Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performance

ORIGINAL RESEARCH

Static and dynamic posterior cingulate cortex nodal topologyof default mode network predicts attention task performance

Pan Lin1,3,6 & Yong Yang2 & Jorge Jovicich3,4 & Nicola De Pisapia3 & Xiang Wang5 &Chun S. Zuo6 & James Jonathan Levitt7,8

# Springer Science+Business Media New York 2015

Abstract Characterization of the default mode network(DMN) as a complex network of functionally interacting dy-namic systems has received great interest for the study ofDMN neural mechanisms. In particular, understanding therelationship of intrinsic resting-state DMN brain network withcognitive behaviors is an important issue in healthy cognitionand mental disorders. However, it is still unclear how DMNfunctional connectivity links to cognitive behaviors duringresting-state. In this study, we hypothesize that static and dy-namic DMN nodal topology is associated with upcoming cog-nitive task performance. We used graph theory analysis inorder to understand better the relationship between the DMNfunctional connectivity and cognitive behavior during resting-state and task performance. Nodal degree of the DMN wascalculated as a metric of network topology. We found that thestatic and dynamic posterior cingulate cortex (PCC) nodaldegree within the DMNwas associated with task performance(Reaction Time). Our results show that the core node PCC

nodal degree within the DMN was significantly correlatedwith reaction time, which suggests that the PCC plays a keyrole in supporting cognitive function.

Keywords Default mode network (DMN) . Functionalconnectivity (FC) . Task performance . Posterior cingulatecortex (PCC) . Degree

Introduction

The intrinsic resting-state networks (RSN) have recentlyattracted increasing attention in neuroscience research. Oneprominent RSN is the default-mode network (DMN), whichrevealed highly correlated intrinsic low-frequency blood oxy-genation level-dependent (BOLD) signal (Greicius et al.2003) or metabolic activity (Buckner et al. 2008; Raichleand Gusnard 2002) during resting-state together with task-

Electronic supplementary material The online version of this article(doi:10.1007/s11682-015-9384-6) contains supplementary material,which is available to authorized users.

* Pan [email protected]

1 Key Laboratory of Biomedical Information Engineering ofEducation Ministry, Institute of Biomedical Engineering, XianJiaotong University, Xian 710049, China

2 School of Information Technology, Jiangxi University of Finance andEconomics, Nanchang, Peoples Republic of China

3 Center for Mind/Brain Sciences, University of Trento,Mattarello 38100, Italy

4 Department of Cognitive and Education Sciences, University ofTrento, Rovereto 38068, Italy

5 Medical Psychological Institute of Second Xiangya Hospital, CentralSouth University, Changsha, China

6 Brain Imaging Center, McLean Hospital, Department of Psychiatry,Harvard Medical School, Belmont, MA, USA

7 Clinical Neuroscience Division, Laboratory of Neuroscience,Department of Psychiatry, VA, Boston Healthcare System, BrocktonDivision, Harvard Medical School, Boston, MA 02301, USA

8 Psychiatry Neuroimaging Laboratory, Department of Psychiatry,Brigham & Womens Hospital, Harvard Medical School,Boston, MA 02215, USA

Brain Imaging and BehaviorDOI 10.1007/s11682-015-9384-6
http://dx.doi.org/10.1007/s11682-015-9384-6

induced deactivation (Buckner et al. 2008; Lin et al. 2011).The DMN is a special brain system that comprises a set ofinteracting brain regions, including medial prefrontal cortex(MPFC), posterior cingulate cortex (PCC), lateral and medialtemporal lobes (MTL), and posterior inferior parietal lobule(pIPL) (Buckner et al. 2008). There is accumulating evidencethat indicates that sub-regions of the DMN perform special-ized functions (Buckner et al. 2008). For example, withinDMN sub-regions or sub-nodes, PCC plays key roles in mon-itoring ones own internal state and emotions, self-referentialprocessing, problem solving and task-independent thoughts,memory consolidation, social cognition and autobiographicalmemory (Buckner et al. 2008; Leech and Sharp 2013; vanVeluw and Chance 2014). The PCC plays a central role withinthe default network and their functional connectivity that issupported by structural connectivity studies (Greicius et al.2009; Patel et al. 2013). In addition, the PCC node shows ahigh metabolic rate during the baseline state (Raichle et al.2001). PCC function might be particularly important forunderstanding brain function and the impact of disease onbrain function (Leech and Sharp 2013; Philip et al. 2013).Accumulating studies suggest the PCC region, itself, hasgreater communication with other brain systems which allowsit to support ongoing task performance (Leech and Sharp2013). Furthermore, most studies have supported the idea thatfunctional integrity of the PCC may be related to executivecontrol processes (Kelly et al. 2008). The DMN sub-regionsmay play important roles in regulating resources competitionduring attention and in supporting higher-level executivefunction tasks (Andrews-Hanna et al. 2007). In short, thePCC plays a central role in regulating the balance betweeninternally and externally driven information for supportingnormal brain cognitive function.

