brain research 1562 (2014) 87–99

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Research Report

Altered default mode and fronto-parietal network subsystems in patients with schizophrenia and their unaffected siblings Xiao Changa,1, Hui Shenb,1, Lubin Wangb, Zhening Liuc, Wei Xina, Dewen Hub,n, Danmin Miaoa,n a

Department of Psychology, Fourth Military Medical University, Xi’an, Shannxi 710032, China College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China c Mental Health Institute, Second Xiangya Hospital, Central South University, Changsha, Hunan, China b

art i cle i nfo

ab st rac t

Article history:

The complex symptoms of schizophrenia have recently been linked to disrupted neural

Accepted 17 March 2014

circuits and corresponding malfunction of two higher-order intrinsic brain networks: The

Available online 25 March 2014

default mode network (DMN) and the fronto-parietal network (FPN). These networks are

Keywords:

both functionally heterogeneous and consist of multiple subsystems. However, the extent

FMRI

to which these subsystems make differential contributions to disorder symptoms and

Schizophrenia

to what degree such abnormalities occur in unaffected siblings have yet to be clarified.

Genetic risk

We used resting-state functional MRI (rs-fMRI) to examine group differences in intra- and

Subsystem

inter-connectivity of subsystems within the two neural networks, across a sample of

Default mode network Fronto-parietal network

patients with schizophrenia (n¼ 24), their unaffected siblings (n ¼25), and healthy controls (n ¼22). We used group independent component analysis (gICA) to identify four network subsystems, including anterior and posterior portions of the DMN (aDMN, pDMN) as well as left- and right-lateralized portions of the FPN (lFPN, rFPN). Intra-connectivity is defined as neural coherence within a subsystem whereas inter-connectivity refers to functional connectivity between subsystems. In terms of intra-connectivity, patients and siblings shared dysconnection within the aDMN and two FPN subsystems, while both groups preserved connectivity within the pDMN. In terms of inter-connectivity, all groups exhibited positive connections between FPN and DMN subsystems, with patients having even stronger interaction between rFPN and aDMN than the controls, a feature that may underlie their psychotic symptoms. Our results implicate that DMN subsystems exhibit different liabilities to the disease risk while FPN subsystems demonstrate distinct interconnectivity alterations. These dissociating manners between network subsystems explicitly suggest their differentiating roles to the disease susceptibility and manifestation. & 2014 Elsevier B.V. All rights reserved.

n

Corresponding authors. E-mail addresses: [email protected] (D. Hu), [email protected] (D. Miao). 1 These authors contributed equally to this paper.

http://dx.doi.org/10.1016/j.brainres.2014.03.024 0006-8993/& 2014 Elsevier B.V. All rights reserved.

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1.

brain research 1562 (2014) 87–99

Introduction

Schizophrenia is a complicated syndrome associated with the malfunction of multiple large-scale brain networks (Bullmore et al., 1997; Friston, 1999; Selemon and Goldman-Rakic, 1999; Stephan et al., 2009). Some of these brain networks appear to be crucial for both daily functioning and disease pathology (Broyd et al., 2009; Menon, 2011; Khadka et al., 2013). In particular, two higher-order functional networks have received particular attention for their potential relevance to schizophrenia (Williamson, 2007; Menon, 2011). The first of these is the default mode network (DMN), with key nodes in the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), precuneus cortex and bilateral angular gyri (AG). It often shows deactivation during externally attention demanding tasks while increases activity during unconstrained thought (Binder et al., 1999; Mason et al., 2007; McKiernan et al., 2006), introspection (Svoboda et al., 2006), and self-related processing (Lin et al., 2011). The other network is fronto-parietal network (FPN), mainly encompassing bilateral dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL). This network is often evoked by various cognitive tasks (Fox et al., 2005; Dosenbach et al., 2006, 2007; Fassbender et al., 2006; Vincent et al., 2008; Cole et al., 2013). Although earlier investigations suggested a striking antagonistic relationship between the two networks, more recent studies have found evidence for their flexible, dynamic engagement according to the task requirement (Fornito et al., 2012; De Pisapia et al., 2012; Cocchi et al., 2013; Spreng et al., 2013). DMN and FPN abnormalities have been suggested to underlie many clinical features of schizophrenia (Greicius et al., 2003; Williamson, 2007; Buckner et al., 2008; Broyd et al., 2009). For example, the DMN is thought to be engaged in self-relevant internal information processing (Raichle and Snyder, 2007). Failure of this function could lead an individual to mistakenly recognize internally generated thoughts as exogenous (Frith, 1995). In addition, multiple executive functions subserved by the FPN are impaired even years before illness onset, including working memory, sustained attention, and verbal declarative memory (Torrey, 2007; Woo et al., 2008; Yildiz et al., 2011). Considering the crucial information process and the potential pathology involved in these two networks, we are motivated to investigate intrinsic connectivity within and between them. Apart from numerous investigations examining the largescale brain network as a whole, some studies have identified functional differentiation within already defined networks, particularly the DMN and the FPN, which have been characterized as heterogeneous systems (Andrews-Hanna et al., 2010; Buckner et al., 2008; Hassabis et al., 2007; Seeley et al., 2007; Uddin et al., 2009, 2010; Leech et al., 2011). For example, areas in anterior portion of DMN (aDMN) are involved in mentalizing (Gilbert et al., 2006), social cognition (Blakemore, 2008), and self-referential processing (D'Argembeau et al., 2005), whereas posterior DMN (pDMN) regions are implicated in episodic memory retrieval (Greicius et al., 2003) and gathering environmental information (Raichle and Snyder, 2007). Leech et al. (2011) reported that the PCC (a central hub in DMN) demonstrates dissociating functions between its ventral and dorsal areas. The dorsal PCC may serve as an interface between attention competitive networks. Investigations also suggest different contributions from network subsystems to the pathology of schizophrenia, with the aDMN

