Distinct patterns of thought mediate the link between brain functional connectome … · Distinct...

Distinct patterns of thought mediate the link betweenbrain functional connectome and psychological well-being

Deniz Vatansever,1, ∗ Theodoros Karapanagiotidis,2 DanielS. Margulies,3 Elizabeth Jefferies,2 and Jonathan Smallwood2

1Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, PR China, 2004332Department of Psychology, University of York, York, United Kingdom, YO10 5DD

3Institut de Cerveau et de la Moelle pinire, Centre National de la Recherche Scientifique, Paris, France, 75013(Dated: September 8, 2019)

Ongoing thought patterns constitute important aspects of both healthy and abnormal humancognition. However, the neural mechanisms behind these daily experiences and their contributionto well-being remain a matter of debate. Here, using resting state fMRI and retrospective thoughtsampling in a large neurotypical cohort (n = 211) we identified two distinct patterns of thought,broadly describing the participants current concerns and future plans, that significantly explainedvariability in the individual functional connectomes. Consistent with the view that ongoing thoughtsare an emergent property of multiple neural systems, network-based analysis highlighted the centralimportance of both unimodal and transmodal cortices in the generation of these experiences. Im-portantly, while state-dependent current concerns predicted better psychological health, mediatingthe effect of functional connectomes; trait-level future plans were related to better social health,yet with no mediatory influence. Collectively, we show that ongoing thoughts can influence the linkbetween brain physiology and well-being.

Keywords: functional connectivity, functional magnetic resonance imaging, graph theory, mental health,resting state, patterns of thought

INTRODUCTION

Recent advances in functional magnetic resonanceimaging (fMRI) methods and data analysis techniqueshave facilitated a new era in the characterisation of theneural representations that underlie human cognition andbehaviour [1]. Big data-driven approaches are utilisedto explore whole-brain functional interactions during un-constrained states of rest, explaining considerable levelsof population-wise variation in complex traits, includ-ing intelligence [2], personality [3], daily habits [4] andself-perceived quality of life [5]. In addition to helpingresearchers derive novel theories on healthy brain pro-cessing, one important goal of these functional connec-tomic mapping initiatives is to devise neural, cognitiveand behavioural links to better understand mental healthdisorders, and to identify “at-risk”groups for future pre-ventative measures [6, 7]. However, most studies oftenneglect the fact that periods of unconstrained states ofrest can be characterised by patterns of self-generatedthoughts that are also associated with well-being, whichmay have unique neurocognitive correlates.

Research from psychology suggest that the ability toself-generate patterns of cognition is a core element ofour mental lives, occupying a considerable portion ofour daily mentation [8–10]. Critically, these thoughtsare particularly prevalent in situations with low externaldemands, such as periods of wakeful rest [11] i.e. the con-ditions when resting state functional data is most com-monly recorded. Importantly, measures of such experi-

∗ [email protected]

ences have a wide range of momentary correlates includ-ing indicators of stress [12], ongoing physiology [13] andtask performance [14], and also have documented linksto both beneficial and deleterious psychological traits.For example, patterns of ongoing thought have been pre-viously linked to individuals’ ability to plan for futuregoals [15], wait for long-term rewards [16], and devisecreative solutions to both personal [17] and social prob-lems [18]. Other forms of self-generated mentation areassociated with unhappiness [19], as well as poor perfor-mance in sustained attention tasks [20] or measures offluid intelligence [21]. In fact, disruptive thought pat-terns are reported to underlie the absent-minded mis-takes in our everyday functioning [22, 23], including traf-fic accidents [24] or medical malpractice [25], and to forma potential basis for cognitive impairments reported inattention-deficit/hyperactivity disorder [26, 27] and clin-ical depression [28].

Furthermore, there is now emerging evidence that linkpatterns of neural activity to trait variance in specificpatterns of thoughts. These investigations highlight thatpatterns of ongoing thought have complex and often het-erogeneous neural correlates [29, 30], which depend onthe functional interaction of multiple neural and cogni-tive components [31–33]. For example, Wang and col-leagues identified distinct patterns of ongoing thoughtsthat were linked to reductions in both within and betweennetwork connectivity for neural systems important for ex-ternal attention (ventral, dorsal and fronto parietal sys-tems), which was in turn related to reduced performancein tasks of cognitive aptitude [34]. Other studies have il-lustrated that different types of spontaneous thought de-pend on differential patterns of functional connectivity

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FIG. 1. Experimental data collection and analysis pipeline. A large-cohort of participants (n = 211) were fMRI scannedduring a state of wakeful rest for a total of 9-minutes. A comprehensive pipeline of fMRI preprocessing and data denoisingprocedures were followed in order to ensure maximal removal of nuisance variables. The thought sampling scores collectedat the end of the resting state scanning session were first hierarchically clustered into two major groups, each of which werethen reduced to three patterns of thought using principal component analysis (PCA). Fully-connected, weighted correlationmatrices based on the Power et al. 2011 parcellation (264 regions of interest) scheme and the individual component scores onthe identified patterns of thought were then carried forward on to Network-Based Statistics (NBS) with the aim of identifyingcomponents of brain connections that related to the participants thought patterns. T-tests were carried out for all six patternsof thought with an initial T-score of 3.2, and a significance level of p < .05 over 5,000 permutations (mean connectivity,age, gender and percentage of invalid scans based on the composite motion score from the scrubbing procedure were enteredas group-level nuisance regressors). The identified brain components that significantly related to the participants thoughtswere first characterized at the group-level and then used as mask graphs to create thresholded connectivity matrices for eachparticipant. Network metrics of positive, negative, total and fractional strength (i.e. the ratio of positive to negative strength)as well as betweenness centrality were measured on individual thresholded matrices and were further used to characterize theidentified neurocognitive profiles. Linear regressions and mediation analyses were then employed to investigate the mediatoryeffect of thought patterns on the link between brain connectivity and psychological and social well-being as measured using across-culturally validated World Health Organization Quality of Life group (WHOQOL-BREF) questionnaire.