Recently, the combination of graph analysis and fMRI of-fers a powerful tool for characterizing brain networks(Rubinov and Sporns 2010). Many groups have explored therelationship between the complex topological properties offunctional brain networks and cognitive or behavioral mea-sures (Bassett et al. 2009; Crossley et al. 2013; Dosenbachet al. 2007; Hagmann et al. 2010; Lin et al. 2014; van denHeuvel et al. 2009). For example, a recent study has suggesteda link between cost and efficiency of complex brain networkmetrics and working memory cognitive performance (Bassettet al. 2009). Moreover, recent studies show that communitystructure of the human brain network is relevant to cognitivefunction (Crossley et al. 2013). These findings further suggestthat brain network topology plays an important role in cogni-tive function.

In addition, brain network functional connectivity is not astatic process over time (Allen et al. 2014). Emerging evidencesuggests that dynamic brain functional connectivity may indexchanges in neural activity patterns underlying critical aspects ofcognition or clinically relevant information (Allen et al. 2014;

Calhoun et al. 2014; Hutchison et al. 2013a; Kucyi and Davis2015; Park and Friston 2013; Tagliazucchi and Laufs 2014;Tagliazucchi et al. 2012). Furthermore, understanding the rela-tionship between static and dynamic DMN functional connec-tivity with brain behavior will be critical to elucidate more fullythe role of DMN signals in both healthy cognition and mentalillness (Arenivas et al. 2014; Tam et al. 2014). Despite the num-ber of studies focusing on DMN static and dynamic functionalconnectivity and mental state, to date, some previous studieshave suggested that DMN functional connectivity is associatedwith brain cognitive function (Bonnelle et al. 2012; Buckneret al. 2008; Kucyi and Davis 2014). However, it still remainsunclear how static or dynamic DMN functional connectivitylinks to cognitive behaviors and mental illness. More recentstudies indicate that nodal topology properties of brain networkassociated brain cognitive function and brain disorder (Achardet al. 2012; Buckner et al. 2009). Given that the central PCCregion has greater communication with other brain systems,wh i c h p l a y a n imp o r t a n t r o l e i n r e g u l a t i n gresources competition during attention, and in supportinghigher-level executive function tasks (Leech and Sharp 2013;Liang et al. 2013), we hypothesized that characteristics ofresting-state PCC nodal degree would be associated with up-coming task performance. This would suggest that static anddynamic PCC nodal temporal topological properties are linkedto underlying behavioral performance.

Materials and methods

Subjects

Fourteen right-handed subjects (6 females, mean age:27.4 years, range: 2335) participated in the experiment. Allsubjects were healthy without a history of neurological orpsychiatric episodes. Participants gave written informed con-sent for a protocol approved by the ethics committee of theUniversity of Trento, Italy.

Experiment design

In order to explore the resting-state brain network, we ran10 min (min) resting-state scanning, prior to the task, wheresubjects were instructed simply to keep their eyes closed andnot to think of anything in particular. After this 10min resting-state scanning, the subjects perform an attention task. SeeFig. 1 for the task design and timing parameters. The primestimuli were a square, diamond, or star. The primes werefollowed by an instruction cue (square or diamond) presentedat the center of the screen. If the instruction cue shape matchedthe prime stimuli, this condition would be congruent, if theinstruction cue had the opposite shape as the prime stimuli, itwould be incongruent; or, if the prime stimuli shape is a star, it

Brain Imaging and Behavior

would be neutral. The participants were instructed to press thecontralateral button if they saw a small central square cue.Alternatively, if they saw a small central diamond cue, partic-ipants were required to press the ipsilateral button. For moredetails about the experimental design of this attention task seeour prior published paper (De Pisapia et al. 2012).

