functional connectivity within and between intrinsic brain ...control.gatech.edu › wp-content ›...

Contents lists available at ScienceDirect

Neuropsychologia

journal homepage: www.elsevier.com/locate/neuropsychologia

Functional connectivity within and between intrinsic brain networkscorrelates with trait mind wandering

Christine A. Godwina,⁎, Michael A. Hunterb, Matthew A. Bezdeka, Gregory Liebermanb,c,d,Seth Elkin-Frankstone, Victoria L. Romeroe, Katie Witkiewitzb, Vincent P. Clarkb,Eric H. Schumachera,⁎

a School of Psychology, Georgia Institute of Technology, United Statesb Department of Psychology, University of New Mexico, United Statesc US Army Research Laboratory, HRED, Aberdeen Proving Ground, MD, United Statesd Department of Bioengineering, University of Pennsylvania, United Statese Charles River Analytics, Cambridge, MA, United States

A R T I C L E I N F O

Keywords:Mind wanderingExecutive functionResting stateDefault mode networkFunctional connectivity

A B S T R A C T

Individual differences across a variety of cognitive processes are functionally associated with individual dif-ferences in intrinsic networks such as the default mode network (DMN). The extent to which these networkscorrelate or anticorrelate has been associated with performance in a variety of circumstances. Despite the es-tablished role of the DMN in mind wandering processes, little research has investigated how large-scale brainnetworks at rest relate to mind wandering tendencies outside the laboratory. Here we examine the extent towhich the DMN, along with the dorsal attention network (DAN) and frontoparietal control network (FPCN)correlate with the tendency to mind wander in daily life. Participants completed the Mind WanderingQuestionnaire and a 5-min resting state fMRI scan. In addition, participants completed measures of executivefunction, fluid intelligence, and creativity. We observed significant positive correlations between trait mindwandering and 1) increased DMN connectivity at rest and 2) increased connectivity between the DMN and FPCNat rest. Lastly, we found significant positive correlations between trait mind wandering and fluid intelligence(Ravens) and creativity (Remote Associates Task). We interpret these findings within the context of currenttheories of mind wandering and executive function and discuss the possibility that certain instances of mindwandering may not be inherently harmful. Due to the controversial nature of global signal regression (GSReg) infunctional connectivity analyses, we performed our analyses with and without GSReg and contrast the resultsfrom each set of analyses.

1. Introduction

The brain at rest may exhibit fundamental organizing principlesof its functional architecture beyond that which appears when in-vestigating task-related activity (Stevens and Spreng, 2014). These in-trinsic networks (often referred to as resting state networks) arise fromlow frequency (< 1 Hz) spontaneous fluctuations in BOLD signal thatform coherent patterns across spatially distinct brain regions (Biswalet al., 1995) and closely mirror functional networks observed duringtask performance (Stevens and Spreng, 2014; Zhang and Raichle,2010). The development of techniques to investigate large-scale brainnetworks observed during awake resting states has allowed researchersto move beyond task-related fMRI activation patterns and beginto associate intrinsic network characteristics to cognitive processing

characteristics measured outside the scanner. The default mode net-work (DMN) is the most studied of these networks. Along with theDMN, prominent intrinsic networks include a dorsal attention and afrontoparietal control network (DAN and FPCN, respectively). The DANis a network comprised of superior parietal regions and the frontal eyefields, and has been consistently linked with attending and respondingto external task demands (Corbetta et al., 2008). The FPCN consists ofexecutive control regions such as the anterior cingulate cortex, lateralprefrontal cortex, and anterior inferior parietal lobule (Vincent et al.,2008). Both of these networks are involved in task-related processesand are sometimes referred to as a task-positive network (TPN; Raichle,2015a). Other frequently observed intrinsic networks include visual,sensorimotor, salience, and auditory networks (Yeo et al., 2011; Zhangand Raichle, 2010).

http://dx.doi.org/10.1016/j.neuropsychologia.2017.07.006Received 19 December 2016; Received in revised form 7 July 2017; Accepted 8 July 2017

⁎ Corresponding author.E-mail addresses: [email protected] (C.A. Godwin), [email protected] (E.H. Schumacher).

Neuropsychologia 103 (2017) 140–153

Available online 10 July 20170028-3932/ © 2017 Elsevier Ltd. All rights reserved.

MARK

http://www.sciencedirect.com/science/journal/00283932

http://www.elsevier.com/locate/neuropsychologia

http://dx.doi.org/10.1016/j.neuropsychologia.2017.07.006


mailto:[email protected]

mailto:[email protected]


http://crossmark.crossref.org/dialog/?doi=10.1016/j.neuropsychologia.2017.07.006&domain=pdf

In recent years, individual differences across a variety of cognitiveprocesses including attention, perception, language, learning, memory,and organization of conceptual knowledge have been functionally as-sociated with individual differences in intrinsic networks (c.f., Stevensand Spreng, 2014). For example, individual differences in executivefunction capacity have been positively correlated with connectivitybetween cerebellar regions and the right frontoparietal network andwith connectivity between frontopolar regions and lateral frontal andsuperior parietal regions (Reineberg et al., 2015). Other researchershave documented relationships between intrinsic functional con-nectivity and individual differences in sustained attention (Rosenberget al., 2015) and distractor suppression (Poole et al., 2016). Stevens andSpreng (2014) proposed that the brain connectivity patterns observedat rest play a dynamic, causal role in cognition, where repeated acti-vation and deactivation across spatially coherent brain regions canfurther modulate connectivity within brain networks at rest in supportof learning and future behavior.

Not only have intrinsic networks been associated with individualdifferences in task performance measured outside the scanner, but therelationships between intrinsic networks have also been shown to havefunctional importance. For example, as summarized by Raichle(2015a), stronger correlations between the DMN and motor planningregions (e.g., premotor and supplementary motor areas) have beenassociated with greater impulsivity in juveniles in prison, whereas in-carcerated juveniles with greater impulse control showed strongerconnectivity between motor planning regions and executive functionand spatial attention networks (Shannon et al., 2011). Poole et al.(2016) noted that increased resting state connectivity between the DMNand attention networks was associated with poorer distractor suppres-sion. Kelly et al. (2008) observed that larger anticorrelations betweenthe DMN and TPN during both task and resting state were associatedwith decreased reaction time (RT) variability on a flanker task. In astudy examining task-related dynamic connectivity, Thompson et al.(2013) had participants complete a sustained attention task in whichthey focused on a black dot centered on the screen and pressed a buttonwhen they detected the dot changed to dark blue. Thompson and col-leagues observed that larger anticorrelations between the DMN andTPN roughly eight seconds prior to stimulus onset preceded faster RTs.The larger anticorrelations preceding fast RTs suggest that the opposingrelationship between the DMN and TPN may be behaviorally relevant.Specifically, researchers have proposed that the opposing relationshipsbetween brain networks such as the DMN and TPN may function as aregulatory mechanism. This mechanism is thought to provide a func-tional balance between the networks, allowing the brain to shift ef-fectively between cognitive processes in support of adaptive behavior(Thompson et al., 2013; Raichle, 2015a). While Thompson and col-leagues observed these patterns during task performance, these re-lationships between intrinsic networks persist during rest and havefunctional importance (Kelly et al., 2008; Stevens and Spreng, 2014).

The current study builds on the growing research of intrinsic net-works and functional connectivity between networks by investigatingthe relationship of intrinsic networks and trait mind wandering. Mindwandering is characteristically described as task-unrelated thought,that is, thought that occurs while an individual is performing a task andwhich is unrelated to the task at hand (Mrazek et al., 2013; Smallwoodand Schooler, 2006). Typically, mind wandering is considered a nega-tive consequence of failing to maintain attention to the task at hand.Increases in mind wandering have been associated with poorer per-formance on a variety of tasks measured in the laboratory (e.g., workingmemory tasks and RT variability on a continuous tapping task; McVayand Kane, 2009; Seli et al., 2013) as well as performance on tasks thatcan occur outside the laboratory (e.g., SAT scores and reading com-prehension; McVay and Kane, 2012; Mrazek et al., 2012). Mind wan-dering has also been associated with negative affect and clinical dis-orders (Killingsworth and Gilbert, 2010; Smallwood et al., 2007).However, benefits of mind wandering have been noted as well. Baird

et al. (2012) found that trait mind wandering (as measured by theDaydreaming Frequency Scale, DDFS, of the Imaginal Processes In-ventory; Singer and Antrobus, 1972) was positively correlated withcreative thought and that more mind wandering during a filled delayperiod was associated with better performance on the Unusual UsesTask. Furthermore, the contents of mind wandering frequently consistof contemplating and planning future events. Prospective thinking, in-cluding prospective mind wandering, can be particularly beneficial inpreparing individuals to obtain their upcoming goals (Smallwood andAndrews-Hanna, 2013; Smallwood and Schooler, 2015). While the costsof mind wandering have been widely documented, the emerging linesof research illuminating potential benefits of mind wandering suggest amore nuanced approach must be taken when understanding the im-plications of mind wandering and the contexts in which it occurs(Smallwood and Schooler, 2015; for a detailed review of the costs andbenefits of mind wandering, see Mooneyham and Schooler, 2013).

The DMN is the most commonly implicated brain network in mindwandering. It has been associated with instances of mind wanderingand related spontaneous thought processes measured during the per-formance of ongoing tasks (Christoff et al., 2009; Mason et al., 2007)and retrospectively – immediately after completion of a scan (e.g.,Andrews-Hanna et al., 2010). Regarding intrinsic network connectivityin particular, O’Callaghan et al. (2015) observed positive correlationsbetween mind wandering frequency during the performance of a taskwith low cognitive demand and resting state functional connectivitybetween the right temporal pole and right lateral temporal cortex of theDMN. However, the DMN is not the only network likely involved inmind wandering. Activation in executive control regions has been re-ported along with DMN activity during instances of mind wandering(Christoff et al., 2009). In a recent meta-analysis of fMRI studies in-vestigating mind wandering, Fox et al. (2015) concluded that alongwith the DMN, mind wandering is likely supported by mechanismsinvolving frontoparietal regions, the secondary somatosensory cortex,insula, and lingual gyrus.

