Tracking resting-state functional connectivity changes and mind wandering: A longitudinal neuroimaging study

Journal Pre-proof Tracking Resting-state Functional Connectivity Changes and Mind Wandering: A Longitudinal Neuroimaging Study Hong He, Yu Li, Qunlin Chen, Dongtao Wei, Liang Shi, Xinran Wu, Jiang Qiu PII:

S0028-3932(20)30346-8

DOI:

https://doi.org/10.1016/j.neuropsychologia.2020.107674

Reference:

NSY 107674

To appear in:

Neuropsychologia

Received Date: 30 November 2019 Revised Date:

30 October 2020

Accepted Date: 4 November 2020

Please cite this article as: He, H., Li, Y., Chen, Q., Wei, D., Shi, L., Wu, X., Qiu, J., Tracking Restingstate Functional Connectivity Changes and Mind Wandering: A Longitudinal Neuroimaging Study, Neuropsychologia, https://doi.org/10.1016/j.neuropsychologia.2020.107674. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Credit Author Statement Hong He, Yu Li, Qunlin Chen, Dongtao Wei: Conceptualization, Investigation. Hong He, Yu Li, Xinran Wu: Formal analysis, Software. Hong He, Qunlin Chen: Methodology. Dongtao Wei: Data curation. Hong He, Jiang Qiu, Yu Li: Writing Original draft preparation, Writing - Reviewing and Editing. Hong He, Liang Shi:

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Dongtao Wei, Qunlin Chen: Funding acquisition.

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Visualization. Jiang Qiu: Supervision. Hong He, Yu Li: Validation. Jiang Qiu,

Tracking Resting-state Functional Connectivity Changes and Mind Wandering: A Longitudinal Neuroimaging Study Hong He1, 2, 4#, Yu Li1, 2#, Qunlin Chen1, 2, Dongtao Wei1, 2, Liang Shi1, 2, Xinran Wu1, 2, Jiang Qiu1, 2, 3*

Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing 400715, China;

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School of Psychology, Southwest University, Chongqing 400715, China

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Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic

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4

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Education Quality at Beijing Normal University

Beijing Key Lab of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal

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*Corresponding author:

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University, Beijing, 100875, China

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Jiang Qiu, Ph.D., Principle Investigator; School of Psychology, Southwest University, Beibei, Chongqing, 400715 China; E-mail: [email protected]; Web: http://www.qiujlab.com/

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Hong He and Yu Li contributed equally to this paper.

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Abstract Mind wandering (MW) refers to a drift of attention away from the ongoing events to internal concerns and activates brain regions in the default mode network (DMN) and the frontoparietal control network (FPCN). Although a number of studies using rest-fMRI data have shown that static and dynamic functional connectivity within the DMN were related to individual variations

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in self-reported MW, whether the brain functional connectivity could predict MW remained

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unclear. Here, we carried out longitudinal data collection from 122 participants that underwent three times of MRI scans and simultaneously completed self-reported MW scales over the course

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of two years to clarify whether a direct relationship existed between brain functional connectivity

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and MW. We identified 16 functional connectivity involving the DMN and FPCN that were

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consistently and stably associated with MW across the three time points. However, there were only significant cross-lagged effects between DMN-involved connections and MW frequency

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rather than FPCN-involved connections. In addition, the results indicated that the mean value of

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functional connectivity involving the DMN (FC-DMN) in the low stable (LS) group was the weakest, followed by mean connectivity in the moderate increasing (MI) group and mean

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connectivity in the high stable (HS) group. These results support previous research linking MW with connections between partial areas involving the DMN and FPCN. Importantly, our findings indicated that brain functional connectivity involving DMN predicted the subsequent MW and provided further support for the trait-based nature of MW.

Keywords: mind wandering; functional connectivity; longitudinal; frontoparietal control network; default mode network

1. Introduction Mind wandering (MW) is a common activity that is not always restricted to events taking 2

place. A broader definition illustrates that MW contains a variety of experiences that vary in accordance with intentionality, content, stimuli-relatedness, and task-relatedness (Christoff, et al., 2018; Seli, et al., 2018). In daily life, people’s minds frequently wander away from the immediate environment toward inner musings regarding people, situations, and places (Killingsworth & Gilbert, 2010; Smallwood & Schooler, 2006). This phenomenon occurs everywhere and composes nearly 50% of our waking lives (Killingsworth & Gilbert, 2010; Klinger, 1999). People dissociate from the external environment when their minds wander, which may result in damaged task performance (Mooneyham & Schooler, 2013; Smallwood & Schooler, 2015), such as working

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memory tasks (Mcvay & Kane, 2009) or reading comprehension tasks (Smallwood, Mcspadden,

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& Schooler, 2008; Unsworth & Mcmillan, 2013). However, we also glean some functional benefit

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from MW (Mooneyham & Schooler, 2013; Schooler, et al., 2011), such as creative problem

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solving (Baird, et al., 2012; Smeekens & Kane, 2016), creative thinking (Baird, et al., 2012),

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autobiographical planning (Baird, Smallwood, & Schooler, 2011; Medea, et al., 2016), alleviation of loneliness (Poerio, Totterdell, Emerson, & Miles, 2015; Poerio, Totterdell, Emerson, & Miles,

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2016), fostering of psychosocial adaptation (Poerio, et al., 2016), and a more patient style of making decisions (Smallwood, Ruby, & Singer, 2013). In view of the diverse effects of MW on

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human life, understanding the neural basis of MW is an issue that is worthy of more research

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attention. Recent neuroimaging studies have found that a series of brain regions, which belong to the default mode network (DMN), frontoparietal control network (FPCN) etc., are related to MW (Fox, Spreng, Ellamil, Andrews-Hanna, & Christoff, 2015); furthermore, the functional connectivity between or within intrinsic networks, which are observed during awake resting states, has been revealed to be associated with MW (Godwin, et al., 2017). However, little is understood regarding whether the functional connectivity predicts MW or the frequency of MW predicts the functional connectivity of the brain. In recent years, with the emphasis on the field of cognitive neuroscience, the neural basis of MW has been the focus of many researchers. MW is associated with the recruitment of regions of the DMN (Christoff, Gordon, Smallwood, Smith, & Schooler, 2009; Fox, et al., 2015). Brain 3

lesion studies also revealed the importance of the DMN in MW. Thus, patients with ventromedial prefrontal cortex (vmPFC) damage have lower frequencies of MW; more specifically, vmPFC damage reduces future-related MW (Bertossi & Ciaramelli, 2016). The study of hippocampal damage reveals that the hippocampus is a key pillar in the biological basis of MW and suggests its impact beyond memory (Stamm, Harlow, & Walls, 2006). The DMN has been considered to be the foundation of many features of the MW state; activations in the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), middle frontal gyrus (MFG), middle temporal gyrus (MTG) and so on were observed prior to reports of MW (Christoff, et al., 2009; Mittner, Hawkins,

