1

Links among resting-state default-mode network, salience network, and symptomatology in

schizophrenia

François Orliac*,b

, Mickael Naveauc, Marc Joliot

c, Nicolas Delcroix

d, Annick

Razafimandimbyb, Perrine Brazo

a,b, Sonia Dollfus

a,b, Pascal Delamillieure

a,b.

a CHU de Caen, Department of Psychiatry, Caen, F-14000, France.

b Université de Caen Basse-Normandie, UFR de Médecine, UMR 6301 ISTCT, ISTS group,

Caen, F-14000, France.

c GIN, UMR 5296 CNRS CEA Université Bordeaux Segalen, Bordeaux, F-33000, France.

d GIP CYCERON, UMS 3408, Caen, F-14000, France.

* To whom correspondence should be addressed ; UMR 6301 ISTCT, ISTS group, GIP

CYCERON, Bd Henri Becquerel, BP5229, F-14074 Caen cedex, France ; tel: +33 (0) 2 31 47

01 57, fax: +33 (0) 2 31 47 02 22, e-mail: orliac@cyceron.fr

CHU de Caen, Department of Psychiatry, Caen, F-14000, France ; Université de Caen Basse-

Normandie, UFR de Médecine, UMR 6301 ISTCT, ISTS group, Caen, F-14000, France.

*Manuscript

Click here to view linked References

2

Abstract

Neuroimaging data support the idea that schizophrenia is a brain disorder with altered brain

structure and function. New resting-state functional connectivity techniques allow us to

highlight synchronization of large-scale networks, such as the default-mode network (DMN)

and salience network (SN). A large body of work suggests that disruption of these networks

could give rise to specific schizophrenia symptoms. We examined the intra-network

connectivity strength and grey matter content (GMC) of DMN and SN in 26 schizophrenia

patients using resting-state functional magnetic resonance imaging and voxel-based

morphometry. Resting-state data were analyzed with independent component analysis and

dual-regression techniques. We reported reduced functional connectivity within both DMN

and SN in patients with schizophrenia. Concerning the DMN, patients showed weaker

connectivity in a cluster located in the right paracingulate cortex. Moreover, patients showed

decreased GMC in this cluster. With regard to the SN, patients showed reduced connectivity

in the left and right striatum. Decreased connectivity in the paracingulate cortex was

correlated with difficulties in abstract thinking. The connectivity decrease in the left striatum

was correlated with delusion and depression scores. Correlation between the connectivity of

DMN frontal regions and difficulties in abstract thinking emphasizes the link between

negative symptoms and the likely alteration of the frontal medial cortex in schizophrenia.

Correlation between the connectivity of SN striatal regions and delusions supports the

aberrant salience hypothesis. This work provides new insights into dysfunctional brain

organization in schizophrenia and its contribution to specific schizophrenia symptoms.

Key words: psychosis, rest, dysconnectivity, cingulate, striatum.

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1. Introduction

Neuroimaging data support the idea that schizophrenia is a brain disorder with altered brain

structure and function (Brown and Thompson, 2010 ; Shenton et al., 2001). The

dysconnectivity theory of schizophrenia proposes that schizophrenic symptoms arise from

abnormalities in neuronal connectivity (Bullmore et al., 1997), and the existence of a

widespread anatomical disconnection is now well established for the condition (Stephan et al.,

2006). Several meta-analyses have documented widespread grey matter (GM) changes in the

brain in patients with schizophrenia (Ellison-Wright et al., 2008 ; Honea et al., 2005), and the

most affected loci were anterior cingulate cortex, medial temporal structures, superior

temporal and inferior frontal gyri. One way of assessing brain connectivity is to study how

multiple brain regions functionally interact while a subject is not engaged in a specific task,

i.e., using resting-state blood oxygen level–dependent (BOLD) functional connectivity

(Rogers et al., 2007). Resting-state functional connectivity is an interesting approach because

it allows partitioning of the brain into functional networks (Damoiseaux et al., 2006 ; Naveau

et al., 2012). Furthermore, resting-state networks have been proposed to overlap the networks

subtending the brain in action (Smith et al., 2009). In other words, functional networks seem

to be continuously and dynamically “active” even when the brain is “at rest.” Disruptions of

these networks may contribute to specific patterns of cognitive and behavioral impairments,

providing new insights into aberrant brain organization in several psychiatric and neurological

disorders (Menon, 2011). Regarding schizophrenia, dysfunction of two networks seems to

play a prominent role: the default mode network (DMN) and the salience network (SN)

(Menon, 2011 ; Palaniyappan et al., 2011 ; Palaniyappan and Liddle, 2012 ; Woodward et al.,

2011).

