Neuroscience Letters 589 (2015) 67–72
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Research article
Increased interhemispheric functional connectivity in college students with non-clinical depressive symptoms in resting state Xin-Hua Wei a , Ji-Liang Ren a , Wen-Hua Liu b , Rui-Meng Yang a , Xiang-Dong Xu a , Jun Liu c , Yong-Mei Guo a , Shao-De Yu d , Li-Sha Lai a , Yao-Qin Xie d , Xin-Qing Jiang a,∗ a
Department of Radiology, Guangzhou first Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, China Faculty of Health Management, Guangzhou Medical University, Guangzhou, Guangdong 510180, China c Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China d Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong 518055, China b
h i g h l i g h t s • We found increased homotopic connectivity in nonclinical depressive symptoms (nCDSs). • The values of homotopic connectivity in the cerebellum lobes can identify nCDSs. • No correlation between the homotopic connectivity and depressive scores in nCDSs.
a r t i c l e
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Article history: Received 22 October 2014 Received in revised form 24 December 2014 Accepted 13 January 2015 Available online 14 January 2015 Keywords: Symptoms Depressive Physiopathology Young adult Magnetic resonance imaging Functional connectivity Voxel-mirrored homotopic connectivity
a b s t r a c t The underlying neural basis of non-clinical depressive symptoms (nCDSs) remains unclear. Interhemispheric functional connectivity has been suggested as one of the most robust characteristics of brain’s intrinsic functional architecture. Here, we investigated the functional connectivity between homotopic points in the brain using the voxel-mirrored homotopic connectivity (VMHC) approach. We performed VMHC analysis on resting-state functional magnetic resonance imaging (rs-fMRI) data from 17 individuals with nCDSs and 20 healthy controls (HCs) who were enrolled from a sample of 1105 college students. We found increased VMHCs in the bilateral posterior cerebellum and fusiform gyrus in nCDSs subjects compared with HCs. Furthermore, receiver operating characteristic (ROC) curves indicated that VMHC values in the posterior cerebellum lobes could use to differente nCDSs from HCs [area under the curve (AUC), 0.756; p < 0.01]. We suggest increased VMHCs indicate a possible compensatory mechanism involved in the pathophysiology of nCDSs. VMHC values of the posterior cerebellum lobes might serve as a reliable biomarker for identifying nCDSs. © 2015 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Non-clinical depressive symptoms (nCDSs) are frequently observed among university students worldwide [1] and their prevalence appears to be increasing [2]. Importantly, depressive symptoms in young adulthood may be associated with an increased risk for other mental health problems, such as suicidal ideation and suicide attempts [3], as well as an increased risks for major depression in the future [4]. However, knowledge about the underling neural basis of nCDS remains unclear. A body of evidence suggests depressive symptoms are associated with the dysregulation and
∗ Corresponding author. Tel.: +86 2081048873. E-mail address: [email protected] (X.-Q. Jiang). http://dx.doi.org/10.1016/j.neulet.2015.01.034 0304-3940/© 2015 Elsevier Ireland Ltd. All rights reserved.
interaction of a brain network encompassing large regions of the cortical, and (para) limbic areas, subcortical structures, and cerebellum [5–7]. Most of the studies on depressive disorder emphasize the role of disrupted functional circuitry within intrahemispheric functional domains [8], whereas depression-related alterations in functional interactions between the cerebral hemispheres are largely unknown. In fact, one of the most robust characteristics of the brain’s intrinsic architecture involves interhemispheric connectivity [9,10], reflecting the process of exchange and the integration of information between the cerebral hemispheres [11]. Interhemispheric processing is known to facilitates discrimination between non-emotional faces [12] and selective attention [13]. Abnormal interhemispheric functional interactions have been found in psychiatric disorders [14]. The corpus callosum, which connects both
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cerebral hemispheres, plays a vital role in interhemispheric functional connectivity and, attention deficit disorder [15]. Neuroimaging plays an important role in the acquisition of evidence of regarding interhemispheric connectivity. For instance, structural MR assumes that hemispheric specialization has evolved as a consequence of reduced interhemispheric connectivity [16]. Abnormalities in the structural integrity of the anterior genu of the corpus callosum in diffusion tensor imaging (DTI) potentially contribute to impairment of interhemispheric connectivity in patients with major depressive disorder (MDD) [17]. Recently, a resting state fMRI (rs-fMRI) approach, which assesses functional connectivity by identifying those regions in which the low frequency blood oxygen level dependent (BOLD) [18] signal exhibits temporal coherence [19], offers a means to directly quantify interhemispheric functional interactions [20]. Temporal coherence between activity in one voxel and its homotopic voxel in the opposite hemisphere is one of the most salient aspects of the brain’s intrinsic functional architecture [21]. Loss of interhemispheric resting BOLD correlations with preserved intrahemispheric correlations is seen in patients performed complete section of the corpus callosum [22]. A recently validated approach referred to as “voxel-mirrored homotopic connectivity” (VMHC) [23] was introduced to quantify the resting-state functional connectivity between each voxel in one hemisphere and its mirrored voxel in the opposite hemisphere. This method has been performed in drug addiction [20], schizophrenia [24], and migraine [25] studies. Evidence from depressed patient demonstrates that decreased VMHCs are observed in the default mode network (DMN) [26], the medial orbitofrontal gyrus, parahippocampal gyrus, fusiform gyrus, and occipital regions [27]. Despite evidence of suggesting interhemispheric interaction deficits in MDD, no studies have explored the functional connectivity between homotopic brain sites in nCDSs. Here we evaluate interhemispheric resting state functional connectivity using VMHC in subjects with nCDSs. Moreover, to exclude the possible confounded influences from structural damage [28], we examine the gray matter volume (GMV) in regions with altered VMHCs using voxel-based morphometry (VBM) approach [29]. Additionally, the correlation between VMHC values and depressive severity scores was explored. Furthermore, receiver operating characteristic (ROC) analysis of VMHC values of each ROI was performed to identify possible biomarker to differentiate nCDSs from HCs. We hypothesized that functional deficiencies in right to left interhemispheric connectivity would be associated within nCDSs.