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Boekel, & Forstmann, 2016). In a focused meditation task, there were activations in the MTG and

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the supplementary motor area (SMA) during MW (Hasenkamp, Wilsonmendenhall, Duncan, &

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Barsalou, 2012). As key regions of the DMN, the mPFC and PCC are related to unconscious states

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(Baars, Ramsøy, & Laureys, 2003). Lateral temporal lobe regions have previously been implicated in the representation of multimodal semantic information (Kajimura, Kochiyama, Nakai, Abe, &

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Nomura, 2016). Medial temporal lobe regions, by contrast, are implicated in memory and

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constructive mental simulation (Andrews ‐ Hanna, Smallwood, & Spreng, 2014; Buckner, Andrews-Hanna, & Schacter, 2008). However, these DMN regions have not been proved to have

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stable relationships with MW.

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Apart from the DMN, MW is also associated with the activation of the FPCN regions, including the dorsolateral and ventrolateral prefrontal cortex, inferior frontal gyrus (IFG) and dorsal anterior cingulate cortex (dACC) (Christoff, et al., 2009; Fox, et al., 2015). In addition, activations in the right inferior frontal gyrus (rIFG), which is involved in stimulus-triggered attentional orienting (Corbetta & Shulman, 2002), have been found during the shift phase when compared with the MW phase (Hasenkamp, et al., 2012). These areas are highly recruited when individuals participate in cognitively demanding tasks (Duncan & Owen, 2000; Smith & Jonides, 1999). Smallwood and collaborators suggest that the recruitment of the FPCN transforms the self-reference thought supported by the DMN into detailed- content thought while MW (Smallwood, Brown, Baird, & Schooler, 2012), which was supported by the result that transcranial 4

direct current stimulation (tDCS) to the lateral prefrontal cortex (LPFC) could increase the meta-awareness of MW (Fox & Christoff, 2015). So, the regions of FPCN play an important role in MW. The development of techniques to investigate functional connectivity observed during awake resting states has allowed researchers to move beyond task-related functional magnetic resonance imaging activation modes and associate connectivity characteristics with cognitive characteristics estimated outside the scanner. Functional connectivity patterns during the resting state are related to MW, and the DMN is the most frequently implicated network in MW. The stronger the internal

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connectivity of the DMN, the higher the trait score of MW (Godwin, et al., 2017; J Smallwood, et

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al., 2016). However, a recent study has challenged this dichotomous view of the connection

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between the DMN and MW, indicating that the functional connectivity between a ventral DMN

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subsystem and the PCC (between two regions of DMN) are negatively correlated with MW frequency (Kucyi & Davis, 2014). Greater functional connectivity between the right hippocampus

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and anterior regions of the DMN promotes more MW (Kucyi, Esterman, Riley, & Valera, 2016).

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Furthermore, the distinct functional connectivity states of the executive control network, DMN, and salience network during sustained attention tasks support the evidence for the MW

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co-recruitment of brain regions within the executive- and default- networks (Mooneyham, et al.,

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2017). Given the surge of research that has emphasized the important role of DMN and DMN involved functional connectivity to MW, it is quite possible that there are interactions between DMN involved edges and MW from a longitudinal perspective. In addition, studies of experience sampling and trait studies of MW have shown the important role of dorsal attention network (DAN) and ventral attention network (VAN) (Hasenkamp, et al., 2012; Turnbull, et al., 2018). The DAN may function to turn attention to the external environment (Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016). And MW is associated with decreased activity in the DAN (Mittner, et al., 2016). VAN is considered to be involved in directing attention to salient perceptual stimuli (Christoff, et al., 2016). Wang et al. have found that DAN and VAN, which reflect the ‘executive-failure’ hypothesis together with FPCN, were related to deliberate planning and social temporal contents of MW, respectively 5

(Wang, et al., 2018). It is often assumed that increased connectivity between the DMN and regions supporting executive control contributes to task functioning. The interactions between the DMN and networks such as the FPCN have been shown to be associated with MW. The connectivity between the FPCN and DMN is positively correlated with the trait of MW (Godwin, et al., 2017). Higher reports of spontaneous MW were related to heightened functional connectivity between a region within the DMN (ventral inferior frontal gyrus) and a region within the FPCN (ventral inferior sulcus) (Golchert, et al., 2016).

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The functional connections observed to have correlations with MW in previous works were

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mixed. To date, empirical studies on the longitudinal level of MW are still lacking. In our current

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work, we focused on the overall MW episodes in daily life and used a cohort of 122 college

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students scanned with structural and functional MRI and subsequently assessed each individual’s frequency of MW at three time points over approximately 2 years. The frequency of MW was

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measured by mind wandering frequency scale (MWFS) (Wang, 2011). MW that was merged by

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daydreaming frequency score of the daydreaming frequency scale (Singer & Antrobus, 1972) and three dimensions of the MWFS showed a significant negative correlation with positive emotion

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and significant positive correlation with negative emotion (He, Chen, Wei, Shi, & Qiu, 2018), as is

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typically the case. Considering the functional importance of the DMN during MW, the cognitive control function of the FPCN, and the close relationship between MW and executive control functions, the current study builds on many studies of functional connectivity between and within networks by investigating the association between MW and these neural networks. The first aim of our study was to examine the stability of the relation between MW and functional connectivity of DMN over time. Furthermore, little study has explored the predicted link between MW and functional connectivity. The second aim of this study, therefore, to test the longitudinal nature of the effect between functional connectivity and MW. Given the fact that the diffusion tensor imaging (DTI) and cortical thickness indicators could predict MW (Golchert, et al., 2016; Karapanagiotidis, Bernhardt, Jefferies, & Smallwood, 2016), we hypothesized some of the stable functional connections (especially the functional connectivity between DMN and FPCN) could 6

also predict the individual frequency of MW. The last aim was to explore whether the importance of these edges can also be seen in the analysis of individual differences of MW.

2. Materials and Methods 2.1 Participants This longitudinal sample was part of an ongoing project to investigate the associations among genes, behavior, and the brain (GBB) project at Southwest University (Liu, et al., 2017), which was approved by the Institutional Review Board of the Southwest University Brain Imaging

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Center. The healthy and right-handed participants were mostly recruited using advertisements on

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leaflets and billboards. All participants provided written informed consent (written informed

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consent and assent were obtained from their guardians for adolescents under the age of 18), and

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they had no history of neurological disorders, substance abuse or psychiatric diseases. Participants received a reward dependent on the tasks they completed and the time they took.