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DMN is a well-known entity, initially described in late 1990s positron emission

tomography studies as a set of brain regions where activity is more important during resting-

state than during a cognitive task (Mazoyer et al., 2001 ; Shulman et al., 1997). Subsequent

work stressed the link between DMN activity and stimulus-independent thoughts, i.e., mind-

wandering (McKiernan et al., 2006). Some authors argue that this network underlies the

construction of complex self-referential simulations, such as mental time travel, perspective-

taking, and theory of mind (Buckner and Carroll, 2007 ; Molnar-Szakacs and Arzy, 2009).

The interaction among these processes would, according to this idea, lead to the construction

of a unique, integrated representation: the Self (Molnar-Szakacs and Arzy, 2009). Several

functional connectivity studies have reported DMN abnormality in schizophrenia, but the

results are mixed: connectivity increase (Mannell et al., 2010 ; Skudlarski et al., 2010 ;

Whitfield-Gabrieli et al., 2009 ; Zhou et al., 2007), connectivity decrease (Bluhm et al., 2007 ;

Bluhm et al., 2009 ; Camchong et al., 2011 ; Jang et al., 2011 ; Rotarska-Jagiela et al., 2010),

or both (Mingoia et al., 2012 ; Ongur et al., 2010). Moreover, one study has found no

significant difference between patients and controls (Wolf et al., 2011). A DMN alteration has

been associated with negative symptoms (Camchong et al., 2011 ; Mingoia et al., 2012),

positive symptoms (Camchong et al., 2011 ; Whitfield-Gabrieli et al., 2009),

attention/concentration deficits (Camchong et al., 2011), and disorganization symptoms

(Rotarska-Jagiela et al., 2010). According to Salgado-Pineda et al. (2011), GM alterations

could constitute a neuroanatomical underpinning of disturbed DMN function in

schizophrenia.

The SN is a network responsible for the integration of sensations, internally generated

thoughts and information about goals and plans to update expectations about the internal and

external environment. If a salient stimulus is presented, SN would allow allocation of

attention, stimulus processing, and initiation of an action (Palaniyappan and Liddle, 2012).

5

Indeed, this network would have a key role in switching among the DMN, the executive

control network, and external attention networks (Doucet et al., 2011 ; Sridharan et al., 2008).

Yet only a few studies have examined SN functional connectivity in schizophrenia: Two

reported no differences between schizophrenia patients and healthy controls (Repovs and

Barch, 2012 ; Woodward et al., 2011), and two others reported a functional connectivity

decrease in schizophrenia patients (Tu et al., 2012 ; White et al., 2010). SN alteration has been

linked to delusions, disorganization symptoms, and psychomotor poverty syndrome

(Palaniyappan and Liddle, 2012 ; Yuan et al., 2012). No study, to our knowledge, has

explored the relation between SN function and GM alteration in schizophrenia, but Schultz et

al. (2012) reported that a disturbed neuronal activation of the dorsal anterior cingulate cortex

(a key region of the SN) during a working memory task was linked to decreased prefrontal

GM thickness.

The inconsistency of findings in schizophrenia patients, especially concerning DMN

connectivity, can be striking. Part of the problem may be different analysis techniques: seed-

based analysis and independent component analysis (ICA). Moreover, results of seed-based

analysis rely on the a priori selection of the seed voxel or region, which differs from one study

to another. Concerning ICA, results rely largely on the reference maps obtained for DMN and

SN, which are based on data from a small number of subjects. As a consequence, reference

maps are also quite variable from one study to another.

To avoid such a bias, here we used reference maps from an ICA analysis on a large

dataset (resting-state functional magnetic resonance imaging [fMRI] from 282 healthy

volunteers). In this way, we were able to reliably explore the functional connectivity of DMN

and SN in schizophrenia patients and its relationships to schizophrenia symptoms. When a

functional connectivity alteration was found, a structural analysis was carried out to determine

whether this functional alteration was linked to a structural (GM) alteration.

