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Distance-dependent alterations in local functional connectivity in drug-naive major depressive disorder
Psychiatry Research: Neuroimaging 270 (2017) 80–85
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Distance-dependent alterations in local functional connectivity in drugnaive major depressive disorder Jiajia Zhua, Xiaodong Linb, Chongguang Linb, Chuanjun Zhuob,c, a b c
MARK
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Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China Department of Psychiatry, Tianjin Mental Health Center, Tianjin, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Major depressive disorder Functional magnetic resonance imaging Resting-state Distance-dependent Functional connectivity strength
Previous studies using resting-state functional magnetic resonance imaging (fMRI) have found abnormal functional connectivity in patients with major depressive disorder (MDD). Yet, effect of distance thresholds on local functional connectivity changes in MDD is largely unknown. Here, we used resting-state fMRI data and functional connectivity strength (FCS) method to test local functional connectivity differences at different distance thresholds between 47 drug-naive patients with MDD and 47 healthy controls. For the distribution of functional brain hubs with high local FCS, the overall changing trend from distance thresholds of 10 mm to 100 mm was from lateral to medial. Compared to controls, MDD patients exhibited decreased local FCS independent of distance threshold in the sensorimotor system (postcentral gyrus, paracentral lobule, and supplementary motor area). MDD Patients exhibited increased local FCS in the inferior temporal gyrus at two lower distance thresholds (20 mm and 30 mm) and a higher distance threshold (100 mm). In addition, MDD patients showed increased local FCS in the putamen at higher distance thresholds (80–100 mm). These findings suggest that local functional connectivity abnormalities in MDD are dependent on distance thresholds and that future studies should take the distance thresholds into account when measuring local functional connectivity in MDD.
1. Introduction Major depressive disorder (MDD) is a debilitating psychiatric disorder characterized by abnormal brain connectivity (Gong and He, 2015; Hamilton et al., 2013; Kaiser et al., 2015; Mulders et al., 2015; Zhang et al., 2016b). Resting-state functional magnetic resonance imaging (fMRI) is a non-invasive imaging technique which allows researchers to measure spontaneous brain activity based on the bloodoxygen-level-dependent (BOLD) signal (Biswal et al., 1995). Restingstate functional connectivity (rsFC), measured as the temporal coherence of the BOLD signal between discrete brain regions during rest (Fox and Raichle, 2007), is a promising approach to investigate disrupted brain communication in MDD. For example, a recent review has demonstrated that consistent findings in MDD revealed by either seedbased correlation or independent component analysis are altered rsFC within the default mode network (DMN) and altered connectivity between DMN and salience network/central executive network (SN/CEN) (Mulders et al., 2015). A recent meta-analysis has provided evidence for large-scale network dysfunction in MDD, including imbalanced connectivity among networks engaged in regulating attention to internal or
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external world, and decreased connectivity between networks engaged in regulating or responding to emotion or salience (Kaiser et al., 2015). Recent advances in brain connectomics through the use of graph theory unravel disrupted topological organization (global topology, modular structure, and network hubs) of large-scale functional brain networks in MDD (Gong and He, 2015). Functional connectivity density (FCD) or functional connectivity strength (FCS), a data-driven method based on graph theory, has been developed to reflect the hub property of a single voxel (Buckner et al., 2009; Liang et al., 2013; Tomasi and Volkow, 2010, 2011a, 2011b). The FCD/FCS is also referred to as the nodal degree centrality of binary/ weighted networks (Buckner et al., 2009; Zuo et al., 2012), and brain regions with high FCD/FCS are considered functional hubs. The global FCD/FCS tests the connectivity of a given voxel with all other voxels in the brain, thus its abnormality could be interpreted as the deficit of a voxel's central role in information transmission in the whole brain network. The global FCD/FCS has been shown to be a powerful and replicable biomarker to be disrupted in MDD (Murrough et al., 2016; Wang et al., 2014a; Wu et al., 2016; Zhang et al., 2016a; Zhuo et al., 2016). The most consistent finding is decreased global FCD/FCS in the
Corresponding author at: Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China. E-mail address: [email protected] (C. Zhuo).
http://dx.doi.org/10.1016/j.pscychresns.2017.10.009 Received 12 June 2017; Received in revised form 6 September 2017; Accepted 23 October 2017 Available online 25 October 2017 0925-4927/ © 2017 Elsevier B.V. All rights reserved.
