher K
    3. Kabat-Zinn J

    (1995) Three-year follow-up and clinical implications of a mindfulness meditation-based stress reduction intervention in the treatment of anxiety disorders

    General Hospital Psychiatry 17:192–200.

    https://doi.org/10.1016/0163-8343(95)00025-m

    • PubMed
    • Google Scholar
81. 1. Mondelli V
    2. Pariante CM
    3. Navari S
    4. Aas M
    5. D’Albenzio A
    6. Di Forti M
    7. Handley R
    8. Hepgul N
    9. Marques TR
    10. Taylor H
    11. Papadopoulos AS
    12. Aitchison KJ
    13. Murray RM
    14. Dazzan P

    (2010) Higher cortisol levels are associated with smaller left hippocampal volume in first-episode psychosis

    Schizophrenia Research 119:75–78.

    https://doi.org/10.1016/j.schres.2009.12.021

    • PubMed
    • Google Scholar
82. 1. Nelson EE
    2. Panksepp J

    (1998) Brain substrates of infant–mother attachment: Contributions of opioids, oxytocin, and norepinephrine

    Neuroscience & Biobehavioral Reviews 22:437–452.

    https://doi.org/10.1016/S0149-7634(97)00052-3

    • Google Scholar
83. 1. O’Doherty DCM
    2. Chitty KM
    3. Saddiqui S
    4. Bennett MR
    5. Lagopoulos J

    (2015) A systematic review and meta-analysis of magnetic resonance imaging measurement of structural volumes in posttraumatic stress disorder

    Psychiatry Research 232:1–33.

    https://doi.org/10.1016/j.pscychresns.2015.01.002

    • PubMed
    • Google Scholar
84. 1. Olsen RK
    2. Moses SN
    3. Riggs L
    4. Ryan JD

    (2012) The hippocampus supports multiple cognitive processes through relational binding and comparison

    Frontiers in Human Neuroscience 6:146.

    https://doi.org/10.3389/fnhum.2012.00146

    • PubMed
    • Google Scholar
85. 1. Palomero-Gallagher N
    2. Kedo O
    3. Mohlberg H
    4. Zilles K
    5. Amunts K

    (2020) Multimodal mapping and analysis of the cyto- and receptorarchitecture of the human hippocampus

    Brain Structure & Function 225:881–907.

    https://doi.org/10.1007/s00429-019-02022-4

    • PubMed
    • Google Scholar
86. 1. Paquola C
    2. Benkarim O
    3. DeKraker J
    4. Larivière S
    5. Frässle S
    6. Royer J
    7. Tavakol S
    8. Valk S
    9. Bernasconi A
    10. Bernasconi N
    11. Khan A
    12. Evans AC
    13. Razi A
    14. Smallwood J
    15. Bernhardt BC

    (2020) Convergence of cortical types and functional motifs in the human mesiotemporal lobe

    eLife 9:e60673.

    https://doi.org/10.7554/eLife.60673

    • PubMed
    • Google Scholar
87. 1. Pascoe MC
    2. Thompson DR
    3. Jenkins ZM
    4. Ski CF

    (2017) Mindfulness mediates the physiological markers of stress: Systematic review and meta-analysis

    Journal of Psychiatric Research 95:156–178.

    https://doi.org/10.1016/j.jpsychires.2017.08.004

    • PubMed
    • Google Scholar
88. Preprint

    1. Pernet CR
    2. Belov N
    3. Delorme A
    4. Zammit A

    (2021) Mindfulness Related Changes in Grey Matter: A Systematic Review and Meta-Analysis

    bioRxiv.

    https://doi.org/10.1101/2020.03.31.20049510

    • Google Scholar
89. 1. Phelps EA

    (2004) Human emotion and memory: interactions of the amygdala and hippocampal complex

    Current Opinion in Neurobiology 14:198–202.

    https://doi.org/10.1016/j.conb.2004.03.015

    • PubMed
    • Google Scholar
90. 1. Plachti A
    2. Eickhoff SB
    3. Hoffstaedter F
    4. Patil KR
    5. Laird AR
    6. Fox PT
    7. Amunts K
    8. Genon S

    (2019) Multimodal parcellations and extensive behavioral profiling tackling the hippocampus gradient

