Progress in Neuro-Psychopharmacology & Biological Psychiatry 49 (2014) 16–20
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Abnormal default-mode network homogeneity in first-episode, drug-naive schizophrenia at rest Wenbin Guo ⁎, Dapeng Yao, Jiajing Jiang, Qinji Su, Zhikun Zhang, Jian Zhang, Liuyu Yu, Changqing Xiao Mental Health Center, the First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi 530021, China
a r t i c l e
i n f o
Article history: Received 24 September 2013 Received in revised form 28 October 2013 Accepted 30 October 2013 Available online 9 November 2013 Keywords: Default mode network Independent component analysis Network homogeneity Resting state Schizophrenia
a b s t r a c t Background: Dysconnectivity hypothesis posits that schizophrenia relates to abnormal resting-state connectivity within the default-mode network (DMN) and this aberrant connectivity is considered as contribution of difficulties in self-referential and introspective processing. However, little is known about the alterations of the network homogeneity (NH) of the DMN in schizophrenia. In the present study, we used an automatic NH method to investigate the NH of the DMN in schizophrenia patients at rest. Methods: Forty-nine first-episode, drug-naive schizophrenia patients and 50 age-, gender-, and educationmatched healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI). An automated NH approach was used to analyze the data. Results: Patients exhibited lower NH than controls in the left medial prefrontal cortex (MPFC) and the right middle temporal gyrus (MTG). Significantly higher NH values in the left posterior cingulate cortex (PCC) and the right cerebellum Crus I were found in the patient group than in the control group. No significant correlation was found between abnormal NH values and Positive and Negative Symptom Scale (PANSS) scores, duration of untreated psychosis (DUP), age or years of education in the patient group. Conclusions: Our findings suggest that abnormal NH of the DMN exists in first-episode, drug-naive schizophrenia and further highlight the importance of the DMN in the pathophysiology of schizophrenia. © 2013 Elsevier Inc. All rights reserved.
1. Introduction Schizophrenia is a devastating psychiatric syndrome affecting about one percent of the population worldwide. This chronic illness is characterized by symptoms of the false attribution of perceptual experience to an external stimulus (hallucinations), distorted thought (delusions), reduction in emotion and motivated behavior (negative symptoms), and deficits in cognition and social functioning (Picchioni and Murray, 2007). Effort has been made to elucidate its neural correlates by advanced imaging techniques, such as functional magnetic resonance imaging (fMRI). Recent evidence suggests that resting-state brain function measured by fMRI is a potentially important tool for the discovery of sensitive markers of the disorder (Broyd et al., 2009). Dysconnectivity hypothesis
Abbreviations: FMRI, functional magnetic resonance imaging; FC, functional connectivity; DMN, default mode network; MPFC, medial prefrontal cortex; PCC/Pcu, posterior cingulate cortex/precuneus; NH, network homogeneity; ADHD, attention-deficit/ hyperactivity disorder; DUP, duration of untreated psychosis; SCID, Structured Clinical Interview of the DSM-IV; PANSS, Positive and Negative Symptom Scale; TR/TE, repetition time/echo time; FOV, field of view; DPARSF, Data Processing Assistant for Resting-State fMRI; ICA, independent component analysis; IC, independent component; GRF, Gaussian Random Field; ROI, region of interest; ACC, anterior cingulate cortex; MTG, middle temporal gyrus; STG, superior temporal gyrus. ⁎ Corresponding author. Tel.: +86 771 3277200. E-mail address: [email protected] (W. Guo). 0278-5846/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.pnpbp.2013.10.021
of schizophrenia postulates that the disease relates to alterations in neuronal connectivity (Stephan et al., 2009). Resting-state functional connectivity (FC) is profoundly disturbed in schizophrenia (Lynall et al., 2010; Skudlarski et al., 2010), providing a support for the dysconnectivity hypothesis. Spontaneous neural activity, inferred by resting-state FC, correlates across brain regions and is organized into spatially segregated FC networks (Fox et al., 2005). Among these networks, a consistently examined network is the default-mode network (DMN), which includes the medial prefrontal cortex (MPFC), posterior cingulate cortex/ precuneus (PCC/PCu), and the lateral posterior cortices (Raichle et al., 2001). The DMN routinely shows decreased activity during task-related cognitive process and is regarded as underlying the construction of complex self-referential stimuli, such as mental time travel, perspective taking and theory of mind (Buckner and Carroll, 2007; Molnar-Szakacs and Arzy, 2009). Evidence has been accumulated that the DMN is abnormal in schizophrenia, but the findings 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; Camchong et al., 2011; Jang et al., 2011; Rotarska-Jagiela et al., 2010), or both (Mingoia et al., 2012; Ongur et al., 2010). One study reported no significant difference between patients and controls (Wolf et al., 2011). The mixed findings may be attributed to many factors, including differences in studied populations, sample size, approaches of data analysis, statistical methods, and the potential effects of medication. In the
W. Guo et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 49 (2014) 16–20
present study, we recruited patients only with first-episode, drug-naive schizophrenia to reduce such confounds as medication and number of recurrence. In particular, the mixed findings are revealed by an FC method focusing on the correlations of two brain regions. It is unclear which brain region is abnormal when two regions exhibit abnormal FC, although this method offers important information. Some aspects of the intrinsic network organization in the DMN might have been long overlooked. Therefore, the advent of new methods for analyzing imaging data is urgently required. Network homogeneity (NH), a method suggested by Uddin et al. (2008), is one potential informative approach that would be to offer an unbiased survey of a distributed network of interest, looking for brain regions showing disease-related reduction in network coherence. NH is a voxel-wise measure that provides an assessment of a voxel's correlation with all other voxels within a given network. The mean correlation of a given voxel is defined as the homogeneity. Alternatively, two methods, independent component analysis (ICA) and seed-based region of interest (ROI), are the most widely employed to analyze the resting-state brain networks previously. ICA is a model-free method with the power to assess largely overlapping spatial process. Although ICA has the power to estimate largely overlapping spatial process, it is not clear how best to compare components across subjects and/or between groups (Fox and Raichle, 2007). The seed-based ROI method is utilized to determine the temporal coherence between the timeseries for a given ROI and the timeseries of all other voxels in the brain by using correlation or regression analysis (Biswal et al., 1995). However, the selection and precise placement of ROI seeds can be somewhat arbitrary, and thus have considerable impact on the patterns of FC obtained. In contrast to these two methods, the NH method enables hypothesisdriven interrogation of large-range networks of clinical interest from the viewpoint of assessing the homogeneity of the whole network and therefore combines the advantages of the two previous methods (Uddin et al., 2008). Furthermore, NH is well applied in attentiondeficit/hyperactivity disorder (ADHD) (Uddin et al., 2008). In the present study, NH was utilized to analyze the data from patients with first-episode, drug-naive schizophrenia. Based on previous FC findings of the DMN abnormalities in schizophrenia (Bluhm et al., 2007; Camchong et al., 2011; Jang et al., 2011; Mannell et al., 2010; Mingoia et al., 2012; Ongur et al., 2010; Rotarska-Jagiela et al., 2010; Skudlarski et al., 2010; Whitfield-Gabrieli et al., 2009; Zhou et al., 2007), we hypothesized that abnormal DMN homogeneity would be observed in the patients compared to the controls. We also explored whether abnormal NH was associated with clinical characteristics in the patients. 2. Methods and materials 2.1. Subjects Forty-nine young adults with schizophrenia were recruited from the Mental Health Center, the First Affiliated Hospital, Guangxi Medical University, China. The subjects were from our previous study (Guo et al., 2014). The patients were right-handed, drug-naive, and at their first episode. The duration of untreated psychosis (DUP) for the patients was less than 3 years. Patients were assessed with the Structured Clinical Interview of the DSM-IV (SCID) (First et al., 1997). To reduce the heterogeneity of symptom manifestations and potentially underlying pathology, only patients who met diagnostic criteria for paranoid schizophrenia according to the DSM-IV were included in the study, although this subtype is not used in the new DSM-V classification system. Exclusion criteria were neurological disorders, severe medical disorders, substance abuse, or electroconvulsive therapy. The symptoms were rated by the Positive and Negative Symptom Scale (PANSS) at the scan time. Fifty right-handed healthy controls were recruited from the community. None of them had a history of severe medical or neuropsychiatric
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illness or a family history of severe medical or neuropsychiatric illness in their first-degree relatives. The controls were group matched with the patients in age, sex and educational level. All subjects gave their written informed consent to participate in the study. The local ethics committee of the First Affiliated Hospital of Guangxi Medical University approved the study.