Psychiatry Research: Neuroimaging 270 (2017) 80–85
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2.2. Data acquisition
ventral medial prefrontal cortex/subgenual anterior cingulate cortex in patients with MDD. The local FCS is defined as the connectivity between a given voxel and other voxels with an anatomical distance less than a certain threshold (e.g., 75 mm) (Achard et al., 2006; He et al., 2007). The local FCS is also applied to investigate connectivity changes in MDD and has revealed decreased local FCS in the insula and superior temporal gyrus (Guo et al., 2016). However, only an arbitrary distance threshold was used in previous studies and the potential effect of distance thresholds on the local FCS analysis remains unclear. Here, we used resting-state fMRI data to test local FCS differences between drug-naive patients with MDD and healthy controls. The purpose of the current study was to investigate the effect of distance thresholds on local FCS changes in MDD. We hypothesized that patients with MDD would show distinct local FCS alteration patterns at different distance thresholds.
MRI data were acquired using a 3.0-Tesla scanner (Magnetom Verio, Siemens, Erlangen, Germany). Tight but comfortable foam padding was used to minimize head motion, and earplugs were used to reduce scanner noise. High resolution structural images were acquired sagittally using a 3D T1-weighted magnetization-prepared rapid gradientecho (MPRAGE) sequence with the following parameters: repetition time (TR) = 1900 ms; echo time (TE) = 2.48 ms; inversion time (TI) = 900 ms; flip angle (FA) = 9°; field of view (FOV) = 250 mm × 250 mm; matrix = 256 × 256; slice thickness = 1 mm, no gap; slice number = 176; and acquisition time = 258 s. Resting-state functional blood-oxygen-level-dependent (BOLD) images were acquired axially using a gradient-echo planar imaging (GRE-EPI) sequence with the following parameters: TR/TE = 2000/25 ms; FA = 90°; FOV = 240 mm × 240 mm; matrix = 64 × 64; slice thickness = 4 mm; no gap; slice number = 36; 240 volumes; and acquisition time = 480 s. Before the scanning, all subjects were instructed to keep their eyes closed, relax, move as little as possible, think of nothing in particular, and not fall asleep during the scans. During and after the scanning, we asked subjects whether they had fallen asleep to confirm that none of them had done so. All MR images were visually inspected to ensure that only images without visible artifacts were included in subsequent analyses.
2. Methods 2.1. Participants A total of ninety-four right-handed individuals were enrolled in the present study, including 47 drug-naive patients with MDD recruited consecutively from the psychiatric outpatient or inpatient department of the local hospital and 47 healthy controls recruited from the local community via advertisements. The patients and controls were wellmatched in terms of age, sex and education (Table 1). The diagnosis of MDD was made according to the Structural Clinical Interview of the DSM-Ⅳ(SCID) (First et al., 1997), patient edition. The severity of depression was assessed using the 24-item Hamilton Rating Scale for Depression (HRSD-24) (Williams, 1988). Only those patients with a HRSD-24 score ≥ 20 were eligible for this study. The detailed clinical characteristics of the patients are shown in Table 1, including the HDRS score, illness duration, onset age, episode number, and current episode duration. Healthy controls were carefully screened for a current or lifetime diagnosis of any Axis Ⅰ and Ⅱ disorder using the SCID, nonpatient edition. Exclusion criteria for all participants were 1) the presence of other Axis Ⅰ psychiatric disorders such as schizophrenia, bipolar disorder, substance-induced mood disorder, anxiety disorders, substance abuse or dependence; 2) a history of neurological diseases or other physical illness; 3) a history of head injury resulting in loss of consciousness; 4) the inability to undergo an MRI. In addition, all healthy controls reported no psychiatric disorders among their firstdegree relatives. This study was approved by the local ethics committee, and written informed consent was obtained from all participants after they had been given a detailed description of the study.
2.3. fMRI data preprocessing BOLD MRI data were preprocessed using SPM8 (http://www.fil.ion. ucl.ac.uk/spm). The first 10 volumes for each participant were discarded to allow the signal to reach equilibrium and the participants to adapt to the scanning noise. The remaining volumes were corrected for the acquisition time delay between slices. Then, realignment was performed to correct the motion between time points. In resting state fMRI, a common finding is that many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased (Power et al., 2012). Therefore, we estimated subject head motion immediately after the fMRI scans to ensure that all participants’ BOLD data were within the defined motion thresholds (i.e., translational or rotational motion parameters less than 2 mm or 2°). We also calculated frame-wise displacement (FD), which indexes the volume-tovolume changes in head position. There were no significant group differences in mean FD (t = 0.601, P = 0.549) between patients with MDD (0.141 ± 0.066) and healthy controls (0.149 ± 0.073). Several nuisance covariates (six motion parameters, their first time derivations, and signals of the global brain, white matter, and cerebrospinal fluid) were regressed out from the data. A recent study has reported that the signal spike caused by head motion significantly contaminated the final resting-state fMRI results even after regressing out the linear motion parameters (Power et al., 2012). Therefore, we further regressed out spike volumes when the FD of the specific volume exceeded 0.5. The datasets were then band-pass filtered using a frequency range of 0.01–0.08 Hz. In the normalization step, individual structural images were firstly co-registered with the mean functional image; then the transformed structural images were segmented and normalized to the Montreal Neurological Institute (MNI) space using a high-level nonlinear warping algorithm, that is, the diffeomorphic anatomical registration through the exponentiated Lie algebra (DARTEL) technique (Ashburner, 2007). Finally, each filtered functional volume was spatially normalized to MNI space using the deformation parameters estimated during the above step and resampled into a 3-mm cubic voxel.