2. Material and methods 2.1. Participants All participants provided their written informed consents and the current research protocol was approved by the local Medical Ethics Committee of Guangzhou First Hospital of Guangzhou Medical University in China. In total, 1105 college students were recruited from Guangzhou Medical University to participate in a survey assessing depressive symptoms. The Beck Depression Inventory (BDI) -IA scale [30], a widely used tool for assessing depressive symptom in nonclinical samples [31], was utilized for assessing depressive severity of participants in the present study. The cut-off value for defining depression is 10. The generally accepted cut-off scores for grading depression are as follows: 0–9, no depression; 10–18, mild depression;19–29, moderate depression; and >30, severe depression [32]. Subjects with nCDSs in this study exhibited a BDI score greater than or equal to10 but did not achieve the DSM-IV criteria for MDD.
A total of 37 participants enrolled in MR studies, including 17 nCDSs (5 men and 12 women) with BDI scores ranging from 10 to 35 as well as 20 sex-, age-,and education-matched HCs (7 men and 13 women) with BDI scores ranging from 0 to 4. In addition, all participants met the following criteria: right-handedness, no visualized disease on MRI, between 19 and 25 years of age, no neurological illness, and no alcohol or drug dependence. Table 1 presents the detailed demographics and depressive scores. 2.2. Imaging data acquisition Scanning was performed with a 3-Tesla MRI scanner (Siemens, Erlangen, Germany) using an 8-channel brain array coil. Participant head movement was minimized using foam padding, and headphones were used to reduce scanner noise. rs-fMRI scans were obtained with an echo-planar imaging sequence (TR = 2500 ms, TE = 21 ms, FA = 90◦ , FOV = 200 mm × 200 mm, matrix = 64 × 64, voxel size = 3.5 mm × 3.1 mm × 3.1 mm, 42 slices, no gap). All subjects underwent a 500 s task-free fMRI scan after being instructed to relax with their eyes closed without falling asleep. After the functional scan, a high-resolution T1-weighted structural image was acquired for each subject. 2.3. Functional imaging data preprocessing Images preprocessing was performed using Data Processing Assistant for Resting-State fMRI (DPARSF, http://www.rest. restfmri.net) which works with SPM8 (http://www.fil.ion.ucl.ac. uk/spm/software/SPM8) on the Matlab platform. For each subject, the first 10 rs-fMRI dataset were discarded for signal equilibrium. The remaining 190 dataset were corrected for slice timing and realigned for motion correction. The steps included slice timing, head motion correction, spatial normalization, spatial smooth (with a Gaussian kernel of 4-mm), linear trend removing and bandpass (0.01–0.08 Hz) filtering. To reduce spurious BOLD variances unlikely to reflect neuronal activity [33], regression of nuisance was performed with white matter, and cerebral spinal fluid BOLD-signals, as well as six headmotion profiles to minimize the effect of head motion. Given the controversy of removing the global signal in the preprocessed rsfMRI data [34], we did not regress the global signal out in the present study. 2.4. Interhemispheric connectivity analysis The VMHC computation was performed using the REST package (http://resting-fmri.sourceforge.net) [35]. The VMHC calculation procedure was previously described [21]. In brief, all normalized T1 images were averaged to generate a mean normalized T1 image. Then, a nonlinear registration was used to normalize each brain to the symmetrical template. We applied the transformation to the symmetrical brain template to the normalized functional data. The VMHC value was then computed as the Pearson correlation (Fischer Z-transformed) between the time-series data of every pair of symmetrical voxels. Intergroup differences were compared with voxels wise t-tests [21]. 2.5. VBM analysis VBM maps were obtained by using DPARSF (http://www.rest.restfmri.net) package which works with SPM8 (http://www.fil.ion.ucl.ac.uk/spm/software/SPM8) on the Matlab platform. The Diffeomorphic Anatomic Registration Through Exponentiated Lie Algebra (DARTEL) algorithm [36] was used to increase the brain registration between participants. The mean GMV of each ROI with altered VMHC was calculated by using
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Table 1 Difference of vmhc in non-clinical depressive symptoms individuals and control subjects. Connected brain region
Non-clinical depressive symptoms > healthy controls Cerebellum posterior Lobe L Cerebellum posterior Lobe R Fusiform gyrus L Fusiform gyrus R
BA
17 17
voxels Size
19 18 18 25
MNI coordinates (mm)
T value
X
Y
Z
9 −9 −30 30
−69 −69 −59 −59
−39 −39 −12 −12
4.6085 4.6737 4.0040 4.0841
BA, Brodman’s area; MNI, Montreal Neurological Institute; L left; R right.