Despite the ubiquitous nature of mind wandering in everyday life(research suggests that mind wandering comprises between 30% and50% of one's daily life; Kane et al., 2007; Killingsworth and Gilbert,2010), little research has examined the extent to which brain activitymeasured during resting state correlates with individuals’ tendencies tomind wander in daily life. However, given the growing amount of re-search that has established associations between intrinsic networks andindividual differences in cognitive functioning, it is quite possible thatthe tendency to mind wander is reflected by intrinsic network patternsas well. The limited existing research supports this claim. Mason et al.(2007) found a positive correlation between BOLD activity in the DMNand scores on the DDFS. More recently, Kucyi and Davis (2014) ob-served negative correlations between functional connectivity of tworegions of the DMN (the posterior cingulate cortex and ventral DMNsubsystem) and scores on the DDFS.

In our current work, we expand on the research reviewed here andfocus on the tendency to mind wander in daily life and the extent towhich connectivity in the DMN, FPCN, and DAN measured duringresting state fMRI correlates with trait mind wandering. Previous re-search on trait mind wandering frequently employed the DDFS (Singerand Antrobus, 1972) to index tendency to mind wander in daily life.However, this scale is limited to the extent that the items reflect ex-periences of stimulus-independent thoughts, rather than task-unrelatedthoughts per se (Mrazek et al., 2013). An item from the DDFS thathighlights this is: On a long bus, train, or airplane ride, I daydream. Thisitem will not necessarily capture one's tendency to mind wander whenone is performing a task. Here, we employ a five-item Mind WanderingQuestionnaire (MWQ) scale recently developed by Mrazek et al. (2013).This scale (shown in Table 1) was developed in part to overcome thelimitations noted with previous usage of the DDFS and specifically in-dexes the occurrence of task-unrelated thoughts in everyday life. TheMWQ has been used in a number of studies with adult participants

C.A. Godwin et al. Neuropsychologia 103 (2017) 140–153

141

(Faber et al., 2017; Kajimura et al., 2016; Kuschpel et al., 2015), but hasyet to be applied to functional connectivity data.

In addition, the majority of resting state research has focused on therelationship between the DMN and mind wandering. As mentionedpreviously, there is reason to suspect that additional networks play animportant role in mind wandering (Fox et al., 2015). While the FPCNconsists of executive control regions that are involved in directing ex-ternally-oriented tasks, recent research suggests that certain forms ofoff-task thought as well as many internally-directed thought processesin general, such as personal goal-directed thoughts (e.g., planning one'sweekend activities), are driven by coupling between the DMN andFPCN (Andrews-Hanna et al., 2014; Christoff et al., 2016; Spreng et al.,2013). In contrast, the DAN is thought to be involved in suppressingmind wandering processes in order to maintain attention to the externalenvironment (Christoff et al., 2016). Here we expand beyond the DMNand examine how connectivity within the DMN, FPCN, and DAN cor-relates with trait mind wandering, and how connectivity between theDMN, FPCN, and DAN correlates with trait mind wandering. To pre-view our results, we found a positive correlation between DMN/FPCNconnectivity and mind wandering where greater tendencies to mindwander were associated with greater correlation between the DMN andFPCN at rest. This result can be interpreted either from a networkregulation perspective, which states that the DMN and FPCN may workin opposition to control attention and suppress detrimental mindwandering processes (e.g., Thompson et al., 2013), or in the context ofrecent research that suggests the DMN and FPCN couple to supportinternally-directed thought processes (Spreng et al., 2013). However,we also observed a positive correlation between trait mind wanderingand within-network connectivity in the DMN. As discussed below, thisresult was somewhat unexpected given research which indicates thatthe integrity of the DMN is often associated with better cognitive pro-cessing (e.g., Mohan et al., 2016). Given that mind wandering is typi-cally considered to be a detrimental facet of cognition (Mooneyham andSchooler, 2013), we sought to further understand the neural and be-havioral relationships associated with trait mind wandering. To do so,we examined the relationships between trait mind wandering, intrinsicnetworks, and several measures of cognitive abilities that have beenassociated with both trait and state mind wandering. Participantscompleted two measures of working memory capacity, the operationspan task (OSPAN) and the symmetry span task (SSPAN; Unsworthet al., 2005). Participants also completed the Ravens Advanced Pro-gressive Matrices, a task that reflects fluid intelligence (Raven et al.,1977), and the Remote Associates Task (RAT), which was developed tomeasure creative thought (Bowden and Jung-Beeman, 2003; Mednick,1962). Previous research has linked patterns of intrinsic networks toboth executive function (Magnuson et al., 2015; Reineberg et al., 2015)and fluid intelligence (Finn et al., 2015). Furthermore, research hasestablished a link between working memory capacity and both stateand trait mind wandering, in which increases in mind wandering havebeen associated with less working memory capacity (Mrazek et al.,2013, 2012), particularly during the performance of cognitively de-manding tasks (Kane et al., 2007). Mrazek et al. (2012) have also

documented a negative correlation between Ravens performance andmind wandering that occurred during Ravens testing. Lastly, mindwandering has also been previously linked with creativity (e.g., Bairdet al., 2012). By including these cognitive metrics in our analyses, wewere able to obtain a more detailed assessment of the implications oftrait mind wandering in cognitive processing and the brain-behaviorrelationships that may possibly underlie these processes.

Lastly, we ran our functional connectivity analyses with andwithout global signal regression (GSReg). GSReg has a controversialhistory in resting state fMRI. The global signal is the time course ob-tained by averaging the time series of all voxels across the brain(Desjardins et al., 2001; Macey et al., 2004; Saad et al., 2012). Theglobal signal has traditionally been considered a nuisance signal thatreflects physiological artifacts and background noise common to allbrain regions. Thus, it has been argued that the global signal should beregressed out of resting state data before correlation analyses (discussedby Saad et al., 2012). However, regressing out signal that is common toall voxels biases correlation coefficients downwards and inevitably in-troduces spurious negative correlations in the data. This can furthercomplicate interpretation of interregional correlations and group dif-ferences (Gotts et al., 2013; Murphy et al., 2009; Saad et al., 2012). Theanticorrelations between the DMN and other networks in early restingstate analyses may have been induced by GSReg (e.g., Fox et al., 2005),however converging evidence across multiple brain imaging modalitiesprovides support for true network anticorrelations (Raichle, 2015a).Regardless, GSReg remains controversial and currently there is no clearconsensus as to whether GSReg should be incorporated in resting statepreprocessing (Murphy and Fox, 2016). To further examine the rolethat GSReg has in functional connectivity analysis, we present our re-sults with and without GSReg and discuss our results within the contextof developing theories of mind wandering.

2. Method

2.1. Participants

Participants (n = 129) were recruited from the Georgia Institute ofTechnology, the University of New Mexico, and the surrounding com-munities of Atlanta and Albuquerque. Participants were recruited aspart of a larger, multi-site study.1 Datasets were included in the analysisif participants completed the initial resting state scan with minimalmotion artifacts (functional volumes with motion greater than 3 mmwere censored, and no participant had greater than 20 volumes cen-sored during preprocessing). In total, datasets from 112 participantswere included in the analysis (mage = 26.07, SDage = 6.98; 61 females).All participants were between 20 and 50 years of age, right handed,reported no neurological or psychiatric disorders, and had completed acollege degree or were currently in at least their third year of uni-versity. Three datasets were missing from the complex span and Ravenstasks, so all analyses using the OSPAN, SSPAN, and Ravens consisted ofdatasets from the remaining 109 participants (mage = 26.18, SDage =7.04; 60 females). Three datasets were missing from the RAT, so allanalyses using the RAT consisted of datasets from the remaining 109participants (mage = 26.22, SDage = 7.06; 60 females).

2.2. Cognitive measures

Participants completed three cognitive tasks to provide measures ofworking memory capacity and fluid intelligence: OSPAN, SSPAN, andRavens. Participants also completed the RAT as an index of creativity.

Table 1Mind Wandering Questionnaire (Mrazek et al., 2013).

Item Min Max

1. I have difficulty maintaining focus on simple or repetitive work. 1 62. While reading, I find I haven’t been thinking about the text and

must therefore read it again.1 6

3. I do things without paying full attention. 1 64. I find myself listening with one ear, thinking about something

else at the same time.1 6

5. I mind-wander during lectures or presentations. 1 6

Note. Responses range on a scale from 1 to 6. 1-almost never; 2-very infrequently; 3-somewhat infrequently; 4-somewhat frequently; 5-very frequently; 6-almost always.

1 This analysis was conducted on a dataset collected as part of a larger project con-ducted at the Georgia Institute of Technology and the University of New Mexico.Participants performed a number of cognitive tasks and scans. Only the tasks describedhere are relevant to the current hypotheses regarding intrinsic networks and mindwandering.


142

In addition, participants completed the MWQ. The tasks and ques-tionnaire were completed on the first study day. Participants returnedto the lab on a second day (on average four days later) to complete theresting state fMRI scan.

2.2.1. Mind Wandering Questionnaire (MWQ)The MWQ (Mrazek et al., 2013) was administered to each partici-

pant at the beginning of the session. The MWQ consists of five itemsdesigned to measure trait mind wandering on a 6-point scale (Table 1).

2.2.2. Operation Span Task (OSPAN)The OSPAN is a working memory task related to verbal working

memory capacity. The procedure is described in detail in Unsworth et al.(2005). In the OSPAN, participants were presented with three to sevenletters one at a time. Between presentation of each letter, an interrupterarithmetic problem was presented. Participants indicated whether the an-swer provided was correct or incorrect. At the end of each trial, participantsrecalled the letters and entered them in order into the computer. Thisprocedure was repeated until 75 letters and math problems were presented.