being implicated in particular. Dost Öngür et al. (2010) found that schizophrenic and bipolar patients shared reduced DMN connectivity in MPFC during a resting state. Camchong et al. (2011) reported that functional and anatomical connectivity abnormalities converge on aDMN regions in patients with schizophrenia. Meanwhile, functional dysconnection of this area showed correlations with patients’ clinical symptom and cognitive ability (attention and concentration). Dysconnection in FPN also plays an important role in the neural mechanism of schizophrenia (Tu et al., 2013; Roiser et al., 2013; Anticevic et al., 2012). Within this network, patients showed decreased separation between two lateralized FPN subsystems, with the right portions of FPN (rFPN) laterality index correlating with disorganization symptom severity (Rotarska-Jagiela et al., 2010). Evidence is beginning to imply that both DMN and FPN subsystems play differential roles in schizophrenia symptomatology, suggesting that it would be helpful to examine functional connectivity at the subsystem level. To further elucidate the degree to which connectivity alterations may reflect the influence of disease risk or illness manifestation, we included patients’ unaffected siblings, who share half of susceptibility genes with the patients. Although siblings of patients with schizophrenia show largely preserved abilities in sensory, motor, emotional, and social interaction domains, studies have detected mild cognitive deficits in these individuals (Sitskoorn et al., 2004). Imaging studies suggested that disruption in specific areas of FPN may underlie these behavior anomalies. In a review of fMRI studies about patients’ relatives, MacDonald et al. (2009) found that the most consistent task-based activation abnormities are in right ventral prefrontal cortex and right parietal cortex. Rasetti et al. (2011) reported a susceptible gene (ZNF804A) modulate right DLPFC coupling with the hippocampus in siblings and patients. Abnormalities in regions of DMN have also been identified in patients’ relatives. Two studies reported abruptions in the aDMN areas while one study found both anterior and posterior DMN anomalies (Whalley et al., 2005; Whitfield-Gabrieli et al., 2009; Jang et al., 2011). To comprehensively explore specific contribution from DMN and FPN areas to the disease pathology, further investigations are still needed. The present study investigated resting-state functional connectivity within and between DMN and FPN subsystems, in patients with schizophrenia, their unaffected siblings, and healthy controls. Since there is currently no consensus as to the exact number of subsystems within DMN and FPN, we used group independent component analysis (gICA), a multivariate data-driven method, instead of the seed-based method to construct large-scale intrinsic networks (Calhoun et al., 2001; Jafri et al., 2008). A component derived from the gICA consists of voxels sharing coherent neural activity, representing a functional entity (i.e. a network subsystem in our study). Voxel-wise z-value reflects intra-connectivity strength of individual voxel to that subsystem (Sorg et al., 2013). We first assessed intra-connectivity differences by comparing spatial extend and intensity of each subsystem map across the three groups. Then we defined interconnectivity as Pearson correlation between subsystem timecourses. Within- and between-group statistical analyses were performed on all inter-connections between subsystems. Taken together, we aimed to comprehensively examine intra- and inter-connectivity alterations in DMN and FPN at the subsystem level, in both patients and their unaffected siblings.

brain research 1562 (2014) 87–99

2.

Results

2.1.

Component visualization

identifying two or more subsystems (Damoiseaux et al., 2006; Kim et al., 2009; Uddin et al., 2009; White et al., 2010).