2.2. Scan acquisition Imaging was obtained on a Siemens 3 T scanner. Subjects were required to lie still, keep their eyes closed, and remain awake. A prototype quadrature birdcage head coil fitted with foam padding was utilized to limit head motion. The following parameters were used for functional imaging: repetition time/echo time (TR/TE) = 2000/30 ms, 30 slices, 64 × 64 matrix, 90˚ flip angle, 24 cm FOV, 4 mm slice thickness, 0.4 mm gap, and 250 volumes (500 s).
2.3. Data preprocessing Data preprocessing was conducted in Matlab (Mathworks) using Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan and Zang, 2010). After the correction for slice timing and head motion, the subjects should have no more than 2 mm maximum displacement in x, y, or z and 2˚ of angular motion. Then the images were normalized and resampled to 3 × 3 × 3 mm3. Afterwards, the generated images were temporally bandpass filtered (0.01–0.08 Hz) and linearly detrended.
2.4. DMN identification As suggested by two recent studies (Guo et al., 2013b; Liu et al., 2012), the group ICA method was implemented to pick out the DMN as a mask from healthy controls. Three main steps, including data reduction, independent component (IC) separation, and back reconstruction, were conducted with the toolbox GIFT (http://mialab.mrn.org/ software/#gica). The DMN was selected according to the templates provided by GIFT, which was used as a mask in the following NH analysis.
2.5. NH analysis NH analysis was performed in Matlab (Mathworks). For each subject, the correlation coefficients were calculated in a given voxel with all other voxels within the DMN mask. The mean correlation coefficient was defined as the homogeneity of the given voxel. The resultant values generated the NH maps, which were smoothed with a Gaussian kernel of 8 mm full-width at half-maximum. Finally, the NH maps within the DMN mask were applied for group comparison.
2.6. Statistical analyses Demographics including age, sex and educational level and imaging data were compared between the patients and the controls. Categorical data were analyzed with Chi-square test and continuous variables were analyzed with two-sample t-test. The NH analysis was implemented with two-sample t-tests via voxel-wise cross-subject statistics in the DMN. The significant level was set at the corrected p b 0.05 for multiple comparisons using Gaussian Random Field (GRF) theory (min z N 1.96, cluster significance: p b 0.05). In addition, brain regions with abnormal NH were identified as ROIs. Mean NH values of these ROIs were extracted for further correlation analysis between these NH values and the PANSS scores or the DUP in the patient group (p b 0.05).
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3. Results 3.1. Demographics and clinical characteristics of the subjects As shown in Table 1, the patient group and the control group did not differ significantly in age (t-tests t = − 1.05, df = 97, p = 0.30), sex ratio (Chi-square test x2 = 2.31, df = 1, p = 0.13), and years of education (t-tests t = −1.23, df = 97, p = 0.22). The mean DUP of the illness was 22.45 ± 6.71 months and the mean PANSS total score was 91.31 ± 10.98 in the patient group. 3.2. The DMN maps determined by group ICA Using a group ICA approach, the DMN mask was picked out from the control group. The DMN included brain regions such as bilateral MPFC, ventral anterior cingulate cortex (ACC), PCC/PCu, lateral temporal cortex, medial, lateral, inferior parietal lobe, and cerebellum Crus I and Crus II (Fig. 1). The obtained DMN mask was utilized in the following NH analysis. 3.3. NH: group differences in the DMN Significant group difference of NH values in the DMN between the patients and the controls was found with two-sample t-tests via voxel-wise cross-subject comparisons within the DMN mask. Compared to the controls, patients had lower NH in the left MPFC and the right middle temporal gyrus (MTG). Significantly higher NH values in the left PCC and the right cerebellum Crus I were found in the patient group than in the control group (Fig. 1 and Table 2). 3.4. Correlations between NH and clinical variables The mean NH values were extracted in the four regions (left MPFC, right MTG, left PCC, and right cerebellum Crus I) with significant group differences. Linear correlations were conducted between NH and PANSS scores, DUP, age or years of education in the patient group. No significant correlation was found between NH and PANSS scores, DUP, age or years of education in the patient group. 4. Discussion Using the NH method, we provided an unbiased survey of the DMN in first-episode, drug-naive schizophrenia at rest. The primary findings were that the patients showed significantly lower NH in the left MPFC and the right MTG and higher NH in the left PCC and the right cerebellum Crus I compared to the controls. There was no significant correlation between abnormal NH of the four above-mentioned brain regions and PANSS scores, DUP, age or years of education in the patient group. The findings of abnormal NH in the DMN are in line with previous results with altered FC in the DMN in schizophrenia (Bluhm et al., 2007; Camchong et al., 2011; Jang et al., 2011; Mannell et al., 2010; Table 1 Characteristics of the participants. Demographic data
Patients (n = 49)
Controls (n = 50)
p value
Gender (male/female) Age (years) Years of education (years) Illness duration (months) PANSS Positive scores Negative scores Total scores
30/19 22.69 ± 4.62 10.94 ± 2.40 22.45 ± 6.71
23/27 23.48 ± 2.49 11.46 ± 1.78 –
0.13a 0.30b 0.22b
22.27 ± 5.33 22.82 ± 6.86 91.31 ± 10.98
– – –
a The P value for gender distribution was obtained by chi-square test. b The P values were obtained by two samples t-tests. PANSS = Positive and Negative Symptom Scale
Mingoia et al., 2012; Ongur et al., 2010; Rotarska-Jagiela et al., 2010; Skudlarski et al., 2010; Whitfield-Gabrieli et al., 2009; Zhou et al., 2007), and add a new clue of abnormal network coherence for the participation of the DMN in the pathophysiology of schizophrenia. As a core component of the DMN, the MPFC is observed to show decreased activity during prefrontal-cortex-related cognitive activity in schizophrenia patients (Spironelli et al., 2011). Meanwhile, evidence of increased MPFC activation has documented on a parametric version of the n-back working memory task in schizophrenia (Callicott et al., 2000). However, studies that examine schizophrenia patients at rest observed decreased activity in the MPFC (Ongur et al., 2010; Schultz and Andreasen, 1999; Tang et al., 2013). Furthermore, gray matter reduction is reported in both chronic schizophrenia (Honea et al., 2005) and first-episode schizophrenia (Chan et al., 2011). The inconsistency of decreased and increased MPFC activation in schizophrenia may attribute to the differences in studied population, such as sample size, symptom severity and illness duration. The discrepancies could also be due to the methodological factors including analytic method, task instructions and parameters for data acquisition (Manoach, 2003). Our sample of first-episode, drug-naive schizophrenia has its advantage to reduce the confounding factors such as medication and number of recurrence. Since NH is a voxel-wise measure of a given voxel's correlation with all other voxels within the DMN, we speculated that gray matter reduction and/or decreased activity at rest in the MPFC would affect the correlations between the MPFC and other voxels within the DMN, leading a decreased NH in the left MPFC like the present results. MPFC links widely with both affective-limbic areas (such as the amygdala, hippocampus, and hypothalamus) and executive control and emotional processing areas (such as the ACC) (Alalade et al., 2011). Through these connections, MPFC may act as its key role in modulation of emotional behavior and self-referential processing in schizophrenia (Yu et al., 2012). Our results of decreased NH in the left MPFC are also in line with the issue that dysregulation of medial frontal areas is associated with selfdirected thoughts, with the consequence that the source of external and internal stimuli would become confused, which may offer a pathophysiological basis for hallucinations (Whitfield-Gabrieli et al., 2009). Of course, this notion warrants more studies. The temporal gyrus has proven to be a common target for both structural and functional study in schizophrenia. Among the many volumetric studies, one of the most widely replicated findings is a volume reduction of the superior temporal gyrus (STG) from neuroimaging studies (Honea et al., 2005). Recently, gray matter deficits in the right MTG are reported to be present during a very early stage of schizophrenia (Lui et al., 2009). The same study also observed a positive correlation between PANSS scores for thought disturbance and MTG-putamen connectivity. Regional gray matter reduction in bilateral MTG is found in first-episode, drug-naive schizophrenia (Hu et al., 2013). In contrast to structural findings, abnormal patterns of FC have been reported in both task-based process and resting state (Hashimoto et al., 2010; Mechelli et al., 2007; Zhou et al., 2008), leading to the idea of schizophrenia as a disconnection syndrome (Friston, 1998). Our results of decreased NH in the right MTG are comparable to the previous studies, supporting that functional deficits in the right MTG may be present during an early stage of schizophrenia. The MTG plays a critical role in semantic memory and language processing (Kiehl et al., 2004). Thus, decreased NH in the right MTG may be related to the cognitive deficits seen in schizophrenia, although cognitive function is not assessed in the present study. As the posterior midline core region of the DMN, the PCC subserves a variety of cognitive functions, especially those associated with longterm memory as well as working memory (Maddock, 1999). Schizophrenia patients exhibited an increased resting perfusion or rate in the PCC compared to controls (Andreasen et al., 1997; Haznedar et al., 1997), which was negatively correlated with Scheiderian first-rank symptoms (Franck et al., 2002). Increased activation and connectivity in the PCC have also been previously reported in schizophrenia at rest
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Fig. 1. The DMN mask determined by ICA from the control group and statistical maps showing NH differences between subject groups. Red and blue denote higher and lower NH respectively and the color bars indicate the T value from two-sample t-tests. DMN = default mode network, ICA = independent component analysis, NH = network homogeneity.