The dependent variables in the OSPAN are the memory score, whichindicates the total number of letters the participant recalled, and thenumber of math errors, which indicates how many math problems theparticipant answered incorrectly. We removed subjects from the OSPANtotal score analysis whose accuracy on the distractor task fell below 85%(following suggested procedures of Conway et al., 2005). There were sevenparticipants with OSPAN distractor scores less than 85%. Therefore, thenumber of subjects included in analyses using OSPAN total scores was 102.

2.2.3. Symmetry Span Task (SSPAN)The SSPAN is a working memory task similar to the OSPAN and is

related to spatial working memory capacity. The procedure is described indetail in Unsworth et al. (2009). In the SSPAN, participants were presentedwith red squares one at a time on a 3 × 3 array. Between the presentationof each square, an interrupter stimulus was presented to which partici-pants made symmetry judgements (vertical symmetry of an 8 × 8 blackand white grid). At the end of each trial, participants recalled the squaresin the order in which they appeared in the trial. This procedure was re-peated until 42 squares and images were presented.

The dependent variables in the SSPAN are the memory score, whichindicates the total number of squares the participant recalled, and thenumber of symmetry errors, which indicates how many symmetryproblems the participant answered incorrectly. As with the OSPAN, weremoved subjects from the SSPAN total score analysis whose accuracyon the distractor task fell below 85%. There were five participants withSSPAN distractor scores less than 85%. Therefore, the number of sub-jects included in analyses using SSPAN total scores was 104.

2.2.4. Raven's Advanced Progressive MatricesRaven's advanced progressive matrices is designed to measure fluid

intelligence (Raven, 2000). In this task, participants were presentedwith a 3 × 3 matrix containing different shapes. The cell in the bottomright corner was always missing. Participants selected from a set ofeight shapes the shape that best completed the pattern in the rows andcolumns. Participants were given 10 min to solve 18 problems selectedfrom the 36 total items in the Ravens task.2 The dependent variable inthe Ravens is the total number of problems solved correctly.

2.2.5. Remote Associates Task (RAT)The RAT is designed to measure creative thought (Mednick, 1962). On

each trial of the RAT, participants were presented with three words, and

were required to type a solution word that would create compound wordsor phrases with the three presented words (e.g., if the three words werecottage, swiss, and cake, the answer would be ‘cheese’). The problems inthis study were taken from Bowden and Jung-Beeman (2003). Participantswere given 23 problems to solve that progressed from easy to difficult.3

Participants had 15 s to press the spacebar to indicate they knew the an-swer. Participants then entered the word using the keyboard. There was notime limit to type the word. The dependent variable in the RAT is the totalnumber of problems solved correctly.

2.3. fMRI data acquisition

Imaging was conducted on Siemens 3 T Trio MRI scanners at theGeorgia Institute of Technology and the University of New Mexico. Allparticipants first completed a T1-weighted MPRAGE anatomical scan withthe following acquisition parameters: FoV = 256 mm; 176 slices; 1.0 ×1.0 × 1.0 mm3 voxels; flip angle = 9°, TE = 3.98 ms; TR = 2250 ms; TI= 850 ms. Participants then completed a 5-min resting state functionalscan (T2*-weighted echo-planar scan) with the following acquisitionparameters: FoV = 204 mm; slices = 37; 3.0 × 3.0 × 3.0 mm3 voxels;interleaved slice acquisition; gap = .5 mm; flip angle = 90°; TE = 30 ms;TR = 2000 ms. Participants were instructed to relax and focus their eyeson the fixation during resting state acquisition.

2.4. Functional connectivity analysis

2.4.1. fMRI data preprocessing without GSRegPreprocessing was performed using Analysis of Functional NeuroImages

(AFNI). The first three volumes were removed from the resting state scans.All resting state EPI images were despiked and corrected for slice timing.EPI images were then registered to the T1 anatomical scan, warped toMNI152 standard space, blurred with a FWHM of 10.0 mm, and bandpassfiltered .01–.08 Hz. Volumes with motion greater than 3 mm (one voxel)were censored and replaced by values interpolated from neighboring vo-lumes. Resting state images were linearly and quadratically detrended andsix motion parameters were regressed out. Per suggestion of Saad et al.(2012), the global signal was not regressed out. The residual functional datawere used for connectivity measures.

2.4.2. fMRI data preprocessing with GSRegWe performed a second functional connectivity analysis in which

we applied GSReg. To do so, we included the average whole brain timecourse as a regressor along with the baseline polynomials and six mo-tion parameters. There were no other differences between this analysisand the non-GSReg analysis. Again, the residual functional data wereused for connectivity measures.

2.4.3. Definition of resting state networksA seed-based ROI approach was used to define the DMN, FPCN, and

DAN. Following procedures of Grady et al. (2016) and of Spreng et al.(2013), the nodes of each network were defined by 5-mm-radius sphericalROIs centered on the coordinates of the regions reported by Spreng et al.(2013).4 There was a total of 17 nodes comprising the DMN, 15 nodescomprising the FPCN, and 11 nodes comprising the DAN (Fig. 1).

For each ROI, the time series were extracted from the constituentvoxels and averaged together to create one average time series. These

2 For purposes of the larger research project, the Ravens items were divided into twoparallel forms consisting of the even or odd items. Participants completed one half of theitems during their first visit, and completed the second half of the items during a secondvisit four weeks later. These forms were counterbalanced across participants. This testingmethod follows procedures of Jaeggi et al. (2008).

3 For purposes of the larger research project, two versions of 23 items were createdfrom the items in Bowden and Jung-Beeman (2003). Participants completed one of theseversions during their first visit, and completed the second version of these items duringtheir second visit. These versions were counterbalanced across participants.

4 We used the nodes from Grady et al. (2016) and Spreng et al. (2013) to create ournetworks. Spreng and colleagues originally assigned the precuneus to the DMN, and aftervisualization, Grady and colleagues reassigned the precuneus to the DAN. We agree withthe assessment of Grady and colleagues and assigned the precuneus to the DAN in thecurrent analysis. All other nodes in the current analysis have been assigned to the samenetworks defined by Spreng and colleagues.


143

time series were then used for connectivity analysis. To examine within-network connectivity in the DMN, FPCN, and DAN, a correlation matrixwas obtained from the time series of each ROI in each network. Thesecorrelation matrices represented the correlation of the time series ofeach pair of ROIs. Each correlation coefficient was normalized using theFisher transformation (z = .5 ln [(1 + r)/(1 − r)]). This method issimilar to that used by Grady et al. (2016). For the triangular half ofeach correlation matrix, the Fisher-transformed correlation coefficientswere averaged to produce measures of within-network mean con-nectivity for each network. We refer to these metrics in this article as“DMN connectivity”, “FPCN connectivity”, and “DAN connectivity”.

For between-network connectivity, the time series from all ROIscomprising each network were correlated with the time series from allROIs comprising each other network. The correlations were normalizedand the Fisher-transformed correlation coefficients were averaged toproduce measures of between-network mean connectivity. We refer tothese metrics in this article as “DMN/FPCN connectivity”, “DMN/DANconnectivity”, and “FPCN/DAN connectivity”.

In summary, six network metrics from each individual were obtained:mean DMN, FPCN, and DAN connectivity as within-network metrics, andDMN/FPCN, DMN/DAN, and FPCN/DAN connectivity as between-net-work metrics. These six metrics were then correlated with scores on theMWQ. To examine the relationships between brain networks and otheraspects of cognition within the same dataset, we also correlated these sixmetrics with performance on the OSPAN, SSPAN, Ravens, and RAT.

2.4.4. Participant motion estimatesIn order to examine the effect that head motion may have had on

the observed relationships, we obtained each participant's average es-timated motion across the resting state scan. For each participant, theEuclidean norm of the derivatives of the motion parameters were cal-culated for each TR and averaged across the time series. These averagemotion estimates were then correlated with MWQ score, performanceon the cognitive tasks, and the network metrics.

2.5. Multiple comparison correction

We corrected for multiple comparisons by using the sequential good-ness of fit (SGoF) method to control for family-wise error (Carvajal-Rodríguez et al., 2009). This method performs a binomial test on the ex-pected distribution from the p-values in the family based on the null

hypothesis. SGoF controls for type I errors, and controls for family-wiseerror by looking for a significantly large group of low p-values rather thanincreasingly small p-values. In this manner, it does not lose power as thenumber of statistical tests increases. This test has been used in previousanalyses of intrinsic network connectivity relationships (e.g., Magnusonet al., 2015; Thompson et al., 2013).

For this study, our comparisons were divided into the followingfourteen families. Our first two families consisted of correlations be-tween cognitive measures. Because our primary focus was on mindwandering, the first family tested the relationship between the MWQand the complex span tasks, Ravens, and RAT (4 tests). The secondfamily examined the relationships between the complex span tasks,Ravens, and RAT (6 tests).

Our primary connectivity analyses focused on the correlations be-tween mind wandering and intrinsic networks. Therefore, our next fa-mily consisted of the correlations between mind wandering and the sixwithin- and between-network metrics for non-GSReg (6 tests) data. Inorder to test the effect of GSReg, our fourth family consisted of the samecorrelations but with the GSReg data (6 tests). The next eight familieswere formed to examine the relationship between intrinsic connectivityand each of our additional cognitive measures (OSPAN, SSPAN, RAT,and Ravens; 6 tests per family) using both non-GSReg data and GSRegdata. The last two families consisted, respectively, of the correlationsbetween each pair of network metrics for the non-GSReg and GSRegdata (12 tests per family).

