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Functional network-based statistics in depression: Theory of mind subnetwork and importance of parietal region
Author’s Accepted Manuscript Functional network-based statistics in depression: theory of mind subnetwork and importance of parietal region Chien-Han Lai, Yu-Te Wu, Yuh-Ming Hou www.elsevier.com/locate/jad
PII: DOI: Reference:
S0165-0327(17)30172-6 http://dx.doi.org/10.1016/j.jad.2017.03.073 JAD8874
To appear in: Journal of Affective Disorders Received date: 25 January 2017 Revised date: 27 March 2017 Accepted date: 30 March 2017 Cite this article as: Chien-Han Lai, Yu-Te Wu and Yuh-Ming Hou, Functional network-based statistics in depression: theory of mind subnetwork and importance of parietal region, Journal of Affective Disorders, http://dx.doi.org/10.1016/j.jad.2017.03.073 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Functional network-based statistics in depression: theory of mind subnetwork and importance of parietal region
Chien-Han Lai1,2,3*, Yu-Te Wu2,3, Yuh-Ming Hou1 1
Department of Psychiatry, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi City, Taiwan, ROC 2 Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan, ROC 3 Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, ROC
*Corresponding author. Chien-Han Lai, M.D., Department of Psychiatry, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi City, Taiwan, ROC, No.539, Zhongxiao Rd.,East Dist.,Chiayi City, Taiwan, ROC, Telephone: +886-5-2765041, Fax: +886 05-2765040, E-mail: [email protected]
Objective The functional network analysis of whole brain is an emerging field for research in depression. We initiated this study to investigate which subnetwork is significantly altered within the functional connectome in major depressive disorder (MDD). Methods The study enrolled 52 first-episode medication-naïve patients with MDD and 40 controls for functional network analysis. All participants received the resting-state functional imaging using a 3-Tesla magnetic resonance scanner. After preprocessing,
we calculated the connectivity matrix of functional connectivity in whole brain for each subject. The network-based statistics of connectome was used to perform group comparisons between patients and controls. The correlations between functional connectivity and clinical parameters were also performed. Results MDD patients had significant alterations in the network involving “theory of mind” regions, such as the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus. The center node of significant network was the left angular gyrus. No significant correlations of functional connectivity within the subnetwork and clinical parameters were noted. Conclusion Functional connectivity of “theory of mind” subnetwork may be the core issue for pathophysiology in MDD. In addition, the center role of parietal region should be emphasized in future study. Keywords: depression; functional connectivity; network-based statistics; theory of mind; left angular gyrus
Significant outcomes: . In addition to traditional theory for depression, “theory of mind” could be proved in
current connectome analysis. . In the subnetwork, the core node is the left angular gyrus. . The connecting spots with the core node include the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus. Limitations: . No significant correlations between depression severity and functional connectivity. . Lack of social cognition data and just functional connectome data also limited the interpretations. . Cross-sectional study design, not the longitudinal design, would also be another limitation.
The brain pathophysiology of major depressive disorder (MDD) is an entry to understand the biological origin of depression. The initial theory of “limbic-cortical-striatal-pallidal-thalamic tract” (Sheline, 2000) suggested possible existence of network-based alterations for MDD. The previous studies also demonstrated the possible involvement of network (de Kwaasteniet et al., 2013; Lai et
al., 2010; Lai and Wu, 2014b). However, the study of whole brain network-based statistics in MDD is very limited. It would be a possible obstacle for the further resolution of network pathophysiology in MDD. The network of “theory of mind” has been discovered in healthy participants. It includes cognitive subnetwork, such as the precuneus, occipital lobe and temporal lobe, combined with affective subnetwork, such as the frontal lobe (Schlaffke et al., 2015). The network is important for implicit and explicit mental activities, such as self monitoring and reflections. They are crucial for social cognition (Frith and Frith, 2012). The frontal lobe serve as the center of human mental processing and control the self thoughts, which is derived from the gateway hypothesis (Burgess et al., 2007). The “theory of mind” is a possible theory for many mental illnesses, including MDD. Impaired domains of “theory of mind”, such as social-perceptual and social-cognitive components, have been discovered in patients with MDD (Wang et al., 2008). The patients with MDD have difficult in reading social interactions (Zobel et al., 2010), which would be associated with chronicity and functional decline. In the system of “theory of mind”, major parts include language processing regions and cognitive regions, such as the angular gyrus (ANG), inferior frontal gyrus (IFG) and temporal gyrus (Mason and Just, 2011). These regions could cooperate with language and emotional processing networks to modulate the perception of emotional content for
sentences in speech (Herve et al., 2012). In the condition of natural social interaction, a mentalizing network also involves the precentral gyrus (PreCG) and other sensory regions, which could process the social stimuli and play an important role in social cognition (Saggar et al., 2014). A recent functional connectivity study showed that alterations in connections between the regions of “theory of mind”, such as the parietal, frontal and temporal regions. In addition, the alterations were associated with emotional dysregulation and extensive processing of self-referential information (O'Neill et al., 2015). The literature suggests the crucial role of “theory of mind” regions in the pathophysiology of depression. However, the study was derived from borderline personality disorder to obtain the results. Therefore the importance of “theory of mind” hypothesis still needs further study with improvements in methodology to be proved. The whole-brain functional connectome has been an emerging area for the investigation of pathophysiology in mental illnesses. Recently a new method, network-based statistics (NBS), has been applied in the field of connectome analysis. It uses the graph theory concept to derive the large-scale brain connectivity with the control of family wise error due to mass-univariate testing for each connection of the functional or structural connectome (Zalesky et al., 2010). It has been applied in several psychiatric illnesses, such as functional connectome in schizophrenia (Zalesky
et al., 2010) and structural connectome in MDD (Korgaonkar et al., 2014; Long et al., 2015). The two reports of structural connectome found alterations of default mod network and other fronto-limbic networks for emotional and cognitive functions in depression. One functional connectome analysis using graph theory found similar disturbances in the network involving mood and cognition (Zhang et al., 2011). From the above literature, we designed this study using resting-state functional magnetic resonance imaging (Rs-FMRI) technique to survey the brain connectivity in MDD patients. In addition, we applied the connectivity matrix analysis and NBS analysis of Rs-FMRI signals in MDD. Based on the “theory of mind” model, we hypothesized that patients with MDD would have reductions in the functional connectome of “theory of mind” network, such as the ANG, PreCG, IFG and temporal lobe, using a relatively unbiased method.