(Whitfield-Gabrieli et al., 2009; Woodward et al., 2011). On the other hand, the PCC showed decreased activation in patients with schizophrenia during memory-related tasks (Hofer et al., 2003; Kiehl and Liddle, 2001). One possible interpretation for this paradox is that overcompensation during the resting state results in a failure to respond to excessive demands (Zhou et al., 2005), suggesting that the functional potential of the PCC in schizophrenia is diminished. Consistent with the findings of hyperactivity in the PCC, our results of increased NH in the left PCC are speculated as a compensation mechanism in schizophrenia at rest. Interestingly, the right cerebellum Crus I exhibited increased NH in the patients. The cerebellum is traditionally considered as a region engaged in motor coordination. This issue has been challenged recently by the evidence that the cerebellum subserves emotion and cognition (Guo et al., 2012; Schmahmann, 2010; Stoodley, 2012). In healthy subjects, the cerebellum is reported to be activated during emotional and cognitive tasks. The cerebellar cognitive–affective syndrome is also observed in patients with cerebellar damage (Parvizi et al., 2001; Schmahmann and Sherman, 1998). The cerebellum possibly links with the cerebrum through the cortico–cerebellar–thalamic–cortical circuit, and dysconnectivity of this circuit is suggested in cognitive dysmetria theory of schizophrenia (Friston, 1998). Our results of increased NH in the right Crus I are in line with the cerebellar functional disconnections involving the DMN. The Crus I is conceptualized as a part of the DMN (Alalade et al., 2011; Guo et al., 2013a). As discussed above, the increased NH in the right Crus I possibly reflects a compensatory effort of this region. Since current psychopathology assessed by PANSS is reported to be correlated to the aberrant intrinsic FC in schizophrenia (RotarskaJagiela et al., 2010), we hypothesized that there were significant
Table 2 Significant differences in NH values between groups. Cluster location
Patients b Controls Left MPFC Right MTG Patients N Controls Left PCC Right Crus I
Peak (MNI)
Number of voxels
T value
−3 −15
20 20
−3.9550 −3.2645
42 −27
23 34
3.2728 3.4638
x
y
z
−6 57
60 −9
0 18
−39 −84
NH = network homogeneity; MNI = Montreal Neurological Institute; MTG = Middle Temporal Gyrus; MPFC = medial prefrontal cortex; PCC = posterior cingulate cortex
correlations between the PANSS scores and the abnormal NH values in the patients. Therefore, no correlation between these factors was somewhat unexpected. One possible explanation of no correlation is that abnormal NH of the DMN might be a trait change for the patients independent of the symptom severity. Another possibility is the concentration of symptom severity. For example, the PANSS positive scores are around 22 in the patient group. The present study has several limitations. First, the DMN mask, derived from healthy controls by using group ICA, might introduce a bias in the analyses. However, it is not biased in the same way as traditional seed-based methods that typically focus on a voxel or cluster of voxels (e.g. seed region) as the starting point of correlation analyses (Uddin et al., 2008). Second, the present study focused on the abnormalities of the DMN. It is helpful for highlighting the pathophysiological contribution of the DMN. For the same reason, some meaningful results from other brain areas might be omitted. Despite the limitations, our findings suggest that abnormal NH of the DMN exists in first-episode, drug-naive schizophrenia. They also suggest an important new avenue of exploring the NH in the patients to improve understanding the nature of schizophrenia. Hence, the present results highlight the importance of the DMN in the pathophysiology of schizophrenia. Acknowledgment The study was supported by Grants from the National Natural Science Foundations of China (Grant nos. 81260210 and 30900483), the Natural Science Foundations of Guangxi (Grant no. 2013GXNSFAA019107), and the special funding by the Ministry of Health of the Peoples’ Republic of China (Grant no. 201002003). The authors express their gratitude to Dr. Feng Liu at Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China for statistical assistance. The authors also appreciate anonymous reviewers for their suggestions and comments. References Alalade E, Denny K, Potter G, Steffens D, Wang L. Altered cerebellar-cerebral functional connectivity in geriatric depression. PLoS One 2011;6:e20035. Andreasen NC, O'Leary DS, Flaum M, Nopoulos P, Watkins GL, Boles Ponto LL, et al. Hypofrontality in schizophrenia: distributed dysfunctional circuits in neurolepticnaive patients. Lancet 1997;349:1730–4.
