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Life events are significant incidences in individuals’ lives that may precipitate changes in behavior (Tibubos, 2020; Wrzus 2013). Life events may be either normative (i.e., common events that happen to most individuals such as marriage or a promotion at work) or non-normative events (i.e., events that happen unexpectedly throughout the lifetime like winning the lottery or the death of a relative) (Tibubos, 2020; Wrzus 2013). The occurrence of negative or positive life events can, respectively, worsen or improve the trajectory of symptoms of depression or anxiety. Specifically, positive life events have been consistently shown to ameliorate depressive symptoms (Disabato, 2017). In fact, the frequency of positive life events can also lead to fewer and less intense depressive symptoms (Disabato, 2017). Contrastingly, negative life events can exacerbate depression and anxiety symptoms (Kraaij, Arensman, & Spinhoven, 2002). Importantly, however, symptoms do not change in all individuals who experience negative or positive life events (Maciejewski et al., 2020). Thus, identifying individual characteristics that affect the impact of life events on pre-existing depressive or anxiety symptoms could be critical for predicting clinical outcomes in untreated individuals suffering from depression and/or anxiety, as well as non-responders to prior treatment as is the case with our current sample. Moreover, there is still much unknown regarding the relationship between life events and specific neural circuits underlying depression and anxiety symptoms. Therefore, uncovering the relationship between positive or negative life events and specific symptoms could possibly illuminate the impact of life events on the neural circuits underlying those symptoms. For the current study, we performed a literature review focusing on current meta-analyses, reviews and large-scale cohort studies regarding the implications of life events on psychiatric symptoms. Negative life events have been found to precipitate the onset of mood disorders and/or to exacerbate existing symptoms (Kraaij, Arensman, & Spinhoven, 2002; Nikolova, 2012; Maciejewski et al., 2020). Biologically, it may be that patients with affective disorders have aberrant functional changes in brain circuits involved in the processing of negative valence information, which could explain over-responsiveness to negative events in certain individuals. We define the negative affect circuit as consisting of the amygdala, anterior cingulate cortex (ACC), and the anterior insula (Williams, 2017; Goldstein-Piekarski et al., 2021). In particular, amygdala hyperactivation in response to negative/sad facial stimuli and reduced amygdala activation in response to positive stimuli has been shown in individuals with depression (Surguladze et al., 2005; Stuhrmann, Suslow, & Dannlowski, 2011). However, recent studies have also reported that individuals with treatment resistant depression or more severe depressive symptoms exhibit blunted amygdala activity (Ferri et al., 2017). Further, hypoactivation of the dorsal anterior cingulate cortex (dACC) and the anterior insula in response to sad facial stimuli is common among depressed individuals (Surguladze et al., 2005; Lawrence et al. 2004; Fu et al., 2008; Goldstein-Piekarsk et al., 2021). Finally, the tendency to see oneself and one's world as negative in individuals with depression and anxiety has also been linked with higher activation in the amygdala, medial prefrontal cortex (mPFC), and the anterior cingulate cortex (ACC) (Williams et al., 2009). Examining activation in these regions may provide insight into the neural characteristics of individuals who are hyper-sensitive to negative stimuli and who, therefore, are more likely to develop worsened clinical symptoms following the experience of a negative life event. In contrast to negative life events, positive events may facilitate symptom improvement in both depression and anxiety (Needles, & Abramson, 1990; Fresco, Alloy & Reilly–Harrington, 2006). Individuals with depression tend to interpret neutral or pleasant events as more negative than do individuals without depression (Gollan et al., 2016). Prior research has shown that this phenomenon may indicate disruptions in the neural circuits underpinning positive valence in depressed individuals, manifesting clinically as anhedonia (Keedwell et al., 2005; Gollan et al., 2016). In particular, functional changes in reward circuitry have been consistently found in patients suffering from mood disorders. We define these positive affect neural circuits as consisting of the ventral striatum—specifically focusing on the nucleus accumbens, ventromedial prefrontal cortex (vmPFC), caudate, cuneus, superior frontal gyrus (SFG), and putamen (Fischer et al., 2019; Borsini et al., 2020; Goldstein-Piekarski et al., 2021). In previous research, decreased ventral striatal activity has been found in depressed individuals during reward processing tasks and has also been associated with anhedonia (Keedwell et al., 2005; Pizzagalli et al., 2009; Zhang et al., 2013; Keren et al., 2018). Further, increased activity in the ventromedial prefrontal cortex (vmPFC) and decreased activity in the anterior cingulate cortex have been associated with anhedonia in individuals with depression (Mitterschiffthaler et al., 2003; Keedwell et al., 2005, Borsini et al., 2020). Finally, hypoactivity in the dorsal striatum, specifically the caudate and putamen, has been linked to increased anxiety as well as reduced reward processing and anhedonia symptoms in depressed adults (Zhang et al., 2013, Borsini et al., 2020). Interestingly, recent studies have shown that individuals with remitted depression display increased activation in the cuneus and the superior frontal gyrus (SFG) during reward processing (Fischer et al., 2019). As a result, lower activation in both the SFG and cuneus during the processing of reward outcomes could underly the presentation and possible continuation of anhedonia symptoms in depressed individuals. Thus, investigating abnormal functioning in reward processing regions may identify individuals who are more or less likely to respond to positive events, and therefore who may have improved clinical outcomes after the occurrence of positive events. Our goal in the present study is to examine if the activity of brain circuits involved in negative valence and positive valence stimuli mediate symptom changes in response to negative and positive events, respectively, in patients with disordered emotional states (Forbes, & Dahl, 2005; Kanske, 2012; Tozzi et al., 2020). Following the Research Domain Criteria (RDoC) framework, we operationalize negative valence as acute threat and positive valence as reward responsiveness and use fMRI tasks designed to probe the neural underpinnings of these constructs (Tozzi et al., 2020). Given previous findings, we focus on the activation of a negative valence network (medial prefrontal cortex (mPFC), amygdala, anterior cingulate cortex (ACC) and anterior insula) during a negative face-matching task to assess responsiveness to negative stimuli, and on the activation of a positive valence network (ventral striatum—specifically the nucleus accumbens, ventromedial prefrontal cortex (vmPFC), cuneus, superior frontal gyrus (SFG) and the caudate and putamen), during a gambling task to assess responsiveness to positive stimuli. With respect to symptoms, we operationalize excessive acute threat as tension and anxious arousal, and we operationalize deficits in reward processing as anhedonia.

Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. The Research Domain Criteria (RDoC) initiative spearheaded by the National Institute of Mental Health (NIMH) offers a framework for characterizing the relations between neural circuits and phenotypic profiles of behavior and self-reported symptoms. Our project integrates an RDoC framework with Human Connectome Project (HCP) imaging protocols to characterize disordered emotional states across units of analysis in a sample of 250 individuals suffering from depression and anxiety as well as 50 healthy controls. We focus on the three RDoC constructs most relevant to depression and anxiety: 1) loss and acute threat within the negative valence system domain; 2) reward valuation and responsiveness within the positive valence system domain; and 3) working memory and cognitive control within the cognitive system domain. Upon project completion, all imaging, behavioral and symptom data will be made publicly available through the Coordinating Connectome Facility and the NIMH Data Archive.

The goal of this project is to test whether brain networks engaged during tasks collected in two large samples at different sites can be used reliably to define clusters of individuals suffering from depression and anxiety. If so, I will then investigate whether cluster assignments are stable in time and relevant for treatment selection.

The inability to regulate emotional responses to stressful events has been found to be associated with the development of symptoms of depression and anxiety. Cortical and subcortical brain regions implicated in emotion regulation, and other aspects of self-control, such as the amygdala, dorsal anterior cingulate cortex (dACC), insula, and hippocampus, may be involved in the neural underpinnings of this association. Self-reported coping strategies are one means to assess the attempt to regulate emotion responses and achieve self-control. However, we do not know the extent to which the association between the explicit use of coping strategies and structural variation in these brain regions contributes to experiences of depression and anxiety in response to stressful events. The COVID-19 pandemic is a salient stressful event, worldwide, that is likely to evoke depression and anxiety in many individuals, especially in those who are already experiencing psychiatric illness. In this context, the temporal onset of the pandemic offers a naturalistic design for investigating whether variations in coping strategies and in neural circuit structure that characterize individuals prior to a major stressful event are associated with the severity and/or development of depression and anxiety symptoms as a consequence of that stressful event (i.e., the pandemic). To our knowledge, no study has investigated how coping strategies and neural circuit structure prior to the COVID-19 pandemic relate to the severity of clinical symptoms during the pandemic. In a sample of adult participants with and without clinical symptoms of depression and anxiety prior to the pandemic, we will first assess whether coping strategies (defined as maladaptive or adaptive) are associated with changes in the severity of depression and anxiety symptoms as a function of the COVID-19 pandemic. Second, we will assess whether characteristic variations in the structure of neural circuit regions implicated in self-control and emotion regulation are also associated with changes in depression and anxiety symptoms as a function of the COVID-19 pandemic. Lastly, we will examine whether coping strategies moderate the relationship between neural circuit structure and clinical symptoms during the COVID-19 pandemic.

