Macro-scale patterns of brain activity at rest have the potential for elucidating the organisation of neural function, different types of psychiatric conditions (Cao et al., 2018; Koban et al., 2021), developmental changes including those during adolescence and old age (Dosenbach et al., 2010; Cui et al., 2020; Gratton et al., 2020; Wen et al., 2020), neurological disorders (Zhang et al., 2021) and are important for revealing the neural basis behind the landscape of self-generated experiences (Karapanagiotidis et al., 2019; Mckeown et al., 2020; Kucyi et al., 2021). However, compared to controlled experimental conditions, interpreting neural activity recorded during resting-state functional MRI (rs-fMRI) is challenging, partly because both trait-level aspects of the individual and the inherently complex and dynamic nature of ongoing experience at rest are both contributory factors to the observed brain activity (Smallwood et al., 2021b). It has recently been suggested that the meaning of different patterns of neural activity can be usefully constrained by pairing imaging data at rest with additional measures (Finn, 2021), for example, by accounting for the patterns of thoughts individuals experience at rest (Karapanagiotidis et al., 2020; Karapanagiotidis et al., 2021; Mckeown et al., 2020; Gonzalez-Castillo et al., 2021; Kucyi et al., 2021), trait variation in how people think during tasks (Smallwood et al., 2016), or features of their personality (Hsu et al., 2018). While this methodological perspective is invaluable, we currently lack a theoretical framework within which to understand the brain–cognition relationships that these observations will establish. To address this gap in the literature, our study explores whether contemporary theories of the neural basis of ongoing conscious thought can provide a framework within which to interpret associations between macro-scale patterns of neural activity observed at rest, and measures of traits and self-reports of ongoing experience.

Emerging views of how the brain supports patterns of ongoing conscious thought highlight interactions between three large-scale networks (Menon, 2011; Huang et al., 2021; Smallwood et al., 2021b): the ventral attention network (VAN), the dorsal attention network (DAN), and the default mode network (DMN) (in this article, we refer to the networks using the taxonomy provided by Yeo et al., 2011). Traditionally, it was argued that the DMN was thought to have an antagonistic relationship with systems linked to external processing (Fox et al., 2005). However, according to the ‘tri-partite’ network accounts the relationship between the DMN and other brain systems is more nuanced. From this perspective, key hubs of the VAN, such as the anterior insula and dorsolateral prefrontal cortex, help gate access to conscious experience regardless of the focus of attention. This is hypothesised to occur because the VAN influences interactions between the DAN, which is more important for external mental content (Corbetta and Shulman, 2002), and the DMN, which is important when states (including tasks) rely more on internal representations (Smallwood et al., 2021a). For example, Huang et al., 2021 established that activity levels in the anterior insula determine whether stimuli presented at perceptual threshold are consciously perceived, and this gating of external input emerged as a consequence of changes in the normal interactions between the DAN and DMN. They also found that disruptions to activity in the insula through anaesthesia resulted in reductions in self-generated mental imagery. Coming from a different perspective, Turnbull et al., 2019a used experience sampling during task performance to link patterns of neural activity to different features of ongoing thought. For example, they found activity in the dorsolateral prefrontal cortex (a member of the VAN according to a parcellation by Yeo et al., 2011) was correlated with apparently contradictory patterns of ongoing thought: (i) self-generated episodic thoughts during periods of low demands, and (ii) patterns of detailed task focus when individuals were engaged in demanding external task. In the same study, however, neural activity within regions of the dorsal parietal cortex within the DAN was exclusively reduced when participants engaged in self-generated thinking, highlighting a parallel neglect of external input seen by Huang and colleagues. Finally, in a second study, Turnbull and colleagues found that at rest trait variance in the ability to focus on self-generated experience in laboratory situations with lower task demands is associated with decoupling of signals arising from the DAN and DMN (Turnbull et al., 2019b). Summarising this emerging evidence, studies focused on understanding ongoing thought patterns from different perspectives converge on the view that regions of the VAN may be important for gating conscious access to different types of content by biasing interactions between the DAN and DMN (Huang et al., 2021; Smallwood et al., 2021b).