REST package (http://resting-fmri.sourceforge.net) [35] for further statistical analysis. 2.6. Statistical analysis The independent-sample, two-samples t test, and chi-squared test were used to compare the demographic data and BDI scores between nCDSs and HCs by using SPSS 16.0 (SPSS Inc. Chicago, IL, USA), A p-value < 0.05 was deemed significant. A general liner model analysis was performed to investigate the difference of VMHC maps in a voxel-by-voxel manner between the nCDSs and HCs using REST package [35]. The GMV, age, gender, and years of education of each subject were included as covariates to avoid the confounding effects. Additionally, given that functional connectivity at rest could be affected by micro-movements from volume to volume [37], the mean framewise displacement (FD) was added as a covariate in the group statistical analyses of VMHC [27]. A threshold of p < 0.01, corrected by a Monte Carlo simulation (see AlphaSim in AFNI http://afni.nih.gov/afni/docpdf/AlphaSim.pdf), was used to calculate the probability of false positive detection, considering both the individual voxel probability threshold and cluster size. Using this program, clusters greater than 18 voxels (486 mm3 ) were applied to the resulting statistical map at a corrected significance level of p < 0.05. To investigate the correlation between VMHC values and the depressive severity in nCDSs, the average VMHC values of all the voxels within the ROIs revealed by VMHC analysis were extracted separately using the REST package. Then, a bivariate correlation using SPSS 16.0 was introduced to indicate the correlation between the VMHC values and BDI scores, and the significance level was set at p < 0.05 (two-tailed). To identify an effective biomarker that can distinguish between nCDSs and HCs subjects, receiver operating characteristic (ROC) curve analysis was used to compute the area under the ROC curve (AUC), sensitivity/specificity characteristics of each ROI, and the optimal cut-off VMHC values for these ROIs. To identify whether the GMV varied between groups in each ROI with altered VMHC values, the extracted GMVs of each ROI were compared between nCDSs and HC subjects. In addition, the correlations between VMHC and GMV were explored. The significance levels were set at p < 0.05 (two-tailed).
3. Results 3.1. Demographics and depressive scores No significant differences were noted between nCDSs and HCs subjects regarding gender, age, or year of education. BDI scores were significantly increased in the nCDSs subjects compared with HCs (p < 0.05) (please see Table S1 in the Supplementary material). Supplementry material related to this article found, in the online version, at http://dx.doi.org/10.1016/j.neulet.2015.01.034.
3.2. VMHC difference between groups A one-sample t-test revealed that the posterior cingulate cortex had a standardized VMHC value that was significantly >1 in the two groups (please see Fig. S1 in the Supplementary material). As indicated in Fig. 1 and Table 1, the two-sample t-tests revealed that increased VMHC values were observed in the bilateral posterior cerebellum lobe (PCL) (VIII) and fusiform gyrus in nCDSs subjects compared with HCs. No region exhibited decreased VMHC values in nCDSs subjects compared with HCs. See Excel sheet 1 as supplementary file. Supplementry material related to this article found, in the online version, at http://dx.doi.org/10.1016/j.neulet.2015.01.034. 3.3. Correlations between VMHC and depressive severity No significant correlation was observed between VMHC values and BDI scores in any brain regions with abnormal VMHC values (p > 0.05). 3.4. ROC analysis of VMHC values in ROIs Among the 4 ROIs that exhibited increased VMHC values in nCDSs subjects, the AUC of both PCLs were 0.756 (p = 0.008). The sensitivity and specificity of left PCL were 76.5% and 80.0%, respectively; the cut-off value was 0.4168. The sensitivity and specificity of the right PCL were 76.5% and 75.0%, respectively; the cut-off value was 0.4077.The AUC of the right FG was 0.688 (p = 0.051), and the left FG AUC was 0.600 (p = 0.300). 3.5. GMV and VMHC values of ROIs Compared with HCs, no significant difference in GMV value in ROIs with abnormal VMHC values were noted in nCDSs (p > 0.05). No significant correlation was observed between VMHC values and GMV values in any of the ROIs with abnormal VMHC values (p > 0.05). 4. Discussion In the present study, we found increased interhemispheric connectivity in the PCL and fusiform gyrus in nCDSs compared with HCs. In addition, the VMHC values in the bilateral PCLs exhibited relative high sensitivity and specificity in differentiating nCDSs subjects and HCs. No significant correlation was observed between VMHC values and BDI scores in regions with altered VMHC values. No alteration was revealed in these regions with regard to the GMV value. To our knowledge, this is the first evidence of abnormal interhemispheric connectivity in nCDSs subjects from a sample of college students. Intriguingly, the posterior cerebellum lobule (VIII) exhibits increased VMHC values in nCDSs in the present study. The cerebellum was traditionally considered to be a region involved in sensorimotor processing. However, several studies revealed that
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Fig. 1. Altered VMHC values in the posterior cerebellum lobe and fusiform gyrus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Regions exhibiting increased (red) VMHC values in nCDSs compared with HCs (two-sample t-tests, with a p < 0.01 threshod, corrected). Each altered functional connectivity brain area is presented as an axial views with the MNI location. Color bar indicates the T score.