Bipolar disorders (BD) are serious chronic mental disorders. 10-20% of patients suffering from BD commit suicide throughout their disease course (Müller-Oerlinghausen et al. 2002). A longer duration of untreated illness leads to more depressive and manic episodes and more suicidal behavior (Drancourt et al. 2013). Hence, early detection and treatment is crucial. Some attempts to detect BD ahead of diagnosis have already been made. For instance, the prognostic accuracy of two clinical interviews has been investigated: The Bipolar at Risk States Revised (BARS; Harrell's C = 0.777) and the Semistructured Interview of At Risk Bipolar States (SIBARS; Harrell's C = 0.742) (Fusar-Poli et al. 2018). Also, in a genetic study, polygenic risk scores differed significantly between control and at-risk groups, but not between at-risk and BD type-I groups (Smigielski et al. 2021). Adding additional data categories to the scales, in particular neuroimaging, could increase the prognostic accuracy and thus lead to an earlier illness detection and better clinical outcomes. With the aim of finding a valid biomarker for BD using neuroimaging, several structural abnormalities have already been identified (Arnone et al. 2009). A currently published mega-analysis with 4,698 participants comparing BD patients with healthy controls (HCs) found significantly smaller volumes of the hippocampus and its subfields (whole hippocampus, GC ML DG, CA4, CA3, CA1, subiculum, presubiculum, molecular layer HP, HATA and hippocampal tail) but not for other subfields (parasubiculum, fimbria and the hippocampal fissure) (Haukvik et al. 2022). Another study found some subfields to be smaller for both, BD and schizophrenia patients, compared to HCs (bilateral CA2/3, CA4/dentate gyrus, subiculum and right CA1) (Haukvik et al. 2015). Meanwhile, presubiculum volumes were smaller only in schizophrenia and, comparing schizophrenia with BD directly, the bilateral subiculum as well as the right presubiculum was found to be smaller for schizophrenia (Haukvik et al. 2015). In an investigation between different psychotic disorders, smaller subfield volumes could be found only in the bilateral CA2/3, the left presubiculum and the right CA4/DG, comparing psychotic bipolar disorder (Mathew et al. 2014). Now, we will investigate if there are observable differences already before any BD diagnosis. Another potentially interesting region for early detection of BD, with less literature to date, is the amygdala and its subnuclei. For instance, a decreased volume was found for the lateral and cortical nuclei, but not for the basal and accessory basal nuclei in BD patients compared to HC (Pantazopoulos et al. 2017). However, Bielau et al. (2005) found no significant differences for the whole amygdala volume in a post-mortem study between BD patients and HC. Thus, the volumes of segmented amygdala nuclei volumes should be considered as potential risk factors for BD. While traditional statistical group comparisons can show mean structural abnormalities, they lack practical clinical impact (Orru et al. 2012). A multivariable machine learning (ML) approach meanwhile allows to make individual inferences potentially useful in clinical prognostics. Thus, we will implement a support vector machine (SVM). This is a widely used algorithm, which classifies huge datasets into groups through weighting so-called features, i.e. structural volumes. These groups are here defined as risk states for BD through three independent assessment tools: EPIbipolar (Leopold et al. 2012), BPSS-P (Correll et al. 2014) and SIBARS (Fusar-Poli et al. 2018). In a currently submitted paper, we already investigated such a SVM classification. We used regional cortical thickness and surface area values as well as subcortical structural volumes (Mikolas et al. 2022), in submission, pre-registered at https://osf.io/c4hfn). Now, we will adapt this procedure to hippocampal subfields and nuclei of the amygdala.

Childhood trauma (CT) has been repeatedly linked to earlier onset and greater severity of bipolar disorder (BD) in adulthood. However, such knowledge is mostly based on retrospective and cross-sectional studies in adults with BD. The first objective of this selective review is to characterize the short-term effects of CT in the development of BD by focusing on studies in young people. The second objective is to describe the longer-term consequences of CT by considering studies with adult participants. This review first outlines the most prominent hypotheses linking CT exposure and the onset of BD. Then, it summarizes the psychological and biological risk factors implicated in the development of BD, followed by a discussion of original studies that investigated the role of CT in young people with early-onset BD, youths at increased risk of developing BD, or young people with BD with a focus on subclinical and clinical outcome measures. The review considers additional biological and psychological factors associated with a negative impact of CT on the long-term course of BD in later adulthood. Finally, we discuss how the integration of information of CT can improve ongoing early identification of BD and mitigate severe clinical expression in later adulthood.