    Cerebral Cortex 29:4595–4612.

    https://doi.org/10.1093/cercor/bhy336

    • PubMed
    • Google Scholar
91. 1. Power JD
    2. Barnes KA
    3. Snyder AZ
    4. Schlaggar BL
    5. Petersen SE

    (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

    NeuroImage 59:2142–2154.

    https://doi.org/10.1016/j.neuroimage.2011.10.018

    • PubMed
    • Google Scholar
92. 1. Pruessner JC
    2. Kirschbaum C
    3. Meinlschmid G
    4. Hellhammer DH

    (2003) Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change

    Psychoneuroendocrinology 28:916–931.

    https://doi.org/10.1016/S0306-4530(02)00108-7

    • Google Scholar
93. 1. Pruessner JC
    2. Baldwin MW
    3. Dedovic K
    4. Renwick R
    5. Mahani NK
    6. Lord C
    7. Meaney M
    8. Lupien S

    (2005) Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood

    NeuroImage 28:815–826.

    https://doi.org/10.1016/j.neuroimage.2005.06.014

    • PubMed
    • Google Scholar
94. 1. Pruessner M
    2. Pruessner JC
    3. Hellhammer DH
    4. Bruce Pike G
    5. Lupien SJ

    (2007) The associations among hippocampal volume, cortisol reactivity, and memory performance in healthy young men

    Psychiatry Research 155:1–10.

    https://doi.org/10.1016/j.pscychresns.2006.12.007

    • PubMed
    • Google Scholar
95. 1. Pruessner JC
    2. Dedovic K
    3. Pruessner M
    4. Lord C
    5. Buss C
    6. Collins L
    7. Dagher A
    8. Lupien SJ

    (2010) Stress regulation in the central nervous system: evidence from structural and functional neuroimaging studies in human populations - 2008 Curt Richter Award Winner

    Psychoneuroendocrinology 35:179–191.

    https://doi.org/10.1016/j.psyneuen.2009.02.016

    • PubMed
    • Google Scholar
96. 1. Przeździk I
    2. Faber M
    3. Fernández G
    4. Beckmann CF
    5. Haak KV

    (2019) The functional organisation of the hippocampus along its long axis is gradual and predicts recollection

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 119:324–335.

    https://doi.org/10.1016/j.cortex.2019.04.015

    • PubMed
    • Google Scholar
97. 1. Puhlmann LMC
    2. Linz R
    3. Valk SL
    4. Vrticka P
    5. Vos de Wael R
    6. Bernasconi A
    7. Bernasconi N
    8. Caldairou B
    9. Papassotiriou I
    10. Chrousos GP
    11. Bernhardt BC
    12. Singer T
    13. Engert V

    (2021a) Association between hippocampal structure and serum Brain-Derived Neurotrophic Factor (BDNF) in healthy adults: A registered report

    NeuroImage 236:118011.

    https://doi.org/10.1016/j.neuroimage.2021.118011

    • PubMed
    • Google Scholar
98. 1. Puhlmann LMC
    2. Vrtička P
    3. Linz R
    4. Stalder T
    5. Kirschbaum C
    6. Engert V
    7. Singer T

    (2021b) Contemplative mental training reduces hair glucocorticoid levels in a randomized clinical trial

    Psychosomatic Medicine 83:894–905.

    https://doi.org/10.1097/PSY.0000000000000970

    • PubMed
    • Google Scholar
99. 1. Radley JJ
    2. Sawchenko PE

    (2011) A common substrate for prefrontal and hippocampal inhibition of the neuroendocrine stress response

    The Journal of Neuroscience 31:9683–9695.

    https://doi.org/10.1523/JNEUROSCI.6040-10.2011

    • PubMed
    • Google Scholar
100. 1. Robillard R
     2. Saad M
     3. Edwards J
     4. Solomonova E
     5. Pennestri MH
     6. Daros A
     7. Veissière SPL
     8. Quilty L
     9. Dion K
     10. Nixon A
     11. Phillips J
     12. Bhatla R
     13. Spilg E
     14. Godbout R
     15. Yazji B
     16. Rushton C
     17. Gifford WA
     18. Gautam M
     19. Boafo A
     20. Swartz R
     21. Kendzerska T