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A total of 701 participants completed the MW questionnaire and MRI assessments at time

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point 1 (tp1), and 145 and 510 participants completed the questionnaire and the MRI assessments at time point 2 (tp2) and time point 3 (tp3), respectively. The interval between tp1 and tp2 was

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278.87 days, and the interval between tp2 and tp3 was 481.88 days. A total of 144 participants

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completed the MRI assessment and MW questionnaire for the three time points. After excluding the individuals whose head motion exceeded 1.5 mm in any direction during the whole fMRI scans at any time point, 122 participants (35 males, 87 females; mean age ± SD = 19.3 ± 1.2 at tp1) were included in the final analysis. They were participants who have chosen the correct answer of the lie test item in the MW questionnaire. 2.2 Questionnaire Measures Functional and structural MRI images were collected, after which the participants were asked to complete several questionnaires developed by the GBB project. In the current study, we only used the MW questionnaire, which was measured at every time point. MW was measured by a 5-point Likert-type MWFS established by Wang (Wang, 2011). The MWFS was developed in Chinese and evaluates an individual’s tendency for MW with 22 questions (see Table 1). The 7

MWFS contains three dimensions; the first is SMW (spontaneous mind wandering) containing 10 items, the second is OEMW (overall evaluation of mind wandering) with 6 items, and the last is UMW (uncontrol mind wandering) with 5 items. Question 15 is a lie test item. The SMW and UMW measure the spontaneous occurrence and the process of MW, respectively. The OEMW serves as a fuzzy self-evaluation of MW. A higher score of MWFS indicated a higher frequency of MW. The Cronbach's alpha for MWFS was 0.94 which showed reasonable retest reliability. The Cronbach's alphas for SMW, UMW and OEMW were 0.90, 0.80 and 0.87, respectively. Participants were asked to report on a 5-point Likert scale, ranging from 1 (never) to 5 (very

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often).

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INSERT TABLE 1 ABOUT HERE

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2.3 MRI Data Acquisition

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At every time point, the rs-fMRI scan lasted 8 minutes. MRI images were collected on a 3.0-T Siemens Trio scanner (Siemens Medical, Erlangen, Germany) using a twelve-channel head

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coil. During the scanning, each subject was instructed to lie down with their eyes closed, to not

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think about anything in particular and to not fall asleep. The scan of 242 whole-brain functional images was collected using gradient echo planar imaging (EPI) sequences (repetition time = 2000

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ms, echo time = 30 ms, flip angle = 90°, field of view = 220 × 220 mm2, slices = 32, thickness = 3

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mm, slice gap = 1 mm, voxel size = 3.4 × 3.4 × 4 mm3). Additionally, a magnetization prepared rapid acquisition gradient-echo (MPRAGE) sequence was used to obtain a 3D high-resolution T1-weighted structural image (repetition time = 1900 ms, echo time = 2.52 ms, field of view = 256 × 256 mm2, flip angle = 90°, slices = 176, thickness = 1 mm, voxel size = 1 × 1 × 1 mm3). 2.4 MRI Data Processing Preprocessing of the resting-state images was performed with the Data Processing Assistant (DPARSF, http://restfmri.net) (Yan & Zang, 2010), which is mostly based on the Statistical Parametric Mapping software (SPM8, http://www.fil.ion.ucl.ac.uk/spm/spm8). Each participant had 232 images after discarding the first 10 scans. Subsequently, slice timing and realignment to the middle image were used for the 232 images to correct for slice order effects and head movement artifacts, respectively. Individuals with head motion exceeding 1.5 mm in translation or 8

in rotation throughout the 8-minute scans were discarded from further analysis. Anatomical CompCor (aCompCor), which uses noise-related principal components of white matter (WM) and cerebrospinal fluid (CSF) time courses, was applied to control the impact of scanner-related and physiological artifacts (Muschelli, et al., 2014; Parkes, Fulcher, Yu¨cel, & Fornitod, 2019). Thus, the 6 motion parameters, top five WM principal components, and top five CSF principal components were regressed out of the data. These functional images were then normalized to the T1 template. Next, we spatially smoothed the normalized data with a 6 mm full width at half maximum (FWHM) Gaussian kernel. Finally, we used a 0.01- to 0.1- Hz bandpass to filter the

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smoothed images to reduce the influences of low-frequency fluctuation and high-frequency noise.

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2.5 Latent growth curve model

The latent growth curve model (Stamm, et al., 2006) was conducted using Mplus (Muthén &

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Muthén, 1998) to estimate individual trajectories of mind wandering over time. The mean initial

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level of mind wandering frequency was reflected on the latent intercept, the mean change over

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time of individuals was modeled by the latent slope. Model fit was determined by the comparative fit index (CFI), root mean square error of approximation (RMSEA) and standardized root mean

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square residual (SRMR).

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2.6 Latent Class Growth Analysis

The MWFS of 122 participants who completed three times of behavioral assessments and

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met the standards of head motion were included in the LCGA in Mplus. This approach is based on finite mixture modeling, and summarizes longitudinal data by modeling variability in developmental trajectories at the individual level (Muthén & Muthén, 2010). The number of classes was set as 1-4 to decide the best-fitting model. The number of classes was determined by the smallest Bayesian information criterion (BIC), the Lo-Mendell-Rubin likelihood ratio test (LMR-LRT), bootstrap likelihood ratio test (BLRT), entropy, and the interpretability of the latent trajectory classes. 2.7 Functional Connectivity Analyses The 268-node atlas based on a group-wise spectral clustering algorithm was used to build the functional connectivity matrix for each subject (Shen, Tokoglu, Papademetris, & Constable, 2013). 9

The whole brain was covered by the atlas, including brainstem, cortical and subcortical structures. This atlas was divided into eight networks based on six separate imaging conditions (Finn, et al., 2015). The 268 × 268 matrices were made up of the functional connectivity values obtained by correlating the average time series in turn, which was extracted from each node. This matrix contained 35778 unique values of Pearson's ρ correlation coefficients. Furthermore, as we did not concern about the cerebellum and subcortical areas, we did not put these areas into the following analysis. Then, we normalized the correlation coefficients by using Fisher’s transformation (Grady, Sarraf, Saverino, & Campbell, 2016). The MW related edges were significantly correlated with

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any one of the dimensions in MWFS. Furthermore, the significant correlation between functional

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connectivity and any one of the three dimensions in the MWFS must exist across three time points

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with a threshold of p < 0.05 (uncorrected). For picture presentation, the network and lobe

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definition were preloaded by an online visualization tool to make the circle plots and the 3D glass brain map (http://bisweb.yale.edu/connviewer/) (Shen, et al., 2017).