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2. Materials and methods.

2.1. Participants.

Twenty-six patients with schizophrenia (SP group) attending at the Department of Psychiatry

of Caen University Hospital and twenty-six matched healthy controls (HC group) were

included in the study. All participants spoke French as their mother tongue. The patient and

control groups were matched for age, sex, handedness, and educational level on a one-to-one

basis. All participants had to be between 18 and 60 years of age. All were screened for

magnetic resonance imaging (MRI) contraindications, and participants with a history of a

major medical condition, neurological disease, or substance abuse were excluded from the

study.

SP group participants were diagnosed by an experienced clinician using the Mini

International Neuropsychiatric Interview (MINIplus v.4.5). They were required to have been

stable on antipsychotic medication for at least four months prior to the study. The Positive and

Negative Syndrome Scale (PANSS) (Kay et al., 1987) was used to assess positive (PANSS-

P), negative (PANSS-N), and general psychopathology (PANSS-G) symptoms. Daily

antipsychotic medication dosage was converted to chlorpromazine equivalents (mg/d).

The local ethics committee (CPP de Basse-Normandie, France) approved the study. All

participants gave written informed consent.

2.2. Imaging procedure.

2.2.1. Data acquisition.

Data acquisition was performed on a 3 T Philips Achieva MRI scanner. Structural data were

acquired using a high-resolution, three-dimensional T1-weighted volume (repetition time

(TR) = 20 ms; echo time (TE) = 4.6 ms; flip angle = 10°; inversion time = 800 ms; turbo field

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echo factor = 65; sense factor = 2; field of view = 256 × 256 × 180 mm; 1 × 1 × 1 mm3

isotropic voxel size), and a T2*-weighted, multi-slice acquisition (T2*-weighted fast-field

echo; TR = 3500 ms; TE = 35 ms; flip angle = 90°; sense factor = 2; 70 axial slices; 2 × 2 × 2

mm3 isotropic voxel size). Spontaneous brain activity was monitored using BOLD fMRI

while the participants performed a resting-state condition for 8 min (T2*-echo planar

imaging; 240 volumes; TR = 2 s; TE = 35 ms; flip angle = 80°; 31 axial slices; 3.75 × 3.75 ×

3.75 mm3 isotropic voxel size). Immediately before fMRI scanning, participants were

instructed to “keep their eyes closed, to relax, to refrain from moving, to stay awake, and to

let their thoughts come and go.”

2.2.2. Functional data.

2.2.2.1. Pre-processing.

Pre-processing of the functional data was based on the methods described in Naveau et al.

(2012). Briefly, it included motion correction, slice-timing correction, band-pass filtering

(0.01 Hz < f < 0.1 Hz), co-registration to structural scan, spatial normalization to the Montreal

Neurological Institute template, and spatial smoothing (6 mm Gaussian kernel). Each

subject’s structural scan was segmented into grey matter, white matter, and cerebrospinal

fluid using the unified segmentation approach implemented in Statistical Parametric Mapping

5 (SPM5; Wellcome Department of Neurology, London, UK; www.fil.ion.ucl.ac.uk/spm5).

2.2.2.2. Reference maps for DMN and SN.

Reference maps for DMN and SN were estimated on a large dataset (resting-state fMRI from

282 healthy volunteers) using a novel group ICA approach based on multi-scale individual

component clustering (MICCA), see Naveau et al. (2012) for more demographic and

methodological considerations. Within the 34 resting-state networks identified by the MICCA

analysis, we retained without ambiguity the two ICA components (i.e., networks)

8

corresponding to the DMN and SN descriptions (Beckmann et al., 2005 ; Sridharan et al.,

2008). Reference maps for DMN and SN are presented in Figure 1. The DMN consists of

eight clusters: a large medial frontal cluster extending up to the superior frontal gyrus; a

posteromedial cluster including the precuneus and part of the posterior cingulate; the angular

gyrus bilaterally; a large part of the middle temporal gyrus bilaterally; and a medial temporal

cluster bilaterally. The SN map is composed of five main clusters: a large medial cluster

located in the anterior cingulate and supplementary motor areas; the middle frontal gyrus

bilaterally; and an insular cluster broadening to striatum bilaterally.