2.2. Fig. 1 illustrates thresholded one-sample t-maps of the four subsystems in the control group. The patients and their siblings exhibited subsystem spatial maps similar to those of the controls, which are in line with prior literature (Greicius et al., 2003; Fransson, 2006; Dosenbach et al., 2006, 2007; Buckner et al., 2008; Menon, 2001, 2011). The left lateralized FPN (lFPN) encompassed a spatial pattern predominantly composed of left hemisphere DLPFC (BA8, 9, 46) and IPL (BA39, 40). Additional regions include ipsilateral middle temporal gyrus (MTG) and small portions of contralateral frontal and parietal areas. The rFPN mainly encompasses corresponding areas in contralateral hemisphere. Though these two subsystems are basically anatomical mirrors of one another, they appear to be associated with different behavioral domains (Smith et al., 2009). The aDMN constitutes large portions of the MPFC, ACC (BA10, 24, 32) and also a very small proportion of PCC/PCu and bilateral AG, which comprises main areas of the pDMN. These two subsystems consist of the canonical DMN, which was first described by Raichle et al. (2001) and Greicius et al. (2003), an important network that consistently deactivates during external goal-directed tasks. Though the DMN was originally regarded as one single functional entity, recent studies have used the gICA

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Group differences for intra-connectivity

Group difference for every subsystem was examined with one-way ANOVA model (po0.001, uncorrected). Main effects for ANOVAs were significant in three subsystems: the lFPN, rFPN and aDMN. Then post hoc analyses were conducted in these subsystems within areas showing significant main effect (voxel-vise FDR corrected, po0.05). Results were presented in Fig. 2 and Table 2. For the lFPN, group differences between the controls and siblings were found for the left PCu and right IPL, with higher z-scores for the controls. The patients also demonstrated lower connectivity in the left PCu and right IPL, as was the case for the siblings. However, compared to the controls, patients had stronger connections in the right lingual gyrus, left parahippocampal cortex, and left inferior frontal gyrus (IFG). Z-scores for the left IFG were also higher in the patients than in the siblings. For the rFPN, a cluster in the left superior frontal gyrus (SFG) was weaker in the siblings than in the controls. The patients exhibited reduced connectivity in the right cerebellum anterior lobe and the left SFG comparing to the controls. No significant differences were found between the patients and their siblings for this subsystem.

Fig. 1 – The spatial maps of the four subsystems derived from gICA during rest: (A) left fronto-parietal network (lFPN) (B) right fronto-parietal network (rFPN) (C) anterior default mode network (aDMN) (D) posterior default mode network (pDMN). Results were from the second-level within-group analyses in the control subjects (po0.05, FDR corrected, cluster size ¼20). Data were displayed on the lateral, medial, and dorsal surfaces of both hemispheres on the ICBM-152 template using the BrainNet Viewer toolbox (http://www.nitrc.org/projects/bnv/).

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Fig. 2 – Second-level between-group comparisons within the subsystem map of lFPN, rFPN and aDMN (voxel-vise FDR corrected, po0.05) after head motion regression. The three columns represent comparisons between controls and siblings, between controls and patients, as well as between siblings and patients, respectively. Significant clusters in the comparisons: Con4Sch, Con4Sb, Sb4Sch were shown in red. Significant clusters in the comparisons: ConoSch, ConoSb were shown in blue. Abbreviations: Con, controls; Sch, patients with schizophrenia; Sb, siblings. Table 1 – Demographic and clinical characteristics of the schizophrenic patients (n ¼25), unaffected siblings (n ¼ 25), and healthy controls (n ¼25). Characteristics

Patients

Siblings

Controls

Age Sex (male/female) Education (years) Duration of illness (months)

25.3676.32 13/12 12.2872.57 18.32715.84

25.5676.78 15/10 12.4872.52 –

25.4875.45 14/11 13.6872.85 –

Positive and negative syndrome scale (PANSS) Total score Positive scale score Negative scale score General psychopathology

77.59711.56 18.7374.25 21.3975.43 37.4776.71

– – – –

– – – –

Groups were matched for age, sex, and education.

For the aDMN, both the patients and siblings demonstrated stronger connectivity in the left MTG than in the controls. Conversely, no significant differences were found within the pDMN, indicating that both patients and their siblings showed preserved connectivity in this subsystem.

2.3.

Inter-connectivity patterns and group differences

Second-level within-group analyses revealed subsystem interaction patterns in the three groups (po0.05, FDR corrected) (Fig. 3). Correlation coefficient r and p-values were provided in Table 3. It was clear that the controls and siblings demonstrate

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Table 2 – Coordinates of post hoc between-group comparisons within the three subsystem maps. Comparison group

aDMN Con vs. Sb Con vs. Sch lFPN Con vs. Sb Con vs. Sch

Sb vs. Sch rFPN Con vs. Sb Con vs. Sch

Anatomical regions

Cluster size

Peak intensitya

Peak coordinates(MNI) x

y

z

Middle temporal gyrus (L) Middle temporal gyrus (L)

19 21

3.28 4.36

58 56

 54 2

4 6

Precuneus Inferior parietal lobule (R) Inferior parietal lobule (R) Precuneus Inferior frontal gyrus (L) Parahippocampa gyrus (L) Lingual gyrus (R) Inferior frontal gyrus (L)

32 22 134 35 31 57 21 20

3.53 3.78 4.82 4.24 4.01 4.79 4.32 3.59

0 40 36 2 42 12 16 42

 72  54  60  74 34  40  92 34

46 54 60 46 6 2  10 2

31 38 23

4.26 4.12 4.00

8 28 4

34  36 24

54  26 58

Superior frontal gyrus (L) Cerebellum anterior lobe(R) Superior frontal gyrus (L)