structural abnormalities [38] and aberrant activity [6] of cerebellum were observed in MDD patients. Furthermore, convergence evidence has suggested that the cerebellum may play an important role in regulating the emotion [39] and cognitive processing of negative stimuli [40]. Despite early controversy, the viewpoint that the cerebellum facilitates sensorimotor processing as well as higher functions (i.e., cognition and emotion control), has achieved consensus [41]. Indeed, evidence strongly suggests important anatomic interactions between the prefrontal cortex and the cerebellum [41], this notion was supported by evidence from diffusion tensor imaging [42]. This anatomic connection has been suggested as the basis of cerebellar involvement in cognitive function [43]. Given that increased interhemispherice connection is evident in the present study, we hypothesize that the posterior cerebellum lobes play a compensatory role in mediating emotional functioning in nCDSs. VMHC in the posterior cerebellum might serve as a biomarker for the identification of individuals experiencing nCDSs. We also observed increased VMHC values in the fusiform gyrus in cCDSs subjects. The fusiform gyrus was suggested to be a component of the visual recognition network [44] involved in the perception of facial emotion, and associated with attentional biases away from happy and toward sad stimuli [45]. Accurate recognition of facial expressions is crucial for social functioning [45]. Altered responsiveness to facial emotional stimuli was proposed as one of the biomarkers for the early diagnosis of MDD [46]. Furthermore, evidence acquired from structural MRI [47], task-fMRI [45], and rs-fMRI [48] studies reported abnormalities in the fusiform gyrus in depressed patients. Recently, a study [27] in MDD patients exhibited reduced VMHC values in the fusiform gyrus. Conversely, increased VMHC values were observed in nCDSs subjects in our
study. The inverse results indicate that nCDSs and MDD subjects undergo different pathophysiologies. The increased VMHC values in nCDSs subjects suggest a compensation process of visual recognition. As expected, no difference in GMV was noted in any of the regions exhibiting altered VMHC values in nCDSs subjects. Additionally, no significant correlation was observed between GMV and VMHC values in nCDSs subjects. This means that the VMHC alternations are not likely caused by GMV. Therefore, the resulting VMHC alterations in nCDSs subjects are potentially related to white-matter abnormalities; to resolve this speculation, additional diffusion tensor imaging studies are needed. Although no significant correlations were observed between BDI scores and VMHC values in nCDS subjects, the ROC analysis demonstrated that VMHC values in the ROIs in the posterior cerebellum (VIII) adequately differentiated between the two groups. The sensitivity and specificity were greater than 70%. We hypothesize that VMHC values in the posterior cerebellum (VIII) may be a biomarker to identify individuals with nCDSs, however, the validity of this potential biomarker should be confirmed by future large-sample studies. Several limitations of the present study should be mentioned. First, the human brain is not exactly structurally symmetrical. In addition, considerable functional lateralization of the brain and large individual variability in the functional organization of the brain has been noted; these characteristics do not support the present strict geometrical method for identifying homotopic brain regions. Although we attempted to resolve this issue by using a symmetric template and smoothing the functional data, the effects of methodological symmetry cannot be completely eliminated. It is possible that the graph theory, which aims to investigate larger
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whole-brain networks could overcome the shortcomings of the VMHC method. Second, given the relatively small sample, a larger data set is required to validate the present findings. Third, given that the relative mild depressive symptoms of nCDS subjects can easily transform over time, a longitudinal study is required to be better address the results. Finally, the present study did not explore the intrahemispheric functional connectivity in nCDS subjects, and the change in the integration of white matter connecting altered VMHC regions. To our knowledge, this is the first rs-fMRI study to provide neurophysiology evidence for abnormalities in interhemispheric connectivity in individual with nCDSs. Increased VMHC in nCDSs subjects indicate a possible compensatory pathophysiological mechanism. Our results provide a new insight to understand the nature of the deficits that contribute to the dysconnectivity hypothesis of nCDSs.
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Acknowledgements This work was partly supported by the Science and Technology Planning Project of Guangdong Province (grant no. 2013B021800063) and the Science and Technology Planning Project of Guangzhou (grant no. 2014J4100071).