MRI data acquisition

Functional images were acquired with a 4-T MRI system(Bruker/SiemensMedSpec, Germany) equippedwith a birdcageRF transmit head coil and an eight-channel phase array receiverheadcoil. Foam padding and headphones were used to limithead motion and reduce scanner noise. The functional imagingdata were acquired using an echo-planar (EPI) sequence,corrected for distortion with the point spread function (PSF)method (time repetition, TR=2500 ms; time echo, TE=33 ms;flip angle, FA=76; 333 mm voxels; field of view, FOV=192) (Zaitsev et al. 2004). Whole-brain coverage including theentire cerebellumwas achieved with 33 slices aligned to AC-PCline. Structural images were acquired using a sagittal magneti-zation prepared rapid gradient echo (MP-RAGE) three-dimensional T1-weighted sequence optimized for gray-whitematter contrast (repetition time, TR=2700 ms, echo time, TE=

4.18 ms, inversion time,TI=1020 ms, flip angle=7, and matrixsize=224256, 111 mm3, GRAPPA iPAT=2).

fMRI analysis

Data preprocessing

The functional images were performed by using a combinationof analysis of fMRI data based on AFNI (http://afni.nimh.nih.gov/afni/) and FSLs software Library (http://www.fmrib.ox.ac.uk/fsl/). The first five volumes were discarded from analysis toallow for initial stabilization of the fMRI signal. For eachsubject, motion correction was performed using 3D imagerealignment with the AFNI program 3dvolreg function, whichuses a weighted least squares rigid-body registration algorithm.Each subjects fMRI datawas registered to theMNI152 standardtemplate by using FSLs linear registration algorithm (FLIRT).For each subject, the data were spatially smoothed using aGaussian kernel of Full width at half maximum (FWHM) 6mm.

Functional connectivity analysis

Prior to performing functional connectivity analysis, severalprocessing steps were used to conduct the fMRI data for

Fig. 1 Experiment design. a Task design order: pre resting-state andattention task. For rest conditions, subjects were instructed to lie still,relaxed, and with eyes close. For the cognitive task condition, subjectsperformed attention task. bAttention task design: The prime stimuli werea square, diamond, or star. The primes were followed by an instructioncue (square or diamond) presented at the center of the screen. If theinstruction cue shape matched the prime stimuli, this condition would

be congruent, if the instruction cue had the opposite shape as the primestimuli, it would be incongruent; or, if the prime stimuli shape is astar, it would be neutral. The participants were instructed to pressthe contralateral button if they saw a small central square cue.Alternatively, if they saw a small central diamond cue, participantswere required to press the ipsilateral button

http://afni.nimh.nih.gov/afni/http://afni.nimh.nih.gov/afni/http://www.fmrib.ox.ac.uk/fsl/http://www.fmrib.ox.ac.uk/fsl/

analysis of voxel-based correlations (Fox et al. 2005). Here,we use temporal band-pass filtering (0.009 Hz < f < 0.08Hz)to reduce the effect of low frequency drift and high frequencyphysiological noise. Several sources of nuisance covariateswere eliminated using linear regression: 1) 6 rigid body mo-tion correction parameters, 2) signal from the white matter andthe signal from a ventricle region of interest. Recently, theissue of whole-brain signal regression has raised challenginginterpretive issues. Further evidence suggests that the BOLDglobal signal, typically removed as a nuisance term in resting-state studies, may have a neural origin. The use of globalsignal regression is still under debate as a preprocessing stepin resting-state fMRI analysis, and its use is not universallyrecommended (Murphy et al. 2009; Scholvinck et al. 2010).Here, we do not remove the global whole brain signal.

In the present study, we defined the DMN regions of inter-est (ROIs) for functional connectivity analysis according toprevious studies (Chikazoe et al. 2007; Corbetta and Shulman2002; Fair et al. 2008; Konishi et al. 2002; Van Dijk et al.2010). For each participant resting-state fMRI data, a seed-

based correlation analysis was calculated by extracting theBOLD time course from the posterior cingular cortex (PCC).The seed was constructed by forming an 8 mm sphere