3. Results

3.1. Cognitive measures

The average score on the MWQ was 16.04 (SEM = .49). The scoresranged between 5 (indicating infrequent tendency to mind wander) and29 (indicating frequent tendency to mind wander). OSPAN scoresaveraged 60.46 (SEM = 1.31) out of 75 (80.61%). The average numberof math processing errors was 5.53 (SEM = .40) out of 75 (7.37%).SSPAN scores averaged 28.94 (SEM = 8.85) out of 42 (68.90%). Theaverage number of symmetry processing errors was 2.74 (SEM = .29)out of 42 (6.52%). The average Ravens score was 11.29 (SEM = .36)out of 18 (62.72%). The average RAT score was 7.49 (SEM = .31) outof 23 (32.57%).

Fig. 1. Correlation matrix depicting the pairwise correlations between all ROIs for non-GSReg (a) and GSReg (b) analyses. The horizontal and vertical black lines divide the ROIs into theDAN, DMN, and FPCN. Correlations are Fisher z-transformed.


144

3.1.1. Correlations between cognitive measures (see Table 2)There was a significant, positive correlation between MWQ score

and Ravens, r = .334, p< .001. There was also a significant positivecorrelation between MWQ score and RAT, r = .257, p = .007. Thecorrelation between MWQ score and RAT remained significant aftercontrolling for Ravens performance, r = .199, p = .04. However, therewere no significant correlations between MWQ score and either com-plex span task. These relationships are shown in Fig. 2.

In addition, there were significant positive correlations betweenOSPAN and Ravens (r= .408, p< .001), between SSPAN and Ravens (r= .546, p< .001), and between OSPAN and SSPAN (r = .517,p< .001). However, there were no significant correlations between thecomplex span tasks and RAT. There was a positive but non-significantcorrelation between RAT and Ravens (r = .162, p = .096). These re-lationships are shown in Fig. 3.

3.2. fMRI measures

Fig. 1 illustrates all pairwise correlations between the ROIs com-prising the three networks for both the GSReg and non-GSReg analyses.In the non-GSReg data, average DMN connectivity across subjects was.43 (SEM = .02), average FPCN connectivity was .46 (SEM = .02), and

average DAN connectivity was .48 (SEM = .02). Average between-network connectivity metrics were .26 (SEM = .02), .18 (SEM = .02)and .33 (SEM = .02) for DMN/FPCN, DMN/DAN, and FPCN/DANconnectivity, respectively. In the GSReg data, average DMN con-nectivity across subjects was .19 (SEM = .01), average FPCN con-nectivity was .21 (SEM = .01), and average DAN connectivity was .23(SEM = .01). Average between-network connectivity metrics were−.01 (SEM = .01), −.11 (SEM = .01) and .05 (SEM = .01) for DMN/FPCN, DMN/DAN, and FPCN/DAN connectivity, respectively.

3.2.1. Network metrics and mind wanderingCorrelations were performed on both the GSReg and non-GSReg

data to examine the relationships between trait mind wandering andnetwork metrics. In the non-GSReg data, positive correlations wereobserved between MWQ score and mean DMN connectivity (Fig. 4a, r= .257, p = .006) as well as between MWQ score and DMN/FPCNconnectivity (Fig. 5a, r = .231, p = .014). There was a positive butnon-significant relationship between MWQ score and DMN/DAN con-nectivity (Fig. 5b, r = .170, p = .072). Statistics are summarized inTable 3.

Regarding the GSReg data, a significant, positive correlation wasalso observed between MWQ score and DMN/FPCN connectivity, r =.188, p = .047 (Fig. 5d). However, this correlation was shifted down-ward relative to the non-GSReg correlation. There were no correlationsbetween MWQ score and mean DMN connectivity (r = .105, p = .268;Fig. 4d) or DMN/DAN connectivity (r = .107, p = .262; Fig. 5e).Statistics are summarized in Table 3.

3.2.2. Network metrics and cognitive measuresWe performed a set of correlations to examine the relationships be-

tween network metrics and our other measures of cognitive ability usingboth GSReg and non-GSReg data. No significant correlations were ob-served between network metrics and complex span scores, RAT scores, orRavens performance in the non-GSReg data. When using GSReg, positivecorrelations were observed between mean DAN connectivity and OSPAN,SSPAN, and Ravens scores (r = .255, p = .010, r = .342, p<.001, and r= .232, p = .015, respectively). Statistics are summarized in Table 4.

Table 2Correlations between measures of cognitive abilities.

Cognitive measure 1 Cognitive measure 2 Correlation p-value

MWQ score OSPAN score .075 .456MWQ score SSPAN score .127 .199MWQ score Ravens .334 < .001*MWQ score RAT .257 .007*OSPAN score Ravens .408 < .001*SSPAN score Ravens .546 < .001*OSPAN score SSPAN score .517 < .001*RAT Ravens .162 .096RAT OSPAN score .105 .299RAT SSPAN score −.137 .169

Note. *Significant after family-wise SGoF multiple comparison correction.

Fig. 2. Correlations between MWQ scores and measures of cognitive abilities: a) MWQ and SSPAN scores; b) MWQ and RAT scores; c) MWQ and OSPAN scores; d) MWQ and Ravensscores.


145

3.2.3. Correlations between network metricsThere were strong positive correlations (all rs> .560, all ps< .001)

between all pairs of metrics in the non-GSReg data, although two cor-relations (DMN and DAN, and DMN and FPCN/DAN) did not survivemultiple comparison correction. In the GSReg data, the correlationsbetween network metrics were substantially reduced. Correlations thatsurvived multiple comparison correction consisted of mean DMN andDAN connectivity (r = .402, p< .001), mean DMN and DMN/DANconnectivity (r = −.506, p< .001), and DAN and DMN/DAN con-nectivity (r = −.462, p< .001). Statistics are summarized in Table 5.

3.2.4. Participant motion estimatesIn the non-GSReg data, there were moderate and strong positive

correlations between all network metrics and motion estimates (allrs> .40, all ps< .001). In the GSReg data, there were small positivecorrelations between motion estimates and DMN/FPCN connectivity (r= .295, p = .002), DMN/DAN connectivity (r = .222, p = .019), andFPCN/DAN connectivity (r = .191, p = .043). However, all correla-tions between network metrics (Table 5) for both GSReg and non-GSReg data remained significant after controlling for motion. Im-portantly, there were no correlations between participant motion esti-mates and any of our cognitive measures or MWQ scores (−.100< allrs< .100, all ps> .300). In addition, partial correlations with the non-GSReg data indicated that the relationships between MWQ score andDMN/FPCN (r = .216, p = .023) and mean DMN connectivity (r =.243, p = .010) remained after controlling for motion estimates. In theGSReg data, the relationship between MWQ score and DMN/FPCNconnectivity remained positive after controlling for motion, r = .216,

yet did not reach significance, p = .076. All correlations with theGSReg data between mean DAN connectivity and OSPAN, SSPAN, andRavens score remained significant after controlling for motion,rs> .23, ps< .01.

4. Discussion

The research presented here investigated the relationship betweenintrinsic functional connectivity within and between the DMN, FPCN,and DAN and the tendency to mind wander in daily life. In addition, weexamined the behavioral relationships between executive function, traitmind wandering, and intrinsic network functional connectivity. Whileoverall, we found similar relationships within our data using bothanalyses, the strength and significance of the results varied when weincluded GSReg and when we excluded GSReg. We discuss and interpretboth of these analyses. We further discuss and interpret our resultswithin the context of developing theories of mind wandering.

4.1. Relationship of trait mind wandering and intrinsic networks

In the non-GSReg analysis, we found a significant, positive corre-lation between DMN/FPCN connectivity and trait mind wandering: Astendency to mind wander in everyday life increased, the extent towhich the DMN and FPCN were correlated at rest increased. A similarpositive correlation was obtained when GSReg was applied, althoughthe overall relationship was shifted downward (see Fig. 5a and d). Inaddition, we observed a smaller positive, yet nonsignificant correlationbetween DMN/DAN connectivity and trait mind wandering in the non-

Fig. 3. Correlations between measures of cognitiveabilities: a) OSPAN and Ravens scores; b) OSPAN andSSPAN scores; c) RAT and SSPAN scores; d) SSPANand Ravens scores; e) RAT and OSPAN scores; f) RATand Ravens scores.


146

GSReg data. Initially, this suggests that these results support the hy-pothesis that increased correlation between opposing intrinsic networksreflects a failure in coordination among networks to support efficientcognitive processing. As tendency to mind wander in daily life de-creased, the DMN and FPCN became less correlated. This relationshipbetween mind wandering and DMN/FPCN connectivity provides sup-port for the hypothesis that anticorrelated intrinsic networks mayfunction as a regulatory mechanism in cognitive processing (Raichle,2015a). That is, the anticorrelation between the DMN, which supportsundirected thought processes (Andrews-Hanna et al., 2010) and theFPCN, which supports executive function processes (Vincent et al.,2008), may serve the function of effectively balancing activity betweenthese intrinsic networks to overcome the detriments of mind wanderingand support adaptive cognition. While these network relationships areimportant during task performance, the same patterns emerge hereduring resting state and are associated with mind wandering tendency.

However, further inspection in conjunction with our cognitive measuressuggests a different interpretation may be warranted. This interpretation

has implications for understanding the function of large-scale networks inmind wandering. In particular, we found a positive correlation betweenmean DMN connectivity and mind wandering in the non-GSReg analysis.This finding might simply reflect the extent of the DMN to support mindwandering processes in general. Similarly, Andrews-Hanna et al. (2010)observed increased resting state connectivity between nodes of the DMN inassociation with amount of time spent thinking about past and futurethought during the scan. O’Callaghan et al. (2015) also observed increasedconnectivity between the temporal pole and the lateral temporal cortex ofthe DMN in relation to mind wandering frequency during a task outside thescanner.

However, this finding appears counterintuitive when compared toother research, which proposes that the integrity of the DMN at rest isrelated to better cognitive performance (Poole et al., 2016) and clinicaloutcomes in cases such as Alzheimer's disease (Greicius, 2008; Mohanet al., 2016) and mild cognitive impairment (Sorg et al., 2007). If mindwandering is an indicator of poorer cognitive functioning, then one mayexpect that increased mind wandering would be associated with decreased

Fig. 4. Correlations between MWQ scores and within-network connectivity metrics for non-GSReg (a–c) and GSReg (d–f) analyses. Connectivity metrics are Fisher z-transformed.