Figure 1. Reference maps for default-mode network and salience network identified by a

multi-scale individual component clustering analysis on resting-state fMRI data from 282

healthy volunteers.

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2.2.2.3. Dual regression.

We used the dual regression approach (Filippini et al., 2009) to build the individual-level

components corresponding with the reference DMN and SN components. In the first

regression equation, the reference maps were used as spatial predictors of the individual fMRI

volumes, resulting in individual time series. These time series were then used as temporal

predictors for the individual functional connectivity map in the second regression equation. At

the end of this process, we obtained one functional connectivity spatial map of DMN and SN

for each individual. Each voxel value of these maps is a Z-normalized regression beta

coefficient, interpreted as a measure of the strength of the association between this voxel time

series and the whole network time series, i.e., functional connectivity within this network.

Dual regression analysis was carried out using FSL tools (www.fmrib.ox.ac.uk/fsl).

2.3. Structural data.

Pre-processing of the structural data was based on the methods described in Michael et al.

(2010), a recent study exploring GM structure-function interactions. Briefly, we applied

optimized Voxel Based Morphometry in SPM5, using unified segmentation approach

(Ashburner and Friston, 2005) and unmodulated normalized parameters with default settings.

The brain was segmented into white matter, GM, and cerebral spinal fluid probabilistic maps.

At any given voxel the GM map indicates the percentage of GM content (GMC) at that voxel.

After segmentation, GM images were smoothed with a Gaussian kernel of 8mm.

2.4. Data analysis.

2.4.1. Group comparison.

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We retained the clusters within which functional connectivity was significantly different

between the two groups using voxel-wise two-sample t-tests (SPM5): height threshold, p <

0.001 uncorrected; extent threshold k > 100 voxels. Anatomical locations were assessed using

the Harvard–Oxford cortical atlas implemented in FSLView

(www.fmrib.ox.ac.uk/fsl/fslview). Mean GMC inside these clusters of interest was calculated

and compared between the two groups using a two sample t-test (JMP9 statistics software;

www.jmp.com/software/jmp9/).

2.4.2. Correlation with clinical variables.

Mean z-score inside each of these clusters was correlated with PANSS-P, PANSS-N, and

PANSS-G scores in the SP group using linear regression (JMP9). When a significant

correlation (p < 0.05 uncorrected) or a trend toward a correlation (p < 0.1) was highlighted

between mean z-score and a subscale, post-hoc regression analysis was carried out using the

individual items of this subscale (p < 0.05, Bonferroni corrected). When a significant

difference in GMC (p < 0.05 uncorrected) between the groups was found inside a cluster of

interest, GMC was correlated with PANSS subscales in the same way as for z-scores. Mean z-

score and GMC were also correlated with antipsychotic medication dosage. Regression

analyses involving PANSS subscales and items or antipsychotic medication dosage were

conducted using Spearman’s nonparametric correlation.

3. Results.

3.1. Clinical data.

Table 1 provides detailed demographic and clinical data. None of the matching criteria were

statistically different between the groups. Mean total PANSS score was 50.4 ± 10.3 (mean ±

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SD). Main subtypes (as defined by the PANSS criteria) were “residual” (n = 12) and

“positive” (n = 8). Mean antipsychotic medication dosage was 326.3 ± 226.9 mg/d, mainly

clozapine (n = 11) or risperidone (n = 7) monotherapy.

Table 1. Population demographics and clinical characteristics (mean ± SD).

Group *

SP HC

N 26 26

Sex (F/M) (6/20) (6/20)

Age (y) 35.6 ± 8.9 33.6 ± 8.9

Handedness (left/right) (4/22) (4/22)

Education (y) 11.9 ± 1.7 13.1 ± 2.5

Illness duration (y) 12.2 ± 8 n/a

Medication

(average chlorpromazine

equivalent, mg/d)

326.3 ± 226.9 n/a

PANSS n/a

Total 50.4 ± 10.3

Positive 12.8 ± 4.9

Negative 12.4 ± 4

General 25.3 ± 5.1

* None of the matching variables were statistically different between the groups.