Participants:
All patients with MDD were met for the following criteria: (1) first-episode patients with a pure MDD diagnosis (DSM-IV criteria) made by the Structured Clinical Interview for DSM-IV; (2) severity of MDD was at least moderate: Clinician Global Impression of Severity > 4, Hamilton Rating Scales for Depression (HDRS) score >
20, Hamilton Rating Scales for Anxiety (HARS) score < 5; (3) no co-morbid psychiatric illnesses or medical illnesses; (4) no previous cognitive behavioural therapy or other psychotherapies; (5) medication-naïve; (6) no abuse of or dependence on alcohol or other substances; and (7) no past history of claustrophobia or discomfort while receiving MR scanning. The healthy controls had no psychiatric illnesses or significant medical illnesses. All of the patients and part of healthy subjects signed the informed consent that was approved by the three Institutional Review Boards at Taipei Tzu Chi Hospital, Cheng Hsin General Hospital and National Yang-Ming University according to the institute where they were recruited. The patients were enrolled at Taipei Tzu Chi Hospital and Cheng Hsin General Hospital. The controls were enrolled from Taipei Tzu Chi Hospital, Cheng Hsin General Hospital and National Yang-Ming University. At the time of the MR imaging, none of the participants in the control group received psychotropic treatment. Handedness was determined by using the Edinburgh Inventory of Handedness (Oldfield, 1971). The sample of participants has some overlaps of our previous reports (Lai and Wu, 2013, 2014a). In this study, we enrolled 52 patients with MDD (26 men and 26 women; 40.26±8.97) and 40 controls (20 men and 20 women; 39±11.81) (Table 1). 44 of the 52 patients and 27 of the controls have been previously reported (Lai and Wu, 2015). This prior article dealt with regional homogeneity alterations in depression patients
whereas in this manuscript we report on the whole-brain functional connectome analysis in depression patients.
Rs-FMRI data acquisition and pulse sequence
Echo planar imaging (EPI) sequence were acquired in 20 axial slices (TR=2000ms, TE=40ms, flip angle=90°, field of view=24cm; 5mm thickness and 1 mm gap; the sequence duration was 300 seconds for each subject, 150 time points were acquired, voxel dimension: 64x64x20) at baseline visit (3T Siemens scanner housed at MRI (magnetic resonance imaging) center of National Yang Ming University) in patients and controls. All the patients and controls were requested to close their eyes with relaxing manner and not sleepy while scanning. The participating subjects were instructed to move as little as possible and stay fully awake while scanning. All these patients and controls reported that they could be fully awake while MRI scanning.
Rs-FMRI data preprocessing
EPI data was first preprocessed by DPARSF (Data Processing Assistant and Resting-State FMRI, version 2.2; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.) (Chao-Gan and Yu-Feng,
2010) working with the statistical parametric mapping 8 (SPM8) on the Matlab platform, which included the removal of first 10 time points due to the consideration for instability of initial MRI signal and patients’ difficult to adapt at first about MRI acquisition circumstance, slice timing with 20th slice as reference slice, realignment, normalization to standard MNI spaces by using EPI templates and re-sampling with 3 x 3 x 3 mm3, smoothing by Full Width at Half Maximum (FWHM) 4x4x4 kernel, to detrend and filter data with residual signals within 0.01-0.08 Hz to discard the bias from high-frequency physiological noise and low-frequency drift. As all subjects’ head movements were less than 0.5 mm in translation and 1 degree in rotation by obtaining the motion time courses of all subjects, no subject was excluded due to no excessive motions were observed. The filtered Rs-FMRI data were registered (nonlinear elastric registration) to the EPI template. The estimated motion parameters were obtained for each subject regressed on each voxel. Besides, the effects of “micromovements” and the nuisance correlation caused by head motion were removed by checking covariates in nuisance regressors in DPARSF (Power et al., 2012; Yan et al., 2013a). Several sources of spurious covariates were removed except global signals due to the controversy about removing the global signal in the resting-state functional connectivity data (Fox et al., 2005; Murphy et al., 2009). The individual-level covariates of motion included Friston-24 parameter model (6 head
motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items) and group-level covariates of motion included framewise displacement motion regression model (Yan et al., 2013b).
Connectivity matrix measures
We used the GRETNA toolbox (https://www.nitrc.org/projects/gretna) to calculate the connectivity matrix for the preprocessed Rs-FMRI data of each subject. GRETNA toolbox has been designed for the graph-theoretical network analysis of Rs-FMRI data. It is a suite of MATLAB functions and MATLAB-based Interfaces to perform the process of conventional functional preprocessing, as well as to calculate most frequently used connectivity matrix.
NBS analysis
The NBS was used to identify significant differences between patients and controls for pairwise associations using whole-brain approach. It incorporated graph model to identify the pairwise association, such as the connection or link, between different pairs of nodes (Zalesky et al., 2010). The processing steps were as follows: (1) The design matrix was set at first to define the group and number for subjects of
patients and controls. Then we put the connectivity matrix file for each subject as the input data. From the probability point, the N(N−1)/2 unique pairwise associations would be possible for an N×N connectivity matrix. For each pairwise association, the test statistic of interest was calculated independently using the values stored in each subject's connectivity matrix, such as Fisher's r-to-z transform for a correlation based measure of association to ensure normality. (2) The AAL-90 nodes and labels were used to define and locate the significant nodes within the significant subnetwork in a graph model. The AAL-90 was chose due to relatively accurate localization of coordinates and similar application in NBS methodology was also mentioned in previous report (Hong et al., 2013). A nonparametric test was used for a breadth first search. This was repeated for multiple permutations to estimate the null distribution. The random permutations were generated independently and the group to which each subject belonged was randomly exchanged for each permutation. The number or size of links the potential nodes comprised according to the threshold (corrected p < 0.05). Permutation testing was used to ascribe a p-value controlled for the FWE to each connected component based on its size. (3) For each permutation, the test statistic of interest is recalculated, after which the
same threshold is applied to define a set of suprathreshold links. The test detected the potential connected structures of suprathreshold links, which could demonstrate the topological extent of all significant structures. The maximal component size in the set of suprathreshold links derived from each of multiple permutations is then determined and stored, thereby yielding an empirical estimate of the null distribution of maximal component size. (4) Finally, the p-value (0.05) of an observed component of size k was estimated by finding the total number of permutations to identify maximal component size.