between regions important for memory (e.g. temporallobe) and those that form the core associative cortices[30, 35, 36]. Notably, tasks such as creative idea gener-ation [37] and future planning [38], that are collectivelyassociated with unconstrained thought [17, 39], dependon similar patterns of interaction between regions of thedefault mode with regions of the fronto-parietal network.

Converging body of evidence from contemporary cog-nitive neuroscience, therefore, highlight that (a) patternsof neural activity at rest have associated patterns of

thought, and (b) that both neural and self-report de-scriptions of patterns of unconstrained activity are pre-dictive of a wide range of psychological features of an in-dividual, including mental well-being. Collectively, theseobservations highlight the need to understand the ex-tent to which relationships between neural activity atrest and aspects of psychological traits are dependentupon the nature of patterns of ongoing experience thatemerges while neural activity is recorded. Our currentstudy reflects an attempt to address this issue by quanti-


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fying the relationship between patterns of thoughts dur-ing rest, and the associated ongoing neural activity, andthen exploring whether descriptions of neural organisa-tion gained in this fashion is associated with variation inmeasures of psychological trait, in this case self-perceivedmental well-being.

In the analysis of a large neurotypical sample (n = 211)we found differential profiles of functional interactionsamongst multiple neural systems that explained individ-ual variation amongst two distinct patterns of thought,broadly corresponding to the participants current con-cerns and future plans. Using a test-retest sample (n =40) we found reasonable concordance between the ten-dency to generate future plans at rest and their brainbasis, indicating that these measures are likely to reflecta trait. On the other hand, current concerns were not sta-ble across individuals, but showed evidence of commonchanges in terms of the pattern of thought and the pat-tern of functional connectivity, suggesting that these neu-rocognitive links reflect a more transient state. Finally,we found that while current concerns indirectly mediatedthe effect of brain functional interactions on psycholog-ical well-being, future plans showed no such mediationeffect.

RESULTS

Dimensions of variation in ongoing thought patterns

With the aim of investigating the differential influenceof distinct thought patterns on the link between brainfunctional network topology and mental well-being, wecollected 9-minutes of fMRI data from a large cohort ofneurotypical participants (n = 211) during a period ofwakeful rest. This neural measure was complementedwith a session of retrospective thought sampling, ad-ministered immediately after the resting state scanning,as well as self-assessed ratings of psychological and so-cial well-being on a cross-culturally validated question-naire from the World Health Organization Quality of Life(WHOQOL) group [40], collected outside the scanner ina separate behavioural session. The workflow for the datacollection and analysis techniques utilized in this studyis presented in Figure 1 with further details provided inthe Methods and Supplementary Information. Parts ofthis dataset have been previously employed in our priorinvestigations [27, 32, 34, 41].

Our first objective was to establish the dimensions ofvariation within the participants ongoing thought pat-terns. The employed retrospective thought samplingmethod required participants to subjectively character-ize their thoughts using 4-choice (Likert scale) ratingson a set of questions derived from our previous investi-gations [18, 30, 33, 39, 42] (Supplementary Table S1).With the aim of reducing dimensionality and improv-ing interpretability, these ratings were first hierarchicallyclustered and then decomposed into distinct dimensions

using principal component analysis (PCA), which estab-lished the main patterns of thought reported in our sam-ple (Fig. 2). The total number of thought patterns wereselected using scree-plots, based on the eigenvalue (>1)and the explanatory power gained by each additional de-composition (Supplementary Fig. S1-2). Typical ratingsfrom participants who scored highest on a given thoughtpattern are provided in Supplementary Figure S3.

Principal Components Analysis (PCA), applied sep-arately to each of the two initial hierarchical clusterson the self-reported ratings, revealed three patterns ofthought each (a total of six), explaining 51% and 35%of the variance, respectively. The identified patternshighlighted aspects of ongoing thoughts that are com-monly characterized in the existing literature [43]. Theseencompassed thoughts that were important and spe-cific (Fig. 2a), perceptually decoupled and hard-to-stopthoughts about the self (Fig. 2b), positive, spontaneousthoughts about others (Fig. 2c), insightful and image-based thoughts (Fig. 2d), deliberate, verbal thoughtsabout the future (Fig. 2e), and negative past-relatedthoughts (Fig. 2f).