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Research article
Increased interhemispheric functional connectivity in college students with non-clinical depressive symptoms in resting state Xin-Hua Wei a , Ji-Liang Ren a , Wen-Hua Liu b , Rui-Meng Yang a , Xiang-Dong Xu a , Jun Liu c , Yong-Mei Guo a , Shao-De Yu d , Li-Sha Lai a , Yao-Qin Xie d , Xin-Qing Jiang a,∗ a
Department of Radiology, Guangzhou first Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510180, China Faculty of Health Management, Guangzhou Medical University, Guangzhou, Guangdong 510180, China c Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China d Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong 518055, China b
h i g h l i g h t s • We found increased homotopic connectivity in nonclinical depressive symptoms (nCDSs). • The values of homotopic connectivity in the cerebellum lobes can identify nCDSs. • No correlation between the homotopic connectivity and depressive scores in nCDSs.
a r t i c l e
i n f o
Article history: Received 22 October 2014 Received in revised form 24 December 2014 Accepted 13 January 2015 Available online 14 January 2015 Keywords: Symptoms Depressive Physiopathology Young adult Magnetic resonance imaging Functional connectivity Voxel-mirrored homotopic connectivity
a b s t r a c t The underlying neural basis of non-clinical depressive symptoms (nCDSs) remains unclear. Interhemispheric functional connectivity has been suggested as one of the most robust characteristics of brain’s intrinsic functional architecture. Here, we investigated the functional connectivity between homotopic points in the brain using the voxel-mirrored homotopic connectivity (VMHC) approach. We performed VMHC analysis on resting-state functional magnetic resonance imaging (rs-fMRI) data from 17 individuals with nCDSs and 20 healthy controls (HCs) who were enrolled from a sample of 1105 college students. We found increased VMHCs in the bilateral posterior cerebellum and fusiform gyrus in nCDSs subjects compared with HCs. Furthermore, receiver operating characteristic (ROC) curves indicated that VMHC values in the posterior cerebellum lobes could use to differente nCDSs from HCs [area under the curve (AUC), 0.756; p < 0.01]. We suggest increased VMHCs indicate a possible compensatory mechanism involved in the pathophysiology of nCDSs. VMHC values of the posterior cerebellum lobes might serve as a reliable biomarker for identifying nCDSs. © 2015 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Non-clinical depressive symptoms (nCDSs) are frequently observed among university students worldwide [1] and their prevalence appears to be increasing [2]. Importantly, depressive symptoms in young adulthood may be associated with an increased risk for other mental health problems, such as suicidal ideation and suicide attempts [3], as well as an increased risks for major depression in the future [4]. However, knowledge about the underling neural basis of nCDS remains unclear. A body of evidence suggests depressive symptoms are associated with the dysregulation and
∗ Corresponding author. Tel.: +86 2081048873. E-mail address: [email protected] (X.-Q. Jiang). http://dx.doi.org/10.1016/j.neulet.2015.01.034 0304-3940/© 2015 Elsevier Ireland Ltd. All rights reserved.
interaction of a brain network encompassing large regions of the cortical, and (para) limbic areas, subcortical structures, and cerebellum [5–7]. Most of the studies on depressive disorder emphasize the role of disrupted functional circuitry within intrahemispheric functional domains [8], whereas depression-related alterations in functional interactions between the cerebral hemispheres are largely unknown. In fact, one of the most robust characteristics of the brain’s intrinsic architecture involves interhemispheric connectivity [9,10], reflecting the process of exchange and the integration of information between the cerebral hemispheres [11]. Interhemispheric processing is known to facilitates discrimination between non-emotional faces [12] and selective attention [13]. Abnormal interhemispheric functional interactions have been found in psychiatric disorders [14]. The corpus callosum, which connects both
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cerebral hemispheres, plays a vital role in interhemispheric functional connectivity and, attention deficit disorder [15]. Neuroimaging plays an important role in the acquisition of evidence of regarding interhemispheric connectivity. For instance, structural MR assumes that hemispheric specialization has evolved as a consequence of reduced interhemispheric connectivity [16]. Abnormalities in the structural integrity of the anterior genu of the corpus callosum in diffusion tensor imaging (DTI) potentially contribute to impairment of interhemispheric connectivity in patients with major depressive disorder (MDD) [17]. Recently, a resting state fMRI (rs-fMRI) approach, which assesses functional connectivity by identifying those regions in which the low frequency blood oxygen level dependent (BOLD) [18] signal exhibits temporal coherence [19], offers a means to directly quantify interhemispheric functional interactions [20]. Temporal coherence between activity in one voxel and its homotopic voxel in the opposite hemisphere is one of the most salient aspects of the brain’s intrinsic functional architecture [21]. Loss of interhemispheric resting BOLD correlations with preserved intrahemispheric correlations is seen in patients performed complete section of the corpus callosum [22]. A recently validated approach referred to as “voxel-mirrored homotopic connectivity” (VMHC) [23] was introduced to quantify the resting-state functional connectivity between each voxel in one hemisphere and its mirrored voxel in the opposite hemisphere. This method has been performed in drug addiction [20], schizophrenia [24], and migraine [25] studies. Evidence from depressed patient demonstrates that decreased VMHCs are observed in the default mode network (DMN) [26], the medial orbitofrontal gyrus, parahippocampal gyrus, fusiform gyrus, and occipital regions [27]. Despite evidence of suggesting interhemispheric interaction deficits in MDD, no studies have explored the functional connectivity between homotopic brain sites in nCDSs. Here we evaluate interhemispheric resting state functional connectivity using VMHC in subjects with nCDSs. Moreover, to exclude the possible confounded influences from structural damage [28], we examine the gray matter volume (GMV) in regions with altered VMHCs using voxel-based morphometry (VBM) approach [29]. Additionally, the correlation between VMHC values and depressive severity scores was explored. Furthermore, receiver operating characteristic (ROC) analysis of VMHC values of each ROI was performed to identify possible biomarker to differentiate nCDSs from HCs. We hypothesized that functional deficiencies in right to left interhemispheric connectivity would be associated within nCDSs.