Our current study explored whether this ‘tri-partite network’ view of ongoing conscious thought derived from studies focused on understanding conscious experience provides a useful organising framework for understanding the relation between observed brain activity at rest and patterns of cognition/personality traits. Such analysis is important because at rest there are multiple features of brain activity that can be identified via complex analyses that include regions that show patterns of coactivation (which are traditionally viewed as forming a cohesive network [Biswal et al., 1995] as well as patterns of anti-correlation with other regions [e.g. Fox et al., 2005]). However, it is unclear which of these relationships reflect aspects of cognition or behaviour or are in fact aspects of the functional organisation of the cortex (Fox and Raichle, 2007). Consequently, our study builds on foundational work (e.g. Vanhaudenhuyse et al., 2011) in order to better understand which aspects of neural function observed at rest are mostly likely linked to cognition and behaviour. With this aim in mind, we examined the links between macro-scale neural activation and both (i) trait descriptions of individuals and (ii) patterns of ongoing thought. Our sample was a cohort of 144 participants who underwent a 1-hr resting-state scan. Across this 1-hr period, participants were interrupted on four occasions to answer a set of questions about their experiences at rest using multidimensional experience sampling (MDES), similar to a number of prior neuroimaging studies (Smallwood et al., 2016; Poerio et al., 2017; Wang et al., 2018). During a different session, the same participants also completed a battery of measures assessing features of their personality (such as the Big Five; Costa and McCrae, 2008) as well as subclinical/psychiatric traits such as trait anxiety and depression (Zigmond and Snaith, 1983). Since our research question depends on understanding the hypothesised relationship between large-scale networks (VAN, DMN, and DAN), we used the BrainSpace toolbox (Vos de Wael et al., 2020) to provide whole-brain low-dimensional representations of functional brain organisation, generating maps which represent the similarities and differences in the activity within different systems. We used R version 4.2.0 (R Development Core Team, 2021) to produce low-dimensional representations of both traits and thoughts using principal component analysis (PCA). Using these two sets of data, we performed multiple regression to identify how brain network organisation varies with traits and states. In these analyses, the low-dimensional representations of brain organisation were the dependent measures, and the components of traits and states were explanatory variables.

Our study investigated whether an emerging ‘tri-partite’ perspective from contemporary views of ongoing conscious thought can provide a framework that can account for the relationship between dispositional traits, self-reports of ongoing thought, and individual differences in large-scale patterns of neural connectivity at rest. This tri-partite network account emphasises the roles of regions embedded within three large-scale networks as important for ongoing experience: VAN, DAN, and DMN (Huang et al., 2021; Smallwood et al., 2021b). We calculated macro-scale connectivity gradients from 1 hr of resting-state fMRI for 144 participants. The variability of these gradients was then analysed as a function of self-reports of ongoing thought patterns (captured by the principal components of MDES) and personality traits (described by the principal components of a battery of personality and habit measures). Given the tendency of certain traits to be correlated with frequency of specific patterns of thought (e.g. depression level with intrusive and negative thought; Konu et al., 2021), we also looked for possible dependencies between trait and thought components through multivariate regression using traits as predictors.

