Brain structure and function link to variation in biobehavioral dimensions across the psychopathological continuum

Over the last decades, functional and structural imaging methods have helped to elucidate the brain circuits involved in psychiatric disorders. However, a growing number of concerns have been raised (Cuthbert and Kozak, 2013). First, the majority of neuroimaging studies in psychiatry have investigated group-level differences between traditional psychiatric diagnoses and healthy controls, often limited to one neuroimaging modality. As a consequence, we are confronted with an overwhelming amount of isolated findings, often unreplicated and without a clear understanding of how these findings relate to each other (Marquand et al., 2016; Specht, 2019). Second, the identification of valid neurobiological mechanisms has been hampered by the somewhat arbitrary boundaries and heterogeneity of traditional diagnostic categories, which fail to map unambiguously on core mechanisms of psychopathology, and also fail to adequately take comorbidity into account (Kotov et al., 2017). The absence of an integrative understanding of transdiagnostic core mechanisms underlying psychopathology may reduce the chance to develop biologically based, personalized forms of treatment.

The Research Domain Criteria (RDoC) have been developed to tackle the abovementioned problems by facilitating a paradigm shift from a categorical approach to a multilevel, transdiagnostic dimensional approach, with brain circuits at the central level (Cuthbert and Insel, 2013; Morris et al., 2022). Linked independent component analysis (ICA) is an innovative analysis technique, which is eminently apt to apply within this framework. Linked ICA performs multiple, simultaneous ICA factorizations that share the same unique mixing matrix and has been shown to be a powerful tool for identifying independent components (ICs) that reflect patterns of shared variance across multiple neuroimaging modalities (Groves et al., 2012, Groves et al., 2011). This allows for a principled integration of information from these imaging modalities at an early stage in the analysis pipeline, rather than a post hoc combination of unimodal results at the stage of final interpretation, and may provide a more integrative understanding at the brain level (Groves et al., 2012, Groves et al., 2011). Subsequently, relationships can be studied between interindividual differences in the resulting (multimodal) imaging components and variation in behavioral dimensions and psychopathology (Llera et al., 2019; Wolfers et al., 2017) (i.e. between the brain circuits level and the other units of analysis of the RDoC matrix).

Linked ICA has already established relationships of interindividual differences in multimodal brain components with behavioral measures and clinical profiles in specific patient groups, such as autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), and Huntington’s disease (Garcia-Gorro et al., 2019; Itahashi et al., 2015; Wolfers et al., 2017). While linked ICA has provided a more coherent insight into the neural mechanisms within these specific populations, it has not yet been used to investigate core mechanisms of psychopathology transdiagnostically. It is important to adopt a transdiagnostic approach since the same disturbances in structural and functional brain networks may be associated with core symptom domains that transcend traditional disorder categories (Menon, 2011).

Therefore, in this study, we used linked ICA within a transdiagnostic, dimensional framework, using the MIND-Set database (Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-related Mental Disorders) (van Eijndhoven et al., 2021). This database provides us with a sample of mentally healthy participants and patients with diverse, highly prevalent non-psychotic psychiatric disorders (i.e. mood disorders, anxiety disorders, addiction, ASD, ADHD, and their comorbidity). A multimodal imaging battery was performed, and all participants were deeply phenotyped. An extensive set of biobehavioral measures was collected, including symptom dimensions, biological/physiological measures (like cortisol and heart rate variability), and also more general measures of physical and mental health. Since psychopathology can best be understood on a continuum from health to mental illness, we also included mentally healthy participants, which allows us to study the brain and biobehavioral dimensions of interest along a wider range from health to psychopathology (Kotov et al., 2017; Morris et al., 2022; van Oort et al., 2022).

We investigated the diverse non-psychotic psychiatric disorders (i.e. mood disorders, anxiety disorders, addiction, ASD, and ADHD) together as high levels of comorbidity suggest shared underlying mechanisms (van Eijndhoven et al., 2021). In addition, numerous studies that investigated these disorders separately provide converging evidence that symptoms across various major domains cut across the diagnostic boundaries of these disorders, with, among others, transdiagnostic symptom dimensions related to the negative valence domain (e.g. repetitive negative thinking) and cognitive systems domain (e.g. regarding cognitive control problems) (Kerns et al., 2015; Koob and Schulkin, 2019; McTeague et al., 2016; Richey et al., 2015; Shaw et al., 2014; van Eijndhoven et al., 2021; Woody and Gibb, 2015). Interestingly, disturbances in the same brain networks may underlie these core symptom dimensions across these diverse disorders (McTeague et al., 2016; Menon, 2011). Therefore, it is crucial to adopt a transdiagnostic approach in order to identify the brain basis for symptom dimensions that transcend these diagnostic categories.