Distinct patterns of thought link to differentialfunctional connectomic profiles

Next, we determined the extent to which the humanfunctional connectomes were associated with individualvariation on the identified patterns of thought. We useda whole-brain parcellation scheme [44]. Defining each ofthe 264 brain regions as nodes, and Pearson correlationcoefficients amongst them as edges, we constructed fully-connected, weighted functional connectomes for each par-ticipant. Utilizing Network-Based Statistics (NBS) [45],we entered the individual component scores for all sixpatterns of thought as variables of interest in a regressionmodel, while removing the effects of nuisance variables in-cluding mean connectivity, age, gender and the compositemotion score (i.e. percentage of invalid volumes identi-fied in the motion artifact detection procedure) (Fig. S4).This revealed two distinct patterns of thought that weresignificantly related to differential profiles of brain func-tional connectomic organisation (Tthreshold = 3.2, 5,000permutations, α = .05). Although we report on the ini-tial Tthreshold = 3.2 threshold, comparable results forTthreshold = 3.1 and Tthreshold = 3.3 are presented in theSupplementary Results section (Fig. S5).

The first thought pattern that was significantly associ-ated with neural patterns reflected high loadings on “ha-bitual”, “realistic”, “specific”and “important”elementsof ongoing cognition, broadly corresponding to the con-struct of current concerns that has been argued to playan important motivational role in the content and formof ongoing thoughts [46]. Population variation in thisexperience was linked to a combination of positive andnegative connections from a wide range of both unimodaland transmodal brain regions (Fig. 3a) (Tthreshold =


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FIG. 2. Decomposing distinct patterns of thought. Following the initial hierarchical clustering of the participants rat-ings on the experience sampling questionnaire, a total of six patterns of thought were identified using PCA. Three principlecomponents in each of the two clusters explained 51% and 35% of the variability in the data, respectively. The Varimaxrotated component loadings are visualized using word clouds. While the size of the text refers to the relative strength of thecomponent loadings, positive and negative loadings are indicated via red and blue fonts, respectively. The components high-lighted (a) important/specific thoughts, (b) perceptually-decoupled/hard-to-stop thoughts, (c) positive/spontaneous thoughts,(d) insightful/image-based thoughts, (e) deliberate/verbal thoughts, and (f ) negative/past-related thoughts. The individualvariation on these patterns of thought were carried forward on to the NBS analysis as between-subject explanatory variables.

3.2, 5,000 permutations, p = .015). These included ar-eas associated with visual, auditory, somato-motor, dor-sal/ventral attention, cingulo-opercular, salience, fronto-parietal, default mode, subcortical networks and a setof regions with memory-related functions, with thestrongest link observed between the right middle frontaland middle cingulate gyri (salience network).

Overall, this neural component was composed of morepositive than negative connections and involved a set ofdistributed brain regions that encompassed a diverse setof large-scale brain networks. Characterization of thisbrain component using network neuroscience measuresrevealed that an area in the left middle frontal gyrus,belonging to the dorsal attention network, showed thehighest positive, negative, and total strength as well asbetweenness centrality. This highlights the left middlefrontal gyrus as a key node in the functional connectivitypatterns linked to ongoing thoughts about the individu-als’ current concerns. In addition, the highest fractionalstrength (i.e. the ratio of positive to negative strength)was observed in a region belonging to the salience net-work, namely the right middle cingulate gyrus.

The second pattern of thought that related to neuralfunction, reflected high loadings on the “future”ratherthan the “past”, as well as “deliberate”and “verbal”focus

on “problems”, which is broadly consistent with stud-ies of ongoing thought that emphasize a deliberate focuson future plans [15, 47]. This thought pattern was re-lated to the functional interaction of brain regions froma small set of large-scale brain networks mainly belong-ing to the higher-order transmodal cortices (Tthreshold =3.2, 5,000 permutations, p = .026) (Fig. 3b). Both posi-tive and negative links between regions belonging to thedefault mode, fronto-parietal, salience, cingulo-opercularand visual regions correlated with higher scores reportedon this pattern of thought, with the strongest link ob-served between regions in the right inferior temporaland middle frontal gyri. Relative to the “current con-cerns”component, this brain component was composedof more negative connections and was drawn from a morelocalized set of brain regions from a smaller number oflarge-scale brain networks. Network-level analysis indi-cated that a left superior frontal gyrus region belongingto the default mode network showed the highest posi-tive, negative and total strength, while a left middle cin-gulate region (salience network) displayed the greatestfractional strength. Betweenness centrality, however, washighest on a right inferior temporal gyrus region.


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FIG. 3. Distinct patterns of thought are linked to differential profiles of functional connectomic organisation.NBS identified two connected components of brain functional interactions at rest that significantly related to the participantsscores on distinct patterns of thought, which highlighted (a) important, specific, realistic and habitual thoughts about theself and others that were not spontaneous; as well as (b) deliberate, verbal, thematic, abstract, problem-based, and future-oriented thoughts in words that were not about the past or in images. The average connectivity patterns of these brain graphsare visualized on an MNI152 smoothed glass brain, with the nodes color-coded according to the original Power et al. 2011parcellation scheme, and the positive/negative connections coloured in red and blue, respectively. The average connectivitypatterns of these brain graphs and the average graph theory metrics calculated across participants are visualized in circularrepresentations. The Automated Anatomical Labeling (AAL) nomenclature, original network parcellation, average positive(red), negative (blue), total (purple) and fractional strengths (orange) as well as betweenness centrality (green) of each regionin this brain component are listed around the rings. The average positive and negative connections between these regions arerepresented by links colour coded red and blue respectively.