Table 1 Demographic and Clinical Characteristics of the Sample. Characteristics
MDD
HC
Number of subjects Age (years) Sex (female/male) Education (years) FD HDRS score Illness duration (months)a Onset age (years)a Episode numbera Current episode duration (months)
47 46.4 ± 13.5 27/20 11.2 ± 3.8 0.141 ± 0.066 30.3 ± 7.1 23.7 ± 36.1
47 47.0 ± 17.9 23/24 11.7 ± 4.1 0.149 ± 0.073 – –
43.4 ± 12.4 1.3 ± 0.7 5.0 ± 6.3
– – –
Statistics
P value
t = 0.182 χ2 = 0.684 t = 0.657 t = 0.601
0.856b 0.408c 0.513b 0.549b
The data are presented as the mean ± SD. Abbreviations: FD, frame-wise displacement; HC, healthy controls; HDRS, Hamilton Depression Rating Scale; MDD, major depressive disorder. a The data are available for 39 of 47 patients. b The P values were obtained by two-sample t-tests. c The P value was obtained by chi-square test.
2.4. Local FCS analysis We computed Pearson's correlation coefficients between the BOLD time courses of all pairs of voxels within the gray matter mask and obtained a whole gray matter functional connectivity matrix for each 81
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Fig. 1. Local FCS maps at different distance thresholds for healthy controls and patients with MDD. Local FCS maps were normalized to z scores and averaged across subjects. Abbreviations: FCS, functional connectivity strength; HC, healthy controls; L, left; MDD, major depressive disorder; R, right.
org/dpabi) was used to perform the correction with the following parameters: single voxel P = 0.001, 5000 simulations, cluster connection radius = 5 mm, voxels in a gray matter mask = 48539, voxel size = 3 mm × 3 mm × 3 mm, estimated smoothness of statistical map = 15.266 mm × 15.578 mm × 14.913 mm. This resulted in a cluster size of at least 74 voxels, which corresponded to a corrected threshold of P < 0.05.
participant. Because removal of the global signal may induce controversial negative correlations (Fox et al., 2009; Murphy et al., 2009), we restricted our analysis to positive correlations. To eliminate weak correlations possibly arising from background noise, we set a correlation threshold of 0.2 according to previous studies (Liu et al., 2015; Wang et al., 2014a, 2015, 2014b). This threshold was selected because lower thresholds may include false-positive connectivity and higher thresholds may exclude some meaningful connectivity. The entry was zero if a functional connectivity was smaller than the threshold. The local FCS of a voxel was computed as the sum of functional connectivity between this voxel and other voxels within a certain anatomical distance to the given voxel. The anatomical distance between two voxels referred to the Euclidean distance between their MNI coordinates. To examine the effects of anatomical distance on local FCS analysis, we employed a distance threshold range of 10 mm to 100 mm with an interval of 10 mm. The local FCS maps were spatially smoothed with an 8 mm × 8 mm × 8 mm full width at half maximum (FWHM) Gaussian kernel.
2.6. Validation analysis In the local FCS computation, we used a correlation coefficient threshold of 0.2 to eliminate weak correlations possibly arising from noise signals. To further evaluate the reproducibility of our main results, we re-calculated the local FCS maps using two other correlation thresholds (i.e., 0.1 and 0.3) and then repeated all of the analyses. 3. Results 3.1. Local FCS mapping
2.5. Statistical analysis Local FCS maps at different distance thresholds in healthy controls and MDD patients are illustrated in Fig. 1. Both groups exhibited similar local FCS spatial distributions. For the distribution of functional brain hubs with high local FCS, the overall changing trend from the distance thresholds of 10–100 mm was from lateral to medial. Specifically, for
We compared local FCS at different distance thresholds between patients with MDD and healthy controls in a voxel-wise fashion. Multiple comparisons were corrected using a Monte Carlo simulation. AlphaSim program in DPABI software (Yan et al., 2016) (http://rfmri. 82
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Fig. 2. Altered local FCS at different distance thresholds in patients with MDD. Abbreviations: FCS, functional connectivity strength; L, left; MDD, major depressive disorder; R, right.