In our multimodal neuroimaging battery, we included functional scans under conditions of rest as well as under conditions of mild experimentally induced stress. It is clinically well established that vulnerability to stress is a common feature across a broad range of psychiatric disorders (Ingram and Luxton, 2005). Mood and anxiety disorders are characterized by a maladaptive stress response as their central feature (de Kloet et al., 2005; Sharma et al., 2016). Vulnerability to stress also plays a key role in addiction disorders, with impaired coping with stress being implicated in the onset, maintenance, and relapse in these disorders (Koob, 2003; Koob and Schulkin, 2019). While neurodevelopmental disorders have a relatively stable, trait-like course, there are clear indications for increased stress sensitivity, as exemplified by arousal and emotion regulation problems, which in turn may lead to the development of negative valence symptoms and stress-related comorbidity (Kerns et al., 2015; Richey et al., 2015; Rommelse et al., 2011; Shaw et al., 2014; van Eijndhoven et al., 2021). We included stress scans within the linked ICA setup as a novel feature of our approach as we hypothesized that shared mechanisms of stress vulnerability would become visible under conditions of stress.

Taken together, we set out to perform a transdiagnostic structural–functional linked ICA analysis across the spectrum from health to diverse non-psychotic psychiatric disorders in order to discover components with shared variance in brain structure and function. Our extensively phenotyped sample made it possible to subsequently perform correlational analyses to investigate how interindividual differences in the neuroimaging components relate to a broad set of biobehavioral measures. Together, these analyses allow us to identify neuroimaging components that are important for the understanding of transdiagnostic biobehavioral dimensions. Because of our primary interest in psychopathology, we decided in advance to focus on the neuroimaging components that are associated with measures of psychopathology and to focus especially on the transdiagnostic symptom dimensions.

In this study, we performed a structural–functional linked ICA analysis to get a more complete understanding of psychopathology from a transdiagnostic perspective, investigating a sample with both mentally healthy participants and psychiatric patients with diverse non-psychotic disorders. Our deeply phenotyped sample allowed us to investigate which brain components may in particular be important for the understanding of transdiagnostic mechanisms in psychopathology by performing extensive, exploratory correlational analyses. Linked ICA resulted in various multimodal components, uncovering shared variance across modalities. While these multimodal components were in particular related to age, sex, BMI, blood pressure, and heart rate variability, we also identified multimodal components that were associated with cognitive symptoms and a diagnosis of ASD. Interestingly, the transdiagnostic symptom dimensions were most strongly related to components that were driven by the large-scale functional networks during the various functional scans.

In line with Llera et al., 2019 and several other linked ICA studies (Garcia-Gorro et al., 2019; Itahashi et al., 2015; Wolfers et al., 2017), we chose to not only focus on the multimodal components but to perform the correlational analyses for all the components that resulted from the linked ICA algorithm. This way, our unbiased analysis was able to identify multimodal components that were related to important aspects of psychopathology (like our multimodal ASD component), but also unimodal components that correlated with a wide range of biobehavioral measures, like our ECN stress-aftermath component. While this ECN-stress aftermath component may contribute less to a more integrative understanding at the brain level, it does contribute to a more integrative understanding of psychopathology by showing how this component is related to various biobehavioral measures at different levels of the RDoC matrix (like physiology and self-reports) and also across different domains (i.e. the negative valence and cognitive systems domain) (Cuthbert and Insel, 2013). Below, we continue with a more in-depth discussion of the components that are most important for the understanding of psychopathology. First, we discuss the multimodal ASD component, before turning to the components related to the transdiagnostic symptom dimensions across the negative valence and cognitive systems domains.