147

connectivity within the DMN at rest. However, as discussed below, themind wandering trait measured here may not be entirely maladaptive. Ifso, the positive correlation between DMN connectivity and mind wan-dering may not be as counterintuitive as one might initially think.

4.2. Relationship of cognitive measures and intrinsic networks

While the primary motivation for including the additional measuresof cognitive ability was to examine their relationship with trait mindwandering, we also investigated the extent to which working memorycapacity, fluid intelligence, and creativity correlated with our intrinsicnetwork metrics. However, there were no significant correlations be-tween any of our network metrics and cognitive abilities in the non-GSReg analysis. In the GSReg analysis, there were significant, positive

Fig. 5. Correlations between MWQ scores and between-network connectivity metrics for non-GSReg (a–c) and GSReg (d–f) analyses. Connectivity metrics are Fisher z-transformed.

Table 3Correlations between MWQ scores and network metrics.

Correlation p-value

Cognitive measure Network metric Non-GSReg GSReg Non-GSReg GSReg

MWQ score DMN .257 .105 .006* .268MWQ score FPCN .133 .026 .161 .787MWQ score DAN .114 .089 .230 .349MWQ score DMN/FPCN .231 .188 .014* .047*MWQ score DMN/DAN .170 .107 .072 .262MWQ score FPCN/DAN .063 −.124 .510 .194



148

correlations only when correlating mean DAN connectivity with com-plex span and Ravens performance. The lack of additional correlationsbetween executive function and intrinsic networks is surprising giventhe growing evidence of such relationships (Magnuson et al., 2015;Reineberg et al., 2015). However, it is possible that the correlationsbetween cognitive abilities and functional connectivity metrics aremore readily detected in task-based studies. Furthermore, it is possiblethat in resting state, it is connectivity between particular brain regionsand networks (e.g., Reineberg et al., 2015) or conversely, global orwhole-brain connectivity (Cole et al., 2012; Rosenberg et al., 2015) thatbest correlate with the overarching types of cognitive ability that wemeasured here.

4.3. Implications of global signal regression

Global signal regression has a controversial history. An increasingamount of research indicates that GSReg induces spurious anticorrelations

in resting state analysis (Gotts et al., 2013; Saad et al., 2012). Our resultshere further indicate the possibility that increased anticorrelations emergewhen GSReg is incorporated into analysis: Fig. 1 illustrates the increasedanticorrelations that occur when GSReg is used. In addition, when ex-amining the relationship between DMN/FPCN connectivity and MWQscore, the trend in the correlation shifted downward when GSReg wasapplied. While anticorrelations have, in theory, functional relevance re-garding the relationships between networks (Cole et al., 2012), in practicethe strength and sign of correlations are difficult to interpret in absoluteterms (Buckner et al., 2013). In our dataset, the majority of relationshipsbetween trait mind wandering and network metrics remained consistentacross both analyses. Importantly, both analyses indicate that relativelymore connectivity between the DMN and FPCN is associated with in-creased mind wandering in daily life. However, the positive correlationbetween mean DMN connectivity and MWQ score did not remain sig-nificant when GSReg was applied. In addition, including GSReg in-troduced significant, positive correlations between DAN connectivity andworking memory capacity and fluid intelligence. GSReg also attenuatedseveral correlations as well as introduced negative correlations betweennetwork metrics (Table 5).

In general, there is no definitive conclusion regarding the inclusionof GSReg in functional connectivity analysis. While issues have beenraised regarding GSReg in functional connectivity analyses (Saad et al.,2012; Murphy et al., 2009), it has also been argued that at this point itis still uncertain the extent to which GSReg should be included oravoided in connectivity analysis (Murphy and Fox, 2016). Here we havepresented our data analyzed both with and without GSReg. The cor-relations between trait mind wandering and intrinsic network con-nectivity calculated without GSReg were in general stronger and sig-nificant. However, the overall pattern between MWQ score and DMN/FPCN connectivity remained across both analyses.

4.4. The role of executive function in mind wandering

The relationships between trait mind wandering and our networkmetrics yielded a set of results seemingly inconsistent with the intrinsicnetwork literature. The increased coupling between the DMN and FPCNwas associated with greater mind wandering tendencies and can beinterpreted from a network regulation standpoint in which the DMNand FPCN work in opposition to suppress detrimental mind wanderingprocesses. However, we also observed a positive correlation betweenmind wandering and DMN connectivity in the non-GSReg analysis. Thislatter result is difficult to interpret in the context of some clinical lit-erature (e.g., Mohan et al., 2016) if mind wandering is considered adetrimental process. Therefore, as an alternative explanation, we con-sider some of the emerging literature regarding the role of executivefunction mechanisms in mind wandering as well as the potential ben-efits of mind wandering.

Executive function is generally thought to provide an inhibitory me-chanism to suppress mind wandering and support on-task processes.Instances of mind wandering result from failures in executive function(McVay and Kane, 2010). However, executive function can also contributeto mind wandering. While the contents of mind wandering (e.g., memoriesand other content decoupled from the external environment) remain de-pendent on DMN involvement, once attention is shifted to task-unrelatedthoughts (either involuntarily, or by means of a deliberate shift of atten-tion away from the task at hand; McVay and Kane, 2010), executivecontrol processes can guide and evaluate thought content for purposes ofprospection, problem solving, and other forms of mentation (Christoffet al., 2009; Fox et al., 2015; Smallwood and Schooler, 2006, 2015). Wesuggest that the increased connectivity of the DMN and the positive re-lationship between DMN/FPCN connectivity and mind wandering mayreflect mechanisms that support the integrative nature of mind wanderingprocesses. The greater the extent to which the DMN couples with theseexecutive regions at rest indicates the tendency for one to mind wander indaily life. In particular, it may indicate the tendency to which one engages

Table 4Correlations between measures of cognitive abilities and network metrics.

Correlation p-value

Cognitivemeasure

Networkmetric

Non-GSReg GSReg Non-GSReg

GSReg

Ravens DMN .174 .159 .071 .099Ravens FPCN .004 −.044 .966 .652Ravens DAN −.016 .232 .866 .015*Ravens DMN/FPCN .060 −.030 .535 .754Ravens DMN/DAN −.072 −.146 .459 .130Ravens FPCN/DAN −.069 −.010 .478 .915OSPAN DMN .061 .128 .545 .198OSPAN FPCN −.036 .084 .717 .403OSPAN DAN .045 .255 .653 .010*OSPAN DMN/FPCN −.045 −.035 .651 .725OSPAN DMN/DAN −.095 −.168 .341 .091OSPAN FPCN/DAN −.069 .045 .494 .656SSPAN DMN .149 .164 .132 .097SSPAN FPCN .185 .188 .060 .056SSPAN DAN .172 .342 .080 < .001*SSPAN DMN/FPCN .010 .040 .316 .690SSPAN DMN/DAN −.035 −.136 .724 .170SSPAN FPCN/DAN .093 .107 .347 .282RAT DMN −.054 −.002 .574 .984RAT FPCN −.120 −.113 .213 .241RAT DAN −.115 −.024 .235 .803RAT DMN/FPCN −.074 −.035 .447 .717RAT DMN/DAN −.070 .007 .472 .940RAT FPCN/DAN −.136 −.069 .157 .474


Table 5Correlations between network metrics.

Correlation p-value

Network metric 1 Network metric 2 Non-GSReg

GSReg Non-GSReg GSReg

DMN FPCN .679 .041 < .001* .666DMN DAN .569 .402 < .001 < .001*DMN DMN/FPCN .782 −.103 < .001* .281DMN DMN/DAN .653 −.506 < .001* < .001*DMN FPCN/DAN .644 .266 < .001 .005FPCN DAN .658 .096 < .001* .314FPCN DMN/FPCN .832 −.066 < .001* .489FPCN FPCN/DAN .831 .179 < .001* .059FPCN DMN/DAN .765 −.100 < .001* .295DAN DMN/FPCN .646 −.066 < .001* .491DAN DMN/DAN .662 −.462 < .001* < .001*DAN FPCN/DAN .829 .376 < .001* < .001



149

in goal-directed forms of mind wandering such as prospective thought andautobiographical planning (Christoff et al., 2016; Smallwood et al., 2009),cognitive processes that recruit a coupling between these two networks(Spreng et al., 2010). Further research is needed to determine if it is indeedthe case that individuals with greater DMN/FPCN connectivity at restexperience mind wandering characterized by greater prospective bias andgoal-directed thinking. However, the current findings are congruent withthis idea.

Our study examined at rest intrinsic connectivity both within andbetween the DMN, FPCN, and DAN, and their relationships to trait mindwandering. Although we did not collect data from participants re-garding their thought content during the resting state, previous re-search indicates that it is likely they were engaged in mind wandering(e.g., Andrews-Hanna et al., 2010). Therefore, the DMN/FPCN re-lationship as well as the relationship between mean DMN connectivityand mind wandering may partly reflect the extent to which an in-dividual is mind wandering in the scanner. However, the static intrinsicnetwork correlations measured in this study likely reflect intrinsic in-dividual differences rather than current experiences of mind wan-dering. For instance, Kucyi and Davis (2014) measured both static anddynamic functional connectivity in the DMN. They found that dynamicconnectivity reflected current daydreaming, but that static connectivitycorrelated with trait mind wandering measured by the DDFS. Kucyi andDavis suggested that this reflected intrinsic differences among in-dividuals independent of current cognitive processing. In addition, al-though we observed a relationship between intrinsic DMN/FPCN con-nectivity and trait mind wandering, it should be noted that executivecontrol regions are not necessarily always involved in mind wanderingprocesses. For instance, Vanhaudenhuyse et al. (2011) associated in-tensity of internal awareness with regions of the DMN but not executivecontrol regions. Relatedly, when examining behavioral performancevariability and mind wandering, Kucyi et al. (2016) observed no cor-relations between self-reported mind wandering and activity in eitherthe FPCN or DAN. Lastly, it should be noted that the correlations be-tween mind wandering and both DMN and DMN/FPCN connectivitywere relatively small (r = .257 and r = .231, respectively, for the non-GSReg analysis). This is common in research linking fMRI measureswith cognition. Physiological processes account for a large proportionof the intrinsic activity in the brain (Raichle, 2015b). However, ourcorrelations are statistically reliable and indicate that at rest, there aresmall but meaningful relationships between these large-scale brainnetworks and self-reported trait mind wandering.