Table 1 Definition of DMN seed regions

Region Abbreviation MNI coordinates

X Y Z

Posterior cingulate cortex PCC 0 53 26Dorsal medial prefrontal cortex dmPFC 3 55 22Ventral medial prefrontal cortex vmPFC 3 59 7Left parahippocampal gyus LPHG 24 33 27Right parahippocampal gyrus RPHG 30 33 27Left lateral parietal cortex LLP 52 69 26Right lateral parietal cortex RLP 48 67 36Left superior frontal cortex LSupF 21 32 47Right superior frontal cortex RSupF 12 44 48

Left inferior temporal cortex LITC 61 17 30Right inferior temporal cortex RITC 61 5 25

Fig. 2 Group functional connectivity mapping of the posterior cingulate cortex during rest (cluster-level FWE-corrected p

centered at foci as defined by MNI space (0,53,26) (An-drews-Hanna et al. 2007; Van Dijk et al. 2010). Then, for eachsubject, we computed the correlation coefficient between thattime course and the time course from all other brain regions.To test for significant connectivity changes in an individualsubject, temporal correlation coefficients relative to PCC wereconverted to z-scores by using Fishers r-to-z transformation.For group-level statistical significance analysis, we computeda one-sample t-test to determine the regions showing signifi-cant functional connectivity with PCC. Finally, group based z-score maps were corrected for multiple comparisons usingFWE-corrected for peak voxels; the corrected threshold wasset at P

to improve the normality of the correlation coefficients.Then, we set the negative functional connectivity to 0.Wefurther use the weighted graph method to calculate thenodal degree topological metrics of DMN. Then, we char-acterized the static nodal degree topological metrics ofDMN by using a weighted complex network analysismethod based on BCT Matlab toolbox.

2) Dynamic DMN complex analysis: first, the mean timeseries for each DMN ROI was then calculated by takingthe mean of the voxel time series with each region. Thedynamic pearsons correlation coefficients were comput-ed between all pair of brain regions time series for eachsubject based on the different sliding window (1 min and2mins) and sliding within a step of one TR, then the 1111 dynamic correlation matrix for each subject is obtain-ed. For further statistical analysis, a Fishers r-to-z trans-formation is applied to improve the normality of the cor-relation coefficients. Then, we set the negative functionalconnectivity to 0. We further used the weighted graphmethod to calculate the nodal degree topological metricsof DMN. The dynamic correlation matrix for each subjectand each sliding window was then obtained base onabove analysis. Then, we characterized the dynamic de-gree topological metrics of DMN for each sliding windowby using a weighted complex network analysis methodbased on BCT Matlab toolbox.

Statistical analysis

To assess task performance differences between reactiontimes, we performed a paired t-test between each condition.To assess the relation between DMN degree topological prop-erties and behavior we used a robust regression approach(Street et al. 1988) against unrepresentative outliers in thedata, as implemented using robust-fit function of the Matlablanguage. Such robust regression methods have been pro-posed for analysis of brain-behavioral associations (Marchantand Driver 2013; van den Bos et al. 2013; Yan et al. 2014).

Results

Behavioral results

Figure 4 shows the mean reaction time for all subjects for thetask run sessions for ipsilateral trials including congruent, neu-tral and incongruent conditions. To assess task performancedifferences between reaction times, we performed a paired t-test between each condition. A paired t-test indicated that re-sponses were reliably slower in the incongruent ipsilateralcondition than the neutral ipsilateral condition (t(13) =3.5931, p=0.003). The congruent ipsilateral condition wasresponded to more quickly than to the incongruent ipsilateral

Fig. 4 Behavioral Results: Mean RTs (s.e.m.) across conditions forincongruent ipsilateral, neutral ipsilateral, and congruent ipsilateralcondition. A paired t-test indicated that responses were reliably slowerin the incongruent ipsilateral condition than the neutral ipsilateral

condition (t(13)=3.5931, p=0.003). The congruent ipsilateralcondition were responded to more quickly than incongruent ipsilateralcondition (t(13)=2.65, p=0.02). Note: CongIpsi-congruent = ipsilateral;NeuIpsi-neutral = ipsilateral; IncIpsi- incongruent = ipsilateral


condition (t(13) = 2.65, p=0.02). There were no significantdifferences found for contralateral trials including congruent,neutral, and incongruent conditions (p>0.05).

DMN functional connectivity mapping

Figure 2 shows the group functional connectivity z-map of thePCC seed after correcting for multiple comparisons usingFWE-corrected for peak voxels; the corrected threshold wasset at P0.05).