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Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34:537–41. Bluhm RL, Miller J, Lanius RA, Osuch EA, Boksman K, Neufeld RW, et al. Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull 2007;33:1004–12. Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 2009;33:279–96. Buckner RL, Carroll DC. Self-projection and the brain. Trends Cogn Sci 2007;11:49–57. Callicott JH, Bertolino A, Mattay VS, Langheim FJ, Duyn J, Coppola R, et al. Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex 2000;10:1078–92. Camchong J, MacDonald III AW, Bell C, Mueller BA, Lim KO. Altered functional and anatomical connectivity in schizophrenia. Schizophr Bull 2011;37:640–50. Chan RC, Di X, McAlonan GM, Gong QY. Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression. Schizophr Bull 2011;37:177–88. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). Washington, DC: American Psychiatric Press; 1997. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007;8:700–11. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005;102:9673–8. Franck N, O'Leary DS, Flaum M, Hichwa RD, Andreasen NC. Cerebral blood flow changes associated with Schneiderian first-rank symptoms in schizophrenia. J Neuropsychiatry Clin Neurosci 2002;14:277–82. Friston KJ. The disconnection hypothesis. Schizophr Res 1998;30:115–25. Guo W, Liu F, Xue Z, Gao K, Liu Z, Xiao C, et al. Abnormal resting-state cerebellar-cerebral functional connectivity in treatment-resistant depression and treatment sensitive depression. Prog Neuropsychopharmacol Biol Psychiatry 2013a;44:51–7. Guo W, Liu F, Zhang J, Zhang Z, Yu L, Liu J, et al. Dissociation of regional activity in the default mode network in first-episode, drug-naive major depressive disorder at rest. J Affect Disord 2013b;151:1097–101. Guo W, Xiao C, Liu G, Wooderson SC, Zhang Z, Zhang J, et al. Decreased resting-state interhemispheric coordination in first-episode, drug-naive paranoid schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2014;48:14–9. Guo WB, Liu F, Chen JD, Gao K, Xue ZM, Xu XJ, et al. Abnormal neural activity of brain regions in treatment-resistant and treatment-sensitive major depressive disorder: A resting-state fMRI study. J Psychiatr Res 2012;46:1366–73. Hashimoto R, Lee K, Preus A, McCarley RW, Wible CG. An fMRI study of functional abnormalities in the verbal working memory system and the relationship to clinical symptoms in chronic schizophrenia. Cereb Cortex 2010;20:46–60. Haznedar MM, Buchsbaum MS, Luu C, Hazlett EA, Siegel Jr BV, Lohr J, et al. Decreased anterior cingulate gyrus metabolic rate in schizophrenia. Am J Psychiatry 1997;154: 682–4. Hofer A, Weiss EM, Golaszewski SM, Siedentopf CM, Brinkhoff C, Kremser C, et al. Neural correlates of episodic encoding and recognition of words in unmedicated patients during an acute episode of schizophrenia: a functional MRI study. Am J Psychiatry 2003;160:1802–8. Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry 2005;162:2233–45. Hu M, Li J, Eyler L, Guo X, Wei Q, Tang J, et al. Decreased left middle temporal gyrus volume in antipsychotic drug-naive, first-episode schizophrenia patients and their healthy unaffected siblings. Schizophr Res 2013;144:37–42. Jang JH, Jung WH, Choi JS, Choi CH, Kang DH, Shin NY, et al. Reduced prefrontal functional connectivity in the default mode network is related to greater psychopathology in subjects with high genetic loading for schizophrenia. Schizophr Res 2011;127:58–65. Kiehl KA, Liddle PF. An event-related functional magnetic resonance imaging study of an auditory oddball task in schizophrenia. Schizophr Res 2001;48:159–71. Kiehl KA, Smith AM, Mendrek A, Forster BB, Hare RD, Liddle PF. Temporal lobe abnormalities in semantic processing by criminal psychopaths as revealed by functional magnetic resonance imaging. Psychiatry Res 2004;130:27–42. Liu CH, Ma X, Li F, Wang YJ, Tie CL, Li SF, et al. Regional homogeneity within the default mode network in bipolar depression: a resting-state functional magnetic resonance imaging study. PLoS One 2012;7:e48181. Lui S, Deng W, Huang X, Jiang L, Ma X, Chen H, et al. Association of cerebral deficits with clinical symptoms in antipsychotic-naive first-episode schizophrenia: an optimized voxel-based morphometry and resting state functional connectivity study. Am J Psychiatry 2009;166:196–205.
Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, et al. Functional connectivity and brain networks in schizophrenia. J Neurosci 2010;30:9477–87. Maddock RJ. The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain. Trends Neurosci 1999;22:310–6. Mannell MV, Franco AR, Calhoun VD, Canive JM, Thoma RJ, Mayer AR. Resting state and task-induced deactivation: a methodological comparison in patients with schizophrenia and healthy controls. Hum Brain Mapp 2010;31:424–37. Manoach DS. Prefrontal cortex dysfunction during working memory performance in schizophrenia: reconciling discrepant findings. Schizophr Res 2003;60:285–98. Mechelli A, Allen P, Amaro Jr E, Fu CH, Williams SC, Brammer MJ, et al. Misattribution of speech and impaired connectivity in patients with auditory verbal hallucinations. Hum Brain Mapp 2007;28:1213–22. Mingoia G, Wagner G, Langbein K, Maitra R, Smesny S, Dietzek M, et al. Default mode network activity in schizophrenia studied at resting state using probabilistic ICA. Schizophr Res 2012;138:143–9. Molnar-Szakacs I, Arzy S. Searching for an integrated self-representation. Commun Integr Biol 2009;2:365–7. Ongur D, Lundy M, Greenhouse I, Shinn AK, Menon V, Cohen BM, et al. Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Res 2010;183:59–68. Parvizi J, Anderson SW, Martin CO, Damasio H, Damasio AR. Pathological laughter and crying: a link to the cerebellum. Brain 2001;124:1708–19. Picchioni MM, Murray RM. Schizophrenia. BMJ 2007;335:91–5. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676–82. Rotarska-Jagiela A, van de Ven V, Oertel-Knochel V, Uhlhaas PJ, Vogeley K, Linden DE. Resting-state functional network correlates of psychotic symptoms in schizophrenia. Schizophr Res 2010;117:21–30. Schmahmann JD. The role of the cerebellum in cognition and emotion: personal reflections since 1982 on the dysmetria of thought hypothesis, and its historical evolution from theory to therapy. Neuropsychol Rev 2010;20:236–60. Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain 1998;121(Pt 4):561–79. Schultz SK, Andreasen NC. Schizophrenia. Lancet 1999;353:1425–30. Skudlarski P, Jagannathan K, Anderson K, Stevens MC, Calhoun VD, Skudlarska BA, et al. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry 2010;68:61–9. Spironelli C, Angrilli A, Calogero A, Stegagno L. Delta EEG band as a marker of left hypofrontality for language in schizophrenia patients. Schizophr Bull 2011;37:757–67. Stephan KE, Friston KJ, Frith CD. Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull 2009;35:509–27. Stoodley CJ. The cerebellum and cognition: evidence from functional imaging studies. Cerebellum 2012;11:352–65. Tang J, Liao Y, Song M, Gao JH, Zhou B, Tan C, et al. Aberrant default mode functional connectivity in early onset schizophrenia. PLoS One 2013;8:e71061. Uddin LQ, Kelly AM, Biswal BB, Margulies DS, Shehzad Z, Shaw D, et al. Network homogeneity reveals decreased integrity of default-mode network in ADHD. J Neurosci Methods 2008;169:249–54. Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A 2009;106:1279–84. Wolf ND, Sambataro F, Vasic N, Frasch K, Schmid M, Schonfeldt-Lecuona C, et al. Dysconnectivity of multiple resting-state networks in patients with schizophrenia who have persistent auditory verbal hallucinations. J Psychiatry Neurosci 2011;36: 366–74. Woodward ND, Rogers B, Heckers S. Functional resting-state networks are differentially affected in schizophrenia. Schizophr Res 2011;130:86–93. Yan C, Zang Y. DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Front Syst Neurosci 2010;4:13. Yu R, Chien YL, Wang HL, Liu CM, Liu CC, Hwang TJ, et al. Frequency-specific alternations in the amplitude of low-frequency fluctuations in schizophrenia. Hum Brain Mapp 2012. http://dx.doi.org/10.1002/hbm.22203. [Epub ahead of print]. Zhou SY, Suzuki M, Hagino H, Takahashi T, Kawasaki Y, Matsui M, et al. Volumetric analysis of sulci/gyri-defined in vivo frontal lobe regions in schizophrenia: precentral gyrus, cingulate gyrus, and prefrontal region. Psychiatry Res 2005;139:127–39. Zhou Y, Liang M, Tian L, Wang K, Hao Y, Liu H, et al. Functional disintegration in paranoid schizophrenia using resting-state fMRI. Schizophr Res 2007;97:194–205. Zhou Y, Shu N, Liu Y, Song M, Hao Y, Liu H, et al. Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophr Res 2008;100:120–32.