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2.8 Cross-lagged Model Analysis

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Cross-lagged model analyses, in which variables were measured three times, were adopted to explore the associations between MW and functional connectivity. The advantage of this approach

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is that it can simultaneously address reciprocal influences on functional connectivity and MW

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frequency (Curran, 2000). The result of a cross-lagged model includes three types of correlations: autocorrelation, synchronous correlation and cross-lagged correlation. To assume that a cross-lagged model is valid, the coefficients of autocorrelation, and synchronous correlation must be high and statistically significant (Turnbull, et al., 2018), and the influence from variable A to variable B is inferred by the standardized regression coefficients of the path from A at tp1 to B at tp2 (cross-lagged correlation). In the current study, correlation analysis and hierarchical regression analysis were performed by using SPSS 22 (SPSS Inc., Chicago, IL, USA) to build the model with the three time point variables, which contains the MWFS scores and the values derived from adding up all the functional-functional connectivity involving the DMN, FPCN, other networks or the all functional connectivity. In order to exclude the influence of irrelevant variables, the mean value of other functional connections among the 16 edges was regressed in the hierarchical 10

regression analysis. For example, when we predict the effects of FC-DMN on mind wandering, the effects of edges except functional connectivity involving DMN among 16 functional connections were controlled. 2.9 Statistical Analyses To examine the homogeneity in MW at different time points, a repeated-measures analysis of variance (ANOVA) was used to compare the differences among three time points of MWFS scores. To determine the functional connectivity of different groups, One-Way ANOVAs were performed for the DMN- and other network-related functional connectivity regardless of the time

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of data collection. A post hoc test was used when a statistically significant effect was found. All of

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these analyses were performed using SPSS 22.

3.1 Longitudinal Behavioral Data

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3. Results

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Table 2 presents the cross-sectional and longitudinal psychological characteristics. After considering the gender variable, the repeated measures ANOVA revealed no significant interaction

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between gender and time points on the MWFS score (F (2, 119) = 0.89, p = 0.42). Furthermore,

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there existed no significant main effect of gender on the MWFS score (F (1) = 0.01, p = 0.91).

INSERT TABLE 2 ABOUT HERE

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In the unconditional latent growth model, all factor loadings of the intercept are constrained to 1, the factor loadings of the latent slope are constrained to 0, 3, and 5 (determined by the intervals between time points of measurement). The linear growth model showed an acceptable fit to data (χ²(1) = 3.3; p = 0.069; RMSEA = 0.112; SRMR = 0.045; CFI = 0.969). The average of the intercept reflects the level of mind wandering frequency of participants at tp1. The non-standardized maximum likelihood estimates of the LGCM are in Table 3. The marginally significant and the positive slope indicates growth in MW frequency across times. The marginally significant variance of the latent slope means a reasonable variability around the average slope (Stamm, et al., 2006). The covariance between the latent slope and intercept is positive, which illustrated that participants with high levels of mind wandering frequency undergo more growth 11

over the 2 years.

INSERT TABLE 3 ABOUT HERE 3.2 Grouping Latent Class Growth Analysis As is recommended (Nylund, Asparouhov, & Muthén, 2007), the optimal number of classes can be determined using an information criterion, which in this case was the BIC and the BLRT. We found that three classes resulted in the lowest BIC and BLRT, which indicates the ‘optimal’ fit of the LCGA models to the data; all other classes led to a significant increase in the value of the information criterion (see Table 4). Figure 1 displays the trajectories of the mean MWFS scores

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for each of the latent group profiles. Here we labeled each group for descriptive purposes: high

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stable (HS) group (N = 24), moderate increasing (MI) group (N = 44) and low stable (LS) group

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(N = 54). Repeated-measures ANOVA yielded statistically significant group differences for the

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MW score among the three time points (F (2) = 181.16, p < 0.01), and the post hoc tests showed that MW in the HS group was higher than that in the MI group (p < 0.01) and the LS group (p <

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0.01). MW in the MI group was higher than that in the LS group (p < 0.05). The repeated

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measures ANOVA indicated there was a significant interaction between group and time points in MWFS scores (F (4, 238) = 16.31, p < 0.01). The following simple effect analysis revealed that at

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tp1, there existed no significant difference between the LS and MI groups (F = 2.97, p = 0.42).

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The MWFS scores of the HS group were higher than those in the MI group and the LS group. At tp2 and tp3, MWFS scores in the HS group were significantly higher than those in the MI group (F = 11.77, p < 0.01; F = 14.50. p < 0.01) MWFS scores in the MI group were significantly higher than those in the LS group (F = 8.06, p < 0.01; F = 21.10. p < 0.01).

INSERT TABLE 4 ABOUT HERE INSERT FIGURE 1 ABOUT HERE 3.3 Robust Functional Connectivity related to MW To assess the reproducibility link between resting-fMRI functional connectivity and MW, we performed a correlation analysis between whole-brain functional connectivity and MWFS at each time point and found that 16 positive functional connections were significantly correlated with several dimensions in the MWFS across the three times (see Table 5 and Figure 2), indicating that 12

these edges could steadily be associated with individual MW. For convenience of description, we called the mean of these 16 functional-connectivity values FC-All. FC-All was used for a cross-lagged analysis. In addition, the results of the correlation analysis were presented in Table 6. There were two positive functional connections, which were composed of at least one node belonging to the DMN (for the convenience of description, we called the mean of these functional-connectivity values as FC-DMN) and were used for a cross-lagged analysis. They were the functional connectivity between right middle frontal gyrus (rMFG) and right supplementary motor area (rSMA), and the functional connectivity between right precentral gyrus (rPG) and right

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middle temporal gyrus (rMTG).