2. Material and methods 2.1. Participants All participants provided their written informed consents and the current research protocol was approved by the local Medical Ethics Committee of Guangzhou First Hospital of Guangzhou Medical University in China. In total, 1105 college students were recruited from Guangzhou Medical University to participate in a survey assessing depressive symptoms. The Beck Depression Inventory (BDI) -IA scale [30], a widely used tool for assessing depressive symptom in nonclinical samples [31], was utilized for assessing depressive severity of participants in the present study. The cut-off value for defining depression is 10. The generally accepted cut-off scores for grading depression are as follows: 0–9, no depression; 10–18, mild depression;19–29, moderate depression; and >30, severe depression [32]. Subjects with nCDSs in this study exhibited a BDI score greater than or equal to10 but did not achieve the DSM-IV criteria for MDD.
A total of 37 participants enrolled in MR studies, including 17 nCDSs (5 men and 12 women) with BDI scores ranging from 10 to 35 as well as 20 sex-, age-,and education-matched HCs (7 men and 13 women) with BDI scores ranging from 0 to 4. In addition, all participants met the following criteria: right-handedness, no visualized disease on MRI, between 19 and 25 years of age, no neurological illness, and no alcohol or drug dependence. Table 1 presents the detailed demographics and depressive scores. 2.2. Imaging data acquisition Scanning was performed with a 3-Tesla MRI scanner (Siemens, Erlangen, Germany) using an 8-channel brain array coil. Participant head movement was minimized using foam padding, and headphones were used to reduce scanner noise. rs-fMRI scans were obtained with an echo-planar imaging sequence (TR = 2500 ms, TE = 21 ms, FA = 90◦ , FOV = 200 mm × 200 mm, matrix = 64 × 64, voxel size = 3.5 mm × 3.1 mm × 3.1 mm, 42 slices, no gap). All subjects underwent a 500 s task-free fMRI scan after being instructed to relax with their eyes closed without falling asleep. After the functional scan, a high-resolution T1-weighted structural image was acquired for each subject. 2.3. Functional imaging data preprocessing Images preprocessing was performed using Data Processing Assistant for Resting-State fMRI (DPARSF, http://www.rest. restfmri.net) which works with SPM8 (http://www.fil.ion.ucl.ac. uk/spm/software/SPM8) on the Matlab platform. For each subject, the first 10 rs-fMRI dataset were discarded for signal equilibrium. The remaining 190 dataset were corrected for slice timing and realigned for motion correction. The steps included slice timing, head motion correction, spatial normalization, spatial smooth (with a Gaussian kernel of 4-mm), linear trend removing and bandpass (0.01–0.08 Hz) filtering. To reduce spurious BOLD variances unlikely to reflect neuronal activity [33], regression of nuisance was performed with white matter, and cerebral spinal fluid BOLD-signals, as well as six headmotion profiles to minimize the effect of head motion. Given the controversy of removing the global signal in the preprocessed rsfMRI data [34], we did not regress the global signal out in the present study. 2.4. Interhemispheric connectivity analysis The VMHC computation was performed using the REST package (http://resting-fmri.sourceforge.net) [35]. The VMHC calculation procedure was previously described [21]. In brief, all normalized T1 images were averaged to generate a mean normalized T1 image. Then, a nonlinear registration was used to normalize each brain to the symmetrical template. We applied the transformation to the symmetrical brain template to the normalized functional data. The VMHC value was then computed as the Pearson correlation (Fischer Z-transformed) between the time-series data of every pair of symmetrical voxels. Intergroup differences were compared with voxels wise t-tests [21]. 2.5. VBM analysis VBM maps were obtained by using DPARSF (http://www.rest.restfmri.net) package which works with SPM8 (http://www.fil.ion.ucl.ac.uk/spm/software/SPM8) on the Matlab platform. The Diffeomorphic Anatomic Registration Through Exponentiated Lie Algebra (DARTEL) algorithm [36] was used to increase the brain registration between participants. The mean GMV of each ROI with altered VMHC was calculated by using
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Table 1 Difference of vmhc in non-clinical depressive symptoms individuals and control subjects. Connected brain region
Non-clinical depressive symptoms > healthy controls Cerebellum posterior Lobe L Cerebellum posterior Lobe R Fusiform gyrus L Fusiform gyrus R
BA
17 17
voxels Size
19 18 18 25
MNI coordinates (mm)
T value
X
Y
Z
9 −9 −30 30
−69 −69 −59 −59
−39 −39 −12 −12
4.6085 4.6737 4.0040 4.0841
BA, Brodman’s area; MNI, Montreal Neurological Institute; L left; R right.