Although set within transdiagnostic research, the linked ICA analysis was sensitive enough to pick up a multimodal component that is associated with a traditional, diagnostic classification of ASD. This relationship was specifically found for ASD as this component was not associated with the other diagnostic groups, nor with the variable that divided our sample in mentally healthy participants versus patients. The multimodal ASD component loaded on the DMN, precentral gyrus, and thalamus. Interestingly, these regions have been implicated in ASD by earlier studies and are associated with core domains of this disorder, like theory of mind (Murdaugh et al., 2012), motor problems (Duffield et al., 2013; Mahajan et al., 2016), and sensitivity to sensory stimuli, respectively (Ayub et al., 2021). While both Itahashi et al., 2015 and Mei et al., 2022 also implicated these regions in ASD in their linked ICA analysis, there were also differences with our findings. In the study of Itahashi et al., 2015, these regions did not show up together in one component, and the multimodal component of Mei et al., 2022 loaded more extensively on the white matter tracts. While the differences with our findings may be related to the differences in study setup and specific sample characteristics (e.g. related to the included MRI modalities, patterns of psychiatric comorbidity, IQ, and age range of the participants), this may also be related to the heterogeneous nature of psychiatric classifications like ASD (Itahashi et al., 2015). Still, these multimodal results may help to get a more coherent understanding of the neurobiology of ASD by not only showing which brain regions are involved but also how these findings covary across different modalities.

Importantly, this multimodal ASD component was associated with a diagnostic label of ASD, but not with the subscales of the AQ-50 across the whole sample. As a diagnosis of ASD reflects a complex clinical phenotype that spans several functional domains, a multimodal brain component spanning brain regions that are involved in these various functions may be more strongly associated with such a complex phenotype than with specific symptom dimensions (Mei et al., 2022). While the AQ-50 subscales did not show any correlations with this multimodal ASD component across the whole sample, the post hoc tests showed that this component was specifically associated with social skill symptoms within the ASD group. This may be explained by the loading of this component in the DMN, which is involved in social functions related to theory of mind (Murdaugh et al., 2012). Based on these results, an alternative explanation for not finding correlations for the AQ-50 across the whole sample could be related to the limitations regarding the application of this questionnaire in our diverse transdiagnostic sample. The AQ-50 may not measure a uniform dimension across our participants. Higher scores on this self-report questionnaire may stem from ASD, but could also stem from other causes, like social interaction problems due to (social) anxiety, or patients may score higher if they have an overly negative judgment of their social skills (e.g. in depression). Thus, when investigating brain behavior relationships using the AQ-50, it may be important to also take into account the judgment of a trained clinician regarding whether a participant has ASD or not. These considerations are important since one of the central goals of RDoC is the identification of reliable and valid measures that can be applied in transdiagnostic research (Morris et al., 2022).

Interestingly, all three functional networks of interest (i.e. DMN, ECN, and FPN) were involved in components that showed transdiagnostic associations with cognitive and/or negative valence symptoms. While the literature thus far indeed suggests that aberrations in these large-scale networks cause major dysfunctions in core cognitive and affective domains across psychiatric disorders, this idea was largely based on indirect evidence in separate psychiatric disorders (Menon, 2011; Sanislow et al., 2010). We now identified these relationships in a diverse sample spanning the psychopathological continuum.

The inclusion of a stress challenge is a novel feature in our linked ICA analysis. While several components that reflect the connectivity between the ECN and FPN (including the resting-state component) were associated with cognitive symptoms, two components that were related to the stress induction were most sensitive in revealing relationships with negative valence symptoms (i.e. the ECN-stress aftermath component and DMN-stress component). This could indicate that it may be especially relevant to include a stress induction paradigm when studying the negative valence domain.

Especially the ECN seems to be important in explaining transdiagnostic symptom dimensions. We found four different components (IC7, IC8, IC13, and IC30) driven by the ECN during different functional scans that reflected connectivity of the ECN with itself and with the FPN. In line with the known role of these networks in a wide range of cognitive tasks (McTeague et al., 2016; Smith et al., 2009), all four components showed significant correlations with executive/higher-order cognitive symptoms. Below, we further elaborate on our ECN-stress aftermath component (i.e. IC7) as this is the only one of these four components that revealed a relationship with both cognitive and negative valence symptoms.