Statistical analysis
For the NBS, we performed the two dimension comparison of MDD and NC. The contrasts were set as “controls > MDD” and “MDD < controls”. The number of permutations was 5000 and the t statistic threshold was set as t >4.0. The threshold of multiple comparisons was set as corrected p < 0.05. The significant subnetwork would be displayed in glass brain and BrainNet Viewer (Xia et al., 2013). For the demographic and clinical data, such as age, educational years and HDRS scores, the differences between patients and controls were estimated by the Mann-Whitney U test. Group differences of gender were analysed by a chi-square test. In addition, we examined the correlations between functional connectivity and clinical variables (e.g., depression severity, anxiety severity, illness duration) and demographic variables (age,
gender) that were used as covariates in the main analyses in each group and across both groups. For the statistical threshold of the correlations, we also used the corrected p < 0.05. The correlation assessment was only used for significant network edges identified by the NBS method.
Results Demographic data
No significant differences of age, gender, educational years, HARS scores and handedness were found between patients and controls. The duration of illness in patient group were 4.52±1.60 months. Only the HDRS scores were significantly different between patients and controls (Table 1).
Functional connectome results by NBS
There was only one subnetwork with significant differences in functional connectivity matrix between patients and controls (corrected p < 0.05). The results were derived from the contrast “controls > MDD”, which meant that MDD patients have lower functional connectivity strength than controls in the above subnetwork. The subnetwork consisted of 5 nodes and 4 edges. The 5 nodes consisted of ANG.L (left AG), PreCG.L (left PreCG), IFGoperc.L (left IFG, opercular part), ROL.L (left
rolandic operculum) and ROL.R (right rolandic operculum). The 4 edges were PreCG.L to ANG.L, IFGoperc.L to ANG.L, ROL.L to ANG.L and ROL.R to ANG.L (Table 2, Figure 1). The difference manifestation suggested that MDD patients had greater decoupling activities than controls over these identified edges. There were no significant results derived from the contrast of “MDD > controls”. Neither MDD severity nor duration of illness were correlated with the network connections identified in the NBS analysis.
Discussion
In current study, we found a “theory of mind” subnetwork with significant reductions in connectivity matrix of the patients with MDD. There were 5 nodes and 4 edges, which seemed to have the ANG.L as the terminal center node for the 4 edges from other 4 nodes, the PreCG.L, IFGoperc.L, ROL.L and ROL.R. The 4 edges all directed to the ANG.L, which suggested possibly crucial role of parietal lobe in the “theory of mind” subnetwork. However, the connectivity measures of the 4 edges were not correlated with severity or illness duration of MDD. Our study results demonstrated improvements in research methods when compared with previous seed-based analysis method (O'Neill et al., 2015). In addition, the importance of “theory of mind” network in MDD was supported due to the first-episode medicine-naïve patients with MDD and positive findings within the network.
However, it seemed not compatible with the right-lateralized core network of temporo-parietal junction (ANG) for the function of filtering external stimulus (Langner et al., 2012). It probably suggested that the alterations of theory of mind network probably might be more related to cognitive function, not sensory part of alterations.
Several previous studies mentioned the deficits of “theory of mind” function in depression patients (Wang et al., 2008; Wolkenstein et al., 2011; Zobel et al., 2010). MDD may be associated with the impairments in integrating contextual information for reasoning of social situation and mental state modality of judgement (Wolkenstein et al., 2011). In current study, all the edges terminated at the ANG.L, which suggested the crucial harbor role of ANG.L for a significant subnetwork in MDD. The ANG.L would play an important role in the integration of linguistic material and world knowledge in context, which is crucial for social communication and speech comprehension ability (Bambini et al., 2011). In addition, ANG.L would also work with the IFG and temporal sulcus (overlapping with ROL.R and ROL.L) for the recognition of communicative intention from others (Bambini et al., 2011). The “theory of mind” network consists of the ANG.L, IFG, PreCG and temporal lobe (Herve et al., 2012; Mason and Just, 2011), which is also in line with our study results. The ANG.L is also associated with memory retrieval and post-retrieval decision
making (Sestieri et al., 2011). The decreased functional connectivity between ANG.L and frontal region has been discovered to correlate with poorer neurocognitive function in treatment-resistant depression patients (de Kwaasteniet et al., 2015). However, other treatment-resistant studies showed dissimilar results, which more focused on default mode network, fronto-limbic network or cerebellar-cerebral network (Li et al., 2013; Sawaya et al., 2015; van Waarde et al., 2015). The similar finding of decreased connectivity between ANG.L, temporal lobe and frontal lobe has also been found in the initial phase of depression. In addition, the pattern of decreased functional connectivity has been observed in longitudinal follow up (Strikwerda-Brown et al., 2014). The results of ANG.L in current study also supported the possible role of cross-modal hub to handle the integration of multi-sensory information, interpersonal information in social situation, mental representations and attention to relevant information (Seghier, 2013; Spreng and Mar, 2012). From the above literature and our study results, we suggested that ANG.L might a center hub for the deficient “theory of mind” subnetwork in MDD. Further work of network analysis in MDD should be more focused on parietal lobe. The weak connection between ANG.L and IFG supported the involvement of cognitive region in the “theory of mind” subnetwork (Bambini et al., 2011). Apart from the role of cognition, the IFG also participate the affective processing inside the
theory of mind network (Bodden et al., 2013). The reduction in functional connectivity of this connection has been mentioned in previous study (Guo et al., 2013). In addition, Granger causality analysis showed inhibitory effect between the ANG.L, IFGoperG.L and temporal lobe in MDD (Guo et al., 2014), which also supported the hypothesis of language processing regions in the theory of mind (Mason and Just, 2011). This connection also participated in the fronto-parietal mirror neuron system, the basis of “theory of mind”, to handle cognitive and affective empathy in social situation (Hooker et al., 2010). The IFG and ROL also participated in working memory and attention (Li et al., 2012). The IFG and ANG were also correlated with the integration of personal and interpersonal information, which facilitate the formation of social conceptual knowledge, social memory and strategic social behaviors (Spreng and Mar, 2012). The connection between ANG.L and ROL.R or ROL.L could correspond to the role of parieto-temporal involvement in the affective “theory of mind” network (Bodden et al., 2013). This connection also involved in the emotional response to auditory information and temporal dynamics of emotional processing (Koelsch et al., 2006). In addition, the ROL is involved in the implicit response of emotion to external information and emotional recognition (Gebauer et al., 2014). The ROL region is also a part of temporal lobe. The clinical symptoms of MDD interact with memory