Test-retest reliability of thought patterns andfunctional connectomic organisation

Next, we examined the stability of the two neurocogni-tive measures over time by exploring their intraclass cor-relation across two sessions in 40 participants for whoma second assessment was performed (i.e. resting statescan and thought sampling). For “important and spe-cific thoughts about the self and others”(i.e. current con-

cerns), individual variability on this thought pattern dis-played no significant intraclass correlation (ICC = .14,CI [-.18, .43], p = .20), however, the associated brainconnectivity (natural log of the fractional strength) wascorrelated between the first and second assessment ses-sions (ICC = .60, CI [.36, .76], p < .0001). Importantly,there was a significant positive correlation between thechange in component scores on this thought pattern andthe change in brain connectivity between the two assess-


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FIG. 4. Test-retest reliability of the identified thought patterns and the associated functional connectomicorganisation. A second session of resting state fMRI scanning and thought sampling was carried out for a total of 40participants. (a) For important/specific thoughts, while individual component scores on this thought pattern did not show asignificant intraclass correlation (ICC = .14, CI [-.18, .43], p = .20), the associated brain connectivity pattern (natural log offractional strength) was largely consistent between the two repeated assessment sessions (ICC = .60, CI [.36, .76], p < .0001).Moreover, the change in thought pattern was positively related to the change in brain connectivity (Pearson r = .40, p = .011).(b) For deliberate/verbal thoughts, both the component scores on this thought pattern (ICC = .48, CI [.21, .69], p = .00065)and the associated brain connectivity (ICC = .61, CI [.38, .55] p < .0001) showed significant intraclass correlations. However,there was no significant link between the change in this thought pattern and the change in brain connectivity between the twoassessment sessions (Pearson r = .24, p = .14).

ment sessions (Pearson r = .40, p = .011) (Fig. 4a).Together this pattern is broadly consistent with a moretransient state.

For “deliberate verbal thoughts about the future”(i.e.future plans) on the other hand, individual variability onboth the thought pattern (ICC = .48, CI [.21, .69], p= .00065) and the associated brain connectivity (ICC =.61, CI [.38, .55] p < .0001) were consistent across thetwo assessment sessions. However, there was no signifi-cant correlation between the change in component scoreson this thought pattern and the change in brain connec-tivity (Pearson r = .24, p = .14) (Fig. 4b). Collectively,these analyses show that the patterns of thought cap-tured by “deliberate and verbal thoughts about the fu-ture”and their neural representations show greater trait-like stability over time than the participants state-like

“important specific thoughts about the self and others”.

Distinct patterns of thought mediate the effect ofbrain connectivity on well-being

Finally, having identified two patterns of ongoingthought, each with an associated profile of complexbrain functional interactions at rest, we tested whetherthese neurocognitive metrics derived from our studyhad mediatory influences on measures of mental healthand well-being in daily life, as indicated by a cross-culturally validated World Health Organization question-naire (i.e. WHOQOL-BREF). Incorporating the relativeimportance of both positive and negative connections[48, 49], we first assessed the predictive power of frac-


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FIG. 5. Model of the functional connectome as a pre-dictor of psychological and social well-being, medi-ated by patterns of thought. While brain connectiv-ity refers to the natural log of the fractional strength, pat-tern of thought represents the individual scores on the identi-fied thought pattern, and the psychological/social well-beingmeasures are self-reported ratings on the WHOQOL-BREFquestionnaire. Only a subset of the participant cohort whofully completed the well-being questionnaire (n = 169) wasutilised in this analysis. The calculation of confidence inter-vals (CI) for the indirect effect was based on the percentilebootstrap estimation approach with 5,000 samples. (a) A sig-nificant indirect effect of brain connectivity on psychologicalwell-being was observed, mediated through the participantsimportant/specific thoughts, broadly related to their currentconcerns. (b) There was no significant indirect effect of brainconnectivity on social well-being, mediated through the par-ticipants deliberate/verbal thoughts on their future plans.

tional strength on psychological and social well-being vialinear regressions, followed by mediation analyses to ex-amine the indirect influence of brain connectivity on well-being, mediated by the participants reported patterns ofthought.

For “important and specific thoughts about the selfand others”(i.e. current concerns), the fractionalstrength (natural log) of the associated brain connec-tivity component significantly predicted the participants’psychological well-being score β = .52, r = .19, p = .014),while no significant link was observed with social well-being β = .075, r = .030, p = .699). A mediation anal-ysis indicated that there was a significant indirect effectof brain connectivity on psychological well-being, medi-ated by the individuals’ scores on the identified patternof thought β' = .35, 95% CI [.029, .68]) (Fig. 5a). For“deliberate and verbal thoughts about the future ”(i.e.future plans) on the other hand, the fractional strength(natural log) of the identified component of brain connec-tivity significantly predicted the participants social well-being score on the WHOQOL-BREF β = .38, r = .17, p =.024), while no significant link was observed with psycho-logical well-being β = -.069, r = .028, p = .718). More-over, a mediation analysis revealed no evidence for anindirect effect of brain connectivity on social well-beingthrough the individuals’ scores on the identified thoughtpattern β' = .11, 95% CI [-.071, .32]) (Fig. 5b).