HC: healthy controls; n/a: not applicable; PANSS: Positive and Negative Syndrome Scale;

SP: schizophrenia patient group

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3.2. Group comparison.

Significant functional connectivity differences were found in one cluster on DMN and two

clusters on SN (Figure 2, Table 2). Within the DMN, the SP group showed weaker

connectivity in a cluster located in the right paracingulate cortex. With regard to the SN, the

SP group showed reduced connectivity in two roughly symmetrical clusters located in the

striatum (mainly in putamen and pallidum).

Figure 2. Schizophrenia patients show a connectivity decrease in several clusters within the

default-mode network and salience network. HC: healthy controls; SP: schizophrenia patients.

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Table 2. Functional connectivity decrease in schizophrenia patients.

Anatomical region

Side BA Peak coordinates

(mm)

Cluster size

(voxels)

T-

value

p value

x y z

DMN

Paracingulate cortex R 32 12 46 20 108 5.50 <0.001*

SN

Striatum L n/a -16 2 -4 170 5.48 <0.001*

Striatum R n/a 24 2 -6 128 4.54 <0.001*

* Statistical significance.

BA: Brodmann area; DMN: default mode network; L: left; n/a: not applicable; R: right; SN:

salience network.

Concerning structural data, SP group showed lower GMC in the paracingulate cluster, but no

correlation was found between mean functional connectivity and mean GMC in this cluster.

No GMC difference was found in striatal clusters (Figure 1 Supplementary data).

3.3. Correlation with clinical variables

Functional connectivity within these clusters of interest was correlated with several clinical

variables. Concerning the DMN (Figure 3), functional connectivity of the paracingulate

cluster showed a trend toward a negative correlation with PANSS-N score (p = 0.09). Post-

hoc analysis revealed that this negative correlation was significant with only one item of the

scale, N5, which refers to “difficulty in abstract thinking” (p = 0.006). Functional connectivity

14

of this cluster showed a negative correlation (p = 0.03) with antipsychotic medication dosage.

Post-hoc analyses were carried out to disentangle possible effects of medication dosage on

PANSS-N items: no correlation was found between N5 score and medication dosage, and the

correlation between functional connectivity of paracingulate cluster and N5 score remained

significant (p = 0.005) after adding medication dosage as a covariate.

Figure 3. Correlations between functional connectivity and clinical variables within the

default-mode network and salience network. Post-hoc analyses are displayed in boxes.

PANSS: Positive and Negative Syndrome Scale; PANSS-G: general psychopathology

subscale of the PANSS; PANSS-N: negative syndrome subscale of the PANSS; PANSS-P:

positive syndrome subscale of the PANSS.

With regard to the SN, functional connectivity of the left striatum cluster (Figure 3) showed a

negative correlation with PANSS-P (p = 0.03) and a trend toward a negative correlation with

PANSS-G (p = 0.07) score. Post-hoc analysis with PANSS-P items revealed that this

correlation was significant with only one item, P1, which refers to “delusions” (p = 0.005).

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The same analysis with PANSS-G items revealed that the negative correlation was found only

with the G6 item “depression” (p = 0.0006). No correlation was identified between functional

connectivity of the right striatum cluster and PANSS subscales. No correlation was identified

between striatal functional connectivity and antipsychotic medication dosage. Concerning

structural data, no correlation was found between GMC of the paracingulate cluster and

PANSS subscales. No correlation was identified between GMC and antipsychotic medication

dosage in any of the clusters of interest.

4. Discussion.

The study results highlight a reduced functional connectivity within both DMN and SN in the

SP group. Concerning the DMN, this reduction was located in the right anterior paracingulate

cortex. Our results are consistent with those of four other studies reporting a loss of functional

connectivity in the frontal medial cluster of the DMN (Camchong et al., 2011 ; Jang et al.,

2011 ; Mingoia et al., 2012 ; Ongur et al., 2010). One study reported a functional connectivity

increase in this region (Whitfield-Gabrieli et al., 2009). This inconsistency may be mainly

attributable to the type of resting data collected: rest only during the whole duration of the

scan (Camchong et al., 2011 ; Jang et al., 2011 ; Mingoia et al., 2012 ; Ongur et al., 2010) or

maintained visual fixation during smaller blocks of time in between task performance