Abbreviations: Con, control subjects; Sch, schizophrenic patients; Sb, siblings; L, left; R, right; aDMN, anterior default mode network; pDMN, posterior default mode network; lFPN, left fronto-parietal network; rFPN, right fronto-parietal network. a Positive number – voxel in the comparisons: Con4Sch, Con4Sb, Sb4Sch; negative number – voxel in the comparisons: ConoSch,ConoSb.

a similar connectivity pattern, whereas the patients showed a somewhat different result. Connections within the originally defined networks (i.e. between the aDMN and the pDMN, and between the lFPN and the rFPN) were of course observed for all three groups. What was unexpected was that all three groups exhibited consistent positive links between the rFPN and the aDMN, as well as between the lFPN and the pDMN. The patient group was unique in that they demonstrated significant positive correlations across all possible interactions, suggestive of enhanced functional interdependence between subsystems. One-way ANOVA analyses were performed on the four positive connections shared by the three groups to detect any significant differences. F- and p-values for these results were listed in Table 4. Among the four connections, only the interaction between the rFPN and aDMN exhibited a significant group difference (F¼ 4.012, p¼ 0.027). Further post hoc analyses revealed that patients had stronger connectivity for this interaction than controls (p¼ 0.033, FDR corrected). The siblings did not differ greatly from the patients (p ¼0.074, FDR corrected) or the controls (p ¼0.783, FDR corrected) (Fig. 4). The possible pathological relevance of such enhanced rFPN to aDMN connectivity will be discussed below.

3.

Discussion

The present study investigated connectivity alteration patterns in subsystems of the DMN and FPN, in both patients with schizophrenia and their unaffected siblings. By performing the gICA on resting-state functional images, we derived four subsystems across the two neural networks, including the aDMN, pDMN, lFPN and rFPN. In terms of intra-connectivity, patients and siblings shared disrupted aDMN neural coherence while enjoying relatively preserved connectivity in the pDMN. For lFPN

and rFPN intra-connectivity abnormality, there was also an overlap between patients and siblings, with patients exhibiting more severe and widespread aberrances. In terms of interconnectivity, all three groups exhibited positive correlations between aDMN and pDMN, lFPN and rFPN, rFPN and aDMN, and lFPN and pDMN. The patients showed other suprathreshold connections and a significantly stronger link between rFPN and aDMN than that evident for the controls. Taken collectively, these results suggested that aDMN, lFPN and rFPN intraconnectivity dysfunction may be partly related to disease risk. Meanwhile, enhanced connection between the rFPN and aDMN may be relevant to the illness manifestation.

3.1.

DMN subsystem intra-connectivity

Compared to controls, patients with schizophrenia and their unaffected siblings exhibited conspicuous alterations in intraconnectivity involving only the aDMN. The two groups shared enhanced neural coherence in the left MTG within the aDMN map while exhibiting no alterations within the pDMN map. This provides clear evidence for the proposal that identified functional entities such as neural networks can show distinct patterns of abnormal development. Such development may be governed by susceptibility genes. It has been reported that patients with schizophrenia and relatives share abnormal hyperactivity and hyperconnectivity within the MPFC, yet only patients show PCC aberrations (Whitfield-Gabrieli et al., 2009). These researchers suggested that MPFC dysfunction is more relevant to the risk of developing the disorder, while PCC dysfunction is related either to the expression of the disorder or to greater disorder risk. In another set of studies, relatives of patients with schizophrenia had genetically mediated connectivity reductions in a medial prefrontal-thalamic-cerebellar network, which overlaps with portions of the aDMN as identified in our study (Whalley et al., 2004, 2005). A more recent study showed that relatives

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Fig. 3 – Connective pattern between subsystems in three participant groups (inter-connection). Red solid line indicates significant positive correlation in all groups, while red dotted line represents positive connection only shown in patients (po0.05, FDR corrected). Table 3 – r and p value of the second-level within-group analyses for inter-connectivity. Inter-connectivity

aDMN &pDMN lFPN & rFPN rFPN & aDMN lFPN & pDMN rFPN & pDMN lFPN & aDMN

Con

Sb

Sch

r

p

r

p

r

p

0.667 0.645 0.313 0.252 0.096 0.082

o0.001 o0.001 0.008 0.015 0.183 0.204

0.583 0.676 0.341 0.227 0.120 0.131

o0.001 o0.001 0.004 0.026 0.126 0.107

0.688 0.537 0.486 0.239 0.334 0.193

o o o o

0.001 0.001 0.001 0.021 0.005 0.047

Note: Bold numbers indicates significant connections in three groups (po0.05, FDR corrected). The controls and the siblings had significant connection in four subsystem combinations, whereas the patients had all connections passed the threshold, implicating the interactions between subsystems were increased in this group. Abbreviations: Con, control subjects; Sch, schizophrenic patients; Sb, siblings; aDMN, anterior default mode network; pDMN, posterior default mode network; lFPN, left fronto-parietal network; rFPN, right fronto-parietal network.

of schizophrenic and bipolar probands were free from PCC dysfunction, further supporting that this region is irrelevant in terms of disease risk (Khadka et al., 2013). Notably, we did not rule out the possibility of pDMN abnormality in patients, which is evidenced in some studies (Fransson, 2005; Whitfield-Gabrieli

et al., 2009). In an exploratory analysis, we chose a more loose criteria (p¼ 0.005, uncorrected) and directly measured group difference using two-sample t-tests, finding that patients demonstrated pDMN dysfunction while siblings were not significantly different from controls.