DISCUSSION

The aim of this study was to examine whether account-ing for the patterns of ongoing thoughts that individualsexperience during periods of wakeful rest (e.g. restingstate fMRI scanning) could allow for a more nuancedunderstanding of the relationships between aspects ofpsychological functioning, patterns of neural organisationand mental health. To achieve this goal, we first iden-tified patterns of neural connectivity at rest that variedwith aspects of self-reported experience during this pe-riod. One dimension of variation was along patterns ofthinking that was indicative of a focus on current con-cerns, which was dominated by connections from the mid-dle frontal gyrus, a region within the salience network.This neural pattern was a stable feature of the assessedindividuals, while the pattern of thinking did not depictsuch reliability. Nevertheless, both neural and self-reportpatterns changed together across time, indicating thatthis neurocognitive profile described a transient mappingbetween brain and experience. A second pattern, asso-ciated with deliberate thoughts about the future, wasdominated by functional connections from the superiorfrontal gyrus, situated within the default mode network.Both neural and experiential features of this mode wereconsistent across individuals and showed little evidence ofcommon changes over time, suggesting a neural patternthat was a relatively stable trait. Importantly, these neu-ral components had differential associations with mea-


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sures of mental health and well-being. The transientneurocognitive component, linked to the participants fo-cus on current concerns, was significantly associated withaspects of self-reported psychological well-being. Media-tion analysis indicated that this brain-behaviour relation-ship was fully mediated by the associated descriptions ofongoing experience. In contrast, while the dimensions ofvariation linked to deliberate problem solving was asso-ciated with social well-being, this relationship was inde-pendent of the associated patterns of experience.

Together these analyses indicate that important as-pects of the commonly reported relationships betweenbrain and behaviour can be understood through theirassociations with patterns of ongoing thoughts that par-ticipants experience during fMRI scanning. First, ourstudy shows that neural profiles, identified through theirassociation with self-reported experience, have differen-tial associations with aspects of well-being. Self-reportsare obviously subject to factors that impact upon theircredibility, however, neural patterns activity identifiedin this fashion have the advantage that they are em-bedded in a cognitive context without the need for re-verse inference [50]. Thus, experience sampling providesa complimentary method for determining whether thesource of observed neural patterns are cognitive in na-ture, or emerge for other reasons, such as cardiovascularfunction or motion related confounds [51]. Second, ourstudy demonstrates that experience sampling is sensitiveto patterns of neural activity that are stable across timeand others that are transient. Our approach, therefore,may have direct relevance to studies that aim to explorethe long-term stability of patterns of functional organi-sation, or those that explore long-term changes in neuralfunction. Based on the current data, for example, expe-rience sampling may provide a reasonably direct way totest hypotheses into why certain neural patterns vary intheir consistency across time. Despite the inherent weak-ness associated with retrospective experience sampling[52], our study shows that the information it providescan address important shortcomings of the conceptual in-terpretations placed on functional connectivity patternsderived from resting state analysis. Given the negligiblecost associated with acquiring descriptions of experienceafter resting state scans, and their prevalence as a tool ofcognitive neuroscience, we see no reason why this methodshould not be employed as a community standard in sim-ilar studies moving forward.

As well as highlighting the value of experience sam-pling to studies investigating the relationship betweenfunctional organisation and behaviour, our study pro-vides valuable information into the neural processes thatcontribute to different types of self-generated thought.Current concerns, either in the form of unfulfilled goalsor personally relevant information, occupy a significantportion of the thoughts we experience in our daily lives,potentially constituting a determining factor in the func-tional outcomes of our ongoing cognition [28, 46]. In linewith these results, a key dimension of thought pattern

that was reported by the participants in our cohort wasrelated to “important and specific thoughts about theself and others”or more generally their current concerns.Our results revealed that important functional connec-tions was observed in the salience network, commonlyimplicated in the detection of behaviourally important,internal or external stimuli for the coordination of neu-ral resources [53]. This neural system has been shownto causally influence the functional interaction betweendefault mode and fronto-parietal networks that are com-monly anti-correlated at rest [54]. We recently combinedmomentary experience sampling with online neural ac-tivity and found that a pre-frontal region of this networkwas associated with the ability to prioritise patterns ofepisodic social thoughts during periods when external de-mands were reduced [55]. Together with such evidenceour study suggests that the role of the saliency networkin patterns of ongoing thought emerges from its generalcapacity for prioritizing patterns sensory, memorial andaffective content [52] that is motivated by their contex-tual relevance [56, 57].

A second component of “deliberate and verbalthoughts about the future”was linked to the connectiv-ity of a limited number of nodes in transmodal corticesincluding regions of the default mode, cingulo-opercular,salience and fronto-parietal networks. This neural pat-tern was dominated by connections from a region of thesuperior frontal gyrus, within the default mode network.A deliberate focus on future goals reflects our ability tosimulate and envision the consequences of our actionsbased on our prior experiences [58]. Importantly, similarinteractions between the default mode network and sys-tems linked to executive control (e.g. fronto-parietal andsalience networks) are observed when participants engagein tasks that mimic this type of thought, such as creativeproblem-solving [37], visuospatial [59] as well as autobio-graphical planning [38], and imagining reward outcomes[60]. This pattern of thought was predictive of bettersocial well-being, an observation consistent with stud-ies linking patterns of ongoing thought to social prob-lem solving [18], and our ability to infer the actions andmental states of others [61]. This component was alsocharacterised by both positive as well as negative brainconnections. Negative or anti-correlations have been his-torically assumed to arise from the analysis techniquesemployed, and head-motion that is thought to lead tospurious connectivity measures [62]. However, recent re-ports suggest a neurophysiological basis and a potentialcognitive importance of such anti-correlations in healthybrain processing [48, 49, 63], and our results raise thepossibility that the tuning of interactions between neu-ral systems may give rise to different types of ongoingexperience a hypothesis which requires further investi-gation. Furthermore, recent perspectives on the genera-tion and maintenance of thought patterns highlight thevital importance of considering the dynamic nature ofongoing cognition and the within individual variation inthis process [52]. Although retrospective thought sam-


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pling methods provide the advantage of acquiring mea-sures related to an undisrupted period of unconstrainedcognition, online thought sampling and the assessment ofthe link between neural and experiential dynamics mightconstitute a fruitful route in deciphering the within andbetween-individual variability in ongoing cognition andthe underlying neural mechanisms [64].