showed increased local FCS in the right putamen, and decreased local FCS in the left paracentral lobule and right supplementary motor area. For the threshold of 100 mm, patients with MDD exhibited increased local FCS in the left inferior temporal gyrus and right putamen, and decreased local FCS in the left paracentral lobule and right supplementary motor area.
the distance thresholds of 10 mm and 20 mm, high local FCS was found primarily in the precuneus/posterior cingulate cortex, posterior parietal cortex, dorsolateral prefrontal cortex, ventromedial prefrontal cortex, and visual cortex. For the distance thresholds of 30 mm and 40 mm, the spatial extent of the lateral functional hubs became smaller, while that of the medial functional hubs became larger. In addition, insular cortex began to exhibit high local FCS at these two distance thresholds. For the distance thresholds from 50 mm to 100 mm, the medial part of the brain had widespread high local FCS, including the precuneus, cingulate cortex, thalamus, and medial visual cortex; the spatial extent of the lateral functional hubs gradually became larger again.
3.3. Validation analyses The spatial distributions of local FCS at the correlation thresholds of 0.1 (Fig. S1) and 0.3 (Fig. S2) were similar to those at the threshold of 0.2. Furthermore, the brain regions exhibiting significant inter-group differences in local FCS at the threshold of 0.2 were largely preserved at the thresholds of 0.1 (Fig. S3) and 0.3 (Fig. S4).
3.2. Local FCS Changes at Different Distance Thresholds in MDD Inter-group differences in local FCS at 10 distance thresholds are shown in Fig. 2 and Table 2 (P < 0.05, AlphaSim corrected). For the distance threshold of 10 mm, patients with MDD exhibited decreased local FCS in the bilateral postcentral gyrus relative to healthy controls. For the threshold of 20 mm, patients with MDD showed increased local FCS in the left inferior temporal gyrus compared to healthy controls. For the threshold of 30 mm, MDD patients exhibited increased local FCS in the left inferior temporal gyrus, and decreased local FCS in the left paracentral lobule. For the thresholds of 40 mm and 50 mm, patients showed decreased local FCS in the left paracentral lobule. For the thresholds of 60 mm and 70 mm, patients with MDD had decreased local FCS in the left paracentral lobule and right supplementary motor area. For the thresholds of 80 mm and 90 mm, patients with MDD
4. Discussion Previous fMRI studies have supported the hypothesis that the brain's repertoire of responses to the external world is effectively modulated by the brain's intrinsic functional activity at rest (Mennes et al., 2011). Apart from the rsFC that reflects simultaneity in neural activity between spatially distinct brain regions, local spontaneous neural activity is also worth exploring (Vargas et al., 2013). Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) are the most frequently used methods to characterize local neural activity in restingstate fMRI studies (Zang et al., 2004, 2007). ALFF measures the power spectrum of low-frequency (0.01–0.08 Hz) fluctuations in the BOLD 83
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healthy controls (Iwabuchi et al., 2015). Despite inconsistency, these findings provide evidence that alterations in local neural activity may be the underlying pathological process engaged in MDD. The human brain is organized into a parallel, segregated complex system, which reaches a balance between global integration and local specialization (Sporns, 2011; Sporns and Zwi, 2004). Based on graph theoretical approaches, FCD/FCS method has been proposed to characterize the complex system at the voxel level (Buckner et al., 2009; Liang et al., 2013; Tomasi and Volkow, 2010, 2011a, 2011b). The global FCD/FCS reflects a voxel's role in global integration and has been applied to MDD research. For instance, Zhang et al. found reduced global FCD in the mid-cingulate cortex and increased global FCD in the occipital cortex in MDD (Zhang et al., 2016a). Compared with healthy controls, MDD patients with and without childhood neglect showed overlapping reduced global FCS in the ventral medial prefrontal cortex/ ventral anterior cingulate cortex (Wang et al., 2014a). Wu et al. reported that patients with MDD had decreased global FCS in the subgenual anterior cingulate cortex, which was correlated with the depressive symptom severity (Wu et al., 2016). Murrough et al. observed reduced global FCS within the ventromedial prefrontal cortex, which