We observed a robust negative correlation between PCCdegree and reaction time, suggesting that higher PCC nodaldegree is associated with shorter reaction time. In addition, wefurther analyzed the relationship between task performanceand nodal degree in two core DMN regions, the vmPFC anddmPFC (see Fig. 6). In contrast, for these regions, we did notfind significant correlations between node degree and reaction

Fig. 5 Relationship between PCC nodal topological metric andbehavioral performance during attention task. Scatterplots of theassociation between task reaction time and PCC mean degree values. ashows the association between RT and PCC mean degree values forcongruent ipsilateral (p=0.028, R2=0.36) and congruent contralateral

(p=0.057, R2=0.28); b shows the association between RT and PCCmean degree values for neutral ipsilateral (p=0.08, R2=0.26) andneutral contralateral (p=0.04, R2=0.32). c shows the associationbetween RT and PCC mean degree values for incongruent ipsilateral(p=0.04, R2=0.34) and incongruent contralateral (p=0.02, R2=0.37)


time. The other DMN sub region degree also did not associ-ated with task performance (Supplementary Table S1).

Dynamic resting-state PCC nodal degree and behavioralperformance

To explore the underlying relationship between dynamicDMN nodal topology and behavioral performance, we corre-lated, in individual subjects, dynamic nodal level topologicalproperties and the attention task reaction time. The effect ofwindow length on DMN topology is significant, the shorterwindow lengths measure for dynamic FC normally usedshorter sliding window length (60 s) (Allen et al. 2014; Changand Glover 2010; Handwerker et al. 2012). Therefore, wefocus on the shorter sliding window length (1 min) dynamicsanalysis. Themean and variance of time-evolving PCC degreeacross different subjects is illustrated in Fig. 7a. Figure 7billustrates the association between RTandmean PCC dynamicdegree values for incongruent ipsilateral (p=0.048, R2=0.34)and incongruent contralateral (p=0.04, R2=0.36) conditions.In order to fully extract the addition information provided by

the dynamic measure, we further investigate the relationshipbetween RT and variance of PCC dynamic degree. Figure 7cillustrates the association between RT and variance of PCCdynamic degree for incongruent ipsilateral (p=0.048,R2=0.13) and incongruent contralateral (p=0.004, R2=0.1)conditions.

Here, we found the dynamic mean values of PCC nodaldegree negative correlated with RT. Higher degree is consis-tent with faster RT. Specifically, the variance of dynamic PCCnodal degree shows postively correlated with RT. However,we did not observe the significant correlation between PCCdynamic degree and RT within other conditions (congruentipsilateral, congruent contralateral, neutral contralateral, andneutral ipsilateral) (Supplementary Fig. S1).

Next, we focus on the longer sliding window length(2 mins) dynamics analysis. The mean and variance of time-evolving PCC degree across different subjects is illustrated inFig. 8a. Figure 8b illustrates the association between RT andaverage values of PCC dynamic degree for incongruent ipsi-lateral (p=0.03, R2=0.35) and incongruent contralateral(p=0.02, R2=0.4). Figure 8c illustrates the association

Fig. 6 Relationship between brainnetwork metrics and behavioral performance. Scatterplots of the association between reaction time and dmPFC andvnPFC mean degree values. No significant correlation between them was observed (p>0.05)


between RT and variance of PCC dynamic degree for incon-gruent ipsilateral (p=0.03, R2=0.28) and incongruent contra-lateral (p=0.01, R2=0.3) conditions. No significant correla-tion was found within other conditions (SupplementaryFig. S2). In contrast, for other DMN region, we did not findstable and significant correlation between dynamic nodaldegree and reaction time during time-sliding window analysis(Supplementary Table S2S5).

Discussion

The resting-state brain network is a complex dynamic networkin which information is continuously processed andtransported between spatially distributed but functionallylinked sub-networks or regions. However, the function of in-trinsic resting-state DMN activity remains poorly understoodwithout being able to show a clear-cut brain-behavior relation-ship. How this resting-state DMN organization is related tocognitive behavioral performance remains an open question.To address this question, we investigated the relationship

between DMN nodal topological properties and behavioralperformance. We found that PCC nodal degree significantlycorrelated with task reaction time. Our results suggest thatbrain network nodal topological properties, in fact, do link tobrain behavioral performance.