In summary, we have shown that distinct patterns ofthought are reflected in the underlying brain functionalconnectomes at rest and that certain types of these ex-periences may mediate the influence of intrinsic brainconnectivity organization on well-being. Our resultshighlight the importance of considering thought patternswhen establishing predictive relationships between func-tional connectomes and complex traits, but also suggestthat taking this aspect of human cognition into consider-ation may lead to better characterization of neural fingerprints of the connectome, with the potential for moreuseful clinical markers. In the future, it may be possi-ble to tailor self-reported questions based on their neuralassociations, allowing development of measures targetingparticular psychiatric populations with well-establishedneural hypotheses, such as mood and neurodegenerativedisorders [65]. Furthermore, the analysis method we em-ployed could be useful in studies that test psychologi-cal or pharmacological interventions designed to improvewell-being [66], allowing these investigations to disentan-gle whether their intervention targets the underlying neu-ral architecture, changes in patterns of thought, or a com-bination of both.

METHODS

Participant Demographics

In accordance with the Declaration of Helsinki on theconduct of research involving human participants, eth-ical approval was obtained for this study from the De-partment of Psychology and York Neuroimaging Centre,University of York ethics committees. Following a stan-dard informed consent procedure, a total of 226 healthy,right-handed (one left-handed), native English speakerundergraduate or post-graduate students with normal tocorrected vision were recruited from the University ofYork. All volunteers received monetary compensation orcourse credit for their participation in line with the de-partmental policies. As per the exclusion criteria, noneof the participants had a history of psychiatric or neu-rological illness, severe claustrophobia, anticipated preg-nancy or drug use that could alter cognitive functioning.Moreover, an extensive motion correction procedure wasfollowed (described in detail below) that resulted in theexclusion of 12 participants due to excessive head motioninside the scanner, and three participants were removeddue to the impartial completion of the thought samplingmethod. In total, 211 participants imaging and thoughtsampling data were used in this analysis. The average

age for this group was 20.85 years (range = 18 - 31, SD= 2.44) with a 129/82 female to male ratio.

Decomposition of Thought Patterns

To ascertain the principal dimensions of variation inthe thought patterns of this participant cohort, a ret-rospective thought sampling questionnaire was adminis-tered immediately after the resting state fMRI scanning.In this session, the participants were asked to subjec-tively rate their thoughts during the resting state scanon a 4-choice Likert scale from “Not at all”to “Com-pletely”based on a randomly presented set of questionsthat probed the content and form of thoughts. Thisset of questions and the accompanying analysis tech-niques have been extensively utilized in various thoughtsampling reports previously published in the literature(Supplementary Table S1) [18, 30, 33, 39, 42]. First,the ratings from each participant were hierarchicallyclustered based on the similarity of responses usingthe Ward linkage method (Supplementary Fig. S1a).This technique was utilized in order to partition thethought ratings into two distinct groups, thus reducingthe number of variables to be decomposed into inter-pretable patterns of thought [67]. Subsequently, bothgroups of ratings were reduced to three factors each (sixin total) using principal component analysis (PCA) inSPSS (Version 23) (https://www.ibm.com/products/spss-statistics).The number of components was cho-sen based on scree-plots indicating the eigenvalue of eachsubsequent decomposition, and its ability to explain vari-ability in the data (Supplementary Fig. 1b). The com-ponent loadings for the total number of six decomposi-tions were then rotated using the Varimax method andthe resulting factors were visualized on heat maps (Sup-plementary Fig. 2a-b) and word clouds (Fig. 2). Thecomponent scores of each participant on these patternsof thought were then used as between-subject covariatesof interest in the subsequent analyses.

Well-being Assessment

For the assessment of the participants self-perceivedwell-being, we employed the brief version of a healthquestionnaire previously established by the World HealthOrganization Quality of Life (WHOQOL) group [40].Termed WHOQOL-BREF, this quality of life assessmenthas been developed to provide a more comprehensive in-dex of overall health of nations that extends beyond mea-sures of mortality and morbidity, hence, it was designedto be readily administered across cultures and countrieswith different economic status. Extensively validated in alarge number of centres, WHOQOL-BREF consists of 26questions that broadly group into four domains of phys-ical, psychological, social and environmental health. Allparticipants were asked to complete this questionnaire


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at a separate session administered outside the scanner.Out of 211 participants, 169 fully completed the question-naire. The standardized responses for the psychologicaland social health domains were then used as covariatesof interest in subsequent linear regression and mediationanalyses.