     (2020) Social, financial and psychological stress during an emerging pandemic: observations from a population survey in the acute phase of COVID-19

     BMJ Open 10:e043805.

     https://doi.org/10.1136/bmjopen-2020-043805

     • PubMed
     • Google Scholar
101. 1. Ross KM
     2. Murphy MLM
     3. Adam EK
     4. Chen E
     5. Miller GE

     (2014) How stable are diurnal cortisol activity indices in healthy individuals? Evidence from three multi-wave studies

     Psychoneuroendocrinology 39:184–193.

     https://doi.org/10.1016/j.psyneuen.2013.09.016

     • PubMed
     • Google Scholar
102. 1. Sanada K
     2. Montero-Marin J
     3. Alda Díez M
     4. Salas-Valero M
     5. Pérez-Yus MC
     6. Morillo H
     7. Demarzo MMP
     8. García-Toro M
     9. García-Campayo J

     (2016) Effects of mindfulness-based interventions on salivary cortisol in healthy adults: A meta-analytical review

     Frontiers in Physiology 7:471.

     https://doi.org/10.3389/fphys.2016.00471

     • PubMed
     • Google Scholar
103. 1. Sánchez MM
     2. Young LJ
     3. Plotsky PM
     4. Insel TR

     (2000) Distribution of corticosteroid receptors in the rhesus brain: relative absence of glucocorticoid receptors in the hippocampal formation

     The Journal of Neuroscience 20:4657–4668.

     https://doi.org/10.1523/JNEUROSCI.20-12-04657.2000

     • PubMed
     • Google Scholar
104. 1. Sapolsky RM
     2. Krey LC
     3. McEwen BS

     (1985a) Prolonged glucocorticoid exposure reduces hippocampal neuron number: implications for aging

     The Journal of Neuroscience 5:1222–1227.

     https://doi.org/10.1523/JNEUROSCI.05-05-01222.1985

     • PubMed
     • Google Scholar
105. 1. Sapolsky RM
     2. Pulsinelli WA

     (1985b) Glucocorticoids potentiate ischemic injury to neurons: therapeutic implications

     Science 229:1397–1400.

     https://doi.org/10.1126/science.4035356

     • PubMed
     • Google Scholar
106. 1. Sapolsky RM

     (2000a) Stress hormones: good and bad

     Neurobiology of Disease 7:540–542.

     https://doi.org/10.1006/nbdi.2000.0350

     • PubMed
     • Google Scholar
107. 1. Sapolsky RM

     (2000b) Glucocorticoids and hippocampal atrophy in neuropsychiatric disorders

     Archives of General Psychiatry 57:925–935.

     https://doi.org/10.1001/archpsyc.57.10.925

     • PubMed
     • Google Scholar
108. Book

     1. Segal ZV
     2. Williams JMG
     3. Teasdale JD

     (2002)

     Mindfulness-Based Cognitive Therapy for Depression

     New York: Guilford Press.

     • Google Scholar
109. Book

     1. Singer T
     2. Kok BE
     3. Bornemann B
     4. Bolz M
     5. Bochow CA

     (2016)

     The Resource Project: Background, Design, Samples and Measurements

     Max Planck Institute for Human Cognitive and Brain Sciences.

     • Google Scholar
110. 1. Smith SM
     2. Miller KL
     3. Salimi-Khorshidi G
     4. Webster M
     5. Beckmann CF
     6. Nichols TE
     7. Ramsey JD
     8. Woolrich MW

     (2011) Network modelling methods for FMRI

     NeuroImage 54:875–891.

     https://doi.org/10.1016/j.neuroimage.2010.08.063

     • Google Scholar
111. 1. Stalder T
     2. Kirschbaum C
     3. Kudielka BM
     4. Adam EK
     5. Pruessner JC
     6. Wüst S
     7. Dockray S
     8. Smyth N
     9. Evans P
     10. Hellhammer DH
     11. Miller R
     12. Wetherell MA
     13. Lupien SJ
     14. Clow A

     (2016) Assessment of the cortisol awakening response: Expert consensus guidelines

     Psychoneuroendocrinology 63:414–432.