was associated with the depressive symptoms in patients with MDD (Murrough et al., 2016). Zhuo et al. found that patients with MDD displayed decreased global FCD mainly in the sensory system including the sensorimotor and visual cortex (Zhuo et al., 2016). The local FCS reflects a voxel's role in local specialization. In this study, we found that the spatial distribution of the local FCS was dependent on the choice of anatomical distance. The functional brain hubs shifted from lateral to medial along with the distance threshold change from 10 to 100 mm, indicating that the lateral cortical hubs may have more relatively short connectivity while the medial hubs may have more relatively long connectivity. We speculate that U-shaped association fibers, which form the major local white matter connections arching through the cortical sulci to connect adjacent gyri, may contribute to the short-connectivity hubs in the lateral cortex; while long white matter fibers, such as the cingulum and thalamic radiation, may contribute to the long-connectivity hubs in the medial brain regions. Furthermore, the alteration patterns of local FCS in MDD were also different along with the distance threshold change. Firstly, we found that disconnection of the sensorimotor system in MDD was independent of distance threshold, although the affected regions were from postcentral gyrus to paracentral lobule and supplementary motor area. Previous studies have demonstrated that dysfunction of the sensorimotor system has modulatory effects on mood and depression; moreover, depression in turn affects sensorimotor processing, resulting in an interaction contributing to the aggravation of depressive symptoms (Canbeyli, 2010). Secondly, patients with MDD exhibited increased local FCS in the inferior temporal gyrus at two lower distance thresholds (20 mm and 30 mm) and a higher distance threshold (100 mm). This finding suggests that MDD-related increase and decrease in connectivity may reach a balance at the medium distance thresholds, which may lead to the seemingly normal local FCS in this region. If distance thresholds were set within this range, one may lose the opportunity to find positive results. Finally, we found increased local FCS in the putamen at higher distance thresholds (80–100 mm), implying a selective functional dysconnectivity of the putamen in the cortico-limbic-thalamic-striatal system in MDD patients. This finding is consistent with prior studies reporting putamen abnormality in MDD, such as elevated D(2) receptor binding potential (Meyer et al., 2006), increased xanthine oxidase (Michel et al., 2010), volumetric/shape changes (Lu et al., 2016), and greater age-related volumetric decreases (Sacchet et al., 2017). There are several limitations in the present study. First, some of the patients had chronic MDD, so we cannot determine whether our findings can be generalized to all stages of depression. In future studies, first-episode drug-naive patients with MDD should be included to validate our findings. Second, only ten distance thresholds (from 10 to
Table 2 Local FCS changes in patients with MDD. Regions
10 mm MDD < HC Left postcentral gyrus Right postcentral gyrus 20 mm MDD > HC Left inferior temporal gyrus 30 mm MDD > HC Left inferior temporal gyrus MDD < HC Left paracentral lobule 40 mm MDD < HC Left paracentral lobule 50 mm MDD < HC Left paracentral lobule 60 mm MDD < HC Left paracentral lobule Right supplementary motor area 70 mm MDD < HC Left paracentral lobule Right supplementary motor area 80 mm MDD > HC Right putamen MDD < HC Left paracentral lobule Right supplementary motor area 90 mm MDD > HC Right putamen MDD < HC Left paracentral lobule Right supplementary motor area 100 mm MDD > HC Left inferior temporal gyrus Right putamen MDD < HC Left paracentral lobule Right supplementary motor area
Brodmann areas
Cluster size (voxels)
Peak t values
Coordinates in MNI (x, y, z)
3 3
119 85
−4.2 −4.5
−51, −6, 33 51, −18, 33
20, 37
209
4.8
−42, −18, −21
20
79
4.7
−39, −18, −24
6
86
−5.7
−15, −21, 78
6
93
−5.9
−15, −21, 78
6
105
−5.9
−15, −21, 78
6 6
126 76
−5.7 −4.3
−15, −18, 78 12, −21, 78
6 6
145 107
−5.8 −4.5
−15, −18, 78 12, −21, 78
80
4.5
27, 21, −6
147 111
−5.7 −4.6
−15, −18, 78 12, −21, 78
97
4.5
24, 18, −3
6 6
144 105
−5.6 −4.6
−15, −18, 78 12, 3, 75
20
75
4.6
−36, −18, −24
103
4.6
24, 18, −3
141 99
−5.5 −4.6
−15, −18, 78 12, 3, 75
6 6
6 6
Abbreviations: HC, healthy controls; MDD, major depressive disorder; MNI, Montreal Neurological Institute; FCS, functional connectivity strength.