functions in the temporal lobe (Goveas et al., 2011). The ROL and PreCG are important for the phonological rehearsal and language processing (Veroude et al., 2010). Affective “theory of mind” regions also included the PreCG (Bodden et al., 2013), which could modulate the affective empathy (Hooker et al., 2010), social interaction (Saggar et al., 2014) and motor-related function within the large-scale networks in MDD (Peng et al., 2015). The decreased functional connectivity between ANG.L and PreCG might represent the abnormalities in affective, social and motor functions of patients with MDD. For the negative results of correlation between functional connectivity of 4 edges and clinical measures, only one previous study revealed the association of network organizations and clinical parameters in MDD (Zhang et al., 2011). However, such association was not replicated in current study. The lack of correlation between functional connectivity and clinical parameters may represent a different type of network revealed by the different type of methodology (Zhang et al., 2011) or probably a distinct endophenotype not accounting for chronicity or symptom severity in depression. There were several limitations in current study. First, the finding regions in current study were not very specific for the “theory of mind”. In addition, the findings were relatively large with encompassing regions not very specific for theory of mind. It
seemed reverse influential and possible real-world applications will go beyond the simple interpretations of psychological process (Poldrack and Farah, 2015). Therefore the potentially different versions of interpretations should not be ignored. Second, the cross-sectional design would limit the interpretations of our study results. A future longitudinal study would help us confirm the importance of theory of mind subnetwork in MDD. Third, this study just provided the functional connectome results without the ground of structural connectome data. The future NBS analysis with structural imaging data derived from as T1 gray matter imaging and diffusion tensor imaging, would help illuminate the structural connectome ground for functional connectome results. Fourth, the bandpass filtering might be considered to be performed after nuisance correction, which is suggested to be with less nuisance-related variability. Fifth, functional connectome measures the functional connectivity according to the signals of hemodynamics. The alterations of functional connectome are probably related to the hemodynamic changes. However, it is still unknown whether the method of functional connectome can be used to detect early neuronal changes, or monitor disease progression in MDD. Sixth, a task-oriented functional MRI study could complete the viewpoint due to Rs-FMRI characteristics of the functional connectome results. Seventh, the lack of social cognition data in current sample might limit the interpretation of “theory of mind” hypothesis. However, past
studies already proved the existence of social cognition deficits in MDD patients (Wang et al., 2008; Wolkenstein et al., 2011; Zobel et al., 2010). At last, the relatively larger scanning thickness (5mm with 1mm gap) would limit the spatial resolution in the axial direction, which should be interpreted with caution. The effects of normalization, segmentation, registration, resampling and interpolation on graph metrics would be another concern.
Conclusion Functional connectivity of “theory of mind” subnetwork is likely the core issue for pathophysiology of MDD. In addition, the center role of parietal region should be emphasized in future study.
Acknowledgements
Author Contributions: Dr. Lai wrote manuscript, researched data. Dr. Wu reviewed/edited manuscript. We want to thank the grant support from Taipei Tzu-Chi General Hospital project TCRD-TPE-100-02 and the project was also supported by grant from the Cheng Hsin General Hospital and National Yang Ming University cooperative project 104F003C03. We also acknowledge MR support from National
Yang-Ming University, Taiwan, which is in part supported by the MOE plan for the top university.
Sources of financial and material support
Funding: This study was funded by Taipei Tzu-Chi General Hospital project TCRD-TPE-100-02 and grant from the Cheng Hsin General Hospital and National Yang Ming University cooperative project 105F003C03.
Conflict of Interest: All authors stated no conflict of interest.
Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent: Informed consent was obtained from all individual participants
included in the study.
Disclosure statement: The authors have nothing to disclose.
Advances in Knowledge This study can provide the evidence of important role of theory of mind network in the pathophysiology of depression, which has been undervalued in recent years. The whole picture of connectome also can delineate the origin of functional alterations. Implications for Patient Care The results can be useful for public education and preventive medicine. In addition, it will enhance the role of social cognition in the treatment of depression.
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Figure 1: The alterations of whole-brain network-based statistics in MDD. Subfigure 1A (the sagittal glass brain view): The patients with MDD had significant reductions in functional connectivity of “theory of mind” subnetwork within whole brain functional connectome. The altered subnetwork consisted of PreCG.L, ANG.L, ROL.L, ROL.R and IFGoperc.L. (PreCG.L: precental gyrus, ANG.L: angular gyrus, ROL.L: left Rolandic operculum, ROL.R: right Rolandic operculum; IFGoperc.L: left inferior frontal gyrus, opercular part). Subfigure 1B (the BrainNet Viewer): The full view and 3-D view of the altered subnetwork.
Table 1: Demographic data of participating patients and controls Patients (N=52)
Controls (N=40)
Sig p (2-tailed), Z df=63
Age, mean (SD), years old 40.26 (8.97)
39 (11.81)
0.238, -1.17
Gender (number)
F(26), M(26)
F(20), M(20)
0.835
Duration of illness, mean
4.52 (1.60)
0 (0)
N/A
15.78 (0.80)
15.90 (0.59)
0.752, -0.316
Handedness
R (51)
R (39)
0.131
HDRS, mean (SD)
22.38 (2.33)
1.92 (0.97)
<0.001, -8.246
HARS, mean (SD)
2.23 (1.02)
2.20 (1.15)
0.316, -0.195
(SD), months Educational years, mean (SD)
N: number; SD: standard deviation; F: female, M: male; HDRS: Hamilton rating scales for depression; HARS: Hamilton rating scales for anxiety; N/A: not applicable; Sig p (significance of p-value) was from Mann-Whitney U test for nonparametric independent 2-sample t-test; df: degree of freedom.
Table 2: The nodes and edges within the significant subnetwork differences between healthy controls and patients Node 1
Node 2
Edge
Test statistic (t values)
PreCG.L
ANG.L
PreCG.L to ANG.L.
4.86
IFGoperc.L
ANG.L
IFGoperc.L to ANG.L.
4.16
ROL.L
ANG.L
ROL.L to ANG.L.
4.31
ROL.R
ANG.L
ROL.R to ANG.L.
4.36
(PreCG.L: left precental gyrus, ANG.L: left angular gyrus, ROL.L: left Rolandic operculum, ROL.R: right Rolandic operculum; IFGoperc.L: left inferior frontal gyrus, opercular part; the right first column meant t test statistical values for the functional connectivity differences between patients and controls)
Highlights
. In addition to traditional theory for depression, “theory of mind” could be proved in current connectome analysis. . In the subnetwork, the core node is the left angular gyrus. . The connecting spots with the core node include the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus.