     https://doi.org/10.1016/j.psyneuen.2015.10.010

     • PubMed
     • Google Scholar
112. 1. Stalder T
     2. Steudte-Schmiedgen S
     3. Alexander N
     4. Klucken T
     5. Vater A
     6. Wichmann S
     7. Kirschbaum C
     8. Miller R

     (2017) Stress-related and basic determinants of hair cortisol in humans: A meta-analysis

     Psychoneuroendocrinology 77:261–274.

     https://doi.org/10.1016/j.psyneuen.2016.12.017

     • PubMed
     • Google Scholar
113. 1. Stalder T
     2. Lupien SJ
     3. Kudielka BM
     4. Adam EK
     5. Pruessner JC
     6. Wüst S
     7. Dockray S
     8. Smyth N
     9. Evans P
     10. Kirschbaum C
     11. Miller R
     12. Wetherell MA
     13. Finke JB
     14. Klucken T
     15. Clow A

     (2022) Evaluation and update of the expert consensus guidelines for the assessment of the cortisol awakening response (CAR)

     Psychoneuroendocrinology 146:105946.

     https://doi.org/10.1016/j.psyneuen.2022.105946

     • Google Scholar
114. 1. Sudheimer KD
     2. O’Hara R
     3. Spiegel D
     4. Powers B
     5. Kraemer HC
     6. Neri E
     7. Weiner M
     8. Hardan A
     9. Hallmayer J
     10. Dhabhar FS

     (2014) Cortisol, cytokines, and hippocampal volume interactions in the elderly

     Frontiers in Aging Neuroscience 6:153.

     https://doi.org/10.3389/fnagi.2014.00153

     • PubMed
     • Google Scholar
115. 1. Trautwein FM
     2. Kanske P
     3. Böckler A
     4. Singer T

     (2020) Differential benefits of mental training types for attention, compassion, and theory of mind

     Cognition 194:104039.

     https://doi.org/10.1016/j.cognition.2019.104039

     • PubMed
     • Google Scholar
116. 1. Ursache A
     2. Wedin W
     3. Tirsi A
     4. Convit A

     (2012) Preliminary evidence for obesity and elevations in fasting insulin mediating associations between cortisol awakening response and hippocampal volumes and frontal atrophy

     Psychoneuroendocrinology 37:1270–1276.

     https://doi.org/10.1016/j.psyneuen.2011.12.020

     • PubMed
     • Google Scholar
117. 1. Valk SL
     2. Bernhardt BC
     3. Trautwein FM
     4. Böckler A
     5. Kanske P
     6. Guizard N
     7. Collins DL
     8. Singer T

     (2017) Structural plasticity of the social brain: Differential change after socio-affective and cognitive mental training

     Science Advances 3:e1700489.

     https://doi.org/10.1126/sciadv.1700489

     • PubMed
     • Google Scholar
118. 1. Valk SL
     2. Kanske P
     3. Park BY
     4. Hong SJ
     5. Böckler A
     6. Trautwein FM
     7. Bernhardt BC
     8. Singer T

     (2023a) Functional and microstructural plasticity following social and interoceptive mental training

     eLife 12:e85188.

     https://doi.org/10.7554/eLife.85188

     • PubMed
     • Google Scholar
119. Preprint

     1. Valk S
     2. Kanske P
     3. Trautwein FM
     4. Böckler-Raettig A
     5. Park B
     6. Hong SJ

     (2023b) Changing the Social Brain: Plasticity along Macro-Scale Axes of Functional Connectivity Following Social Mental Training

     bioRxiv.

     https://doi.org/10.1101/2020.11.11.377895

     • Google Scholar
120. Software

     1. Valk S

     (2024) Valk_hippocampal_change, version swh:1:rev:cf489dca61cd8e37d65d07b77cce111c5bca8df6

     Software Heritage.

     https://archive.softwareheritage.org/swh:1:dir:53baa3e2749d954d9d835a2e94f23e393b0fdda8;origin=https://github.com/CNG-LAB/valk_hippocampal_change;visit=swh:1:snp:b3e8cd1bf1df40c6cfd0e899fe77ca502ac31c40;anchor=swh:1:rev:cf489dca61cd8e37d65d07b77cce111c5bca8df6
121. 1. Vincent JL
     2. Snyder AZ
     3. Fox MD
     4. Shannon BJ
     5. Andrews JR
     6. Raichle ME
     7. Buckner RL