signal (Zang et al., 2007); ReHo reflects the neural coherence of a given voxel with its nearest voxels (Zang et al., 2004). Both approaches have been widely applied to explore the neural mechanisms of MDD. For example, two meta-analyses using ALFF method have revealed that MDD patients show increased ALFF in the anterior cingulate cortex, supplementary motor area, insula, striatum and middle frontal gyrus, and decreased ALFF in the cerebellum, superior temporal gyrus, middle temporal gyrus and calcarine fissure cortex (Li et al., 2017; Zhou et al., 2017). Another meta-analysis using ReHo method has identified increased ReHo in the medial superior frontal gyrus and fusiform gyrus, and decreased ReHo in the cerebellum, postcentral gyrus, rolandic operculum, cuneus, inferior parietal gyrus in MDD patients compared to 84
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100 mm) were used in the local FCS analysis, which prevents us from exploring the more exact relationship between local FCS alteration and distance threshold. However, these distance thresholds were defined on the anatomical basis. Further studies using more elaborate strategy may facilitate a better understanding of the distance-dependent local FCS alteration in MDD. Finally, artifacts from cardiac and respiratory noise are prevalent in resting-state fMRI analyses (Murphy et al., 2013). Thus, an advisable pre-processing step is to remove physiological noise from the data using simultaneously collected pulse and respiration data. However, physiological data were not collected in this study. In conclusion, this is the first study to investigate local functional connectivity changes in drug-naive MDD at different distance thresholds. Our results indicate that local functional connectivity abnormalities in MDD are dependent on distance thresholds. The findings suggest that future studies should take distance threshold effects into account when examining local functional connectivity changes in MDD. Conflict of interest The authors declare no conflict of interests. Role of funding source This study was supported by grants from the Key project of Wenzhou Science and Technology Bureau (ZS2017011). Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.pscychresns.2017.10. 009. References Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E., 2006. A resilient, lowfrequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 63–72. Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541. Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., AndrewsHanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860–1873. Canbeyli, R., 2010. Sensorimotor modulation of mood and depression: an integrative review. Behav. Brain Res. 207, 249–264. First, M.B., S, R., Gibbon, M., JBW, Williams, 1997. Structured Clinical Interview for DSM-IV Axis I Disorders. American Psychiatric Presse. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Fox, M.D., Zhang, D., Snyder, A.Z., Raichle, M.E., 2009. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283. Gong, Q., He, Y., 2015. Depression, neuroimaging and connectomics: a selective overview. Biol. Psychiatry 77, 223–235. Guo, W., Liu, F., Chen, J., Wu, R., Zhang, Z., Yu, M., Xue, Z., Zhao, J., 2016. Decreased long- and short-range functional connectivity at rest in drug-naive major depressive disorder. Aust. N. Z. J. Psychiatry 50, 763–769. Hamilton, J.P., Chen, M.C., Gotlib, I.H., 2013. Neural systems approaches to understanding major depressive disorder: an intrinsic functional organization perspective. Neurobiol. Dis. 52, 4–11. He, Y., Chen, Z.J., Evans, A.C., 2007. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407–2419. Iwabuchi, S.J., Krishnadas, R., Li, C., Auer, D.P., Radua, J., Palaniyappan, L., 2015. Localized connectivity in depression: a meta-analysis of resting state functional imaging studies. Neurosci. Biobehav. Rev. 51, 77–86. Kaiser, R.H., Andrews-Hanna, J.R., Wager, T.D., Pizzagalli, D.A., 2015. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry 72, 603–611. Li, W., Chen, Z., Wu, M., Zhu, H., Gu, L., Zhao, Y., Kuang, W., Bi, F., Kemp, G.J., Gong, Q., 2017. Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: a multimodal meta-analysis. J. Affect. Disord. 210, 303–311. Liang, X., Zou, Q., He, Y., Yang, Y., 2013. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the
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Distance-dependent alterations in local functional connectivity in drugnaive major depressive disorder Jiajia Zhua, Xiaodong Linb, Chongguang Linb, Chuanjun Zhuob,c, a b c
MARK
⁎
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China Department of Psychiatry, Tianjin Mental Health Center, Tianjin, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Major depressive disorder Functional magnetic resonance imaging Resting-state Distance-dependent Functional connectivity strength
Previous studies using resting-state functional magnetic resonance imaging (fMRI) have found abnormal functional connectivity in patients with major depressive disorder (MDD). Yet, effect of distance thresholds on local functional connectivity changes in MDD is largely unknown. Here, we used resting-state fMRI data and functional connectivity strength (FCS) method to test local functional connectivity differences at different distance thresholds between 47 drug-naive patients with MDD and 47 healthy controls. For the distribution of functional brain hubs with high local FCS, the overall changing trend from distance thresholds of 10 mm to 100 mm was from lateral to medial. Compared to controls, MDD patients exhibited decreased local FCS independent of distance threshold in the sensorimotor system (postcentral gyrus, paracentral lobule, and supplementary motor area). MDD Patients exhibited increased local FCS in the inferior temporal gyrus at two lower distance thresholds (20 mm and 30 mm) and a higher distance threshold (100 mm). In addition, MDD patients showed increased local FCS in the putamen at higher distance thresholds (80–100 mm). These findings suggest that local functional connectivity abnormalities in MDD are dependent on distance thresholds and that future studies should take the distance thresholds into account when measuring local functional connectivity in MDD.