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There were 10 positive edges, which were composed of at least one node belonging to FPCN

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(for the convenience of description, we called the mean values of these functional connections as

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FC-FPCN) and were also used for a cross-lagged analysis. They were as follows: the functional connectivity between rPG and rMTG (one of FC-DMN), the functional connectivity between right

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orbital part of superior frontal gyrus and right superior parietal gyrus, the functional connectivity

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between right triangular part of inferior frontal gyrus and right superior temporal gyrus, the functional connectivity between right orbital part of inferior frontal gyrus and right precentral

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gyrus, the functional connectivity between right precentral gyrus and right superior temporal gyrus,

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the functional connectivity between right precentral gyrus and right middle temporal gyrus, the functional connectivity between right precentral gyrus and left middle temporal gyrus, and the functional connectivity between right precentral gyrus and left median cingulate and paracingulate gyri. Moreover, 5 functional connections belonged to other networks. We called the mean values of these functional-connectivity values FC-other and we also used them into a cross-lagged model. The edges were the functional connectivity between right middle frontal gyrus and right superior parietal gyrus, the functional connectivity between right postcentral gyrus and right superior temporal gyrus, the functional connectivity between right middle temporal gyrus and left precentral gyrus, and the functional connectivity between the right fusiform gyrus and right precuneus, the functional connectivity between the right fusiform gyrus and left cuneus. 13

INSERT TABLE 5 ABOUT HERE INSERT TABLE 6 ABOUT HERE INSERT FIGURE 2 ABOUT HERE 3.4 The predictions of the selected Functional Connectivity involving DMN or FPCN to MW Figure 3a shows the predicted relationship between the FC-DMN and the MWFS. First, the synchronous correlation coefficients (r tp3-FC-DMN × tp3-MWFS

tp1-FC-DMN × tp1-MWFS

= 0.211, r

= 0.243) and the autocorrelation coefficients (r = 0.462, r

tp1-MWFS × tp2-MWFS

= 0.351, r

= 0.309, r

tp1-FC-DMN × tp2-FC-DMN

= 0.579, r

tp2-MWFS × tp3-MWFS

= 0.498) were

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tp2-FC-DMN × tp3-FC-DMN

tp2-FC-DMN × tp2-MWFS

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statistically significant. Second, the standardized regression coefficient of the path from the tp1 FC-DMN score to the tp2 MWFS score was significant (β = 0.214, p < 0.05), and the standardized

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regression coefficient of the path from the tp2 FC-DMN score to the tp3 MWFS score was

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marginally significant (β = 0.187, p < 0.07). However, the standardized regression coefficient of

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the path from the tp1 MWFS score to the tp2 FC-DMN score (β = 0.096, p = 0.21) was not significant, and the standardized regression coefficient of the path from the tp2 MWFS score to

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the tp3 FC-DMN score (β = 0.012, p = 0.89) was not significant. FC-DMN had positive

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cross-lagged impacts on MWFS scores, which indicated previous FC-DMN did increase subsequent MWFS. However, the results of FC-FPCN did not reflect the same effect as FC-DMN

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did. Specifically, the standardized regression coefficient of the path from the tp2 FC-FPCN score to the tp3 MWFS score was not significant (Figure 3b). In addition. there existed no cross-lagged effect neither between MWFS scores and FC-FPCN nor between MWFS scores and FC-other (see Figures 3c and 3d).

INSERT FIGURE 3 ABOUT HERE 3.5 Group Differences in Functional Connectivity Involving the DMN Considering the prediction of the DMN-involved functional connectivity to MW, we conducted One-Way ANOVAs for two edges involving DMN to explore the group differences. Describe statistics of functional connections involving the DMN are presented in Table 7. Regardless of the time of data collection, the mean functional connections involving the DMN in 14

the HS group and mean functional connections in the MI group were the strongest, followed by the functional-connectivity values involving the DMN in the LS group (see Figure 4). There was group difference for the FC-DMN result (F (2) = 12.52, p < 0.01). The post hoc test revealed that the functional connectivity involving DMN of HS group and MI group was significantly higher than the LS group. However, there existed no significant difference between HS and the MI group. There was no significant difference in the variability functional connectivity involving the DMN among these groups (rMFG-rSMA: F = 1.14, p = 0.32; rPG-rMTG: F = 0.03, p = 0.97).

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INSERT TABLE 7 ABOUT HERE

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INSERT FIGURE 4 ABOUT HERE

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4. Discussion

To our knowledge, the current study is the first to report longitudinal relationships between

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functional connectivity and MW. The stable functional connectivity and predicted relationship

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between connectivity and MW have been analyzed by conducting rs-fMRI and the MWFS at three time points. We found that the stable functional connectivity included nodes belonging to the

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FPCN and DMN, as expected. In the following cross-lagged analysis, the FC-DMN could predict

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the MW frequency stably. However, edges related to the FPCN or the whole connectivity did not affect MW. The latent class growth analysis divided individuals into 3 groups. The FC-DMN of the HS group and MI group were significantly higher than the LS group. However, there existed no significant difference between the HS and MI group. The robust functional connectivity was composed of 16 edges; the functional connections are presented in Figure 3. As hypothesized, the functional connectivity, whose nodes involved the DMN, were correlated to MW. This association supports the important role of the DMN in supporting MW processing (Buckner, et al., 2008; Christoff, et al., 2009; Godwin, et al., 2017; Mason, et al., 2007; Mittner, et al., 2014; Mooneyham, et al., 2017; Stawarczyk, Majerus, Maquet, & D'Argembeau, 2011). Furthermore, the functional connections contained several edges involving the FPCN, which has been demonstrated to have associations with MW in previous 15

studies. The result supports the hypothesis that increased functional connectivity of nodes between the executive network and other functional networks reflects a failure to support cognitive process (Mcvay & Kane, 2009). Furthermore, a very significant proportion of edges involved functional connections between the FPCN and temporal lobe, which revealed that MW is associated with memory extraction from executive control of the FPCN and was consistent with the fact that long-term memory processing contributes strongly to spontaneous thought (Christoff, Ream, & Gabrieli, 2004). Moreover, there were brain features within the visual-related network, which is consistent with mentally traveling in the visual imagery of MW (Nathan, Jorge, Turner, Dale, &

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Schacter, 2013). Taken together, the robust functional connectivity was consistent with previous

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studies, and reflected that MW may be organized in an interactive manner of internal-oriented and

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executive control.