Static PCC nodal topology and behavioral performance

Recent studies suggest that whole network topological metricsare not sufficient to characterize cognitive behavioral perfor-mance (Achard et al. 2012). The network topology can also beestimated from each individual node in the brain network, thusallowing a more refined analysis of changes in brain functionassociated with behavioral performance. More importantly,the local topological features, such as DMN PCC node, arelikely to be more important in understanding human cognitivebehavior (Leech and Sharp 2013; Power et al. 2013). Wecalculated the correlation between nodal topology and behav-ioral response times.We found that PCC nodal degree shows asignificant negative correlation with task reaction times in the

Fig. 7 The relationship between dynamic resting-state DMN PCCdegree and task performance based on 1 min sliding window analysis. aThe mean dynamic time-evolving PCC degree topological propertieswithin 14 subjects across the whole resting state period. b The associationbetween RT and PCC dynamic degree values for incongruent ipsilateral

(p=0.048, R2=0.34) and incongruent contralateral (p=0.04, R2=0.36). cThe association between RT and variance of PCC dynamic degree forincongruent ipsilateral (p=0.048, R2=0.13) and incongruent contralateral(p=0.004, R2=0.1). Shaded regions indicate standard error


incongruent ipsilateral and incongruent contralateralconditions.

As expected, we found the degree of the PCC node predictstask performance in an attention task. The PCC nodemay playan important role to support the higher communication ratewithin the DMN (Buckner et al. 2008; Utevsky et al. 2014).Evidence from functional studies of brain DMN developmenthave demonstrated age-related increases in connectionstrength within the DMN and in functional integration of theinitial subsystems into a more coherent network (Supekaret al. 2009; Supekar et al. 2010). Furthermore, DTI-basedstudies show that the PCC is a hub region that widely connectswith other DMN regions (Gong et al. 2009). Our results showPCC-degree negatively correlated with the incongruent con-dition reaction time. A variety of studies have proved that thehigh interference during the incongruent condition is associ-ated with a longer RT (Kucyi and Davis 2015; Sharp et al.2011).

In line with previous studies, processing efficiency duringinterference related tasks is correlated with degree of DMNactivity (De Pisapia et al. 2012; Weissman et al. 2006). This

longer reaction time during the incongruent condition, and inturn, the production of less PCC deactivation (Weissman et al.2006). Accumulating evidence indicates efficient behaviorwas associated with increased PCC deactivation or its func-tional connectivity (Leech et al. 2011; Leech and Sharp 2013).Further studies show that greater PCC deactivation is associ-ated with a higher amount of focused attention resource com-pared with a low-level difficult task condition (De Pisapiaet al. 2012; Mantini and Vanduffel 2013; Marchant and Driver2013). Our results show higher PCC nodal degree is associat-ed with faster reaction time.

One possible interpretation of these results is that strongeranatomical and functional connectivity probably permits moreefficient communication among DMN regions for the supportof incongruent task performance. Our results may indicate thathigher PCC node degree enables efficient information flowacross the whole DMN and, thus, facilitates integrative infor-mation processing speed during the incongruent conditioncompared with the congruent condition. Increased PCC deac-tivation in the DMN typically indexes more effortful process-ing and it is associated with increased task difficulty

Fig. 8 The relationship between dynamic resting-state DMN PCC de-gree and task performance based on 2min sliding window analysis. a Themean dynamic time-evolving PCC degree topological properties within14 subjects across the whole resting state period. b The associationbetween RT and PCC dynamic degree values for incongruent

ipsilateral (p=0.03, R2=0.35) and incongruent contralateral (p=0.02,R2=0.4). c The association between RT and variance of PCC dynamicdegree for incongruent ipsilateral (p=0.03, R2=0.28) and incongruentcontralateral (p=0.01, R2=0.3). Shaded regions indicate standard error


(McKiernan et al. 2003). Thus, we can expect that PCC playless effortful processing during less task stimuli condition,such as congruent and neutral conditions. For example, previ-ous studies have suggested that the level of PCC deactivationduring incongruent condition is significantly higher comparedto neutral and congruent conditions (De Pisapia et al. 2012).Furthermore, PCC functional connectivity is correlated withthe task performance of cognitive processing in various cog-nitive tasks (Kucyi and Davis 2014; Leech and Sharp 2013;Liang et al. 2013). For example, recent studies show that in-creased functional connectivity of the PCCwithin the DMN iscorrelated with faster reaction times to external task stimuli inboth healthy and traumatic brain injury subjects (Bonnelleet al. 2012; Sharp et al. 2014). Our results further demonstratethat the degree of functional integration within the PCC nodecould influence ongoing cognitive behavior.