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Progress in Neuro-Psychopharmacology & Biological Psychiatry journal homepage: www.elsevier.com/locate/pnp
Abnormal default-mode network homogeneity in first-episode, drug-naive schizophrenia at rest Wenbin Guo ⁎, Dapeng Yao, Jiajing Jiang, Qinji Su, Zhikun Zhang, Jian Zhang, Liuyu Yu, Changqing Xiao Mental Health Center, the First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi 530021, China
a r t i c l e
i n f o
Article history: Received 24 September 2013 Received in revised form 28 October 2013 Accepted 30 October 2013 Available online 9 November 2013 Keywords: Default mode network Independent component analysis Network homogeneity Resting state Schizophrenia
a b s t r a c t Background: Dysconnectivity hypothesis posits that schizophrenia relates to abnormal resting-state connectivity within the default-mode network (DMN) and this aberrant connectivity is considered as contribution of difficulties in self-referential and introspective processing. However, little is known about the alterations of the network homogeneity (NH) of the DMN in schizophrenia. In the present study, we used an automatic NH method to investigate the NH of the DMN in schizophrenia patients at rest. Methods: Forty-nine first-episode, drug-naive schizophrenia patients and 50 age-, gender-, and educationmatched healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI). An automated NH approach was used to analyze the data. Results: Patients exhibited lower NH than controls in the left medial prefrontal cortex (MPFC) and the right middle temporal gyrus (MTG). Significantly higher NH values in the left posterior cingulate cortex (PCC) and the right cerebellum Crus I were found in the patient group than in the control group. No significant correlation was found between abnormal NH values and Positive and Negative Symptom Scale (PANSS) scores, duration of untreated psychosis (DUP), age or years of education in the patient group. Conclusions: Our findings suggest that abnormal NH of the DMN exists in first-episode, drug-naive schizophrenia and further highlight the importance of the DMN in the pathophysiology of schizophrenia. © 2013 Elsevier Inc. All rights reserved.
1. Introduction Schizophrenia is a devastating psychiatric syndrome affecting about one percent of the population worldwide. This chronic illness is characterized by symptoms of the false attribution of perceptual experience to an external stimulus (hallucinations), distorted thought (delusions), reduction in emotion and motivated behavior (negative symptoms), and deficits in cognition and social functioning (Picchioni and Murray, 2007). Effort has been made to elucidate its neural correlates by advanced imaging techniques, such as functional magnetic resonance imaging (fMRI). Recent evidence suggests that resting-state brain function measured by fMRI is a potentially important tool for the discovery of sensitive markers of the disorder (Broyd et al., 2009). Dysconnectivity hypothesis
Abbreviations: FMRI, functional magnetic resonance imaging; FC, functional connectivity; DMN, default mode network; MPFC, medial prefrontal cortex; PCC/Pcu, posterior cingulate cortex/precuneus; NH, network homogeneity; ADHD, attention-deficit/ hyperactivity disorder; DUP, duration of untreated psychosis; SCID, Structured Clinical Interview of the DSM-IV; PANSS, Positive and Negative Symptom Scale; TR/TE, repetition time/echo time; FOV, field of view; DPARSF, Data Processing Assistant for Resting-State fMRI; ICA, independent component analysis; IC, independent component; GRF, Gaussian Random Field; ROI, region of interest; ACC, anterior cingulate cortex; MTG, middle temporal gyrus; STG, superior temporal gyrus. ⁎ Corresponding author. Tel.: +86 771 3277200. E-mail address: [email protected] (W. Guo). 0278-5846/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.pnpbp.2013.10.021
of schizophrenia postulates that the disease relates to alterations in neuronal connectivity (Stephan et al., 2009). Resting-state functional connectivity (FC) is profoundly disturbed in schizophrenia (Lynall et al., 2010; Skudlarski et al., 2010), providing a support for the dysconnectivity hypothesis. Spontaneous neural activity, inferred by resting-state FC, correlates across brain regions and is organized into spatially segregated FC networks (Fox et al., 2005). Among these networks, a consistently examined network is the default-mode network (DMN), which includes the medial prefrontal cortex (MPFC), posterior cingulate cortex/ precuneus (PCC/PCu), and the lateral posterior cortices (Raichle et al., 2001). The DMN routinely shows decreased activity during task-related cognitive process and is regarded as underlying the construction of complex self-referential stimuli, such as mental time travel, perspective taking and theory of mind (Buckner and Carroll, 2007; Molnar-Szakacs and Arzy, 2009). Evidence has been accumulated that the DMN is abnormal in schizophrenia, but the findings 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; Camchong et al., 2011; Jang et al., 2011; Rotarska-Jagiela et al., 2010), or both (Mingoia et al., 2012; Ongur et al., 2010). One study reported no significant difference between patients and controls (Wolf et al., 2011). The mixed findings may be attributed to many factors, including differences in studied populations, sample size, approaches of data analysis, statistical methods, and the potential effects of medication. In the
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present study, we recruited patients only with first-episode, drug-naive schizophrenia to reduce such confounds as medication and number of recurrence. In particular, the mixed findings are revealed by an FC method focusing on the correlations of two brain regions. It is unclear which brain region is abnormal when two regions exhibit abnormal FC, although this method offers important information. Some aspects of the intrinsic network organization in the DMN might have been long overlooked. Therefore, the advent of new methods for analyzing imaging data is urgently required. Network homogeneity (NH), a method suggested by Uddin et al. (2008), is one potential informative approach that would be to offer an unbiased survey of a distributed network of interest, looking for brain regions showing disease-related reduction in network coherence. NH is a voxel-wise measure that provides an assessment of a voxel's correlation with all other voxels within a given network. The mean correlation of a given voxel is defined as the homogeneity. Alternatively, two methods, independent component analysis (ICA) and seed-based region of interest (ROI), are the most widely employed to analyze the resting-state brain networks previously. ICA is a model-free method with the power to assess largely overlapping spatial process. Although ICA has the power to estimate largely overlapping spatial process, it is not clear how best to compare components across subjects and/or between groups (Fox and Raichle, 2007). The seed-based ROI method is utilized to determine the temporal coherence between the timeseries for a given ROI and the timeseries of all other voxels in the brain by using correlation or regression analysis (Biswal et al., 1995). However, the selection and precise placement of ROI seeds can be somewhat arbitrary, and thus have considerable impact on the patterns of FC obtained. In contrast to these two methods, the NH method enables hypothesisdriven interrogation of large-range networks of clinical interest from the viewpoint of assessing the homogeneity of the whole network and therefore combines the advantages of the two previous methods (Uddin et al., 2008). Furthermore, NH is well applied in attentiondeficit/hyperactivity disorder (ADHD) (Uddin et al., 2008). In the present study, NH was utilized to analyze the data from patients with first-episode, drug-naive schizophrenia. Based on previous FC findings of the DMN abnormalities in schizophrenia (Bluhm et al., 2007; Camchong et al., 2011; Jang et al., 2011; Mannell et al., 2010; Mingoia et al., 2012; Ongur et al., 2010; Rotarska-Jagiela et al., 2010; Skudlarski et al., 2010; Whitfield-Gabrieli et al., 2009; Zhou et al., 2007), we hypothesized that abnormal DMN homogeneity would be observed in the patients compared to the controls. We also explored whether abnormal NH was associated with clinical characteristics in the patients. 2. Methods and materials 2.1. Subjects Forty-nine young adults with schizophrenia were recruited from the Mental Health Center, the First Affiliated Hospital, Guangxi Medical University, China. The subjects were from our previous study (Guo et al., 2014). The patients were right-handed, drug-naive, and at their first episode. The duration of untreated psychosis (DUP) for the patients was less than 3 years. Patients were assessed with the Structured Clinical Interview of the DSM-IV (SCID) (First et al., 1997). To reduce the heterogeneity of symptom manifestations and potentially underlying pathology, only patients who met diagnostic criteria for paranoid schizophrenia according to the DSM-IV were included in the study, although this subtype is not used in the new DSM-V classification system. Exclusion criteria were neurological disorders, severe medical disorders, substance abuse, or electroconvulsive therapy. The symptoms were rated by the Positive and Negative Symptom Scale (PANSS) at the scan time. Fifty right-handed healthy controls were recruited from the community. None of them had a history of severe medical or neuropsychiatric
17
illness or a family history of severe medical or neuropsychiatric illness in their first-degree relatives. The controls were group matched with the patients in age, sex and educational level. All subjects gave their written informed consent to participate in the study. The local ethics committee of the First Affiliated Hospital of Guangxi Medical University approved the study.