PII: DOI: Reference:
S0165-0327(17)30172-6 http://dx.doi.org/10.1016/j.jad.2017.03.073 JAD8874
To appear in: Journal of Affective Disorders Received date: 25 January 2017 Revised date: 27 March 2017 Accepted date: 30 March 2017 Cite this article as: Chien-Han Lai, Yu-Te Wu and Yuh-Ming Hou, Functional network-based statistics in depression: theory of mind subnetwork and importance of parietal region, Journal of Affective Disorders, http://dx.doi.org/10.1016/j.jad.2017.03.073 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Functional network-based statistics in depression: theory of mind subnetwork and importance of parietal region
Chien-Han Lai1,2,3*, Yu-Te Wu2,3, Yuh-Ming Hou1 1
Department of Psychiatry, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi City, Taiwan, ROC 2 Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan, ROC 3 Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, ROC
*Corresponding author. Chien-Han Lai, M.D., Department of Psychiatry, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi City, Taiwan, ROC, No.539, Zhongxiao Rd.,East Dist.,Chiayi City, Taiwan, ROC, Telephone: +886-5-2765041, Fax: +886 05-2765040, E-mail: [email protected]
Objective The functional network analysis of whole brain is an emerging field for research in depression. We initiated this study to investigate which subnetwork is significantly altered within the functional connectome in major depressive disorder (MDD). Methods The study enrolled 52 first-episode medication-naïve patients with MDD and 40 controls for functional network analysis. All participants received the resting-state functional imaging using a 3-Tesla magnetic resonance scanner. After preprocessing,
we calculated the connectivity matrix of functional connectivity in whole brain for each subject. The network-based statistics of connectome was used to perform group comparisons between patients and controls. The correlations between functional connectivity and clinical parameters were also performed. Results MDD patients had significant alterations in the network involving “theory of mind” regions, such as the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus. The center node of significant network was the left angular gyrus. No significant correlations of functional connectivity within the subnetwork and clinical parameters were noted. Conclusion Functional connectivity of “theory of mind” subnetwork may be the core issue for pathophysiology in MDD. In addition, the center role of parietal region should be emphasized in future study. Keywords: depression; functional connectivity; network-based statistics; theory of mind; left angular gyrus
Significant outcomes: . In addition to traditional theory for depression, “theory of mind” could be proved in
current connectome analysis. . In the subnetwork, the core node is the left angular gyrus. . The connecting spots with the core node include the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus. Limitations: . No significant correlations between depression severity and functional connectivity. . Lack of social cognition data and just functional connectome data also limited the interpretations. . Cross-sectional study design, not the longitudinal design, would also be another limitation.
The brain pathophysiology of major depressive disorder (MDD) is an entry to understand the biological origin of depression. The initial theory of “limbic-cortical-striatal-pallidal-thalamic tract” (Sheline, 2000) suggested possible existence of network-based alterations for MDD. The previous studies also demonstrated the possible involvement of network (de Kwaasteniet et al., 2013; Lai et
al., 2010; Lai and Wu, 2014b). However, the study of whole brain network-based statistics in MDD is very limited. It would be a possible obstacle for the further resolution of network pathophysiology in MDD. The network of “theory of mind” has been discovered in healthy participants. It includes cognitive subnetwork, such as the precuneus, occipital lobe and temporal lobe, combined with affective subnetwork, such as the frontal lobe (Schlaffke et al., 2015). The network is important for implicit and explicit mental activities, such as self monitoring and reflections. They are crucial for social cognition (Frith and Frith, 2012). The frontal lobe serve as the center of human mental processing and control the self thoughts, which is derived from the gateway hypothesis (Burgess et al., 2007). The “theory of mind” is a possible theory for many mental illnesses, including MDD. Impaired domains of “theory of mind”, such as social-perceptual and social-cognitive components, have been discovered in patients with MDD (Wang et al., 2008). The patients with MDD have difficult in reading social interactions (Zobel et al., 2010), which would be associated with chronicity and functional decline. In the system of “theory of mind”, major parts include language processing regions and cognitive regions, such as the angular gyrus (ANG), inferior frontal gyrus (IFG) and temporal gyrus (Mason and Just, 2011). These regions could cooperate with language and emotional processing networks to modulate the perception of emotional content for
sentences in speech (Herve et al., 2012). In the condition of natural social interaction, a mentalizing network also involves the precentral gyrus (PreCG) and other sensory regions, which could process the social stimuli and play an important role in social cognition (Saggar et al., 2014). A recent functional connectivity study showed that alterations in connections between the regions of “theory of mind”, such as the parietal, frontal and temporal regions. In addition, the alterations were associated with emotional dysregulation and extensive processing of self-referential information (O'Neill et al., 2015). The literature suggests the crucial role of “theory of mind” regions in the pathophysiology of depression. However, the study was derived from borderline personality disorder to obtain the results. Therefore the importance of “theory of mind” hypothesis still needs further study with improvements in methodology to be proved. The whole-brain functional connectome has been an emerging area for the investigation of pathophysiology in mental illnesses. Recently a new method, network-based statistics (NBS), has been applied in the field of connectome analysis. It uses the graph theory concept to derive the large-scale brain connectivity with the control of family wise error due to mass-univariate testing for each connection of the functional or structural connectome (Zalesky et al., 2010). It has been applied in several psychiatric illnesses, such as functional connectome in schizophrenia (Zalesky
et al., 2010) and structural connectome in MDD (Korgaonkar et al., 2014; Long et al., 2015). The two reports of structural connectome found alterations of default mod network and other fronto-limbic networks for emotional and cognitive functions in depression. One functional connectome analysis using graph theory found similar disturbances in the network involving mood and cognition (Zhang et al., 2011). From the above literature, we designed this study using resting-state functional magnetic resonance imaging (Rs-FMRI) technique to survey the brain connectivity in MDD patients. In addition, we applied the connectivity matrix analysis and NBS analysis of Rs-FMRI signals in MDD. Based on the “theory of mind” model, we hypothesized that patients with MDD would have reductions in the functional connectome of “theory of mind” network, such as the ANG, PreCG, IFG and temporal lobe, using a relatively unbiased method.