More importantly, recent studies have reported the PCCnode consistently exhibits the highest cerebral blood flow(CBF) and cerebral metabolism rate for oxygen (CMRO2)during the resting-state (Pfefferbaum et al. 2011; Raichle andGusnard 2002; Raichle et al. 2001), and baseline CBF may berelevant to individual deactivation variability during task per-formance. Our results highlight the important link of PCCnodal topology and brain behavior. Further, structural connec-tivity studies indicate that brain spontaneous fluctuations arethought to stem from anatomical connectivity (Greicius et al.2009; van den Heuvel et al. 2008). For example, structuralconnectivity brain studies have reported that fractional anisot-ropy of the posterior cingulum bundle correlated with reactiontime task performance (Tam et al. 2014). These studies pro-vide further evidence that brain network activity is coordi-nated by the PCC and relies on the structural integrity of itswhite matter connections.

Taken together, previous functional connectivity and struc-tural connectivity studies both support that PCC is the DMNcore region, which facilitates information integration withinthe entire DMN and, thus, aids in ongoing behavioralperformance.

Dynamic PCC nodal topology and behavioralperformance

Brain network functional connectivity is not a static processover time (Allen et al. 2014; Chang and Glover 2010; Chenet al. 2013; Cocchi et al. 2011; Handwerker et al. 2012;Hutchison et al. 2013a; Hutchison et al. 2013b; Kucyi et al.2013; Lee et al. 2013; Leonardi et al. 2013; Tagliazucchi andLaufs 2014; Tagliazucchi et al. 2012; Thompson et al. 2013).As dynamic brain network functional connectivity may carryimportant, cognitive and clinically relevant information (Allenet al. 2014; Hellyer et al. 2014; Ma et al. 2014; Mayhew et al.2013), in order to better understand dynamic brain networkfunctional connectivity and cognition over time, it is

necessary to investigate the relationship between dynamicfunctional connectivity and brain behavior. Emerging evi-dence suggests that dynamic brain network or functional con-nectivity may index changes in neural activity patterns under-lying critical aspects of cognition and clinically relevant infor-mation (Allen et al. 2014; Hutchison et al. 2013a; Hutchisonet al. 2013b). Growing interest in these dynamic networks as anew approach with which to understand information process-ing across different brain states (task or resting-state), braindevelopment, and disease states, should show a complex anddynamic pattern associated with brain cognition.

PCC is a key cortical region within the DMN system that isinvolved in several higher brain cognitive functions (Buckneret al. 2008; Leech and Sharp 2013). To better understand howPCC activity dynamic changes in resting-state and its associ-ation with ongoing brain cognitive function, one very usefuland simple approach is to characterize the dynamic PCC nodeproperties during different brain mental states. Dynamic PCCnode topology properties of resting-state function brain activ-ity can account for individual task performance differences inseveral cognitive functions. Dynamic functional connectivityis associated with internal mental state during rest or taskstates (Fornito et al. 2012). Recently, characterization of thedynamic resting-state network properties has received greatinterest for the study of DMN neural mechanisms (Changand Glover 2010). However, what remains unclear is the keyissue of how the DMN organization dynamic configurationlinks to task performance. Here, we conducted a robust regres-sion, between dynamic PCC node degree and attention cogni-tive task performance and we observe that DMN PCC dynam-ic nodal topology property changes are associated with taskperformance. We found that mean dynamic degree robustlynegatively correlated with RT across incongruent ipsilateraland incongruent contralateral. One possible explain is thathigher PCC degree indicate more efficient information trans-fer performance within brain for support higher difficult on-going task (Liang et al. 2013). We also found the variancevalues of dynamic degree positively correlated with RTacross incongruent ipsilateral and incongruent contralater-al. Our results show that PCC nodal topological propertiesvariability is linked to task performance. In particular,emerging evidence suggests that brain region variabilityis correlated with task performance (Calhoun et al. 2014;Kucyi and Davis 2015). Furthermore, one previous studyfound that dynamic DMN FC variability reflects the degreeof ongoing mind-wandering (Kucyi and Davis 2014). TheDMN sub-regions show decreasing variability activity forsupporting external task stimuli (Garrett et al. 2011; Lianget al. 2013). This evidence indicates that higher mind-wandering ratio would link to worse task performance dur-ing task stimuli (Kucyi and Davis 2014). Conversely, low-er mind-wandering ratio would link to better task perfor-mance. Further work is needed to uncover the potential


relationship between dynamic mind-wandering ratio, PCCnodal topology and task performance.