2.2. Scan acquisition Imaging was obtained on a Siemens 3 T scanner. Subjects were required to lie still, keep their eyes closed, and remain awake. A prototype quadrature birdcage head coil fitted with foam padding was utilized to limit head motion. The following parameters were used for functional imaging: repetition time/echo time (TR/TE) = 2000/30 ms, 30 slices, 64 × 64 matrix, 90˚ flip angle, 24 cm FOV, 4 mm slice thickness, 0.4 mm gap, and 250 volumes (500 s).
2.3. Data preprocessing Data preprocessing was conducted in Matlab (Mathworks) using Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan and Zang, 2010). After the correction for slice timing and head motion, the subjects should have no more than 2 mm maximum displacement in x, y, or z and 2˚ of angular motion. Then the images were normalized and resampled to 3 × 3 × 3 mm3. Afterwards, the generated images were temporally bandpass filtered (0.01–0.08 Hz) and linearly detrended.
2.4. DMN identification As suggested by two recent studies (Guo et al., 2013b; Liu et al., 2012), the group ICA method was implemented to pick out the DMN as a mask from healthy controls. Three main steps, including data reduction, independent component (IC) separation, and back reconstruction, were conducted with the toolbox GIFT (http://mialab.mrn.org/ software/#gica). The DMN was selected according to the templates provided by GIFT, which was used as a mask in the following NH analysis.
2.5. NH analysis NH analysis was performed in Matlab (Mathworks). For each subject, the correlation coefficients were calculated in a given voxel with all other voxels within the DMN mask. The mean correlation coefficient was defined as the homogeneity of the given voxel. The resultant values generated the NH maps, which were smoothed with a Gaussian kernel of 8 mm full-width at half-maximum. Finally, the NH maps within the DMN mask were applied for group comparison.
2.6. Statistical analyses Demographics including age, sex and educational level and imaging data were compared between the patients and the controls. Categorical data were analyzed with Chi-square test and continuous variables were analyzed with two-sample t-test. The NH analysis was implemented with two-sample t-tests via voxel-wise cross-subject statistics in the DMN. The significant level was set at the corrected p b 0.05 for multiple comparisons using Gaussian Random Field (GRF) theory (min z N 1.96, cluster significance: p b 0.05). In addition, brain regions with abnormal NH were identified as ROIs. Mean NH values of these ROIs were extracted for further correlation analysis between these NH values and the PANSS scores or the DUP in the patient group (p b 0.05).
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3. Results 3.1. Demographics and clinical characteristics of the subjects As shown in Table 1, the patient group and the control group did not differ significantly in age (t-tests t = − 1.05, df = 97, p = 0.30), sex ratio (Chi-square test x2 = 2.31, df = 1, p = 0.13), and years of education (t-tests t = −1.23, df = 97, p = 0.22). The mean DUP of the illness was 22.45 ± 6.71 months and the mean PANSS total score was 91.31 ± 10.98 in the patient group. 3.2. The DMN maps determined by group ICA Using a group ICA approach, the DMN mask was picked out from the control group. The DMN included brain regions such as bilateral MPFC, ventral anterior cingulate cortex (ACC), PCC/PCu, lateral temporal cortex, medial, lateral, inferior parietal lobe, and cerebellum Crus I and Crus II (Fig. 1). The obtained DMN mask was utilized in the following NH analysis. 3.3. NH: group differences in the DMN Significant group difference of NH values in the DMN between the patients and the controls was found with two-sample t-tests via voxel-wise cross-subject comparisons within the DMN mask. Compared to the controls, patients had lower NH in the left MPFC and the right middle temporal gyrus (MTG). Significantly higher NH values in the left PCC and the right cerebellum Crus I were found in the patient group than in the control group (Fig. 1 and Table 2). 3.4. Correlations between NH and clinical variables The mean NH values were extracted in the four regions (left MPFC, right MTG, left PCC, and right cerebellum Crus I) with significant group differences. Linear correlations were conducted between NH and PANSS scores, DUP, age or years of education in the patient group. No significant correlation was found between NH and PANSS scores, DUP, age or years of education in the patient group. 4. Discussion Using the NH method, we provided an unbiased survey of the DMN in first-episode, drug-naive schizophrenia at rest. The primary findings were that the patients showed significantly lower NH in the left MPFC and the right MTG and higher NH in the left PCC and the right cerebellum Crus I compared to the controls. There was no significant correlation between abnormal NH of the four above-mentioned brain regions and PANSS scores, DUP, age or years of education in the patient group. The findings of abnormal NH in the DMN are in line with previous results with altered FC in the DMN in schizophrenia (Bluhm et al., 2007; Camchong et al., 2011; Jang et al., 2011; Mannell et al., 2010; Table 1 Characteristics of the participants. Demographic data
Patients (n = 49)
Controls (n = 50)
p value
Gender (male/female) Age (years) Years of education (years) Illness duration (months) PANSS Positive scores Negative scores Total scores
30/19 22.69 ± 4.62 10.94 ± 2.40 22.45 ± 6.71
23/27 23.48 ± 2.49 11.46 ± 1.78 –
0.13a 0.30b 0.22b
22.27 ± 5.33 22.82 ± 6.86 91.31 ± 10.98
– – –
a The P value for gender distribution was obtained by chi-square test. b The P values were obtained by two samples t-tests. PANSS = Positive and Negative Symptom Scale
Mingoia et al., 2012; Ongur et al., 2010; Rotarska-Jagiela et al., 2010; Skudlarski et al., 2010; Whitfield-Gabrieli et al., 2009; Zhou et al., 2007), and add a new clue of abnormal network coherence for the participation of the DMN in the pathophysiology of schizophrenia. As a core component of the DMN, the MPFC is observed to show decreased activity during prefrontal-cortex-related cognitive activity in schizophrenia patients (Spironelli et al., 2011). Meanwhile, evidence of increased MPFC activation has documented on a parametric version of the n-back working memory task in schizophrenia (Callicott et al., 2000). However, studies that examine schizophrenia patients at rest observed decreased activity in the MPFC (Ongur et al., 2010; Schultz and Andreasen, 1999; Tang et al., 2013). Furthermore, gray matter reduction is reported in both chronic schizophrenia (Honea et al., 2005) and first-episode schizophrenia (Chan et al., 2011). The inconsistency of decreased and increased MPFC activation in schizophrenia may attribute to the differences in studied population, such as sample size, symptom severity and illness duration. The discrepancies could also be due to the methodological factors including analytic method, task instructions and parameters for data acquisition (Manoach, 2003). Our sample of first-episode, drug-naive schizophrenia has its advantage to reduce the confounding factors such as medication and number of recurrence. Since NH is a voxel-wise measure of a given voxel's correlation with all other voxels within the DMN, we speculated that gray matter reduction and/or decreased activity at rest in the MPFC would affect the correlations between the MPFC and other voxels within the DMN, leading a decreased NH in the left MPFC like the present results. MPFC links widely with both affective-limbic areas (such as the amygdala, hippocampus, and hypothalamus) and executive control and emotional processing areas (such as the ACC) (Alalade et al., 2011). Through these connections, MPFC may act as its key role in modulation of emotional behavior and self-referential processing in schizophrenia (Yu et al., 2012). Our results of decreased NH in the left MPFC are also in line with the issue that dysregulation of medial frontal areas is associated with selfdirected thoughts, with the consequence that the source of external and internal stimuli would become confused, which may offer a pathophysiological basis for hallucinations (Whitfield-Gabrieli et al., 2009). Of course, this notion warrants more studies. The temporal gyrus has proven to be a common target for both structural and functional study in schizophrenia. Among the many volumetric studies, one of the most widely replicated findings is a volume reduction of the superior temporal gyrus (STG) from neuroimaging studies (Honea et al., 2005). Recently, gray matter deficits in the right MTG are reported to be present during a very early stage of schizophrenia (Lui et al., 2009). The same study also observed a positive correlation between PANSS scores for thought disturbance and MTG-putamen connectivity. Regional gray matter reduction in bilateral MTG is found in first-episode, drug-naive schizophrenia (Hu et al., 2013). In contrast to structural findings, abnormal patterns of FC have been reported in both task-based process and resting state (Hashimoto et al., 2010; Mechelli et al., 2007; Zhou et al., 2008), leading to the idea of schizophrenia as a disconnection syndrome (Friston, 1998). Our results of decreased NH in the right MTG are comparable to the previous studies, supporting that functional deficits in the right MTG may be present during an early stage of schizophrenia. The MTG plays a critical role in semantic memory and language processing (Kiehl et al., 2004). Thus, decreased NH in the right MTG may be related to the cognitive deficits seen in schizophrenia, although cognitive function is not assessed in the present study. As the posterior midline core region of the DMN, the PCC subserves a variety of cognitive functions, especially those associated with longterm memory as well as working memory (Maddock, 1999). Schizophrenia patients exhibited an increased resting perfusion or rate in the PCC compared to controls (Andreasen et al., 1997; Haznedar et al., 1997), which was negatively correlated with Scheiderian first-rank symptoms (Franck et al., 2002). Increased activation and connectivity in the PCC have also been previously reported in schizophrenia at rest
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Fig. 1. The DMN mask determined by ICA from the control group and statistical maps showing NH differences between subject groups. Red and blue denote higher and lower NH respectively and the color bars indicate the T value from two-sample t-tests. DMN = default mode network, ICA = independent component analysis, NH = network homogeneity.