Our analyses confirmed that both patterns of thought and indices of traits contribute to patterns of brain organisation in a manner that converges with the emerging tri-partite view of ongoing conscious thought. For example, it has been hypothesised that the VAN helps adjudicate between internal and external influences on ongoing thought (Huang et al., 2021; Smallwood et al., 2021b), and we found that individuals who were high on dispositional ‘introversion’ showed variation in anterior insula, overlying operculum, and ACC: all regions making up part of the VAN. For more introverted people, these regions showed greater alignment with somatomotor regions and less with visual cortex (gradient 2), and greater alignment with the DMN than the fronto-parietal network (gradient 3). Notably, prior studies have found that regions of sensorimotor cortex are linked to deliberate mind-wandering (Golchert et al., 2017) and individuals who tend to generate patterns of episodic social cognition during periods of low task demands show greater temporal correlation between the VAN and sensorimotor cortex (Turnbull et al., 2019b). Introversion reflects a predisposition towards internal subjective states rather than external objects (Jung, 1995), and so our analysis adds to an emerging literature (Golchert et al., 2017; Turnbull et al., 2019b) that suggests that there may be an important relationship between sensorimotor cortex and the VAN in patterns of internal self-generated thought.

Our study also highlighted that associations between observed patterns of neural organisation at rest and patterns of ongoing thought are consistent with the hypothesised tri-partite view (Huang et al., 2021; Smallwood et al., 2021b). For example, we found a pattern of detailed thinking during wakeful rest that was correlated with stronger decoupling between a region within dorsal parietal cortex from the visual network (as indexed by changes in both gradients 1 and 2), and greater separation from the DMN (gradient 3). This region overlaps with a region within the DAN identified by Turnbull et al., 2019a in which brain activity was reduced during self-generated thoughts relative to external task focus, suggesting an important role for the DAN in external facing attention. Furthermore, using a technique known as ‘echoes’ analysis (see Leech et al., 2012), Turnbull et al., 2019a established that individuals who engaged in self-generated thought during situations of low external demand at rest showed greater separation between the DAN and lateral regions of the DMN in a region of the dLPFC, also a member of the VAN. Thus, our study confirms prior studies that highlight that greater segregation between the DAN and the DMN in the capacity to engage in thoughts that are less linked to the external environment. Together, the convergence between the current analysis and perspectives from research on conscious experience highlights a high degree of overlap in both the regions identified and the hypothesised functions. Our observations are important, therefore, because they help establish that with the appropriate methodology (Mckeown et al., 2020; Finn, 2021; Gonzalez-Castillo et al., 2021) neural accounts of conscious experience (Huang et al., 2021; Smallwood et al., 2021b) provide an important valuable way to make sense of brain-cognition links observed at rest. Second, our data provides evidence for the ‘decoupling’ hypothesis of self-generated experience (Smallwood, 2013b). This perspective emerged from observations that cortical processing of external inputs is reduced when individuals focus on internal self-generated thought (Smallwood et al., 2008; Kam et al., 2011; Baird et al., 2014) and assumes that this reduced processing of external input allows an internal train of thought to persist in a more detailed manner (p.524, Smallwood, 2013a; Smallwood, 2013b). Our data is consistent with this view since both personality traits linked to internal focus (‘introversion’) and patterns of detail experience that are not directed externally (‘specific internal thought’) are linked to reductions in the similarity between neural activity in regions linked to attention and cognition, with regions of visual cortex. In this way, our study provides novel insight into how the macro-scale functional patterns across the cortex support the emergence of detailed patterns of internal experience. Critically, in our study there was no external task from which thinking needed to be decoupled from, ruling out accounts of this process as a ‘lapse’ in the normal upregulation of task-relevant material needed for task completion (for discussion, see Franklin et al., 2013; Smallwood, 2013a; Smallwood, 2013b).