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Table 4 – F and p value of one-way ANOVA betweengroup comparisons for inter-connectivity. Subsystem

aDMN F/p

pDMN F/p

lFPN F/p

pDMN lFPN rFPN

1.125/0.426 – 4.012/0.027

0.325/0.637 –

3.173/0.102

Note: Bold number indicates significant group difference in ANOVA analyses (po0.05, FDR corrected). Post hoc t-test revealed significant group difference came from controls and patients comparison (p¼ 0.033, FDR corrected). In other words, significant alteration was only found in the patients between rFPN and aDMN. Considering the connections between rFPN and pDMN as well as lFPN and aDMN were not significant in both controls and siblings, they were not included in the between-group comparisons. Abbreviations: aDMN, anterior default mode network; pDMN, posterior default mode network; lFPN, left fronto-parietal network; rFPN, right fronto-parietal network.

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the study of Whalley et al. (2005), who examined healthy controls, genetic high risk subjects with and without isolated psychotic symptoms. They found functional connectivity between DLPFC and IPL increased successively from controls to high risk subjects without symptoms and to high risk subjects with symptoms. This task-induced connectivity enhancement was interpreted as a compensatory mechanism, supported by studies of healthy controls (Honey et al., 2002) and patients with schizophrenia (Quintana et al., 2003). Repovs and Barch (2012) also found aberrant connectivity between FPN and other networks in both siblings and patients during task, further corroborated FPN alterations in these two groups. Apart from decreased connectivity, patients also showed three clusters (left IFG, left parahippocampal cortex, and right lingual gyrus) that evidenced increased synchronization within the lFPN map. The abnormal connectivity within FPN often leads to a more widespread connectivity pattern, which is evidenced in previous work (Tu et al., 2013). Enhanced connectivity in the left parahippocampal cortex and right lingual gyrus would appear in siblings if a more loose threshold was adopted (po0.005, uncorrected). A DTI study found that patients and their healthy siblings shared white matter disruptions in the left prefrontal cortex and hippocampus regions, where abnormalities showed good convergence across imaging modalities (Hao et al., 2009).

3.3. Positive correlations between DMN and FPN subsystems

Fig. 4 – Connectivity strength (Pearson correlation coefficient r) between rFPN and aDMN in three participant groups. The controls and siblings had comparable r value in this connection, while the patients exhibited significant stronger connection than the controls (po0.05, FDR corrected).

Left MTG connectivity abnormality has been reported by Neil Woodward et al. (2011), who found stronger connections between a cluster in left MTG and DMN seed region (PCC) in patients with schizophrenia. Interestingly, this cluster overlapped with the executive control network of healthy participants, implying that normal segregation between networks may be compromised in schizophrenia.

3.2.

FPN subsystem intra-connectivity

Intra-connections within FPN subsystems were diminished in the patients, which also appeared in their siblings, albeit to a lesser extent. Both groups showed decreased IPL and precuneus coherence within the lFPN map, as well as decreased SFG coherence within the rFPN map. These results suggested that resting-state functional connectivity alterations within FPN may originate from the complex interplay between disease risk and symptom manifestation. Our finding was directly supported by

It is worth noting that correlations between DMN and FPN subsystem activity were positive for all three groups studied. Considering that we did not remove global and other nuisance signals which may lead to pseudo correlations, these positive correlations probably reflect functional cooperation between subsystems in a resting-state. Vincent et al. (2008) proposed a model suggesting the existence of two “rival” networks: the dorsal attention network (or DAN) (including the frontal eye fields and intraparietal sulcus), which supports externally directed cognition, and the DMN, which facilitates internal mental activities (Corbetta and Shulman, 2002; Fox et al., 2006; Hopfinger et al., 2000; Mantini et al., 2007; Ptak and Schnider, 2010). A third network, the FPN, serves as the executive control center, flexibly engaging either the DMN or the DAN in internally versus externally focused goal-directed cognition. This model has been supported by research examining mind wandering (Christoff et al., 2009), mental simulations (Gerlach et al., 2011; Spreng et al., 2010) and creative thinking (Kounios et al., 2006; Subramaniam et al., 2009). The resting-state is closely related to spontaneous mental simulations and mind wandering, and it therefore seems likely that the DMN and FPN networks may be engaged simultaneously during this state. In the important work of Fox et al. (2005), it was found that the DAN demonstrates an antagonistic relationship with the DMN, competing for limited attentional resources. Our findings appear to be broadly consistent with earlier work.