4.5. Trait mind wandering as a positive characteristic

We observed a positive correlation between intrinsic DMN con-nectivity and trait mind wandering. We also observed a positive re-lationship between mind wandering and DMN/FPCN connectivity. Wepropose that instead of our functional connectivity metrics reflectingregulatory mechanisms of intrinsic networks, these patterns of con-nectivity at rest reflect the extent to which functionally relevant networkscouple to guide mind wandering processes. To further understand thebrain-behavior relationships underlying trait mind wandering, we ex-amined how trait mind wandering correlates with several measures ofcognitive abilities. While we observed no relationship between trait mindwandering and working memory capacity, we did obtain a positive cor-relation between mind wandering and the Ravens progressive matricestest, a measure of fluid intelligence: The greater one's fluid intelligence, thegreater the tendency to mind wander in daily life. This finding was un-expected given its counterintuitive nature and the negative relationshippreviously observed between Ravens scores and mind wandering duringtesting (Mrazek et al., 2012).

In many instances, mind wandering is considered a negative char-acteristic detrimental to many aspects of cognitive function and dailylife (Mooneyham and Schooler, 2013). However, in the context of ourobserved relationship between mind wandering and fluid intelligence

as well as the relationships between mind wandering and the networkmetrics described above, our results indicate that a different inter-pretation of trait mind wandering may be warranted. Namely, mindwandering may not be inherently negative and increased connectivitybetween intrinsic networks at rest may not always be a detrimentalcharacteristic of cognition. In line with the literature regarding execu-tive function discussed above, it may be that the tendency to mindwander, as measured in this study, reflects instances in which partici-pants were simply able to afford to mind wander. For example, previousresearch has indicated that the extent to which executive functioncontributes to mind wandering varies based on the demands of thecurrent task. Smallwood and Schooler (2015) recently proposed thecontext regulation hypothesis: When cognitive demand is high, executivefunction can suppress mind wandering; when cognitive demand is low,executive function allocates excess central processing resources toguide task-unrelated thoughts without a detriment to task performance.It is during these instances that increased goal-directed, future-orientedthought can frequently occur (Smallwood et al., 2009), supported bycoupling between the DMN and FPCN (Spreng et al., 2010). Althoughthe items on the MWQ were written to measure mind wandering in avariety of daily task situations, it is possible that our sample of parti-cipants introspected particularly on instances in their lives that werelow in cognitive demand during which they could mind wander. (Thismay especially be the case given that our participants were all highlyeducated and had above average working memory capacity scoreswhen compared to normative data from non-Georgia Tech students andAtlanta community members summarized by Redick et al., 2012.) Thefirst item on the scale highlights this possibility: I have difficulty main-taining focus on simple or repetitive tasks. Other items had the potential tobe interpreted more ambiguously in terms of demand. When a parti-cipant responded to the item I do things without paying full attention, heor she could have been thinking about an instance where they werechatting online (low cognitive demand) or completing a programmingassignment (high cognitive demand). In general, most of the items onthe MWQ could be interpreted as examples of low-load tasks. If this isthe case, then intrinsic DMN/FPCN connectivity may indicate the ten-dency of the two networks to coordinate in support of mind wanderingin general, perhaps primarily during undemanding tasks, as opposed toan inability to regulate cognition when task demands become challen-ging.

Furthermore, it is difficult to fully apply a negative interpretation ofmind wandering measured here in light of our behavioral data. Thepositive correlation between mind wandering and fluid intelligence wasunexpected given the negative relationship between Ravens scores andmind wandering during testing (Mrazek et al., 2012). However, thispositive correlation makes more sense if, as we propose, mind wan-dering is not an entirely negative attribute (c.f., Baird et al., 2012;Mooneyham and Schooler, 2013; Smallwood and Andrews-Hanna,2013; Smallwood and Schooler, 2015). Consistent with this idea is therelationship between trait mind wandering and the RAT, a test ofcreativity. One of the hypothesized benefits of increased mind wan-dering is that it has been associated with increased creativity (Bairdet al., 2012; Smeekens and Kane, 2016). Indeed, we observed a positivecorrelation between RAT performance and trait mind wandering, evenafter controlling for the effect of fluid intelligence (i.e., Ravens scores)on this relationship. While we cannot determine a causal relationshipbetween mind wandering and creativity within our dataset, we canspeculate about the mechanisms that may relate these two processes.For instance, the tendency to mind wander may facilitate broader as-sociative processes in semantic networks that bridge previously un-related concepts (Baird et al., 2012; Yaniv and Meyer, 1987). However,it is possible that the relationship between mind wandering and crea-tivity is not causally related, but rather emerges from a shared re-lationship with a third variable (Smeekens and Kane, 2016). Moregenerally, both increased trait mind wandering and creativity may berelated to a general tendency to engage in moderately constrained


150

internally-directed thought processes (such as creative and spontaneousthought; Christoff et al., 2016) which may engage both the DMN andFPCN. While we did not find significant correlations between creativityand functional connectivity in our network metrics, other research hasimplicated cooperation between these networks in creative processes(Beaty et al., 2016). The lack of similar patterns in our data may be dueto the possibility that these relationships are stronger during task-basedanalysis, or perhaps creativity is better associated with intrinsic con-nectivity between sub-regions of these networks (e.g., Beaty et al.,2014). In general, the positive correlations between trait mind wan-dering, fluid intelligence, and creativity suggest that there may be ad-ditional beneficial aspects of mind wandering that should be furtherinvestigated.

4.6. Remaining questions

During development and testing of the MWQ, Mrazek et al. (2013)found a positive correlation between MWQ scores and mind wanderingduring the OSPAN task, as well as a negative correlation between MWQscores and OSPAN performance. It is surprising that our data did notcorrelate in a similar manner. However, it is possible that workingmemory capacity does not always predict trait mind wandering. Forinstance, Kane et al. (2007) found that working memory capacity didnot predict overall rate of mind wandering in daily life, but rathermoderated the amount of mind wandering reported in various contexts,such as amount of concentration toward a task. To what extent in ourcurrent study did the MWQ measure mind wandering only in low loadsituations? Mrazek and colleagues did not develop the scale to focus onthese situations alone. If the MWQ was actually tapping into mindwandering tendency only during low load conditions, we might haveexpected a positive correlation between the MWQ scores and complexspan scores, since previous work has documented that individuals withhigh working memory capacity tend to mind wander more in low loadsituations (Levinson et al., 2012). We did not observe this relationship.However, Kane et al. (2007) observed a positive correlation betweenmind wandering tendency and boredom/preference for activities otherthan the one currently engaged in. In addition, working memory ca-pacity did not moderate this relationship. Related to this, the extent towhich mind wandering occurs intentionally may be important. A recentstudy has demonstrated that task-unrelated thoughts can occur bothintentionally and unintentionally (Seli et al., 2015). The authors discussthe possibility that intentional mind wandering may occur withoutdetriment to task performance, such as during a lecture of familiarmaterial. Perhaps the MWQ items best index mind wandering in thesecontexts, which are more general motivational contexts than that of lowcognitive demand and have been shown elsewhere to be importantpredictors of mind wandering (Unsworth and McMillan, 2013).

It is important to note that while working memory capacity did notcorrelate with mind wandering in this study, both measures of workingmemory capacity correlated with Ravens scores. This was expected andreplicated a large number of studies (e.g., Conway et al., 2002) that havedocumented the strong correlation between working memory capacity andintelligence. However, working memory capacity and fluid intelligence arenot isomorphic (Kane et al., 2005). The results here may be of further in-terest to researchers interested in discriminating between fluid intelligenceand working memory capacity, and the predictability of these constructs inother aspects of life such as trait mind wandering.

5. Concluding remarks

Emerging research indicates that intrinsic networks play function-ally important roles in many cognitive processes (Stevens and Spreng,2014). Additionally, brain networks interact in meaningful ways andthe extent to which they coordinate can be indexed by between-net-work correlations and anticorrelations (Raichle, 2015a). This has fre-quently been studied by examining anticorrelations between the DMN

and executive function and attention networks, with the implicationthat across a variety of cognitive processes, cognition is most efficientwhen these networks are anticorrelated (Magnuson et al., 2015;Thompson et al., 2013). The DMN/FPCN connectivity observed in thecurrent study may reflect the interaction between the DMN and ex-ecutive function regions that support unconstrained thought processes.However, the extent to which this connectivity indexes a failure ofregulatory mechanisms is unclear. Alternatively, the role of functionalcoupling between networks is relative to the task being performed andthe person performing it. If task demands are low, or if an individuallacks motivation to perform a particular task to his or her best ability,then the individual may lapse into mind wandering processes supportedby an interaction between the DMN and FPCN. In this case, the be-tween-network correlations may not indicate a failure to regulatecognitive processing, but rather may be a marker of efficient processingof the chosen cognitive process at the time. These same patterns emergeat rest, as they did here, and may correlate with the tendency withwhich mind wandering occurs. Related to this, research indicates thatregions of the default mode and executive networks couple in supportof other cognitive processes including future planning and creativethought (Beaty et al., 2016; Spreng et al., 2010). Additional work hasassociated DMN activity with task-relevant working memory processes(Smallwood et al., 2013). Overall, an emerging trend in intrinsic net-work literature indicates that internally-oriented cognition in generaldepends on interactions within and between large-scale brain networks(Zabelina and Andrews-Hanna, 2016). Our current results providefurther support for this concept.