Our results suggest the important role that the dynamictemporal-topological structure of the PCC hub within theDMN may play in brain adaptive information processingand cognitive integration. Furthermore, the topological struc-ture of PCC node degree shows dynamic change over time.The observed dynamic topological structure of PCC couldrepresent a brain microstate temporal signature for supportinginternal and ongoing different brain state cognitive processes(Kucyi and Davis 2014; Kucyi and Davis 2015); especially,the dynamic topological properties of PCC correlated withtask performance during an attention task. Our results suggestthat PCC node temporal topological structure dynamicallychanges during different brain resting-state microstates in or-der to support ongoing brain information processing.

In addition, of note, a new finding in our research is thatPCC is the only core region within the DMN, which stablycorrelated with cognitive task performance. The PCC may bethe key node to which information converges allowing forfunctional cooperation within the DMN. Here, our findingsstrongly indicate that the dynamic PCC node communicationstructure within the DMN is associated with cognitive func-tion, which can support a variety of task performance. Moreimportantly, our results suggest that the PCC node plays acritical role for coordinating brain network information com-munication in order to support cognition function.

Limitations

There were a number of limitations in our study. First, in thisstudy, we only use one attention cognitive task. Therefore, itwill be important for future studies to collect both behavioraland multiple cognitive tasks imaging data to understand therelationship between performance in different cognitive tasksand DMN topological metrics. Second, we used traditionalBOLD fMRI for which temporal resolution is not high enoughto capture the underling dynamics information. In our futurework, we intend to study brain network dynamics using fasterneuroimaging techniques such as electroencephalography(EEG), magnetoencephalography (MEG) and multi-band im-aging. More importantly, the combination of fMRI-EEGmethods can provide better understanding of the dynamicfunctional connectivity in humans across multiple cognitivetasks.

Conclusions

In conclusion, this study shows that there is a relationshipbetween static, intrinsic resting-state DMN brain network ac-tivity and cognitive behavior. Our results show that theresting-state PCC nodal degree is significantly associated with

task performance. In addition, the dynamic PCC nodal degreeis also related to task performance. Our findings suggest theimportant link between both static and dynamic PCC regiontemporal topological properties and underlying brain cogni-tion and adaptive information processing function. Futurestudies of PCC nodal topology properties will continue to behighly important for greater understanding of brain functionand brain disorders.

Acknowledgments Government of the Provincia Autonoma di Trento,Italy, Project PAT Post-doc 2006; Fondazione Cassa di Risparmio diTrento e Rovereto; and University of Trento, Italy. This work was sup-ported by the National Natural Science Foundation of China (61473221,61462031, 61262034, 31271061), by Doctoral Fund of Ministry of Edu-cation of China (20120201120071), by the Young Scientist Foundation ofJiangxi Province (20122BCB23017), by the Project of the EducationDepartment of Jiangxi Province (KJLD14031), by the FundamentalResearch Funds for the Central Universities of China, by the Programfor New Century Excellent Talents in University of China (NCET-12-0557 to XW).

Competing interests Pan Lin, Yong Yang, Jorge Jovicich, Nicola DePisapia, Xiang Wang, Chun S. Zuo, and James Jonathan Levitt declarethat they have no conflicts of interest.

Informed consent All procedures followed were in accordance withthe ethical standards of the responsible committee on human experimen-tation (institutional and national) and with the Helsinki Declaration of1975, and the applicable revisions at the time of the investigation.Informed consent was obtained from all patients for being included inthe study.

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Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performanceAbstractIntroductionMaterials and methodsSubjectsExperiment designMRI data acquisitionfMRI analysisData preprocessingFunctional connectivity analysisStatic and dynamic graph theory analysisStatistical analysisResultsBehavioral resultsDMN functional connectivity mappingStatic resting-state PCC nodal degree and behavioral performanceDynamic resting-state PCC nodal degree and behavioral performanceDiscussionStatic PCC nodal topology and behavioral performanceDynamic PCC nodal topology and behavioral performanceLimitationsConclusionsReferences

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Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performance