The ECN stress-aftermath component reflects connectivity of the ECN with itself and with the right FPN in the aftermath of stress. Interestingly, the anterior insula and dorsal anterior cingulate cortex are part of as well this component as the ECN template (Smith et al., 2009), and are both considered core regions of the salience network (SN) (Hermans et al., 2011; Seeley et al., 2007; Shirer et al., 2012). In line with the known role of these regions in the dynamic reallocation of resources under stress (Hermans et al., 2014), our results suggest that the ECN engages the right FPN in the aftermath of stress for emotion regulation/top-down control (Buhle et al., 2014; Hermans et al., 2014; Hermans et al., 2011; Kohn et al., 2014; McTeague et al., 2016). The negative relationship between this component and negative valence symptoms suggests that an inadequate engagement of the FPN by the ECN can be seen as a crucial transdiagnostic vulnerability factor for the development of negative valence symptoms (under stress).

Our extensive, exploratory analyses allowed us to identify not only a relationship of this ECN component with symptom dimensions, but also with a broad range of negative health outcomes related to physiological/cardiovascular measures, BMI, and also general health/functioning. It is known from the literature that there are complex relationships between mental health, physical health, and coping with stress (de Kloet et al., 2005; Juster et al., 2010; McEwen, 2003). Physical and mental health problems may affect each other and may also result in a maladaptive stress response. Vice versa a repeatedly maladaptive stress response, including inadequate recovery in the aftermath of stress, may lead to mental and physical wear-and-tear (Juster et al., 2010; McEwen, 2000; McEwen, 2003). As the brain is the central organ that coordinates the stress response (Ulrich-Lai and Herman, 2009), our results suggest the key importance of the ECN and FPN in this complex web of relationships. Together, these results emphasize the importance of an integrative approach to stress, and physical and mental health.

The results for our DMN-stress component add to a growing body of evidence on the importance of the DMN in the stress response in health and psychopathology (van Oort et al., 2020; van Oort et al., 2017; Zhang et al., 2019). This component reflects the connectivity of the DMN with the FPN and visual regions during stress induction with a stressful movie clip with an eyewitness instruction. The connectivity between the DMN (which is known to be involved in self-referential processing) and visual regions may reflect the processing of the stressful movie from a self-referential/eyewitness perspective (Buckner et al., 2008; van Oort et al., 2017). The connectivity with the FPN may reflect the role of the FPN in top-down control during stress (Kohn et al., 2014). Interestingly, this component is negatively associated with the fear of losing one’s mind under stress. Together, this may suggest that stronger connectivity of the DMN with itself, the FPN and visual regions, may be an adaptive response to facilitate the (self-referential) processing of the stressor, while there is top-down control of this fear-related symptom.

The main strength of our study is that this is the first study performing an integrative structural–functional linked ICA analysis, investigating the relationship between the brain and broad set of symptoms and other (bio)behavioral measures transdiagnostically. However, our study has to be interpreted in light of some limitations. First, linked ICA is characterized by sign indeterminacy, which made it necessary to infer the direction of the associations between our components and biobehavioral measures of interest, by studying the relationship between global gray matter volume and age. While we think that this is a valid way of determining the direction of the relationships for our results, we would like to note here that it is a limitation that the direction of the effects is not a mathematical certainty but an inference. Future studies should focus on replicating the findings described in this study. Second, our results were found in a specific sample of patients with mainly mood, anxiety, addiction, and neurodevelopmental disorders. Future studies should investigate whether our results can be replicated in other (transdiagnostic) samples. Third, the cross-sectional nature of our study prevents us from making definitive causal inferences about the relationships that were found. Future studies should prospectively investigate the relationship between brain structure, function, and psychiatric symptoms. Finally, although we did include measures related to psychotropic medication and substance use in our correlational analyses, we did not correct for these factors in the analyses. We did not include these factors as covariates as these factors are not independent from our other measures of interest, like the symptom dimensions and diagnostic labels. Future studies in samples that are well matched on these measures of interest and only differ in medication status or substance use could help to further disentangle the effects of these factors from other aspects of psychopathology.