(Whitfield-Gabrieli et al., 2009). In our study, medial frontal reduction in functional

connectivity was correlated with difficulties in abstract thinking, i.e., difficulties in forming

generalizations and proceeding beyond egocentric thinking. This region does not seem to be

engaged in abstract concept processing (Wang et al., 2010) but seems to be specifically

involved in self-referential processing (Gusnard et al., 2001). An altered pattern of interaction

between the whole DMN and this region could lead to a difficulty in disengaging from self-

focus when it is appropriate to do so. Of interest, such a relationship between the connectivity

16

of this region within the DMN and dysfunctional self-focused thinking has already been

stressed in depressed patients (Zhu et al., 2012). Moreover, SP group showed lower GMC in

this region. The early alteration of the medial frontal cortex in schizophrenia is well

documented (Fornito et al., 2009 ; Pantelis et al., 2005), but this structural alteration doesn’t

seem to drive the functional abnormalities in our study. Moreover, recent data suggest that

GM structural-functional coupling may be decreased in schizophrenia (Michael et al., 2010).

Concerning the SN, we highlighted a reduced functional connectivity located in the

striata, thus replicating the results of Tu et al. (2012). White et al. (2010) reported a functional

connectivity decrease between the dorsal anterior cingulate and insula in an SP group during a

somatosensory stimulus processing task, but the basal ganglia were not included in their

analysis. In our study, the connectivity decrease in the left striatum was correlated with

delusion scores. These results are in line with those of previous work. Indeed, structural

(Brandt and Bonelli, 2008) and functional (Heinz and Schlagenhauf, 2010) basal ganglia

abnormalities are well documented in patients with schizophrenia. Moreover, schizophrenia-

like delusive symptoms have been reported following left putaminal infarction (Farid and

Mahadun, 2009). According to the aberrant salience hypothesis of schizophrenia (Heinz and

Schlagenhauf, 2010 ; Kapur, 2003), altered cortico-striatal-thalamic neurocircuitry is

responsible for chaotic firing of dopaminergic neurons in the striatum, leading to aberrant

assignment of salience to innocuous stimuli. Delusions in this framework are seen as a “top-

down” cognitive explanation that the individual imposes on these experiences of aberrant

salience in an effort to make sense of them (Kapur, 2003). It is noteworthy that our SN

reference map obtained from ICA on a large dataset included subcortical regions, bridging the

gap between the striatal “aberrant salience” hypothesis (Heinz and Schlagenhauf, 2010 ;

Kapur, 2003) and the cingulo-opercular “proximal salience” hypothesis (Palaniyappan and

Liddle, 2012) of positive symptoms in patients with schizophrenia. In our study, the

17

connectivity decrease in the left striatum was also correlated with depression score. As

stressed by a recent functional imaging study in an SP group (Simon et al., 2010),

dysfunctional activity of the striatum can alter the neural coding of rewarding outcomes,

contributing to the neurobiological origin of depression in SP.

This study had several limitations. Although reference maps derived from a large dataset

were used, the sample size for each group was modest. Moreover, we must consider the fact

that patients were on antipsychotic medication, suspension of which would not have been

justified on ethical grounds. In our study, antipsychotic medication dosage was negatively

correlated with DMN medial frontal functional connectivity. Interestingly, Sambataro et al.

(2010) reported that, in previously unmedicated SP, a four weeks treatment with olanzapine

was associated with increases in DMN connectivity with the ventromedial prefrontal cortex

during a working memory task. This discrepancy suggests differential effects of antipsychotic

medication on DMN connectivity according to the paradigm of the study: resting state versus

explicit task.

In conclusion, this study highlighted connectivity decreases within two key functional

networks in SP, the DMN and SN. These results are in line with the dysconnectivity theory of

schizophrenia. Loss of connectivity within DMN frontal regions was correlated with

difficulties in abstract thinking, stressing the link between negative symptoms and the likely

alteration of the frontal medial cortex in the SP group. Loss of connectivity within striatal

regions of the SN was correlated with delusions and depressive symptoms, supporting the

aberrant salience hypothesis. Thus, this work provides new insights into the dysfunctional

brain organization in schizophrenia and its contribution to specific schizophrenia symptoms.

18

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