This study has examined the relationship of the DMN, DAN, andFPCN in regard to trait mind wandering. The static correlations withinthe DMN and between the DMN and FPCN likely reflect intrinsic in-dividual differences related to mind wandering that are stable overtime. However, mind wandering is a complicated and dynamic process.The necessity to maintain focus is ubiquitous in daily life, yet the extentto which one maintains focus can fluctuate substantially. Disentanglingthe nuances of mind wandering processes and how brain networkscoordinate these complicated streams of thought will depend on ana-lysis methods such as dynamic functional connectivity and further in-vestigations into the relationships between subcomponents of largescale networks (e.g., Spreng, 2012). In addition, the complicated natureof mind wandering further calls on researchers to consider the im-portance of task context and individual differences when parsing outneural mechanisms. In the current study, we can only speculate aboutthe extent to which cognitive load, intentionality, or general motivationmay contribute to self-reported mind wandering tendencies and therelationship of intrinsic networks. Future research should activelymeasure and manipulate these variables in order to gain a more thor-ough understanding of mind wandering.

Acknowledgments

The research is based upon work supported by the Office of theDirector of National Intelligence (ODNI), Intelligence AdvancedResearch Projects Activity (IARPA), via No. 2014-13121700006. Theviews and conclusions contained herein are those of the authors andshould not be interpreted as necessarily representing the official po-licies or endorsements, either expressed or implied, of the ODNI,IARPA, or the U.S. Government. The U.S. Government is authorized toreproduce and distribute reprints for Governmental purposes notwith-standing any copyright annotation thereon.

References

Andrews-Hanna, J.R., Reidler, J.S., Huang, C., Buckner, R.L., 2010. Evidence for thedefault network's role in spontaneous cognition. J. Neurophysiol. 104 (1), 322–335.

Andrews-Hanna, J.R., Smallwood, J., Spreng, R.N., 2014. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. A.N. Y. Acad. Sci. 1316 (1), 29–52.


151

http://refhub.elsevier.com/S0028-3932(17)30259-2/sbref1





Baird, B., Smallwood, J., Mrazek, M.D., Kam, J.W., Franklin, M.S., Schooler, J.W., 2012.Inspired by distraction mind wandering facilitates creative incubation. Psychol. Sci.23 (10), 1117–1122.

Beaty, R.E., Benedek, M., Silvia, P.J., Schacter, D.L., 2016. Creative cognition and brainnetwork dynamics. Trends Cogn. Sci. 20 (2), 87–95.

Beaty, R.E., Benedek, M., Wilkins, R.W., Jauk, E., Fink, A., Silvia, P.J., Neubauer, A.C.,2014. Creativity and the default network: a functional connectivity analysis of thecreative brain at rest. Neuropsychologia 64, 92–98.

Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity inthe motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med.34 (4), 537–541.

Bowden, E.M., Jung-Beeman, M., 2003. Normative data for 144 compound remote as-sociate problems. Behav. Res. Methods Instrum. Comput. 35 (4), 634–639.

Buckner, R.L., Krienen, F.M., Yeo, B.T., 2013. Opportunities and limitations of intrinsicfunctional connectivity MRI. Nat. Neurosci. 16 (7), 832–837.

Carvajal-Rodríguez, A., de Uña-Alvarez, J., Rolán-Alvarez, E., 2009. A new multitestcorrection (SGoF) that increases its statistical power when increasing the number oftests. BMC Bioinform. 10 (1), 1–14.

Christoff, K., Gordon, A.M., Smallwood, J., Smith, R., Schooler, J.W., 2009. Experiencesampling during fMRI reveals default network and executive system contributions tomind wandering. Proc. Natl. Acad. Sci. USA 106 (21), 8719–8724.

Christoff, K., Irving, Z.C., Fox, K.C., Spreng, R.N., Andrews-Hanna, J.R., 2016. Mind-wandering as spontaneous thought: a dynamic framework. Nat. Rev. Neurosci.

Cole, M.W., Yarkoni, T., Repovš, G., Anticevic, A., Braver, T.S., 2012. Global connectivityof prefrontal cortex predicts cognitive control and intelligence. J. Neurosci. 32 (26),8988–8999.

Conway, A.R., Cowan, N., Bunting, M.F., Therriault, D.J., Minkoff, S.R., 2002. A latentvariable analysis of working memory capacity, short-term memory capacity, pro-cessing speed, and general fluid intelligence. Intelligence 30 (2), 163–183.

Conway, A.R., Kane, M.J., Bunting, M.F., Hambrick, D.Z., Wilhelm, O., Engle, R.W., 2005.Working memory span tasks: a methodological review and user's guide. Psychon.Bull. Rev. 12 (5), 769–786.

Corbetta, M., Patel, G., Shulman, G.L., 2008. The reorienting system of the human brain:from environment to theory of mind. Neuron 58 (3), 306–324.

Desjardins, A.E., Kiehl, K.A., Liddle, P.F., 2001. Removal of confounding effects of globalsignal in functional MRI analyses. NeuroImage 13 (4), 751–758.

Faber, M., Bixler, R., D’Mello, S.K., 2017. An automated behavioral measure of mindwandering during computerized reading. Behav. Res. Methods 1–17.

Finn, E.S., Shen, X., Scheinost, D., Rosenberg, M.D., Huang, J., Chun, M.M., Constable,R.T., 2015. Functional connectome fingerprinting: identifying individuals usingpatterns of brain connectivity. Nat. Neurosci. 18 (11), 1664–1671.

Fox, K.C., Spreng, R.N., Ellamil, M., Andrews-Hanna, J.R., Christoff, K., 2015. Thewandering brain: meta-analysis of functional neuroimaging studies of mind-wan-dering and related spontaneous thought processes. NeuroImage 111, 611–621.

Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005.The human brain is intrinsically organized into dynamic, anticorrelated functionalnetworks. Proc. Natl. Acad. Sci. USA 102 (27), 9673–9678.

Gotts, S.J., Saad, Z.S., Jo, H.J., Wallace, G.L., Cox, R.W., Martin, A., 2013. The perils ofglobal signal regression for group comparisons: a case study of Autism SpectrumDisorders. Front. Hum. Neurosci. 7 (356), 1–20.

Grady, C., Sarraf, S., Saverino, C., Campbell, K., 2016. Age differences in the functionalinteractions among the default, frontoparietal control, and dorsal attention networks.Neurobiol. Aging 41, 159–172.

Greicius, M., 2008. Resting-state functional connectivity in neuropsychiatric disorders.Curr. Opin. Neurol. 21 (4), 424–430.

Jaeggi, S.M., Buschkuehl, M., Jonides, J., Perrig, W.J., 2008. Improving fluid intelligencewith training on working memory. Proc. Natl. Acad. Sci. 105 (19), 6829–6833.

Kajimura, S., Kochiyama, T., Nakai, R., Abe, N., Nomura, M., 2016. Causal relationshipbetween effective connectivity within the default mode network and mind-wanderingregulation and facilitation. NeuroImage 133, 21–30.

Kane, M.J., Brown, L.H., McVay, J.C., Silvia, P.J., Myin-Germeys, I., Kwapil, T.R., 2007.For whom the mind wanders, and when an experience-sampling study of workingmemory and executive control in daily life. Psychol. Sci. 18 (7), 614–621.

Kane, M.J., Hambrick, D.Z., Conway, A.R., 2005. Working memory capacity and fluidintelligence are strongly related constructs: comment on Ackerman, Beier, and Boyle(2005). Psychol. Bull. 131 (1), 66–71.

Kelly, A.C., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P., 2008. Competitionbetween functional brain networks mediates behavioral variability. Neuroimage 39(1), 527–537.

Killingsworth, M.A., Gilbert, D.T., 2010. A wandering mind is an unhappy mind. Science330 (6006) (932–932).

Kucyi, A., Davis, K.D., 2014. Dynamic functional connectivity of the default mode net-work tracks daydreaming. NeuroImage 100, 471–480.

Kucyi, A., Esterman, M., Riley, C.S., Valera, E.M., 2016. Spontaneous default networkactivity reflects behavioral variability independent of mind-wandering. Proc. Natl.Acad. Sci. USA 113 (48), 13899–13904.

Kuschpel, M.S., Liu, S., Schad, D.J., Heinzel, S., Heinz, A., Rapp, M.A., 2015. Differentialeffects of wakeful rest, music and video game playing on working memory perfor-mance in the n-back task. Front. Psychol. 6, 1683.

Levinson, D.B., Smallwood, J., Davidson, R.J., 2012. The persistence of thought evidencefor a role of working memory in the maintenance of task-unrelated thinking. Psychol.Sci. 23 (4), 375–380.

Macey, P.M., Macey, K.E., Kumar, R., Harper, R.M., 2004. A method for removal of globaleffects from fMRI time series. NeuroImage 22 (1), 360–366.

Magnuson, M.E., Thompson, G.J., Schwarb, H., Pan, W.J., McKinley, A., Schumacher,E.H., Keilholz, S.D., 2015. Errors on interrupter tasks presented during spatial and

verbal working memory performance are linearly linked to large-scale functionalnetwork connectivity in high temporal resolution resting state fMRI. Brain ImagingBehav. 9 (4), 854–867.

Mason, M.F., Norton, M.I., Van Horn, J.D., Wegner, D.M., Grafton, S.T., Macrae, C.N.,2007. Wandering minds: the default network and stimulus-independent thought.Science 315 (5810), 393–395.

McVay, J.C., Kane, M.J., 2009. Conducting the train of thought: working memory capa-city, goal neglect, and mind wandering in an executive-control task. J. Exp. Psychol.:Learn. Mem. Cogn. 35 (1), 196–204.