Participants:
All patients with MDD were met for the following criteria: (1) first-episode patients with a pure MDD diagnosis (DSM-IV criteria) made by the Structured Clinical Interview for DSM-IV; (2) severity of MDD was at least moderate: Clinician Global Impression of Severity > 4, Hamilton Rating Scales for Depression (HDRS) score >
20, Hamilton Rating Scales for Anxiety (HARS) score < 5; (3) no co-morbid psychiatric illnesses or medical illnesses; (4) no previous cognitive behavioural therapy or other psychotherapies; (5) medication-naïve; (6) no abuse of or dependence on alcohol or other substances; and (7) no past history of claustrophobia or discomfort while receiving MR scanning. The healthy controls had no psychiatric illnesses or significant medical illnesses. All of the patients and part of healthy subjects signed the informed consent that was approved by the three Institutional Review Boards at Taipei Tzu Chi Hospital, Cheng Hsin General Hospital and National Yang-Ming University according to the institute where they were recruited. The patients were enrolled at Taipei Tzu Chi Hospital and Cheng Hsin General Hospital. The controls were enrolled from Taipei Tzu Chi Hospital, Cheng Hsin General Hospital and National Yang-Ming University. At the time of the MR imaging, none of the participants in the control group received psychotropic treatment. Handedness was determined by using the Edinburgh Inventory of Handedness (Oldfield, 1971). The sample of participants has some overlaps of our previous reports (Lai and Wu, 2013, 2014a). In this study, we enrolled 52 patients with MDD (26 men and 26 women; 40.26±8.97) and 40 controls (20 men and 20 women; 39±11.81) (Table 1). 44 of the 52 patients and 27 of the controls have been previously reported (Lai and Wu, 2015). This prior article dealt with regional homogeneity alterations in depression patients
whereas in this manuscript we report on the whole-brain functional connectome analysis in depression patients.
Rs-FMRI data acquisition and pulse sequence
Echo planar imaging (EPI) sequence were acquired in 20 axial slices (TR=2000ms, TE=40ms, flip angle=90°, field of view=24cm; 5mm thickness and 1 mm gap; the sequence duration was 300 seconds for each subject, 150 time points were acquired, voxel dimension: 64x64x20) at baseline visit (3T Siemens scanner housed at MRI (magnetic resonance imaging) center of National Yang Ming University) in patients and controls. All the patients and controls were requested to close their eyes with relaxing manner and not sleepy while scanning. The participating subjects were instructed to move as little as possible and stay fully awake while scanning. All these patients and controls reported that they could be fully awake while MRI scanning.
Rs-FMRI data preprocessing
EPI data was first preprocessed by DPARSF (Data Processing Assistant and Resting-State FMRI, version 2.2; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.) (Chao-Gan and Yu-Feng,
2010) working with the statistical parametric mapping 8 (SPM8) on the Matlab platform, which included the removal of first 10 time points due to the consideration for instability of initial MRI signal and patients’ difficult to adapt at first about MRI acquisition circumstance, slice timing with 20th slice as reference slice, realignment, normalization to standard MNI spaces by using EPI templates and re-sampling with 3 x 3 x 3 mm3, smoothing by Full Width at Half Maximum (FWHM) 4x4x4 kernel, to detrend and filter data with residual signals within 0.01-0.08 Hz to discard the bias from high-frequency physiological noise and low-frequency drift. As all subjects’ head movements were less than 0.5 mm in translation and 1 degree in rotation by obtaining the motion time courses of all subjects, no subject was excluded due to no excessive motions were observed. The filtered Rs-FMRI data were registered (nonlinear elastric registration) to the EPI template. The estimated motion parameters were obtained for each subject regressed on each voxel. Besides, the effects of “micromovements” and the nuisance correlation caused by head motion were removed by checking covariates in nuisance regressors in DPARSF (Power et al., 2012; Yan et al., 2013a). Several sources of spurious covariates were removed except global signals due to the controversy about removing the global signal in the resting-state functional connectivity data (Fox et al., 2005; Murphy et al., 2009). The individual-level covariates of motion included Friston-24 parameter model (6 head
motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items) and group-level covariates of motion included framewise displacement motion regression model (Yan et al., 2013b).
Connectivity matrix measures
We used the GRETNA toolbox (https://www.nitrc.org/projects/gretna) to calculate the connectivity matrix for the preprocessed Rs-FMRI data of each subject. GRETNA toolbox has been designed for the graph-theoretical network analysis of Rs-FMRI data. It is a suite of MATLAB functions and MATLAB-based Interfaces to perform the process of conventional functional preprocessing, as well as to calculate most frequently used connectivity matrix.
NBS analysis
The NBS was used to identify significant differences between patients and controls for pairwise associations using whole-brain approach. It incorporated graph model to identify the pairwise association, such as the connection or link, between different pairs of nodes (Zalesky et al., 2010). The processing steps were as follows: (1) The design matrix was set at first to define the group and number for subjects of
patients and controls. Then we put the connectivity matrix file for each subject as the input data. From the probability point, the N(N−1)/2 unique pairwise associations would be possible for an N×N connectivity matrix. For each pairwise association, the test statistic of interest was calculated independently using the values stored in each subject's connectivity matrix, such as Fisher's r-to-z transform for a correlation based measure of association to ensure normality. (2) The AAL-90 nodes and labels were used to define and locate the significant nodes within the significant subnetwork in a graph model. The AAL-90 was chose due to relatively accurate localization of coordinates and similar application in NBS methodology was also mentioned in previous report (Hong et al., 2013). A nonparametric test was used for a breadth first search. This was repeated for multiple permutations to estimate the null distribution. The random permutations were generated independently and the group to which each subject belonged was randomly exchanged for each permutation. The number or size of links the potential nodes comprised according to the threshold (corrected p < 0.05). Permutation testing was used to ascribe a p-value controlled for the FWE to each connected component based on its size. (3) For each permutation, the test statistic of interest is recalculated, after which the
same threshold is applied to define a set of suprathreshold links. The test detected the potential connected structures of suprathreshold links, which could demonstrate the topological extent of all significant structures. The maximal component size in the set of suprathreshold links derived from each of multiple permutations is then determined and stored, thereby yielding an empirical estimate of the null distribution of maximal component size. (4) Finally, the p-value (0.05) of an observed component of size k was estimated by finding the total number of permutations to identify maximal component size.