REST package (http://resting-fmri.sourceforge.net) [35] for further statistical analysis. 2.6. Statistical analysis The independent-sample, two-samples t test, and chi-squared test were used to compare the demographic data and BDI scores between nCDSs and HCs by using SPSS 16.0 (SPSS Inc. Chicago, IL, USA), A p-value < 0.05 was deemed significant. A general liner model analysis was performed to investigate the difference of VMHC maps in a voxel-by-voxel manner between the nCDSs and HCs using REST package [35]. The GMV, age, gender, and years of education of each subject were included as covariates to avoid the confounding effects. Additionally, given that functional connectivity at rest could be affected by micro-movements from volume to volume [37], the mean framewise displacement (FD) was added as a covariate in the group statistical analyses of VMHC [27]. A threshold of p < 0.01, corrected by a Monte Carlo simulation (see AlphaSim in AFNI http://afni.nih.gov/afni/docpdf/AlphaSim.pdf), was used to calculate the probability of false positive detection, considering both the individual voxel probability threshold and cluster size. Using this program, clusters greater than 18 voxels (486 mm3 ) were applied to the resulting statistical map at a corrected significance level of p < 0.05. To investigate the correlation between VMHC values and the depressive severity in nCDSs, the average VMHC values of all the voxels within the ROIs revealed by VMHC analysis were extracted separately using the REST package. Then, a bivariate correlation using SPSS 16.0 was introduced to indicate the correlation between the VMHC values and BDI scores, and the significance level was set at p < 0.05 (two-tailed). To identify an effective biomarker that can distinguish between nCDSs and HCs subjects, receiver operating characteristic (ROC) curve analysis was used to compute the area under the ROC curve (AUC), sensitivity/specificity characteristics of each ROI, and the optimal cut-off VMHC values for these ROIs. To identify whether the GMV varied between groups in each ROI with altered VMHC values, the extracted GMVs of each ROI were compared between nCDSs and HC subjects. In addition, the correlations between VMHC and GMV were explored. The significance levels were set at p < 0.05 (two-tailed).
3. Results 3.1. Demographics and depressive scores No significant differences were noted between nCDSs and HCs subjects regarding gender, age, or year of education. BDI scores were significantly increased in the nCDSs subjects compared with HCs (p < 0.05) (please see Table S1 in the Supplementary material). Supplementry material related to this article found, in the online version, at http://dx.doi.org/10.1016/j.neulet.2015.01.034.
3.2. VMHC difference between groups A one-sample t-test revealed that the posterior cingulate cortex had a standardized VMHC value that was significantly >1 in the two groups (please see Fig. S1 in the Supplementary material). As indicated in Fig. 1 and Table 1, the two-sample t-tests revealed that increased VMHC values were observed in the bilateral posterior cerebellum lobe (PCL) (VIII) and fusiform gyrus in nCDSs subjects compared with HCs. No region exhibited decreased VMHC values in nCDSs subjects compared with HCs. See Excel sheet 1 as supplementary file. Supplementry material related to this article found, in the online version, at http://dx.doi.org/10.1016/j.neulet.2015.01.034. 3.3. Correlations between VMHC and depressive severity No significant correlation was observed between VMHC values and BDI scores in any brain regions with abnormal VMHC values (p > 0.05). 3.4. ROC analysis of VMHC values in ROIs Among the 4 ROIs that exhibited increased VMHC values in nCDSs subjects, the AUC of both PCLs were 0.756 (p = 0.008). The sensitivity and specificity of left PCL were 76.5% and 80.0%, respectively; the cut-off value was 0.4168. The sensitivity and specificity of the right PCL were 76.5% and 75.0%, respectively; the cut-off value was 0.4077.The AUC of the right FG was 0.688 (p = 0.051), and the left FG AUC was 0.600 (p = 0.300). 3.5. GMV and VMHC values of ROIs Compared with HCs, no significant difference in GMV value in ROIs with abnormal VMHC values were noted in nCDSs (p > 0.05). No significant correlation was observed between VMHC values and GMV values in any of the ROIs with abnormal VMHC values (p > 0.05). 4. Discussion In the present study, we found increased interhemispheric connectivity in the PCL and fusiform gyrus in nCDSs compared with HCs. In addition, the VMHC values in the bilateral PCLs exhibited relative high sensitivity and specificity in differentiating nCDSs subjects and HCs. No significant correlation was observed between VMHC values and BDI scores in regions with altered VMHC values. No alteration was revealed in these regions with regard to the GMV value. To our knowledge, this is the first evidence of abnormal interhemispheric connectivity in nCDSs subjects from a sample of college students. Intriguingly, the posterior cerebellum lobule (VIII) exhibits increased VMHC values in nCDSs in the present study. The cerebellum was traditionally considered to be a region involved in sensorimotor processing. However, several studies revealed that
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Fig. 1. Altered VMHC values in the posterior cerebellum lobe and fusiform gyrus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Regions exhibiting increased (red) VMHC values in nCDSs compared with HCs (two-sample t-tests, with a p < 0.01 threshod, corrected). Each altered functional connectivity brain area is presented as an axial views with the MNI location. Color bar indicates the T score.