3.4.

Inter-connectivity group differences

Positive connectivity between the rFPN and the aDMN was exhibited in all three groups, with the patients demonstrating

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an even stronger correlation than the controls. The rFPN has been consistently reported to underlie environmental monitoring and intrinsic alertness, representing a network involved in the cognitive control of wakefulness and arousal (Shallice et al., 2008; Sturm et al., 1999; Vallesi and Crescentini, 2011). Portions of the aDMN are frequently involved in self-referential processing (D'Argembeau et al., 2005), such that a positive correlation between these two subsystems might reflect the continuous process of monitoring internal mental states across all participants. The greater connectivity found in our patients may reflect oversensitivity to self-relevant information, potentially causing them to mistakenly interpret ordinary events as having mysterious and worrisome self-referential implications. It has been argued that failures in effective self-monitoring are the source of hallucinatory experiences and delusional preoccupations (Stephan et al., 2009). Detailed analysis of subsystem interaction provides new insight into possible link between malfunction in cognitive process such as selfmonitoring and the underlying neural mechanism, which merits further corroboration. An interesting point need to be mentioned is that patients’ unaffected siblings showed disrupted intra-connectivity while relatively preserved inter-connectivity between DMN and FPN. This aberrance pattern in siblings is consistent with previous findings (Liu et al., 2012; Unschuld et al., 2013; Meda et al., 2012). Liu et al. (2012) investigated task-negative and task-positive networks (i.e. DMN and FPN) in patients with schizophrenia and their unaffected siblings, reporting disrupted within network connectivity but not between network connectivity. A recent work verified aberrances in DMN and FPN intra-connectivity in both patients and relatives (Unschuld et al., 2013). These abnormalities inversely correlated with participant’s cognitive performance. However in the work of Meda et al. (2012), who examined functional connectivity among 16 resting-state networks, no inter-connectivity alterations were identified between DMN and FPN either in patients or in relatives. In some task-based functional studies, on the other hand, patients and relatives would show aberrant activation and connection in DMN and FPN (Whitfield-Gabrieli et al., 2009; Repovs and Barch, 2012). Task and resting state studies are not directly comparable, but the differences merits further exploration. Two possible explanations may account for these results. First, disrupted relationship between DMN and FPN is suspected to associate with the characteristic symptoms of schizophrenia, which are apparent only in patients (Buckner et al., 2008; Broyd et al., 2009; Rotarska-Jagiela et al., 2010; Menon, 2011). In this study, patients’ unaffected siblings were free from severe psychotic symptoms. Therefore, in terms of DMN and FPN functions, their aberrance may restrict to focal areas rather than large-scale network interaction. Second, siblings of the adult-onset patients only increase 9% risk for the disease, and therefore maybe inadequate to reflect some brain abnormalities evidenced in patients (Moran et al., 2013). Siblings of childhood-onset patients or discordant twins who bear more genetic burdening will demonstrate more similar aberrance pattern to the patients (de la Serna et al., 2010).

3.5.

Limitations

There are several limitations of the present study. First, due to improper storage, the individual patient’s symptomology record was unavailable, only the average PANSS score was kept and exhibited in Table 1. Therefore, we are unable to conduct correlation analysis between symptomology score and connectivity strength. To compensate on this issue, we performed correlation analysis on another group of patients with schizophrenia, who have complete symptomology records (n¼ 13). The patients in this study and in the other data set were closely matched on the following measures: age (25.3676.32 and 27.076.57), sex (13/12 and 7/6), duration of illness (18.32715.84 and 12.76710.54) and PANSS score (77.59711.56 and 76.4276.64). Data analysis procedure was the same as described in Section 5. No significant relationships were found between symptomatology measures and inter-connectivity in the patient group (Table S1). However, this result should be interpreted with caution due to the relative small sample size of the other patient group. Further work with a larger sample size will make conclusions more reliable. Second, the majority of patients included in the present analyses were receiving antipsychotic medications at the time of scanning. Some studies suggest that medications can influence resting-state brain network activity (Moncrieff and Leo, 2010; Lui et al., 2010). To address this issue, we compared inter-connectivity strength of the rFPN-aDMN association across the unmedicated (n¼ 6) and medicated patients (n¼ 18). There was no significant group difference (p ¼0.243). Nevertheless, a study of first-episode nonmedicated patients would provide a conclusive test of this potentially spurious explanation of our findings. Another limitation that merits some consideration is the relatively slow sampling rates (every 2 s for each brain volume) used in the present study. Spontaneous restingstate BOLD signals may be contaminated by low-frequency physiological fluctuations such as cardiac or respiratory activity (Fox and Raichle, 2007). However, gICA is known for the capability to maximally separate spatially distinct components, regressing out signals that are more likely to be physiological artifacts rather than bona fide neuronal activity of interest (Beckmann et al., 2005). Moreover, prior work suggests that cardiac and respiratory signals make only small contributions to measures of neural activity (Birn et al., 2006), such that physiological “noise” is unlikely to provide a comprehensive explanation of our results.