Statistical analysis
For the NBS, we performed the two dimension comparison of MDD and NC. The contrasts were set as “controls > MDD” and “MDD < controls”. The number of permutations was 5000 and the t statistic threshold was set as t >4.0. The threshold of multiple comparisons was set as corrected p < 0.05. The significant subnetwork would be displayed in glass brain and BrainNet Viewer (Xia et al., 2013). For the demographic and clinical data, such as age, educational years and HDRS scores, the differences between patients and controls were estimated by the Mann-Whitney U test. Group differences of gender were analysed by a chi-square test. In addition, we examined the correlations between functional connectivity and clinical variables (e.g., depression severity, anxiety severity, illness duration) and demographic variables (age,
gender) that were used as covariates in the main analyses in each group and across both groups. For the statistical threshold of the correlations, we also used the corrected p < 0.05. The correlation assessment was only used for significant network edges identified by the NBS method.
Results Demographic data
No significant differences of age, gender, educational years, HARS scores and handedness were found between patients and controls. The duration of illness in patient group were 4.52±1.60 months. Only the HDRS scores were significantly different between patients and controls (Table 1).
Functional connectome results by NBS
There was only one subnetwork with significant differences in functional connectivity matrix between patients and controls (corrected p < 0.05). The results were derived from the contrast “controls > MDD”, which meant that MDD patients have lower functional connectivity strength than controls in the above subnetwork. The subnetwork consisted of 5 nodes and 4 edges. The 5 nodes consisted of ANG.L (left AG), PreCG.L (left PreCG), IFGoperc.L (left IFG, opercular part), ROL.L (left
rolandic operculum) and ROL.R (right rolandic operculum). The 4 edges were PreCG.L to ANG.L, IFGoperc.L to ANG.L, ROL.L to ANG.L and ROL.R to ANG.L (Table 2, Figure 1). The difference manifestation suggested that MDD patients had greater decoupling activities than controls over these identified edges. There were no significant results derived from the contrast of “MDD > controls”. Neither MDD severity nor duration of illness were correlated with the network connections identified in the NBS analysis.
Discussion
In current study, we found a “theory of mind” subnetwork with significant reductions in connectivity matrix of the patients with MDD. There were 5 nodes and 4 edges, which seemed to have the ANG.L as the terminal center node for the 4 edges from other 4 nodes, the PreCG.L, IFGoperc.L, ROL.L and ROL.R. The 4 edges all directed to the ANG.L, which suggested possibly crucial role of parietal lobe in the “theory of mind” subnetwork. However, the connectivity measures of the 4 edges were not correlated with severity or illness duration of MDD. Our study results demonstrated improvements in research methods when compared with previous seed-based analysis method (O'Neill et al., 2015). In addition, the importance of “theory of mind” network in MDD was supported due to the first-episode medicine-naïve patients with MDD and positive findings within the network.
However, it seemed not compatible with the right-lateralized core network of temporo-parietal junction (ANG) for the function of filtering external stimulus (Langner et al., 2012). It probably suggested that the alterations of theory of mind network probably might be more related to cognitive function, not sensory part of alterations.
Several previous studies mentioned the deficits of “theory of mind” function in depression patients (Wang et al., 2008; Wolkenstein et al., 2011; Zobel et al., 2010). MDD may be associated with the impairments in integrating contextual information for reasoning of social situation and mental state modality of judgement (Wolkenstein et al., 2011). In current study, all the edges terminated at the ANG.L, which suggested the crucial harbor role of ANG.L for a significant subnetwork in MDD. The ANG.L would play an important role in the integration of linguistic material and world knowledge in context, which is crucial for social communication and speech comprehension ability (Bambini et al., 2011). In addition, ANG.L would also work with the IFG and temporal sulcus (overlapping with ROL.R and ROL.L) for the recognition of communicative intention from others (Bambini et al., 2011). The “theory of mind” network consists of the ANG.L, IFG, PreCG and temporal lobe (Herve et al., 2012; Mason and Just, 2011), which is also in line with our study results. The ANG.L is also associated with memory retrieval and post-retrieval decision
making (Sestieri et al., 2011). The decreased functional connectivity between ANG.L and frontal region has been discovered to correlate with poorer neurocognitive function in treatment-resistant depression patients (de Kwaasteniet et al., 2015). However, other treatment-resistant studies showed dissimilar results, which more focused on default mode network, fronto-limbic network or cerebellar-cerebral network (Li et al., 2013; Sawaya et al., 2015; van Waarde et al., 2015). The similar finding of decreased connectivity between ANG.L, temporal lobe and frontal lobe has also been found in the initial phase of depression. In addition, the pattern of decreased functional connectivity has been observed in longitudinal follow up (Strikwerda-Brown et al., 2014). The results of ANG.L in current study also supported the possible role of cross-modal hub to handle the integration of multi-sensory information, interpersonal information in social situation, mental representations and attention to relevant information (Seghier, 2013; Spreng and Mar, 2012). From the above literature and our study results, we suggested that ANG.L might a center hub for the deficient “theory of mind” subnetwork in MDD. Further work of network analysis in MDD should be more focused on parietal lobe. The weak connection between ANG.L and IFG supported the involvement of cognitive region in the “theory of mind” subnetwork (Bambini et al., 2011). Apart from the role of cognition, the IFG also participate the affective processing inside the
theory of mind network (Bodden et al., 2013). The reduction in functional connectivity of this connection has been mentioned in previous study (Guo et al., 2013). In addition, Granger causality analysis showed inhibitory effect between the ANG.L, IFGoperG.L and temporal lobe in MDD (Guo et al., 2014), which also supported the hypothesis of language processing regions in the theory of mind (Mason and Just, 2011). This connection also participated in the fronto-parietal mirror neuron system, the basis of “theory of mind”, to handle cognitive and affective empathy in social situation (Hooker et al., 2010). The IFG and ROL also participated in working memory and attention (Li et al., 2012). The IFG and ANG were also correlated with the integration of personal and interpersonal information, which facilitate the formation of social conceptual knowledge, social memory and strategic social behaviors (Spreng and Mar, 2012). The connection between ANG.L and ROL.R or ROL.L could correspond to the role of parieto-temporal involvement in the affective “theory of mind” network (Bodden et al., 2013). This connection also involved in the emotional response to auditory information and temporal dynamics of emotional processing (Koelsch et al., 2006). In addition, the ROL is involved in the implicit response of emotion to external information and emotional recognition (Gebauer et al., 2014). The ROL region is also a part of temporal lobe. The clinical symptoms of MDD interact with memory