MRI Data Acquisition

All MRI data acquisition was carried out at the YorkNeuroimaging Centre, York with a 3T GE HDx ExciteMagnetic Resonance Imaging (MRI) scanner using aneight-channel phased array head coil. A single run of9-minute resting state fMRI scan was carried out usingsingle-shot 2D gradient-echo-planar imaging. The pa-rameters for this sequence was as follows: TR = 3 s, TE= minimum full, flip angle = 90, matrix size = 64 x 64,60 slices, voxel size = 3 x 3 x 3 mm3, 180 volumes. Dur-ing resting state scanning, the participants were askedto focus on a fixation cross in the middle of the screen.Subsequently, a T1-weighted structural scan with 3D fastspoiled gradient echo was acquired (TR = 7.8 s, TE =minimum full, flip angle= 20, matrix size = 256 x 256,176 slices, voxel size = 1.13 x 1.13 x 1 mm3),

MRI Data Preprocessing

All preprocessing and denoising steps for the MRIdata were carried out using the SPM software package(Version 12.0) (http://www.fil.ion.ucl.ac.uk/spm/)and Conn functional connectivity toolbox (Version 17.f)(https://www.nitrc.org/projects/conn) [68], basedon the MATLAB platform (Version 16.a) (https://uk.mathworks.com/products/matlab.html). The firstthree functional volumes were removed in order toachieve steady state magnetization. The remaining datawas first corrected for motion using six degrees of freedom(x, y, z translations and rotations), and adjusted for dif-ferences in slice-time. Subsequently, the high-resolutionstructural images were co-registered to the mean func-tional image via rigid-body transformation, segmentedinto grey/white matter and cerebrospinal fluid probabil-ity maps, and were spatially normalized to Montreal Neu-rological Institute (MNI) space alongside with all func-tional volumes using the segmented images and a prioritemplates. This indirect procedure utilizes the unifiedsegmentationnormalization framework, which combinestissue segmentation, bias correction, and spatial normal-ization in a single unified model [69]. No smoothing wasemployed, complying with recent reports on the negativeinfluence of this procedure on the construction of func-tional connectomes and graph theoretical analyses [70].

Furthermore, a growing body of literature indicatesthe potential impact of volunteer head motion inside thescanner on the subsequent estimates of functional connec-tivity and network neuroscience metrics [71–74]. In order

to ensure that motion and other artefacts did not con-found our data, we have employed an extensive motion-correction procedure and denoising steps, comparable tothose reported in the literature [75, 76]. In additionto the removal of six realignment parameters and theirsecond-order derivatives using the general linear model(GLM) [77], a linear detrending term was applied aswell as the CompCor method that removed five principlecomponents of the signal from white matter (WM) andcerebrospinal fluid (CSF) [78]. Moreover, the volumesaffected by motion were identified and scrubbed basedon the conservative settings of motion greater than 0.5mm and global signal changes larger than z = 3. A to-tal of 12 participants, who had more than 15% of theirdata affected by motion was excluded from this study[76]. Though recent reports suggest the ability of globalsignal regression to account for head motion, it is alsoknown to introduce spurious anti-correlations, and wasthus not utilized in our analysis [62, 79, 80]. Never-theless, the composite motion score (i.e. percentage ofinvalid scans) for each participant was also added as acovariate in group-level analyses to further account forthe potential influence of head motion on functional con-nectome estimations. Finally, a band-pass filter between0.009 Hz and 0.08 Hz was employed in order to focuson low frequency fluctuations [81, 82]. The maximum,and mean motion parameters and global signal change,the percentage invalid volumes that were scrubbed, andthe distribution of correlation coefficients before and af-ter denoising steps are provided in Supplementary FigureS4.

Functional Connectome Analysis

Brain Parcellation We adopted a set of 264 regionsbased on the Power et al. 2011 brain parcellation schemethat has been previously shown to produce reliable net-work topologies at rest and task conditions [44, 83, 84].The network partitions outlined by Cole et al. 83 wereutilized to pre-assign each one of the 264 ROIs to oneof the 13 LSNs documented in the original publication[44]. Namely, 10 well-established networks covering dor-sal (DAN) and ventral attention (VAN), salience (SAN),cingulo-opercular (CON), fronto-parietal control (FPN),default mode (DMN), visual (VN), auditory (AN), so-matomotor (hand and mouth) (SMN), subcortical net-works (SCN); as well as 3 networks that fall into memoryretrieval, cerebellum, and a network of uncertain functionwere used as the 13 network partitions [44].Connectome Construction. Fully-connected, undi-

rected and weighted matrices (264 ROI x 264 ROI) ofbivariate correlation coefficients (Pearson r) were con-structed for each participant using the average BOLD sig-nal time series obtained from the 6 mm (radius) spheresplaced on the MNI coordinates of all the 264 ROIs de-scribed above. The matrices reflected both positive andnegative weighted correlations. The arbitrary thresh-


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olding and binarization processes in graph theoreticalanalysis often lead to loss of information, especially inthe case of negative correlations [85]. Given recent re-ports suggesting a neurophysiological basis and potentialcognitive importance of such anti-correlations in healthybrain function [48, 49, 86], we focused on fully connected,weighted connectomes.