4.

Conclusion

The present study comprehensively examined DMN and FPN subsystem intra- and inter-connectivity in patients with schizophrenia, their unaffected siblings, and healthy controls. Intraconnectivity analyses revealed that patients and siblings shared dysconnection within aDMN and l/rFPN subsystems while relatively preserved pDMN connectivity. Inter-connectivity analyses exhibited functional cooperation between FPN and DMN subsystems in three groups, although the patients demonstrated stronger connectivity between rFPN and aDMN as compared to

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the controls. Taken together, these results corroborate differentiation between the subsystems and their individual relevance to the disorder pathology. Genetic factors may exert different influences on DMN subsystems, with aDMN functioning related to greater disorder liability. Enhanced interaction between the rFPN and aDMN may underlie characteristic manifestations of schizophrenia such as delusions and hallucinations. Detailed analysis of neural subsystems provides a fruitful avenue for the exploration of the neural bases of both health and illness.

5.

Experimental Procedure

5.1.

Participants

Participants consisted of 25 patients with schizophrenia, 25 unaffected siblings of the patients, and 25 healthy controls. All participants were informed as to the potential benefits and risks of their participation and gave their written informed consent to take part in the study. This study was approved by the ethics committee of the Second Xiangya Hospital, Central South University, following the principles set forth by the Declaration of Helsinki. All patients were recruited from the inpatients units at Department of Psychiatry, Second Xiangya Hospital of Central South University, Changsha, China. Patients met the DSM-IV diagnostic criteria for schizophrenia on the basis of the Structured Clinical Interview (SCID-I) (First, 2007). They were in clinical stable status with current psychotic symptoms at the time-point of scanning, as indicated by the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987) and confirmed by the doctors in charge (Table 1). Psychiatrists who have been professionally trained for SCID and PANSSbased interviews performed clinical-psychometric assessment with inter-rater reliability for diagnoses and scores of more than 95%. Patients were excluded if they had a history of neurological disorder, severe medical disorder, substance abuse or dependence, prior electroconvulsive therapy or head injury resulting in loss of consciousness. In addition, every patient participant had at least one unaffected sibling who is willing to take part in this experiment. The included patients were further examined not having a major psychiatric illness other than schizophrenia. Nineteen of the twenty-five patients were receiving antipsychotic medications (risperidone [n¼ 10, 2–6 mg/day], clozapine [n¼ 4, 200–350 mg/day], quetiapine [n¼ 4, 400–600 mg/day], and sulpiride [n ¼1, 200 mg/day]). Twenty-five unaffected siblings of the patients were recruited in the present study, with each patient had a sibling. Siblings were excluded if they met the DSM-IV criteria for any Axis I psychiatric disorders or the exclusion criteria for the patients. Those who retained in the study report no experience of any psychotic symptoms. Both the siblings and the controls were free from any current or past psychiatric, neurological or systemic disorder, nor did they ever taken antipsychotics medications. Twenty-five healthy controls were recruited from the Changsha City area by word-of-mouth advertising. The inclusion and exclusion criteria were the same as those for the siblings except that the controls had no first-degree relatives

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with a history of psychiatric disorders. Groups were wellmatched on age, sex, and education level (Table 1).

5.2.

Image acquisition

Images were acquired on a 1.5-T GE Signa Twinspeed scanner (General Electric Medical System, Milwaukee, Wisconsin). Participants underwent a 6-min resting-state fMRI scan as well as a high-resolution structural scan. All participants were instructed to be as still as possible, keeping their eyes closed and staying relaxed without falling asleep. They were judged to be awake at the start and conclusion of the scanning. A standard head coil was used for radio frequency transmission and reception of the magnetic resonance signal. Foam pads and earplugs were used to minimize head motion and dampen scanner noise. The functional scans were acquired using a gradient-echo echoplanar imaging (EPI) sequence (TR¼2 s, TE¼ 40 ms, flip angle¼ 901, FOV¼ 240  240 mm2). Whole-brain volumes comprised 20 contiguous transverse slices with 5 mm thickness, 1 mm gap and 3.75  3.75 mm2 in-plane resolution. For every participant, 180 whole-brain volumes were acquired and the first five volumes were discarded to allow for longitudinal equilibrium. T1-weighted images were obtained using a high resolution sequence (TR¼2045 ms, TE¼9.6 ms, flip angle¼ 901, FOV¼ 220  220 mm2).

5.3.