(Whitfield-Gabrieli et al., 2009; Woodward et al., 2011). On the other hand, the PCC showed decreased activation in patients with schizophrenia during memory-related tasks (Hofer et al., 2003; Kiehl and Liddle, 2001). One possible interpretation for this paradox is that overcompensation during the resting state results in a failure to respond to excessive demands (Zhou et al., 2005), suggesting that the functional potential of the PCC in schizophrenia is diminished. Consistent with the findings of hyperactivity in the PCC, our results of increased NH in the left PCC are speculated as a compensation mechanism in schizophrenia at rest. Interestingly, the right cerebellum Crus I exhibited increased NH in the patients. The cerebellum is traditionally considered as a region engaged in motor coordination. This issue has been challenged recently by the evidence that the cerebellum subserves emotion and cognition (Guo et al., 2012; Schmahmann, 2010; Stoodley, 2012). In healthy subjects, the cerebellum is reported to be activated during emotional and cognitive tasks. The cerebellar cognitive–affective syndrome is also observed in patients with cerebellar damage (Parvizi et al., 2001; Schmahmann and Sherman, 1998). The cerebellum possibly links with the cerebrum through the cortico–cerebellar–thalamic–cortical circuit, and dysconnectivity of this circuit is suggested in cognitive dysmetria theory of schizophrenia (Friston, 1998). Our results of increased NH in the right Crus I are in line with the cerebellar functional disconnections involving the DMN. The Crus I is conceptualized as a part of the DMN (Alalade et al., 2011; Guo et al., 2013a). As discussed above, the increased NH in the right Crus I possibly reflects a compensatory effort of this region. Since current psychopathology assessed by PANSS is reported to be correlated to the aberrant intrinsic FC in schizophrenia (RotarskaJagiela et al., 2010), we hypothesized that there were significant
Table 2 Significant differences in NH values between groups. Cluster location
Patients b Controls Left MPFC Right MTG Patients N Controls Left PCC Right Crus I
Peak (MNI)
Number of voxels
T value
−3 −15
20 20
−3.9550 −3.2645
42 −27
23 34
3.2728 3.4638
x
y
z
−6 57
60 −9
0 18
−39 −84
NH = network homogeneity; MNI = Montreal Neurological Institute; MTG = Middle Temporal Gyrus; MPFC = medial prefrontal cortex; PCC = posterior cingulate cortex
correlations between the PANSS scores and the abnormal NH values in the patients. Therefore, no correlation between these factors was somewhat unexpected. One possible explanation of no correlation is that abnormal NH of the DMN might be a trait change for the patients independent of the symptom severity. Another possibility is the concentration of symptom severity. For example, the PANSS positive scores are around 22 in the patient group. The present study has several limitations. First, the DMN mask, derived from healthy controls by using group ICA, might introduce a bias in the analyses. However, it is not biased in the same way as traditional seed-based methods that typically focus on a voxel or cluster of voxels (e.g. seed region) as the starting point of correlation analyses (Uddin et al., 2008). Second, the present study focused on the abnormalities of the DMN. It is helpful for highlighting the pathophysiological contribution of the DMN. For the same reason, some meaningful results from other brain areas might be omitted. Despite the limitations, our findings suggest that abnormal NH of the DMN exists in first-episode, drug-naive schizophrenia. They also suggest an important new avenue of exploring the NH in the patients to improve understanding the nature of schizophrenia. Hence, the present results highlight the importance of the DMN in the pathophysiology of schizophrenia. Acknowledgment The study was supported by Grants from the National Natural Science Foundations of China (Grant nos. 81260210 and 30900483), the Natural Science Foundations of Guangxi (Grant no. 2013GXNSFAA019107), and the special funding by the Ministry of Health of the Peoples’ Republic of China (Grant no. 201002003). The authors express their gratitude to Dr. Feng Liu at Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China for statistical assistance. The authors also appreciate anonymous reviewers for their suggestions and comments. References Alalade E, Denny K, Potter G, Steffens D, Wang L. Altered cerebellar-cerebral functional connectivity in geriatric depression. PLoS One 2011;6:e20035. Andreasen NC, O'Leary DS, Flaum M, Nopoulos P, Watkins GL, Boles Ponto LL, et al. Hypofrontality in schizophrenia: distributed dysfunctional circuits in neurolepticnaive patients. Lancet 1997;349:1730–4.