Network-Based Statistics. Next, we aimed to ascer-tain components of individual functional connectomesthat significantly predicted the participants between-subject variation on the identified patterns of thought.For that purpose, we employed the Network-BasedStatistics (NBS) toolbox (Version 1.2) (https://www.nitrc.org/projects/nbs/) [45], which provides en-hanced power to identify connected brain componentsformed by suprathreshold edge links that are associatedwith a covariate of interest, while controlling for Family-Wise Error (FWE) at the component-level. Utilizing thismethod, we entered the individual component scores forall six patterns of thought as variables of interest, whileaccounting for the effects of mean connectivity, age, gen-der and percentage of motion-related invalid scans identi-fied by the scrubbing procedure. Using these regressors,T-tests were first carried out on fully connected whole-brain network edges for each pattern of thought to assessthe relationship between the strength of an edge linkand component scores on patterns of thought, storingthe size of the connected components that survived thechosen T threshold. Next, over a total of 5,000 permuta-tions in which the outcome measures were randomised,random null distributions of maximal component sizeabove the chosen threshold was generated. The num-ber of permutations in which the maximal componentsize was greater than the empirical component size, nor-malised by the total number of permutations, was usedto estimate p-values (.05 level of significance). While theinitial Tthreshold = 3.2 was used for the main analysis,comparable results for Tthreshold = 3.1 and Tthreshold =3.3 are reported in the Supplementary Results section(Supplementary Fig. S5). The resulting connected braincomponents, the links of which showed a significant rela-tionship with individual variability in thought patterns,were then defined as mask graphs to threshold individualfunctional connectomes, which were then carried forwardonto graph theoretical analyses [87].

Network Neuroscience Analysis. Graph theoreticalmetrics in this study were calculated using MATLABfunctions obtained from the publicly available Brain Con-nectivity Toolbox (https://sites.google.com/site/bctnet/). Commonly used in the identification of hub re-gions that greatly influence the efficiency of a network indistributing information [88], network strength denotesone of the most fundamental measures in weighted func-tional connectomes and thus formed the basis of our net-work neuroscience analysis approach. Calculated as thesum of all neighbouring link weights [88], we measuredthe strength of connected components across all partic-ipants that were previously identified as illustrating sig-

nificant relations to individual variability in thought pat-terns. Based on recent reports suggesting the importanceof anti-correlations, we calculated positive, negative aswell as total strength for each individual. Furthermore,given recent evidence suggesting a contribution of thebalance between both positive and negative correlationsto healthy brain processing [48, 49, 86], we aimed to uti-lize a metric that incorporated the importance of theinterplay between these links when assessing its predic-tive power for explaining individual variability in self-reported mental well-being. Thus, we defined fractionalstrength as the ratio of the sum of positive to negativelinks, which was later used as the graph metric of inter-est in subsequent linear regression and mediation analy-ses. Finally, to identify central nodes in the functionallyconnected components that significantly related to thethought structures, betweenness centrality, i.e. the frac-tion of all shortest paths in the network that pass througha given node, was calculated. The average positive andnegative links and the employed graph theoretical met-rics were visualized on circular plots using Circos [89].

Test-Retest Reliability Analysis. Our next aim in thisanalysis was to determine the reliability of the identi-fied patterns of thought and brain connectivity measures.This would not only establish the generalizability of ourresults but would also allude to the potential differencesin the state versus trait-level variability of our neurocog-nitive measures. For that purpose, a second session ofresting state scan and experience sampling was carriedout for 44 participants using the same parameters out-lined above. This behavioural and imaging data werepreprocessed using the same procedures, resulting in theexclusion of four participants due to excessive motion.For the thought decomposition scores, the hierarchicalclustering and PCA decompositions obtained from theinitial cohort was imposed on the ratings obtained fromthe second session [90]. Intraclass correlation coefficients(ICC) were employed to assess the reliability of thoughtcomponent scores and the associated brain connectivity(as denoted by fractional strength) from the first and sec-ond sessions. Furthermore, we assessed a potential linkbetween the change in thought patterns between the twosessions and the change in the associated brain functionalconnections using Pearson correlations.

Mediation Analysis. Finally, we used linear regres-sions to identify the relationship between componentscores on the identified patterns of thought, the frac-tional strength (natural log) of connected componentsand the participants self-reported scores on the psycho-logical and social domains of the WHOQOL-BREF ques-tionnaire. The relationships with health were Bonferronicorrected for multiple comparisons across the two healthdomains. After establishing linear relationships betweenall three measures, subsequent mediation analyses werecarried out with the aim of determining the indirect effectof brain connectivity on psychological and social well-being through participants thought patterns. As it issuggested for small to medium sample sizes [91], the per-


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centile bootstrap estimation approach with 5,000 sampleswere used to ascertain the presence of a mediation effect.

ACKNOWLEDGMENTS

This study was supported by the European ResearchCouncil (Project ID: 646927) (https://cordis.europa.eu/project/rcn/198057/factsheet/en) awarded to JSand a grant from the John Templeton Foundation,“Prospective Psychology Stage 2: A Research Competi-tion”. The funders had no role in study design, data col-lection and analysis, decision to publish, or preparationof the manuscript. The authors extend their gratitudeto Mladen Sormaz, Charlotte Murphy, Hao-Ting Wang

and Giulia Poerio for their invaluable contribution to thescanning of participants. In addition, the authors thankAndre Gouws, Ross Devlin, Jane Hazell and the rest ofthe York Neuroimaging Centre staff for their support insetting up the imaging protocol and scanning. Finally,we thank all the participants for their time and effort intaking part in this study. The authors declare no conflictof interest.Competing Interests: The authors declare no com-

peting interests.Author Contributions: D.M., E.J. and J.S. de-

signed the experiment. D.V. and T.K. developed experi-mental materials, performed data collection and analyzedthe data. D.V., T.K., E.J., and J.S. interpreted results.D.V. wrote the manuscript with comments from all otherauthors.

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