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The cross-lagged model suggested that functional connectivity involving the DMN (rMFG-rSMA, rPG-rMTG) could predict MW frequency over the 2 years. The predicted

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relationship between functional connectivity and MW supports the results of previous studies,

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which have indicated that the variation in MW could be influenced by altered brain function through the tDCS and brain lesion approach (Axelrod, Rees, Lavidor, & Bar, 2015; Bertossi &

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Ciaramelli, 2016; Kajimura & Nomura, 2015). The present result shows that interaction between

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the DMN and other networks forecasts MW. Consistent with findings that have been discussed in a previous study (Golchert, et al., 2016), the current result may reveal that MW occurs because of a person’s intention. In conclusion, the result may demonstrate that the executive system allows information to guarantee the process of MW. However, recent studies indicate that DMN should not be reduced to or considered as synonymous of MW (Nelson, et al., 2016; Whitfield-Gabrieli & Ford, 2012); DMN engagement was not always found during MW episodes in task setting (Denkova, Nomi, Uddin, & Jha, 2019). Thus, the results should be interpreted with some caution. The rMFG-rSMA was one of the DMN-involved edges. The MFG is known to be involved in MW processes. Moreover, the rMFG has been delineated as one of the core regions for self-reference (Muthén & Muthén, 1998), and it has been observed as active prior to reports of MW, and was more active when subjects were unaware of their MW (Christoff, et al., 2009). 16

These results indicate that the MFG, especially the rMFG, plays an important role in the MW state. The SMA is also a region that activated in the MW phase (Hasenkamp, et al., 2012), and it has been implicated in the executive control process (Kucyi, Salomons, & Davis, 2013). The activation of the SMA is related to the initiation of movement (Grady, et al., 2016). We speculate that the rSMA serves as an executive controller and could be critical in regulating the rMFG to regulate the frequency of MW. Not surprisingly, the rPG-rMTG was another one of the DMN-involved edges that influenced the frequency of MW. As a part of the DMN, the MTG was associated with MW (Christoff, et al., 2009; Hasenkamp, et al., 2012; Golchert, et al., 2016),

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which was consistent with our current results. The precentral gyrus has been demonstrated to have

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a relationship with memory (Nelson, et al., 2016). The rPG belongs to a superordinate cognitive

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control network, which subserves diverse executive functions such as flexibility, working memory

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and inhibition (Niendam, et al., 2012), and the encodes valence of task-free states during the resting period (Tusche, Smallwood, Bernhardt, & Singer, 2014). The edge between node of FPCN

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and node of DMN was positively correlated with MW frequency, which is consistent with

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previous research results (Godwin, et al., 2017) and supports the coactivation of the DMN and the FPCN during the MW process (Christoff, et al., 2009; Fox, et al., 2015). As rMTG was associated

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with memory processes (Christoff, et al., 2004), the association between the edge and MW

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suggests that MTG allows mind wandering relevant to some scenes by extracting memories. It demonstrated a perceptual decoupling theory that memory processes contribute to thought content during MW.

The results of the latent growth curve model reveled the individual trajectories of participants. Results in laboratory have shown age-related decreases in MW frequency (Jordão, et al., 2019; Maillet & Schacter, 2016), but there was a study reporting an increase in future-oriented thoughts in older adults (Gardner & Ascoli, 2015). Thus, it is possible that age differences in MW frequency are determined by several factors. MW frequency alters in different conditions, especially with different task difficulty (Konishi, Brown, Battaglini, & Smallwood, 2017; Smallwood, et al., 2013), or in different emotions (He, et al., 2018; Killingsworth & Gilbert, 2010). We think the unstable results may due to the different environments and emotions of participants 17

among three times. The DMN-related edges in the HS group were significantly stronger than the connections in the LS group and the connections in the MI group was significantly stronger than that in the LS group. This result supports the relationship between MW frequency and FC-DMN; thus, the stronger the functional connectivity of DMN-related functional connectivity is, the more often the frequency of MW becomes. These results indicated that functional connectivity is not only crucial for longitudinal investigation, but also valuable for studying individual differences. However, there exists no statistically significant difference between the HS group and the MI group, and we considered this may because that the mind wandering of the MI group has a rising

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trend. This uptrend may firstly reflect in the connectivity of the brain, which may narrow the gap

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between the HS group and the MI group.

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The present study had several limitations. Firstly, we only used functional connectivity as an

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indicator to measure the cross-lagged effects, however, cortical thickness (Golchert, et al., 2016), structural connectivity (Karapanagiotidis, et al., 2016), and brain activation pattern (Christoff, et

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al., 2009) have been indicated to correlated with MW. Secondly, MW varies between individuals,

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such that older people report less MW than younger people (Jackson & Balota, 2012). The recruited participants were all college students, and we have not explored the influence of age

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characteristics on MW. Thirdly, the questionnaire we used only focused on the general property of

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MW. However, we did not address the heterogeneity of MW category in the current study. Future studies will be needed to uncover the age-related differences in MW by using the larger sample size and exploring the relationship between MW and its neural basis at a multimodal level. Additionally, we may find relationships between MW and other behavioral indicators through brain features that predict MW. Furthermore, measuring multiple aspects of experience (e.g. multiple dimensional experience Sampling) may help us to explore more aspects of MW.

5. Conclusions In conclusion, it is important to note that MW is a phenomenon that comprises a large part of our waking hours and is closely related to cognitive function and task performance. Exploring the stable functional connectivity of MW and finding the predicted relationship between brain features 18

and MW are crucial to more deeply understand the relevant theories of MW and to establish the connections between MW and other variables at the neural basis level. The functional connectivity of MW in the present study using longitudinal data from three time points is more stable and efficient compared with single time point statistics. The results reveal that although the FPCN-related functional connections were significantly related to MW, the DMN-related functional connections were the determining factors of MW. Our demonstration that two functional connections involving the DMN, which predict MW, supports the vital role of the

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Acknowledgments

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DMN.

This work was supported by the National Natural Science Foundation of China (31771231, Natural

Science

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32071070),

Foundation

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Chongqing

(cstc2019jcyj-msxmX0520,

cstc2020jcyj-msxmX0299), the planned project of Chongqing humanities and Social Sciences (2018PY80, 2019PY51), and Fundamental Research Funds for the Central Universities (SWU119007), Chang Jiang Scholars Program, National Outstanding Young People Plan, Chongqing Talent Program. Conflict interest The authors declare no conflicts of interest with respect to the authorship or the publication of this article. Data and code availability statement The data and code used to support the findings of this study have not been made 19

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available.

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Tables

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Table 1. Questions of mind wandering frequency scale Question

SMW

Some scattered thoughts or imaginations appear in my mind. When it comes to mind wandering, I'll describe myself as a person whose mind wanders. Things about the past or future appear in my mind uncontrollably. I find it difficult to stay focused. I unconsciously fall into an aimless fantasy. My mind works in a state of dissociation that beyond my control. Some ideas come to me by accident. When I am doing something, I unconsciously began to do another thing. One idea after another come to my mind. I am the person whose mind wanders. I unconsciously have a lot of associations. I am doing something with thinking about something else. My thoughts are interrupted by thoughts that pop into my head. I am in a daze. Select option 4. My mind seems to think things on its own. I find myself not thinking when I am reading, so I have to go back and read it again. I can’t help thinking about many things. I am a person who in a daze. There are a lot of things that come into my mind. I find myself not paying attention to what I am doing. I feel my mind go blank.