     (2006) Coherent spontaneous activity identifies a hippocampal-parietal memory network

     Journal of Neurophysiology 96:3517–3531.

     https://doi.org/10.1152/jn.00048.2006

     • Google Scholar
122. 1. Vogel JW
     2. La Joie R
     3. Grothe MJ
     4. Diaz-Papkovich A
     5. Doyle A
     6. Vachon-Presseau E
     7. Lepage C
     8. Vos de Wael R
     9. Thomas RA
     10. Iturria-Medina Y
     11. Bernhardt B
     12. Rabinovici GD
     13. Evans AC

     (2020) A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems

     Nature Communications 11:960.

     https://doi.org/10.1038/s41467-020-14518-3

     • PubMed
     • Google Scholar
123. 1. Vos de Wael R
     2. Larivière S
     3. Caldairou B
     4. Hong SJ
     5. Margulies DS
     6. Jefferies E
     7. Bernasconi A
     8. Smallwood J
     9. Bernasconi N
     10. Bernhardt BC

     (2018) Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding

     PNAS 115:10154–10159.

     https://doi.org/10.1073/pnas.1803667115

     • PubMed
     • Google Scholar
124. 1. Wennig R

     (2000) Potential problems with the interpretation of hair analysis results

     Forensic Science International 107:5–12.

     https://doi.org/10.1016/s0379-0738(99)00146-2

     • PubMed
     • Google Scholar
125. 1. Whelan CD
     2. Hibar DP
     3. van Velzen LS
     4. Zannas AS
     5. Carrillo-Roa T
     6. McMahon K
     7. Prasad G
     8. Kelly S
     9. Faskowitz J
     10. deZubiracay G
     11. Iglesias JE
     12. van Erp TGM
     13. Frodl T
     14. Martin NG
     15. Wright MJ
     16. Jahanshad N
     17. Schmaal L
     18. Sämann PG
     19. Thompson PM

     (2016) Heritability and reliability of automatically segmented human hippocampal formation subregions

     NeuroImage 128:125–137.

     https://doi.org/10.1016/j.neuroimage.2015.12.039

     • PubMed
     • Google Scholar
126. 1. Wisse LEM
     2. Adler DH
     3. Ittyerah R
     4. Pluta JB
     5. Robinson JL
     6. Schuck T
     7. Trojanowski JQ
     8. Grossman M
     9. Detre JA
     10. Elliott MA
     11. Toledo JB
     12. Liu W
     13. Pickup S
     14. Das SR
     15. Wolk DA
     16. Yushkevich PA

     (2017) Comparison of in vivo and ex vivo MRI of the human hippocampal formation in the same subjects

     Cerebral Cortex 27:5185–5196.

     https://doi.org/10.1093/cercor/bhw299

     • PubMed
     • Google Scholar
127. 1. Wisse LEM
     2. Chételat G
     3. Daugherty AM
     4. de Flores R
     5. la Joie R
     6. Mueller SG
     7. Stark CEL
     8. Wang L
     9. Yushkevich PA
     10. Berron D
     11. Raz N
     12. Bakker A
     13. Olsen RK
     14. Carr VA

     (2021) Hippocampal subfield volumetry from structural isotropic 1 mm³ MRI scans: A note of caution

     Human Brain Mapping 42:539–550.

     https://doi.org/10.1002/hbm.25234

     • PubMed
     • Google Scholar
128. Book

     1. Wittchen HU
     2. Pfister H

     (1997)

     Diagnostisches Expertensystem Für Psychische Störungen (DIA-X)

     Frankfurt: Swets & Zeitlinger.

     • Google Scholar
129. Book

     1. Wittchen HU
     2. Zaudig M
     3. Fydrich T

     (1997)

     SKID-Strukturiertes Klinisches Interview Für DSM-IV, Achse I Und II

     Göttingen: Hogrefe.

     • Google Scholar
130. 1. Wolf OT
     2. Fujiwara E
     3. Luwinski G
     4. Kirschbaum C
     5. Markowitsch HJ

     (2005) No morning cortisol response in patients with severe global amnesia

     Psychoneuroendocrinology 30:101–105.

     https://doi.org/10.1016/j.psyneuen.2004.05.001

     • PubMed
     • Google Scholar
131. 1. Worsley K
     2. Taylor J
     3. Carbonell F
     4. Chung M
     5. Duerden E
     6. Bernhardt B
     7. Lyttelton O
     8. Boucher M
     9. Evans A

     (2009) SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory

     NeuroImage 47:S102.

     https://doi.org/10.1016/S1053-8119(09)70882-1

     • Google Scholar
132. 1. Yeo BTT
     2. Krienen FM
     3. Sepulcre J
     4. Sabuncu MR
     5. Lashkari D
     6. Hollinshead M
     7. Roffman JL
     8. Smoller JW
     9. Zöllei L
     10. Polimeni JR
     11. Fischl B
     12. Liu H
     13. Buckner RL

     (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity

     Journal of Neurophysiology 106:1125–1165.

     https://doi.org/10.1152/jn.00338.2011

     • PubMed
     • Google Scholar
133. 1. Yushkevich PA
     2. Amaral RSC
     3. Augustinack JC
     4. Bender AR
     5. Bernstein JD
     6. Boccardi M
     7. Bocchetta M
     8. Burggren AC
     9. Carr VA
     10. Chakravarty MM
     11. Chételat G
     12. Daugherty AM
     13. Davachi L
     14. Ding SL
     15. Ekstrom A
     16. Geerlings MI
     17. Hassan A
     18. Huang Y
     19. Iglesias JE
     20. La Joie R
     21. Kerchner GA
     22. LaRocque KF
     23. Libby LA
     24. Malykhin N
     25. Mueller SG
     26. Olsen RK
     27. Palombo DJ
     28. Parekh MB
     29. Pluta JB
     30. Preston AR
     31. Pruessner JC
     32. Ranganath C
     33. Raz N
     34. Schlichting ML
     35. Schoemaker D
     36. Singh S
     37. Stark CEL
     38. Suthana N
     39. Tompary A
     40. Turowski MM
     41. Van Leemput K
     42. Wagner AD
     43. Wang L
     44. Winterburn JL
     45. Wisse LEM
     46. Yassa MA
     47. Zeineh MM
     48. Hippocampal Subfields Group (HSG)

     (2015) Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol

     NeuroImage 111:526–541.

     https://doi.org/10.1016/j.neuroimage.2015.01.004

     • PubMed
     • Google Scholar
134. 1. Zhong Q
     2. Xu H
     3. Qin J
     4. Zeng LL
     5. Hu D
     6. Shen H

     (2019) Functional parcellation of the hippocampus from resting-state dynamic functional connectivity

     Brain Research 1715:165–175.

     https://doi.org/10.1016/j.brainres.2019.03.023

     • PubMed
     • Google Scholar

Author details

1. Sofie Louise Valk

   1. Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. INM-7, FZ Jülich, Jülich, Germany
   3. Institute for System Neurosciences, Heinrich Heine University, Düsseldorf, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Veronika Engert

   For correspondence

   s.valk@fz-juelich.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2998-6849

2. Veronika Engert

   1. Institute for Psychosocial Medicine, Psychotherapy and Psychooncology, Jena University Hospital, Friedrich-Schiller University, Jena, Germany
   2. Research Group Social Stress and Family Health, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Sofie Louise Valk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5317-933X

3. Lara Puhlmann

   1. Research Group Social Stress and Family Health, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. Leibniz Institute for Resilience Research, Mainz, Germany

   Contribution

   Data curation, Validation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0870-8770

4. Roman Linz

   Research Group Social Stress and Family Health, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

   Contribution

   Data curation, Validation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Benoit Caldairou

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada

   Contribution

   Resources, Software, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

6. Andrea Bernasconi

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada

   Contribution

   Resources, Software, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

7. Neda Bernasconi

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada

   Contribution

   Resources, Software, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

8. Boris C Bernhardt

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Tania Singer

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9256-6041

9. Tania Singer

   Social Neuroscience Lab, Max Planck Society, Berlin, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Boris C Bernhardt

   Competing interests

   No competing interests declared

• 885

  views

• 58

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.