McVay, J.C., Kane, M.J., 2010. Does mind wandering reflect executive function or ex-ecutive failure? Comment on Smallwood and Schooler (2006) and Watkins (2008).Psychol. Bull. 136 (2), 188–197.

McVay, J.C., Kane, M.J., 2012. Why does working memory capacity predict variation inreading comprehension? On the influence of mind wandering and executive atten-tion. J. Exp. Psychol.: Gen. 141 (2), 302.

Mednick, S., 1962. The associative basis of the creative process. Psychol. Rev. 69 (3), 220.Mohan, A., Roberto, A.J., Mohan, A., Lorenzo, A., Jones, K., Carney, M.J., Lapidus, K.A.,

2016. Focus: the aging brain: the significance of the default mode network (DMN) inneurological and neuropsychiatric disorders: a review. Yale J. Biol. Med. 89 (1), 49.

Mooneyham, B.W., Schooler, J.W., 2013. The costs and benefits of mind-wandering: areview. Can. J. Exp. Psychol. 67 (1), 11–18.

Mrazek, M.D., Phillips, D.T., Franklin, M.S., Broadway, J.M., Schooler, J.W., 2013. Youngand restless: validation of the Mind-Wandering Questionnaire (MWQ) reveals dis-ruptive impact of mind-wandering for youth. Front. Psychol. 4, 560.

Mrazek, M.D., Smallwood, J., Franklin, M.S., Chin, J.M., Baird, B., Schooler, J.W., 2012.The role of mind-wandering in measurements of general aptitude. J. Exp. Psychol.:Gen. 141 (4), 788.

Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., Bandettini, P.A., 2009. The impactof global signal regression on resting state correlations: are anti-correlated networksintroduced? NeuroImage 44 (3), 893–905.

Murphy, K., Fox, M.D., 2016. Towards a consensus regarding global signal regression forresting state functional connectivity MRI. NeuroImage.

O’Callaghan, C., Shine, J.M., Lewis, S.J., Andrews-Hanna, J.R., Irish, M., 2015. Shaped byour thoughts – a new task to assess spontaneous cognition and its associated neuralcorrelates in the default network. Brain Cogn. 93, 1–10.

Poole, V.N., Robinson, M.E., Singleton, O., DeGutis, J., Milberg, W.P., McGlinchey, R.E.,Esterman, M., 2016. Intrinsic functional connectivity predicts individual differencesin distractibility. Neuropsychologia 86, 176–182.

Raichle, M.E., 2015a. The brain's default mode network. Ann. Rev. Neurosci. 38,433–447.

Raichle, M.E., 2015b. The restless brain: how intrinsic activity organizes brain function.Philos. Trans. R. Soc. B 370 (1668).

Raven, J., 2000. The Raven's progressive matrices: change and stability over culture andtime. Cogn. Psychol. 41, 1–48.

Raven, J.C., Court, J.H., Raven, J., 1977. Raven's Progressive Matrices and VocabularyScales. Psychological Corporation, New York.

Redick, T.S., Broadway, J.M., Meier, M.E., Kuriakose, P.S., Unsworth, N., Kane, M.J.,Engle, R.W., 2012. Measuring working memory capacity with automated complexspan tasks. Eur. J. Psychol. Assess. 28 (3), 164–171.

Reineberg, A.E., Andrews-Hanna, J.R., Depue, B.E., Friedman, N.P., Banich, M.T., 2015.Resting-state networks predict individual differences in common and specific aspectsof executive function. Neuroimage 104, 69–78.

Rosenberg, M.D., Finn, E.S., Scheinost, D., Papademetris, X., Shen, X., Constable, R.T.,Chun, M.M., 2015. A neuromarker of sustained attention from whole-brain functionalconnectivity. Nat. Neurosci. 19 (1), 165–171.

Saad, Z.S., Gotts, S.J., Murphy, K., Chen, G., Jo, H.J., Martin, A., Cox, R.W., 2012. Troubleat rest: how correlation patterns and group differences become distorted after globalsignal regression. Brain Connect. 2 (1), 25–32.

Seli, P., Cheyne, J.A., Smilek, D., 2013. Wandering minds and wavering rhythms: linkingmind wandering and behavioral variability. J. Exp. Psychol.: Hum. Percept. Perform.39 (1), 1–5.

Seli, P., Cheyne, J.A., Xu, M., Purdon, C., Smilek, D., 2015. Motivation, intentionality, andmind wandering: implications for assessments of task-unrelated thought. J. Exp.Psychol.: Learn. Mem. Cogn. 41 (5), 1417.

Shannon, B.J., Raichle, M.E., Snyder, A.Z., Fair, D.A., Mills, K.L., Zhang, D., Stevens, A.A.,2011. Premotor functional connectivity predicts impulsivity in juvenile offenders.Proc. Natl. Acad. Sci. USA 108 (27), 11241–11245.

Singer, J.L., Antrobus, J.S., 1972. Daydreaming, imaginal processes, and personality: anormative study. Funct. Nat. Imag. 175–202.

Smallwood, J., Andrews-Hanna, J., 2013. Not all minds that wander are lost: the im-portance of a balanced perspective on the mind-wandering state. Front. Psychol. 4,441.

Smallwood, J., Nind, L., O’Connor, R.C., 2009. When is your head at? An exploration ofthe factors associated with the temporal focus of the wandering mind. Conscious.Cogn. 18 (1), 118–125.

Smallwood, J., O'Connor, R.C., Sudbery, M.V., Obonsawin, M., 2007. Mind-wanderingand dysphoria. Cogn. Emot. 21 (4), 816–842.

Smallwood, J., Schooler, J.W., 2006. The restless mind. Psychol. Bull. 132 (6), 946.Smallwood, J., Schooler, J.W., 2015. The science of mind wandering: empirically navi-

gating the stream of consciousness. Ann. Rev. Psychol. 66, 487–518.Smallwood, J., Tipper, C., Brown, K., Baird, B., Engen, H., Michaels, J.R., Schooler, J.W.,

2013. Escaping the here and now: evidence for a role of the default mode network inperceptually decoupled thought. NeuroImage 69, 120–125.

Smeekens, B.A., Kane, M.J., 2016. Working Memory Capacity, Mind Wandering, andCreative Cognition: An Individual-Differences Investigation into the Benefits ofControlled versus Spontaneous Thought.


152










































































































































































Sorg, C., Riedl, V., Mühlau, M., Calhoun, V.D., Eichele, T., Läer, L., Wohlschläger, A.M.,2007. Selective changes of resting-state networks in individuals at risk for Alzheimer'sdisease. Proc. Natl. Acad. Sci. USA 104 (47), 18760–18765.

Spreng, R.N., 2012. The fallacy of a “task-negative” network. Front. Psychol. 3 (145), 1–5.Spreng, R.N., Sepulcre, J., Turner, G.R., Stevens, W.D., Schacter, D.L., 2013. Intrinsic

architecture underlying the relations among the default, dorsal attention, and fron-toparietal control networks of the human brain. J. Cogn. Neurosci. 25 (1), 74–86.

Spreng, R.N., Stevens, W.D., Chamberlain, J.P., Gilmore, A.W., Schacter, D.L., 2010.Default network activity, coupled with the frontoparietal control network, supportsgoal-directed cognition. NeuroImage 53 (1), 303–317.

Stevens, W.D., Spreng, R.N., 2014. Resting-state functional connectivity MRI reveals ac-tive processes central to cognition. Wiley Interdiscip. Rev.: Cogn. Sci. 5 (2), 233–245.

Thompson, G.J., Magnuson, M.E., Merritt, M.D., Schwarb, H., Pan, W.J., McKinley, A.,Keilholz, S.D., 2013. Short-time windows of correlation between large-scale func-tional brain networks predict vigilance intraindividually and interindividually. Hum.Brain Mapp. 34 (12), 3280–3298.

Unsworth, N., Heitz, R.P., Schrock, J.C., Engle, R.W., 2005. An automated version of theoperation span task. Behav. Res. Methods 37 (3), 498–505.

Unsworth, N., McMillan, B.D., 2013. Mind wandering and reading comprehension: ex-amining the roles of working memory capacity, interest, motivation, and topic

experience. J. Exp. Psychol.: Learn. Mem. Cogn. 39 (3), 832.Unsworth, N., Redick, T.S., Heitz, R.P., Broadway, J.M., Engle, R.W., 2009. Complex

working memory span tasks and higher-order cognition: a latent-variable analysis ofthe relationship between processing and storage. Memory 17 (6), 635–654.

Vanhaudenhuyse, A., Demertzi, A., Schabus, M., Noirhomme, Q., Bredart, S., Boly, M.,Laureys, S., 2011. Two distinct neuronal networks mediate the awareness of en-vironment and of self. J. Cogn. Neurosci. 23 (3), 570–578.

Vincent, J.L., Kahn, I., Snyder, A.Z., Raichle, M.E., Buckner, R.L., 2008. Evidence for afrontoparietal control system revealed by intrinsic functional connectivity. J.Neurophysiol. 100 (6), 3328–3342.

Yaniv, I., Meyer, D.E., 1987. Activation and metacognition of inaccessible stored in-formation: potential bases for incubation effects in problem solving. J. Exp. Psychol.:Learn. Mem. Cogn. 13 (2), 187.

Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M.,Fischl, B., 2011. The organization of the human cerebral cortex estimated by intrinsicfunctional connectivity. J. Neurophysiol. 106 (3), 1125–1165.

Zabelina, D.L., Andrews-Hanna, J.R., 2016. Dynamic network interactions supportinginternally-oriented cognition. Curr. Opin. Neurobiol. 40, 86–93.

Zhang, D., Raichle, M.E., 2010. Disease and the brain's dark energy. Nat. Rev. Neurol. 6(1), 15–28.


153









































functional connectivity within and between intrinsic brain ...control.gatech.edu › wp-content ›...

Documents