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W. Guo et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 49 (2014) 16–20
Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34:537–41. Bluhm RL, Miller J, Lanius RA, Osuch EA, Boksman K, Neufeld RW, et al. Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull 2007;33:1004–12. Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 2009;33:279–96. Buckner RL, Carroll DC. Self-projection and the brain. Trends Cogn Sci 2007;11:49–57. Callicott JH, Bertolino A, Mattay VS, Langheim FJ, Duyn J, Coppola R, et al. Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex 2000;10:1078–92. Camchong J, MacDonald III AW, Bell C, Mueller BA, Lim KO. Altered functional and anatomical connectivity in schizophrenia. Schizophr Bull 2011;37:640–50. Chan RC, Di X, McAlonan GM, Gong QY. Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression. Schizophr Bull 2011;37:177–88. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). Washington, DC: American Psychiatric Press; 1997. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007;8:700–11. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005;102:9673–8. Franck N, O'Leary DS, Flaum M, Hichwa RD, Andreasen NC. Cerebral blood flow changes associated with Schneiderian first-rank symptoms in schizophrenia. J Neuropsychiatry Clin Neurosci 2002;14:277–82. Friston KJ. The disconnection hypothesis. Schizophr Res 1998;30:115–25. Guo W, Liu F, Xue Z, Gao K, Liu Z, Xiao C, et al. Abnormal resting-state cerebellar-cerebral functional connectivity in treatment-resistant depression and treatment sensitive depression. Prog Neuropsychopharmacol Biol Psychiatry 2013a;44:51–7. Guo W, Liu F, Zhang J, Zhang Z, Yu L, Liu J, et al. Dissociation of regional activity in the default mode network in first-episode, drug-naive major depressive disorder at rest. J Affect Disord 2013b;151:1097–101. Guo W, Xiao C, Liu G, Wooderson SC, Zhang Z, Zhang J, et al. Decreased resting-state interhemispheric coordination in first-episode, drug-naive paranoid schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2014;48:14–9. Guo WB, Liu F, Chen JD, Gao K, Xue ZM, Xu XJ, et al. Abnormal neural activity of brain regions in treatment-resistant and treatment-sensitive major depressive disorder: A resting-state fMRI study. J Psychiatr Res 2012;46:1366–73. Hashimoto R, Lee K, Preus A, McCarley RW, Wible CG. An fMRI study of functional abnormalities in the verbal working memory system and the relationship to clinical symptoms in chronic schizophrenia. Cereb Cortex 2010;20:46–60. Haznedar MM, Buchsbaum MS, Luu C, Hazlett EA, Siegel Jr BV, Lohr J, et al. Decreased anterior cingulate gyrus metabolic rate in schizophrenia. Am J Psychiatry 1997;154: 682–4. Hofer A, Weiss EM, Golaszewski SM, Siedentopf CM, Brinkhoff C, Kremser C, et al. Neural correlates of episodic encoding and recognition of words in unmedicated patients during an acute episode of schizophrenia: a functional MRI study. Am J Psychiatry 2003;160:1802–8. Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry 2005;162:2233–45. Hu M, Li J, Eyler L, Guo X, Wei Q, Tang J, et al. Decreased left middle temporal gyrus volume in antipsychotic drug-naive, first-episode schizophrenia patients and their healthy unaffected siblings. Schizophr Res 2013;144:37–42. Jang JH, Jung WH, Choi JS, Choi CH, Kang DH, Shin NY, et al. Reduced prefrontal functional connectivity in the default mode network is related to greater psychopathology in subjects with high genetic loading for schizophrenia. Schizophr Res 2011;127:58–65. Kiehl KA, Liddle PF. An event-related functional magnetic resonance imaging study of an auditory oddball task in schizophrenia. Schizophr Res 2001;48:159–71. Kiehl KA, Smith AM, Mendrek A, Forster BB, Hare RD, Liddle PF. Temporal lobe abnormalities in semantic processing by criminal psychopaths as revealed by functional magnetic resonance imaging. Psychiatry Res 2004;130:27–42. Liu CH, Ma X, Li F, Wang YJ, Tie CL, Li SF, et al. Regional homogeneity within the default mode network in bipolar depression: a resting-state functional magnetic resonance imaging study. PLoS One 2012;7:e48181. Lui S, Deng W, Huang X, Jiang L, Ma X, Chen H, et al. Association of cerebral deficits with clinical symptoms in antipsychotic-naive first-episode schizophrenia: an optimized voxel-based morphometry and resting state functional connectivity study. Am J Psychiatry 2009;166:196–205.
Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, et al. Functional connectivity and brain networks in schizophrenia. J Neurosci 2010;30:9477–87. Maddock RJ. The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain. Trends Neurosci 1999;22:310–6. Mannell MV, Franco AR, Calhoun VD, Canive JM, Thoma RJ, Mayer AR. Resting state and task-induced deactivation: a methodological comparison in patients with schizophrenia and healthy controls. Hum Brain Mapp 2010;31:424–37. Manoach DS. Prefrontal cortex dysfunction during working memory performance in schizophrenia: reconciling discrepant findings. Schizophr Res 2003;60:285–98. Mechelli A, Allen P, Amaro Jr E, Fu CH, Williams SC, Brammer MJ, et al. Misattribution of speech and impaired connectivity in patients with auditory verbal hallucinations. Hum Brain Mapp 2007;28:1213–22. Mingoia G, Wagner G, Langbein K, Maitra R, Smesny S, Dietzek M, et al. Default mode network activity in schizophrenia studied at resting state using probabilistic ICA. Schizophr Res 2012;138:143–9. Molnar-Szakacs I, Arzy S. Searching for an integrated self-representation. Commun Integr Biol 2009;2:365–7. Ongur D, Lundy M, Greenhouse I, Shinn AK, Menon V, Cohen BM, et al. Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Res 2010;183:59–68. Parvizi J, Anderson SW, Martin CO, Damasio H, Damasio AR. Pathological laughter and crying: a link to the cerebellum. Brain 2001;124:1708–19. Picchioni MM, Murray RM. Schizophrenia. BMJ 2007;335:91–5. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676–82. Rotarska-Jagiela A, van de Ven V, Oertel-Knochel V, Uhlhaas PJ, Vogeley K, Linden DE. Resting-state functional network correlates of psychotic symptoms in schizophrenia. Schizophr Res 2010;117:21–30. Schmahmann JD. The role of the cerebellum in cognition and emotion: personal reflections since 1982 on the dysmetria of thought hypothesis, and its historical evolution from theory to therapy. Neuropsychol Rev 2010;20:236–60. Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain 1998;121(Pt 4):561–79. Schultz SK, Andreasen NC. Schizophrenia. Lancet 1999;353:1425–30. Skudlarski P, Jagannathan K, Anderson K, Stevens MC, Calhoun VD, Skudlarska BA, et al. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry 2010;68:61–9. Spironelli C, Angrilli A, Calogero A, Stegagno L. Delta EEG band as a marker of left hypofrontality for language in schizophrenia patients. Schizophr Bull 2011;37:757–67. Stephan KE, Friston KJ, Frith CD. Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull 2009;35:509–27. Stoodley CJ. The cerebellum and cognition: evidence from functional imaging studies. Cerebellum 2012;11:352–65. Tang J, Liao Y, Song M, Gao JH, Zhou B, Tan C, et al. Aberrant default mode functional connectivity in early onset schizophrenia. PLoS One 2013;8:e71061. Uddin LQ, Kelly AM, Biswal BB, Margulies DS, Shehzad Z, Shaw D, et al. Network homogeneity reveals decreased integrity of default-mode network in ADHD. J Neurosci Methods 2008;169:249–54. Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A 2009;106:1279–84. Wolf ND, Sambataro F, Vasic N, Frasch K, Schmid M, Schonfeldt-Lecuona C, et al. Dysconnectivity of multiple resting-state networks in patients with schizophrenia who have persistent auditory verbal hallucinations. J Psychiatry Neurosci 2011;36: 366–74. Woodward ND, Rogers B, Heckers S. Functional resting-state networks are differentially affected in schizophrenia. Schizophr Res 2011;130:86–93. Yan C, Zang Y. DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Front Syst Neurosci 2010;4:13. Yu R, Chien YL, Wang HL, Liu CM, Liu CC, Hwang TJ, et al. Frequency-specific alternations in the amplitude of low-frequency fluctuations in schizophrenia. Hum Brain Mapp 2012. http://dx.doi.org/10.1002/hbm.22203. [Epub ahead of print]. Zhou SY, Suzuki M, Hagino H, Takahashi T, Kawasaki Y, Matsui M, et al. Volumetric analysis of sulci/gyri-defined in vivo frontal lobe regions in schizophrenia: precentral gyrus, cingulate gyrus, and prefrontal region. Psychiatry Res 2005;139:127–39. Zhou Y, Liang M, Tian L, Wang K, Hao Y, Liu H, et al. Functional disintegration in paranoid schizophrenia using resting-state fMRI. Schizophr Res 2007;97:194–205. Zhou Y, Shu N, Liu Y, Song M, Hao Y, Liu H, et al. Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophr Res 2008;100:120–32.