Although our study establishes how contemporary work on conscious experience can help understand patterns of brain organisation observed at rest and highlights how these approaches can be leveraged to understand the neural correlates of both an individual’s traits and their thoughts, there are nonetheless important questions that our study leaves open. For example, contemporary work on ongoing conscious thought highlights time and context as key variables necessary for understanding the neural correlates of different features of thinking (Smallwood et al., 2021b). Since the aim of our study was to focus on the brain at rest, interpretations of our results should bear in mind that under different task conditions neural correlates between thinking, personality, and neural activity may be different. For example, prior studies have established that posterior elements of the DMN can become integrated into task-positive systems (Krieger-Redwood et al., 2016; Vatansever et al., 2017) and under demanding task conditions the DMN is linked to patterns of task-focused cognition (Sormaz et al., 2018). Similarly, our analysis focused on ‘static’ indices of neural activity rather than dynamic measures. We chose to focus on static indices of neural activity because our prior studies have shown that brain–behaviour correlations can be relatively stable over time (Wang et al., 2020), and as we establish in our study these patterns show reasonable stability across a 1-hr session. Furthermore, our study, particularly with respect to the findings relating to the DAN, map closely onto studies that use experience sampling to identify momentary correlations between neural activity and experience (Turnbull et al., 2019a). Nonetheless, there are likely to be important dynamic features of ongoing experience that our analysis of static brain organisation cannot capture (Kucyi, 2018). Recent discussions in cognitive neuroscience have highlighted the link between sample size and the reproducibility of brain-wide associations with behavioural phenotypes (Marek et al., 2022; Spisak et al., 2023). Our analysis of the trait and thought data alone revealed that ‘neuroticism’ was related to high negative and episodic thoughts; however, we did not find any other significant relationships among traits and thought patterns. In the current data, neuroticism was the most prominent out of all five traits included in the analysis, accounting for 29% of the total variance explained by them. It is therefore likely that more extensive and robust correlations between thoughts patterns and other traits, as well as thoughts, traits, and macro-scale connectivity patterns, would emerge with data sets with larger sample sizes. In the future, it may also be important to consider measures of traits that could have relationships to both neural activity and/or experience at rest (e.g. self-consciousness [de Caso et al., 2017] or autistic tendencies [Turnbull et al., 2020a]). It is worth noting that mapping momentary changes between ongoing experience and neural activity will likely depend on a data set tailored to this question, in particular in which (i) experience sampling measures are collected more frequently as well as (ii) methodological advances that allow patterns of activity to be mapped without using temporal correlation. and that measure thinking across multiple contexts.

All data analyesed in the manuscript has previously been published in Mendes et al., 2019. The code used for analysis and visualiszation is available at https://github.com/samyogita-hardikar/trait_state_lsd (copy archived at Hardikar, 2024).

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Author details

1. Samyogita Hardikar

   1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. Max Planck School of Cognition, Leipzig, Germany

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   samyogita@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4380-5055

2. Bronte Mckeown

   Department of Psychology, Queen's University, Kingston, Canada

   Contribution

   Resources, Software, Writing – review and editing

   Competing interests

   No competing interests declared

3. H Lina Schaare

   1. Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany

   Contribution

   Resources, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4259-0793

4. Raven Star Wallace

   Department of Psychology, Queen's University, Kingston, Canada

   Contribution

   Resources, Software, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0003-0414-0254

5. Ting Xu

   Center for the Developing Brain, Child Mind Institute, New York, United States

   Contribution

   Resources, Software, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Mark Edgar Lauckener

   Max Planck Research Group: Adaptive Memory, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

   Contribution

   Resources, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Sofie Louise Valk

   1. Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
   3. Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

   Contribution

   Resources, Software, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

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

8. Daniel S Margulies

   Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France

   Contribution

   Resources, Data curation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Adam Turnbull

   1. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States
   2. Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

10. Boris C Bernhardt

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

    Contribution

    Resources, Software, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

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

11. Reinder Vos de Wael

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

    Contribution

    Resources, Software, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Arno Villringer

    1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. Max Planck School of Cognition, Leipzig, Germany
    3. Day Clinic of Cognitive Neurology, Universitätsklinikum Leipzig, Leipzig, Germany
    4. MindBrainBody Institute, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
    5. Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany

    Contribution

    Resources, Data curation, Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

13. Jonathan Smallwood

    Department of Psychology, Queen's University, Kingston, Canada

    Contribution

    Conceptualization, Resources, Data curation, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7298-2459

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