functions in the temporal lobe (Goveas et al., 2011). The ROL and PreCG are important for the phonological rehearsal and language processing (Veroude et al., 2010). Affective “theory of mind” regions also included the PreCG (Bodden et al., 2013), which could modulate the affective empathy (Hooker et al., 2010), social interaction (Saggar et al., 2014) and motor-related function within the large-scale networks in MDD (Peng et al., 2015). The decreased functional connectivity between ANG.L and PreCG might represent the abnormalities in affective, social and motor functions of patients with MDD. For the negative results of correlation between functional connectivity of 4 edges and clinical measures, only one previous study revealed the association of network organizations and clinical parameters in MDD (Zhang et al., 2011). However, such association was not replicated in current study. The lack of correlation between functional connectivity and clinical parameters may represent a different type of network revealed by the different type of methodology (Zhang et al., 2011) or probably a distinct endophenotype not accounting for chronicity or symptom severity in depression. There were several limitations in current study. First, the finding regions in current study were not very specific for the “theory of mind”. In addition, the findings were relatively large with encompassing regions not very specific for theory of mind. It
seemed reverse influential and possible real-world applications will go beyond the simple interpretations of psychological process (Poldrack and Farah, 2015). Therefore the potentially different versions of interpretations should not be ignored. Second, the cross-sectional design would limit the interpretations of our study results. A future longitudinal study would help us confirm the importance of theory of mind subnetwork in MDD. Third, this study just provided the functional connectome results without the ground of structural connectome data. The future NBS analysis with structural imaging data derived from as T1 gray matter imaging and diffusion tensor imaging, would help illuminate the structural connectome ground for functional connectome results. Fourth, the bandpass filtering might be considered to be performed after nuisance correction, which is suggested to be with less nuisance-related variability. Fifth, functional connectome measures the functional connectivity according to the signals of hemodynamics. The alterations of functional connectome are probably related to the hemodynamic changes. However, it is still unknown whether the method of functional connectome can be used to detect early neuronal changes, or monitor disease progression in MDD. Sixth, a task-oriented functional MRI study could complete the viewpoint due to Rs-FMRI characteristics of the functional connectome results. Seventh, the lack of social cognition data in current sample might limit the interpretation of “theory of mind” hypothesis. However, past
studies already proved the existence of social cognition deficits in MDD patients (Wang et al., 2008; Wolkenstein et al., 2011; Zobel et al., 2010). At last, the relatively larger scanning thickness (5mm with 1mm gap) would limit the spatial resolution in the axial direction, which should be interpreted with caution. The effects of normalization, segmentation, registration, resampling and interpolation on graph metrics would be another concern.
Conclusion Functional connectivity of “theory of mind” subnetwork is likely the core issue for pathophysiology of MDD. In addition, the center role of parietal region should be emphasized in future study.
Acknowledgements
Author Contributions: Dr. Lai wrote manuscript, researched data. Dr. Wu reviewed/edited manuscript. We want to thank the grant support from Taipei Tzu-Chi General Hospital project TCRD-TPE-100-02 and the project was also supported by grant from the Cheng Hsin General Hospital and National Yang Ming University cooperative project 104F003C03. We also acknowledge MR support from National
Yang-Ming University, Taiwan, which is in part supported by the MOE plan for the top university.
Sources of financial and material support
Funding: This study was funded by Taipei Tzu-Chi General Hospital project TCRD-TPE-100-02 and grant from the Cheng Hsin General Hospital and National Yang Ming University cooperative project 105F003C03.
Conflict of Interest: All authors stated no conflict of interest.
Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent: Informed consent was obtained from all individual participants
included in the study.
Disclosure statement: The authors have nothing to disclose.
Advances in Knowledge This study can provide the evidence of important role of theory of mind network in the pathophysiology of depression, which has been undervalued in recent years. The whole picture of connectome also can delineate the origin of functional alterations. Implications for Patient Care The results can be useful for public education and preventive medicine. In addition, it will enhance the role of social cognition in the treatment of depression.
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Figure 1: The alterations of whole-brain network-based statistics in MDD. Subfigure 1A (the sagittal glass brain view): The patients with MDD had significant reductions in functional connectivity of “theory of mind” subnetwork within whole brain functional connectome. The altered subnetwork consisted of PreCG.L, ANG.L, ROL.L, ROL.R and IFGoperc.L. (PreCG.L: precental gyrus, ANG.L: angular gyrus, ROL.L: left Rolandic operculum, ROL.R: right Rolandic operculum; IFGoperc.L: left inferior frontal gyrus, opercular part). Subfigure 1B (the BrainNet Viewer): The full view and 3-D view of the altered subnetwork.
Table 1: Demographic data of participating patients and controls Patients (N=52)
Controls (N=40)
Sig p (2-tailed), Z df=63
Age, mean (SD), years old 40.26 (8.97)
39 (11.81)
0.238, -1.17
Gender (number)
F(26), M(26)
F(20), M(20)
0.835
Duration of illness, mean
4.52 (1.60)
0 (0)
N/A
15.78 (0.80)
15.90 (0.59)
0.752, -0.316
Handedness
R (51)
R (39)
0.131
HDRS, mean (SD)
22.38 (2.33)
1.92 (0.97)
<0.001, -8.246
HARS, mean (SD)
2.23 (1.02)
2.20 (1.15)
0.316, -0.195
(SD), months Educational years, mean (SD)
N: number; SD: standard deviation; F: female, M: male; HDRS: Hamilton rating scales for depression; HARS: Hamilton rating scales for anxiety; N/A: not applicable; Sig p (significance of p-value) was from Mann-Whitney U test for nonparametric independent 2-sample t-test; df: degree of freedom.
Table 2: The nodes and edges within the significant subnetwork differences between healthy controls and patients Node 1
Node 2
Edge
Test statistic (t values)
PreCG.L
ANG.L
PreCG.L to ANG.L.
4.86
IFGoperc.L
ANG.L
IFGoperc.L to ANG.L.
4.16
ROL.L
ANG.L
ROL.L to ANG.L.
4.31
ROL.R
ANG.L
ROL.R to ANG.L.
4.36
(PreCG.L: left precental gyrus, ANG.L: left angular gyrus, ROL.L: left Rolandic operculum, ROL.R: right Rolandic operculum; IFGoperc.L: left inferior frontal gyrus, opercular part; the right first column meant t test statistical values for the functional connectivity differences between patients and controls)
Highlights
. In addition to traditional theory for depression, “theory of mind” could be proved in current connectome analysis. . In the subnetwork, the core node is the left angular gyrus. . The connecting spots with the core node include the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus.