Linking macroscale resting-state functional connectivity to acute and chronic stress

Why study stress? Stress exposure leads to an intricate psychophysiological response (Dhabhar, 2018). Acute or short-term stress, triggered by a challenging external or internal stimulus, influences subjective experience and physiological (e.g., the autonomic and endocrine) systems (Dhabhar, 2018; Lupien et al., 2018; Tsigos et al., 2000). This expansive yet transient shift in the state of the organism is adaptive for an ever-changing environment. However, when stress becomes prolonged or chronic, cognitive, physiological, and behavioural impairments may unfold (Davis et al., 2017; McEwen, 2017). Consequently, chronic stress is an indicator for several psychiatric disorders including depression, anxiety, and psychosis (Davis et al., 2017). Hence, a better understanding of the effects of stress on the brain are important for both healthy and clinical populations. Yet, despite the prevalence and impact of both acute and chronic stress on general health, our understanding of its effects on the brain remains incomplete. How to operationalise stress? To assess the neurobiology of stress, researchers ideally induce an experimentally valid and reliable stressor and stress evaluation measures. The Trier Social Stress Test (TSST) is a widely used and ecologically valid procedure. It induces acute stress through psychological pressure in a social situation: a 5-minute preparation period for a job interview followed by 5 minutes of interviewing and 5 minutes of performing mental arithmetic in front of an evaluation committee (Kirschbaum et al., 1993). On the other hand, chronic stress is typically induced by the lives of people and assessed through self reports. The Trier Inventory of Chronic Stress (TICS) is a questionnaire to quantify chronic stress levels (Schulz & Schlotz, 1999). It includes 57 items and covers nine domains of stress such as “work overload”,” social tension”, or “chronic worrying”. The TICS screening scale is a weighted average of the nine domains, to yield an integrated (i.e., single-value) measurement of chronic stress. What do we know about the brain and stress? Resting-state functional magnetic imaging (rs-fMRI) is a central tool for the investigation of both brain activation and functional connectivity - and of stress-induced changes therein (Fox & Greicius, 2010; Soares et al., 2013). As a non-invasive measure of spontaneous brain activity during rest, rs-fMRI can provide insights into the organisation of functional systems without the presence of an external task (Foster et al., 2016; Raimondo et al., 2021). Both acute and chronic stress can induce brain changes in single regions (Berretz et al., 2021) and inter-regional connectivity in functional networks (Reinelt et al., 2019) as well as the large-scale brain-wide reconfigurations (Wang et al., 2022; Zhang et al., 2019). For example, in the framework by Hermans et al. (2014) acute stress exposure results in more resources being allocated to the salience network (SN), which is interpreted as increased emotional reactivity, alertness or vigilance (Hermans et al., 2014). When the acute stress wanes, resources are then shifted back to the executive control network (ECN), associated with higher-order mental processes. Chronic stress, in turn, has been related to decreased connectivity within the ECN and between the SN and ECN, as well as aberrant activity in the DMN in students during the examination period (Massullo et al., 2022; Soares et al., 2013). Similarly, chronic pain, a different type of chronic stress, leads to atypical functional connectivity in the DMN and medial prefrontal cortex (Kucyi & Davis, 2015). Those shifts of activity and connectivity may be linked to altered information processing and disrupted attentional control as adaptation to a stressor (Soares et al., 2013; Zhang et al., 2019). In summary, previous findings indicate widespread stress-related shifts in functional networks and their interactions. Such interactions are suitably investigated in an integrative (i.e., whole-brain and “macroscale”) manner that considers all voxels at once and without regional restrictions. What are gradients? A promising approach to describe macroscale organisation uses multivariate machine learning and dimension reduction to identify different axes of cortical organisation (Margulies et al., 2016). These axes are derived through a decomposition of rs-fMRI data in terms of similarity of functional connectivity networks at every point of measurement of the cortex. Dimension reduction then produces axes of explained variance called “cortical gradients” (Cross et al., 2021; Margulies et al., 2016). Such cortical gradients represent topographical organisation of the brain in a hierarchical manner (Eickhoff et al., 2018). Thus, this data-driven approach allows every point of measurement to be visualised along multiple dimensions of cortical organisation (Bajada et al., 2020; Craddock et al., 2013; Margulies et al., 2016). Differentiation of points of measurement (also called parcels) along one gradient implies dissimilarity in terms of functional connectivity networks and, hence, segregation of their networks. Since every parcel has a position based on the scores of the three gradients we chose to analyse, the position can be interpreted together in a 3D space (the manifold space). Since the manifold space consists of three axes (one per gradient), it has the advantage of all gradients being analysed and visualised simultaneously for every parcel (Park et al., 2021). Further, each datapoint or network isn't fixed along these hierarchies but may change position with age (Bethlehem et al., 2020), states such as sleep deprivation (Cross et al., 2021) or pharmacological intervention (Girn et al., 2021). Typically networks, consisting of multiple parcels, are analysed in terms of the dispersion of their parcels and their dispersion relative to other networks along the gradients (Bethlehem et al., 2020; Cross et al., 2021). Focusing on networks and their relation rather than brain areas allows one to understand the entire brain's response to a change in environment. Why should we use gradients to investigate stress? The analysis of cortical gradients has multiple benefits for the investigation of stress: As stress causes a host of changes in the entire organism and therefore at multiple brain sites, stress-related shifts in neuronal activity and in information processing could be reflected in the gradient changes. For instance, stress has been shown to decrease cognitive flexibility (Cremer et al., 2021), a process which heavily relies on transmodal information processing of the brain. Using gradient analysis, we may be able to capture a shift of the areas involved in cognitive flexibility along the first gradient which typically reflects information processing on a unimodal-multimodal spectrum. Furthermore, stress (both acute and chronic) has been implied to change network balance. Specifically, acute stress can shift the brain into a more integrated state, reducing dispersion between networks (especially in the fronto-parietal regions) and variability of dynamic transitions between states (Zang et al 2022). In this study, we use rs-fMRI data to investigate macroscale functional connectivity (cortical gradients) related to acute and chronic stress. We will focus on three networks: the DMN, ECN, and SN previously implied in stress research (see above) and the related dispersion change along the gradients within and between those networks. This dispersion analysis may aid the understanding of behavioural changes such as vigilance or lack of control of internal thoughts and their link to the underlying cortical processes. The strength of this analysis is that we not only analyse the network independent of each other but also their interplay. In this way, we may understand the brain more as a network than as connected single entities. 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