structural abnormalities [38] and aberrant activity [6] of cerebellum were observed in MDD patients. Furthermore, convergence evidence has suggested that the cerebellum may play an important role in regulating the emotion [39] and cognitive processing of negative stimuli [40]. Despite early controversy, the viewpoint that the cerebellum facilitates sensorimotor processing as well as higher functions (i.e., cognition and emotion control), has achieved consensus [41]. Indeed, evidence strongly suggests important anatomic interactions between the prefrontal cortex and the cerebellum [41], this notion was supported by evidence from diffusion tensor imaging [42]. This anatomic connection has been suggested as the basis of cerebellar involvement in cognitive function [43]. Given that increased interhemispherice connection is evident in the present study, we hypothesize that the posterior cerebellum lobes play a compensatory role in mediating emotional functioning in nCDSs. VMHC in the posterior cerebellum might serve as a biomarker for the identification of individuals experiencing nCDSs. We also observed increased VMHC values in the fusiform gyrus in cCDSs subjects. The fusiform gyrus was suggested to be a component of the visual recognition network [44] involved in the perception of facial emotion, and associated with attentional biases away from happy and toward sad stimuli [45]. Accurate recognition of facial expressions is crucial for social functioning [45]. Altered responsiveness to facial emotional stimuli was proposed as one of the biomarkers for the early diagnosis of MDD [46]. Furthermore, evidence acquired from structural MRI [47], task-fMRI [45], and rs-fMRI [48] studies reported abnormalities in the fusiform gyrus in depressed patients. Recently, a study [27] in MDD patients exhibited reduced VMHC values in the fusiform gyrus. Conversely, increased VMHC values were observed in nCDSs subjects in our
study. The inverse results indicate that nCDSs and MDD subjects undergo different pathophysiologies. The increased VMHC values in nCDSs subjects suggest a compensation process of visual recognition. As expected, no difference in GMV was noted in any of the regions exhibiting altered VMHC values in nCDSs subjects. Additionally, no significant correlation was observed between GMV and VMHC values in nCDSs subjects. This means that the VMHC alternations are not likely caused by GMV. Therefore, the resulting VMHC alterations in nCDSs subjects are potentially related to white-matter abnormalities; to resolve this speculation, additional diffusion tensor imaging studies are needed. Although no significant correlations were observed between BDI scores and VMHC values in nCDS subjects, the ROC analysis demonstrated that VMHC values in the ROIs in the posterior cerebellum (VIII) adequately differentiated between the two groups. The sensitivity and specificity were greater than 70%. We hypothesize that VMHC values in the posterior cerebellum (VIII) may be a biomarker to identify individuals with nCDSs, however, the validity of this potential biomarker should be confirmed by future large-sample studies. Several limitations of the present study should be mentioned. First, the human brain is not exactly structurally symmetrical. In addition, considerable functional lateralization of the brain and large individual variability in the functional organization of the brain has been noted; these characteristics do not support the present strict geometrical method for identifying homotopic brain regions. Although we attempted to resolve this issue by using a symmetric template and smoothing the functional data, the effects of methodological symmetry cannot be completely eliminated. It is possible that the graph theory, which aims to investigate larger
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whole-brain networks could overcome the shortcomings of the VMHC method. Second, given the relatively small sample, a larger data set is required to validate the present findings. Third, given that the relative mild depressive symptoms of nCDS subjects can easily transform over time, a longitudinal study is required to be better address the results. Finally, the present study did not explore the intrahemispheric functional connectivity in nCDS subjects, and the change in the integration of white matter connecting altered VMHC regions. To our knowledge, this is the first rs-fMRI study to provide neurophysiology evidence for abnormalities in interhemispheric connectivity in individual with nCDSs. Increased VMHC in nCDSs subjects indicate a possible compensatory pathophysiological mechanism. Our results provide a new insight to understand the nature of the deficits that contribute to the dysconnectivity hypothesis of nCDSs.
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Acknowledgements This work was partly supported by the Science and Technology Planning Project of Guangdong Province (grant no. 2013B021800063) and the Science and Technology Planning Project of Guangzhou (grant no. 2014J4100071).
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