1. Introduction Major depressive disorder (MDD) is a debilitating psychiatric disorder characterized by abnormal brain connectivity (Gong and He, 2015; Hamilton et al., 2013; Kaiser et al., 2015; Mulders et al., 2015; Zhang et al., 2016b). Resting-state functional magnetic resonance imaging (fMRI) is a non-invasive imaging technique which allows researchers to measure spontaneous brain activity based on the bloodoxygen-level-dependent (BOLD) signal (Biswal et al., 1995). Restingstate functional connectivity (rsFC), measured as the temporal coherence of the BOLD signal between discrete brain regions during rest (Fox and Raichle, 2007), is a promising approach to investigate disrupted brain communication in MDD. For example, a recent review has demonstrated that consistent findings in MDD revealed by either seedbased correlation or independent component analysis are altered rsFC within the default mode network (DMN) and altered connectivity between DMN and salience network/central executive network (SN/CEN) (Mulders et al., 2015). A recent meta-analysis has provided evidence for large-scale network dysfunction in MDD, including imbalanced connectivity among networks engaged in regulating attention to internal or
⁎
external world, and decreased connectivity between networks engaged in regulating or responding to emotion or salience (Kaiser et al., 2015). Recent advances in brain connectomics through the use of graph theory unravel disrupted topological organization (global topology, modular structure, and network hubs) of large-scale functional brain networks in MDD (Gong and He, 2015). Functional connectivity density (FCD) or functional connectivity strength (FCS), a data-driven method based on graph theory, has been developed to reflect the hub property of a single voxel (Buckner et al., 2009; Liang et al., 2013; Tomasi and Volkow, 2010, 2011a, 2011b). The FCD/FCS is also referred to as the nodal degree centrality of binary/ weighted networks (Buckner et al., 2009; Zuo et al., 2012), and brain regions with high FCD/FCS are considered functional hubs. The global FCD/FCS tests the connectivity of a given voxel with all other voxels in the brain, thus its abnormality could be interpreted as the deficit of a voxel's central role in information transmission in the whole brain network. The global FCD/FCS has been shown to be a powerful and replicable biomarker to be disrupted in MDD (Murrough et al., 2016; Wang et al., 2014a; Wu et al., 2016; Zhang et al., 2016a; Zhuo et al., 2016). The most consistent finding is decreased global FCD/FCS in the
Corresponding author at: Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China. E-mail address: [email protected] (C. Zhuo).
http://dx.doi.org/10.1016/j.pscychresns.2017.10.009 Received 12 June 2017; Received in revised form 6 September 2017; Accepted 23 October 2017 Available online 25 October 2017 0925-4927/ © 2017 Elsevier B.V. All rights reserved.
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2.2. Data acquisition
ventral medial prefrontal cortex/subgenual anterior cingulate cortex in patients with MDD. The local FCS is defined as the connectivity between a given voxel and other voxels with an anatomical distance less than a certain threshold (e.g., 75 mm) (Achard et al., 2006; He et al., 2007). The local FCS is also applied to investigate connectivity changes in MDD and has revealed decreased local FCS in the insula and superior temporal gyrus (Guo et al., 2016). However, only an arbitrary distance threshold was used in previous studies and the potential effect of distance thresholds on the local FCS analysis remains unclear. Here, we used resting-state fMRI data to test local FCS differences between drug-naive patients with MDD and healthy controls. The purpose of the current study was to investigate the effect of distance thresholds on local FCS changes in MDD. We hypothesized that patients with MDD would show distinct local FCS alteration patterns at different distance thresholds.
MRI data were acquired using a 3.0-Tesla scanner (Magnetom Verio, Siemens, Erlangen, Germany). Tight but comfortable foam padding was used to minimize head motion, and earplugs were used to reduce scanner noise. High resolution structural images were acquired sagittally using a 3D T1-weighted magnetization-prepared rapid gradientecho (MPRAGE) sequence with the following parameters: repetition time (TR) = 1900 ms; echo time (TE) = 2.48 ms; inversion time (TI) = 900 ms; flip angle (FA) = 9°; field of view (FOV) = 250 mm × 250 mm; matrix = 256 × 256; slice thickness = 1 mm, no gap; slice number = 176; and acquisition time = 258 s. Resting-state functional blood-oxygen-level-dependent (BOLD) images were acquired axially using a gradient-echo planar imaging (GRE-EPI) sequence with the following parameters: TR/TE = 2000/25 ms; FA = 90°; FOV = 240 mm × 240 mm; matrix = 64 × 64; slice thickness = 4 mm; no gap; slice number = 36; 240 volumes; and acquisition time = 480 s. Before the scanning, all subjects were instructed to keep their eyes closed, relax, move as little as possible, think of nothing in particular, and not fall asleep during the scans. During and after the scanning, we asked subjects whether they had fallen asleep to confirm that none of them had done so. All MR images were visually inspected to ensure that only images without visible artifacts were included in subsequent analyses.