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UMW

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SMW UMW SMW OEMW SMW UMW SMW OEMW SMW UMW SMW OEMW Lie test item SMW

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Dimension

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SMW OEMW SMW UMW OEMW

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SMW, spontaneous mind wandering (one dimension of mind wandering frequency scale); OEMW, overall evaluation of mind wandering (one dimension of mind wandering frequency scale); UMW, uncontrol mind wandering (one dimension of mind wandering frequency scale).

Table 2. Descriptive characteristics of the longitudinal study participants Tp1: Mean (SD)

Tp2: Mean (SD)

Tp3: Mean (SD)

Age

19.33 (1.25)

20.09 (1.29)

21.41 (1.36)

Interval days

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278.87 (91.94)

481.88 (65.47) #

MWFS

49.77 (14.14)

53.48 (13.72)

52.26 (15.28)

Tp1, time point 1; Tp2, time point 2; Tp3, time point 3; MWFS, mind wandering frequency scale. 26

#

The interval days for Tp3 the interval between Tp2 and Tp3.

Table 3. Maximum likelihood estimates of LGCM of mind wandering frequency. Intercept

Slope

Var.intercept

Var.slope

Covar.intercept and slope

Fixed at zero

0.50 (p = 0.055)

-6.65 (p = 0.865)

-5.70 (p = 0.066)

24.30 (p < 0.05)

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LGCM = latent growth curve model.

< 0.001 0.0605 0.3786

< 0.001 < 0.001 0.375

BIC

Entropy

2956.611 2948.108 2958.800

0.835 0.816 0.823

-p

BLRT

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Two classes Three classes Four classes

LMR-LRT

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Table 4. The model fits of the LCGA

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LCGA, latent class growth analysis; LMR-LRT, Lo-Mendell-Rubin likelihood ratio test; BLRT,

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ur

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bootstrap likelihood ratio test; BIC, Bayesian information criterion.

27

Table 5. Functional connections of mind wandering and the repeated-measure ANOVA of functional connectivity among three groups.

Edge2

Middle temporal gyrus

DMN

R

FPCN

R

FPCN

R

FPCN

R

Edge3 Edge4 Edge5

Superior frontal gyrus, orbital part Inferior frontal gyrus, triangular part Inferior frontal gyrus, orbital part

23.9, 30.7, 36.4 48.9, -58.1, 14.4 30.5, 54.9, -3.5 40, 17.6, 29.2 44.6, 46.2, -4.9 39.7, 3.4, 34 39.7, 3.4, 34 39.7, 3.4, 34 39.7, 3.4, 34 39.7, 3.4, 34

Precentral gyrus

FPCN

R

Edge7

Precentral gyrus

FPCN

R

Edge8

Precentral gyrus

FPCN

R

Edge9

Precentral gyrus

FPCN

R

Edge10

Precentral gyrus

FPCN

R

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Edge6

Supplementary motor area

28

regions2 Network affiliation

Hemisphere

SCN

R

FPCN

R

Superior parietal gyrus

VAN

R

Superior temporal gyrus

MN

R

Precentral gyrus

SCN

R

Temporal pole: superior temporal gyrus

MFN

R

Middle temporal gyrus

MFN

R

Middle temporal gyrus

MFN

L

Middle temporal gyrus

MFN

L

Middle temporal gyrus

VAN

L

f

R

Regions

oo

DMN

Pr

Middle frontal gyrus

rn

Edge1

MNI coordinates

Precentral gyrus

epr

Hemisphere

al

Regions

regions1 Network affiliation

MNI coordinates 13.7, 6.3, 65.4 39.7, 3.4, 34 25.2, -52.4, 68.1 59.2, -3.4, 2.7 32, -5.4, 52.1 52.8, 10.9, -21.8 56.6, -8.5, -14.3 -57.6, -6.4, -22.7 -57.8, -47.5, 5.2 -48.3, -67.4, 1.1

Edge12

Middle frontal gyrus

SCN

R

Edge13

Postcentral gyrus

MN

R

Edge14

Middle temporal gyrus

MFN

R

Edge15

Fusiform gyrus

VN1

R

Edge16

Fusiform gyrus

VN1

R

39.7, 3.4, 34 37.6, 35.4, 31.1 57.8, -8.3, 27.3 56.5, -8.5, -14.3 25.2, -44.6, -12.2 25.2, -44.6, -12.2

Median cingulate and paracingulate gyri

MN

L

Superior parietal gyrus

VAN

R

Superior temporal gyrus

MN

R

MFN

L

Precuneus

VN1

R

7.7, -75, 25

Cuneus

VN1

L

-9.5, -71, 31.9

f

R

Precentral gyrus

oo

FPCN

epr

Precentral gyrus

Pr

Edge11

-7.8, -22.4, 46 25.2, -52.4, 68.1 59.2, -3.4, 2.7 -45.8. -0.4, 49.3

al

The name of the region was based on the AAL template. FPCN, frontalparietal control network; SCN, subcortical-cerebellum network; DMN, default mode network;

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rn

MN, motor network; MFN, medial frontal network; VN1, visual 1 network; VAN, visual association network; L, left hemisphere, R, right hemisphere.

29

Table 6. Results of functional connectivity analyses.

Tp2

Tp3

UMW

0.18* 0.14 0.13 0.18* 0.13 0.16 0.11 0.09 0.08 0.16 0.22* 0.16 0.22* 0.08 0.14 0.20*

0.10

0.20**

0.14

Edge2

0.21*

0.19*

0.15

Edge3

0.19*

0.21*

0.17

Edge4

0.19*

0.14

0.16

Edge5

0.18*

0.13

0.07

0.09 0.26** 0.21* 0.15 0.20* 0.23* 0.19* 0.14 0.27** 0.21* 0.17 0.11 0.09 0.09 0.21* 0.19*

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UMW

SMW

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0.14 0.25** 0.24** 0.10 0.08 0.28** 0.20* 0.20* 0.19* 0.21* 0.19* 0.24** 0.08 0.18* 0.17 0.22*

Tp2 OEMW

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0.02 0.18 0.21* 0.16 0.18* 0.23* 0.13 0.16 0.18* 0.19* 0.16 0.17 0.17 0.20* 0.18* 0.16

SMW

-p

UMW

re

Tp1 OEMW

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Tp1

Tp3 OEMW

Edge1 Edge2 Edge3 Edge4 Edge5 Edge6 Edge7 Edge8 Edge9 Edge10 Edge11 Edge12 Edge13 Edge14 Edge15 Edge16 Edge1 Edge2 Edge3 Edge4 Edge5 Edge6 Edge7 Edge8 Edge9 Edge10 Edge11 Edge12 Edge13 Edge14 Edge15 Edge16 Edge1

SMW

30

0.17 0.28** 0.28* 0.20* 0.23* 0.19* 0.24** 0.25** 0.23* 0.19* 0.23* 0.22* 0.18* 0.18* 0.23* 0.27**

0.29** 0.12 0.09 0.13 0.21* 0.14 0.16 0.17 0.14 0.07 0.20* 0.10 0.19* 0.05 0.08 0.16

Edge6

0.20*

0.08

0.08

Edge7

0.23*

0.13

0.13

Edge8

0.17

0.12

0.18*

Edge9

0.19*

0.11

0.05

Edge10

0.21*

0.11

0.09

Edge11

0.19*

0.13

0.10

Edge12

0.20*

0.19*

0.21*

Edge13

0.15

0.18

0.19*

Edge14

0.19*

0.12

0.15

Edge15

0.22*

0.29**

0.21*

Edge16

0.18*

0.22*

0.18*

of

Tp1, time point 1; Tp2, time point 2; Tp3, time point 3; SMW, spontaneous mind wandering (one

ro

dimension of mind wandering frequency scale); OEMW, overall evaluation of mind wandering

-p

(one dimension of mind wandering frequency scale); UMW, uncontrol mind wandering (one

re

dimension of mind wandering frequency scale); Edge1, middle frontal gyrus-supplementary motor area; Edge2, middle temporal gyrus-precentral gyrus; Edge3,

superior frontal gyrus-superior

Edge5,

inferior frontal gyrus, orbital part-precentral

gyrus;

Edge6,

precentral

na

gyrus;

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parietal gyrus, orbital part; Edge4, inferior frontal gyrus, triangular part-superior temporal

gyrus-temporal pole: superior temporal gyrus; Edge7, precentral gyrus-middle temporal gyrus;

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Edge8, precentral gyrus-middle temporal gyrus; Edge9, precentral gyrus-middle temporal gyrus;

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Edge10, precentral gyrus-middle temporal gyrus; Edge11, precentral gyrus-median cingulate and paracingulate gyri; Edge12, middle frontal gyrus-superior parietal gyrus; Edge13,

postcentral

gyrus-superior temporal gyrus; Edge14, middle temporal gyrus-precentral gyrus; Edge15, fusiform gyrus-precuneus; Edge16, fusiform gyrus-cuneus. * p < 0.05, ** p < 0.01.

Table 7. Describe statistics of functional connection involving the DMN for each group and each time point.

LS group MI group

rMFG-rSMA rPG-rMTG rMFG-rSMA rPG-rMTG

Tp1: Mean FC (SD)

Tp2: Mean FC (SD)

Tp3: Mean FC (SD)

0.22 (0.25) 0.19 (0.24) 0.35 (0.27) 0.31 (0.24)

0.21 (0.25) 0.24 (0.23) 0.33 (0.29) 0.35 (0.25)

0.27 (0.27) 0.20 (0.26) 0.37 (0.27) 0.38 (0.24)

31

HS group

rMFG-rSMA

0.34 (0.32)

0.37 (0.29)

0.38 (0.27)

rPG-rMTG

0.36 (0.27)

0.44 (0.32)

0.32 (0.25)

Tp1, time point 1; Tp2, time point 2; Tp3, time point 3; FC, functional connectivity; LS, low stable; MI, moderate increasing; HS, high stable; rMFG-rSMA, right middle frontal gyrus-right supplementary motor area; rPG-rMTG, right precentral gyrus-right middle temporal gyrus.

Figure legends

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HS, high stable; MI, moderate increasing; LS, low stable.

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Figure 1. MWFS scores of the three trajectory classes. MWFS, mind wandering frequency scale;

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Figure 2. Functional connectivity significantly correlated with the MWFS score. (a) Circle plots:

re

The 268 nodes are arranged in two half circles, which are split into left and right hemispheres,

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from anterior (12 o’clock position) to posterior (6 o’clock position) brain anatomy. The inner circle indicates 268 nodes, and the outer circle reflects 10 lobes. Lines represent the positive edges

na

significantly related to the MWFS, blue lines indicate that the edges composed of at least one node

ur

belonging to the DMN, and red lines indicate other network-related connections. (b) Glass brain plots: Each edge related to the DMN is coded blue, and the set of other features is coded red. The

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number of features is represented by the size of each node emanating from that sphere. L, left hemisphere; R, right hemisphere; DMN, default mode network.

Figure 3. The cross-lagged analysis model for exploring the relationship between (a) the MWFS and FC-DMN, (b) the MWFS and FC-FPCN, (c) the MWFS and FC-Other, (d) the MWFS and FC-All. The double-sided arrows in the figure indicate the results of correlation analysis, and the data are the correlation coefficients. One-way arrows indicate regression analysis results obtained using hierarchical regression techniques, with regression coefficients being standardized. Each solid line indicates a significant effect, and the dotted lines reflect the nonsignificant effects. FC-DMN, mean value of functional connections involving the default mode network; FC-FPCN, 32

mean value of functional connections involving the frontoparietal network; FC-Other, mean value of functional connections related to nodes that are independent of the default mode network and frontoparietal network; FC-All, mean value of all 16 functional connections; Tp1, time point1; Tp2, time point2; Tp3, time point3; MWFS, mind wandering frequency scale; ** p < 0.01, * p < 0.05.

Figure 4. Mean functional connectivity values of two DMN-related edges for three different groups, regardless of time points. (a) Post hoc tests of mean functional connectivity of

of

rMFG-rSMA among the three groups. (b) Post hoc tests of mean functional connectivity of

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rPG-rMTG among the three groups. LS, low stable; MI, moderate increasing; HS, high stable; FC,

-p

functional connectivity; rMFG-rSMA, right middle frontal gyrus-right supplementary motor area;

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ur

na

lP

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rPG-rMTG, right precentral gyrus-right middle temporal gyrus; ** p < 0.01, * p < 0.05.

33

Jo na

ur re

lP ro

-p

of

Jo na

ur re

lP ro

-p

of

Jo na

ur re

lP ro

-p

of

Jo na

ur re

lP ro

-p

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Highlights We identified 16 functional connectivity involving the DMN and FPCN that were consistently and stably associated with MW across the three time points. There were only significant cross-lagged effects between DMN-involved connections and MW frequency rather than FPCN-involved connections.

Jo

ur

na

lP

re

-p

ro

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The mean value of functional connectivity involving the DMN (FC-DMN) in the low stable (LS) group was the weakest, followed by mean connectivity in the moderate increasing (MI) group and mean connectivity in the high stable (HS) group.