Taken together, the structural–functional linked ICA analysis followed by extensive correlational analysis revealed several components that are important for the understanding of psychopathology. We identified a multimodal component that was specifically associated with ASD. The involvement and covariation of the DMN, precentral gyrus, and thalamus across the different modalities of this component may reflect the broad functional domains that may be affected in ASD, like theory of mind, motor problems, and sensitivity to sensory stimuli, respectively. Furthermore, the results emphasized the importance of especially the ECN for the understanding of transdiagnostic symptom dimensions. The aftermath of stress revealed that the connectivity between the ECN and FPN was associated with symptom dimensions across both the cognitive systems and negative valence domain of the RDoC framework, and also with various other health-related (bio)behavioral measures. This suggests a key role for these networks in adaptive coping with stress and thereby for mental and physical health.

Our results provide initial insight into the neural mechanisms underlying transdiagnostic (bio)behavioral dimensions and provide avenues for future research. First, further research is necessary into which (biobehavioral) dimensions serve as reliable and valid measures for transdiagnostic research and how they can be assessed best (Morris et al., 2022). While various dimensions can be measured well with a self-report questionnaire, in other cases it may be important to complement these measures with more objective measures (e.g. neuropsychological tests or assessments by a trained clinician). In addition, the field of multimodal imaging is still relatively new. Multimodal imaging techniques have the potential to take maximal advantage of the strengths of the different types of imaging data, with each data type having a limited but complementary view of the brain (Sui et al., 2012). Since our results show the potential of the functional scans for revealing relationships with transdiagnostic symptom dimensions, future studies should investigate how multimodal analysis techniques may further capitalize on this potential of the functional scans. It should be investigated which structural and functional modalities can best be combined and how multimodal analyses techniques can be optimized in order to further the integrative understanding of brain function and structure (Sui et al., 2012).

All data analyzed in this study is stored in a Data Sharing Collection (DSC) (https://data.donders.ru.nl/collections/di/dccn/DSC_3013061.01_147?5) on the institutional repository of the Donders Institute for Brain, Cognition and Behavior, and is available upon reasonable request (N.B. the data in this DSC is only available for scientific, non-profit research). When a request for data access is submitted through the website mentioned above, the data collection manager receives this request. For getting access to the data, a project proposal has to be submitted and the data use agreement (DUA) has to be signed. For European Union (EU)-countries (and countries that offer an adequate level of data protection according to the EU; see the list of non-EU countries with an adequate protection level) a standard DUA is in place (RU-RA-DUA-1.0). Based on the project proposal and signed DUA the collection manager will assess whether the data can be shared. For non-EU countries (that do not offer an adequate level of data protection according to the EU), the data manager will reach out to the data steward within the Donders Institute, who in turn will reach out to the legal department of the Radboud university, providing details on who requests access to the data (including affiliation, country, and intended purpose of data usage). Based on this information a DUA will be drafted on a case by case basis.Furthermore, relevant data generated by the analyses we performed are included in the manuscript and supporting files. The linked ICA decomposition was performed using the Linked ICA toolbox, which was made available earlier by Llera et al., 2019 (https://github.com/allera/Llera_elife_2019_1/tree/master/matlab_flica_toolbox; Llera, 2019).

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Author details

1. Jasper van Oort

   1. Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

   Contribution

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

   For correspondence

   jasper.vanoort@radboudumc.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2281-0349

2. Alberto Llera

   1. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Nils Kohn

   1. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Ting Mei

   1. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Data curation, Software, Supervision, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Rose M Collard

   Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

   Contribution

   Conceptualization, Supervision, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

6. Fleur A Duyser

   Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

   Contribution

   Conceptualization, Data curation, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

7. Janna N Vrijsen

   1. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Pro Persona Mental Health Care, Depression Expertise Center, Nijmegen, Netherlands

   Contribution

   Conceptualization, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

8. Christian F Beckmann

   1. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands
   3. Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Supervision, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Aart H Schene

   1. Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

   Contribution

   Conceptualization, Supervision, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Guillén Fernández

    1. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands

    Contribution

    Conceptualization, Supervision, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Indira Tendolkar

    1. Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

    Contribution

    Conceptualization, Supervision, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

12. Philip FP van Eijndhoven

    1. Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    2. Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

    Contribution

    Conceptualization, Formal analysis, Supervision, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

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  citation for umbrella DOI https://doi.org/10.7554/eLife.85006