Image preprocessing

Data preprocessing was performed using the statistical parametric mapping software package (SPM5, Welcome Department of Cognitive Neurology, Institute of Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm5) and MATLAB 7.1 (MathWorks). Image preprocessing comprised motion correction (using a 6-parameter affine transformation), spatial normalization (using the T1-weighted structural images of each participant, resampling to 3  3  3 mm3) and spatial smoothing (using an 8 mm full-width half-maximum Gaussian kernel). We checked head motion parameters for all participants and discarded data from one patient and three controls (translational 42.0 mm and/or rotational 421). The remaining sample (n¼71) were further compared by the root-mean-square (RMS) of the translation parameters (displacement¼ square root (x2þy2þz2)), which is estimated from realign parameters in the x (left/right), y (anterior/posterior), and z (superior/inferior) directions (Van Dijk et al., 2012). For the patients, siblings and controls, the mean and standard deviation of RMS were 0.239 (0.0754), 0.274 (0.142) and 0.311 (0.165) in millimeters respectively, yielding no significant differences across groups (F¼0.517, p¼0.609).

5.4. Independent component analysis and component identification The gICA is commonly used to extract resting-state functional brain networks, with this approach deriving a set of “components” that are maximally independent of each other. Compared to seed-based correlation analysis, another method commonly used to construct large-scale intrinsic networks, gICA does not require the researcher to specify regions of interest beforehand, thus avoiding selection bias prior to analysis (Garrity et al., 2007). In addition, gICA can

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isolate noise that can contaminate neural signals (Beckmann et al., 2005). Finally, this algorithm can also reveal temporal characteristics of component activities, making it possible to analyze dynamic interaction across components. We performed gICA on data from all 71 participants, the “Group ICA of fMRI Toolbox” (GIFT toolbox; http://icatb.sourceforge.net/) (Correa et al., 2005). Using the minimum description length (MDL) criterion, preliminary dimension estimation was performed, such that 27 independent components were identified (Li et al., 2007). Data from all participants were then concatenated and this aggregate data set was reduced to 27 temporal dimensions using principle components analysis (PCA), followed by an independent component estimation using the infomax algorithm (Bell and Sejnowski, 1995; Calhoun et al., 2001). Single participant time-courses and spatial maps were back-reconstructed using the aggregated components and the results from the data reduction step to compute components for each individual participant (Calhoun et al., 2001). The stability of the independent components was investigated using ICASSO, on the basis of a random initiation method (Himberg et al., 2004). Based on previous studies, a two-step process was used to select components of interest (Calhoun et al., 2001; Stevens et al., 2007). First, spatial multiple regression was performed for every component with a priori probabilistic maps of gray matter, white matter and cerebral spinal fluid provided in SPM5. Components evidencing high correlations with CSF or white matter, along with low correlations with gray matter, are likely to be artifactual and should be discarded. These discarded components were visually checked to ensure that they did indeed represent irrelevant noise before exclusion. Next, using a template produced by Laird et al. (2011), four DMN and FPN subsystems were selected via voxelwise spatial correlations. These subsystems were examined in subsequent analyses.

5.5.

Intra-connectivity analyses

The subsystem map extracted using gICA reflects a collection of voxels each contributing to that component. Thus, intraconnectivity strength of a given subsystem can be inferred from its’ z-map (van de Ven et al., 2004; Manoliu et al., 2013). To ensure that only highly connected regions were analyzed for each subsystem, one-sample t-tests (voxel-vise FDR corrected, po0.05, cluster size¼20 voxels) were performed individually on the three participant groups (Genovese et al., 2002). Statistical maps for each group were binarized and then combined together using a logical AND to create a subsystem mask (Garrity et al., 2007). This subsystem mask was used in group comparisons with an ANOVA model (voxel-wise po0.001, uncorrected), with age and gender as covariates. Clusters showing a main effect of group differences were binarized and saved as the mask for further post hoc analysis, which was performed using twosample t-tests to compare across patients, siblings, and controls (voxel-vise FDR corrected, po0.05).

5.6.

Inter-connectivity analyses

The capability of gICA to reconstruct component timecourses enables analysis of dynamic interactions across coherent neuronal activity patterns. Prior to analysis, we

band-pass filtered (0.01–0.08 Hz) subsystem time-courses and then calculated the Pearson correlation coefficients for all subsystem time-course pairs. These coefficients were converted to z values using Fisher’s r-to-z transformation to improve normality. Consequently, we obtained a 4  4 correlation matrix for every participant. Statistical analyses were conducted both within and between groups, with age and gender as covariates. First, one-sample t-tests were performed on the correlation matrixes to probe the interconnectivity pattern for each participant group (po0.05, FDR corrected). Significantly anomalous correlations were then detected using a one-way ANOVA model for group comparisons (po0.05), with abnormal connections being further tested with post hoc analysis (po0.05, FDR corrected). We chose this relatively loose criterion for inter-connectivity analyses because we predicted that the subsystem dependencies would not be as strong as connections within a subsystem map (Calhoun et al., 2003).

Conflicts of interest We declare that we have no financial or personal relationships with individuals or organizations that could inappropriately influence our work.

Acknowledgments This work was supported by the National Basic Research Program of China (2011CB707802), Nation Science Foundation of China (61375111, 90820304). We sincerely thank the volunteers and patients for their participation and the two anonymous referees for their insightful comments and suggestions.

Appendix A.

Supporting information

Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.brainres. 2014.03.024.

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