2. Methods 2.1. Participants A total of ninety-four right-handed individuals were enrolled in the present study, including 47 drug-naive patients with MDD recruited consecutively from the psychiatric outpatient or inpatient department of the local hospital and 47 healthy controls recruited from the local community via advertisements. The patients and controls were wellmatched in terms of age, sex and education (Table 1). The diagnosis of MDD was made according to the Structural Clinical Interview of the DSM-Ⅳ(SCID) (First et al., 1997), patient edition. The severity of depression was assessed using the 24-item Hamilton Rating Scale for Depression (HRSD-24) (Williams, 1988). Only those patients with a HRSD-24 score ≥ 20 were eligible for this study. The detailed clinical characteristics of the patients are shown in Table 1, including the HDRS score, illness duration, onset age, episode number, and current episode duration. Healthy controls were carefully screened for a current or lifetime diagnosis of any Axis Ⅰ and Ⅱ disorder using the SCID, nonpatient edition. Exclusion criteria for all participants were 1) the presence of other Axis Ⅰ psychiatric disorders such as schizophrenia, bipolar disorder, substance-induced mood disorder, anxiety disorders, substance abuse or dependence; 2) a history of neurological diseases or other physical illness; 3) a history of head injury resulting in loss of consciousness; 4) the inability to undergo an MRI. In addition, all healthy controls reported no psychiatric disorders among their firstdegree relatives. This study was approved by the local ethics committee, and written informed consent was obtained from all participants after they had been given a detailed description of the study.
2.3. fMRI data preprocessing BOLD MRI data were preprocessed using SPM8 (http://www.fil.ion. ucl.ac.uk/spm). The first 10 volumes for each participant were discarded to allow the signal to reach equilibrium and the participants to adapt to the scanning noise. The remaining volumes were corrected for the acquisition time delay between slices. Then, realignment was performed to correct the motion between time points. In resting state fMRI, a common finding is that many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased (Power et al., 2012). Therefore, we estimated subject head motion immediately after the fMRI scans to ensure that all participants’ BOLD data were within the defined motion thresholds (i.e., translational or rotational motion parameters less than 2 mm or 2°). We also calculated frame-wise displacement (FD), which indexes the volume-tovolume changes in head position. There were no significant group differences in mean FD (t = 0.601, P = 0.549) between patients with MDD (0.141 ± 0.066) and healthy controls (0.149 ± 0.073). Several nuisance covariates (six motion parameters, their first time derivations, and signals of the global brain, white matter, and cerebrospinal fluid) were regressed out from the data. A recent study has reported that the signal spike caused by head motion significantly contaminated the final resting-state fMRI results even after regressing out the linear motion parameters (Power et al., 2012). Therefore, we further regressed out spike volumes when the FD of the specific volume exceeded 0.5. The datasets were then band-pass filtered using a frequency range of 0.01–0.08 Hz. In the normalization step, individual structural images were firstly co-registered with the mean functional image; then the transformed structural images were segmented and normalized to the Montreal Neurological Institute (MNI) space using a high-level nonlinear warping algorithm, that is, the diffeomorphic anatomical registration through the exponentiated Lie algebra (DARTEL) technique (Ashburner, 2007). Finally, each filtered functional volume was spatially normalized to MNI space using the deformation parameters estimated during the above step and resampled into a 3-mm cubic voxel.
Table 1 Demographic and Clinical Characteristics of the Sample. Characteristics
MDD
HC
Number of subjects Age (years) Sex (female/male) Education (years) FD HDRS score Illness duration (months)a Onset age (years)a Episode numbera Current episode duration (months)
47 46.4 ± 13.5 27/20 11.2 ± 3.8 0.141 ± 0.066 30.3 ± 7.1 23.7 ± 36.1
47 47.0 ± 17.9 23/24 11.7 ± 4.1 0.149 ± 0.073 – –
43.4 ± 12.4 1.3 ± 0.7 5.0 ± 6.3
– – –
Statistics
P value
t = 0.182 χ2 = 0.684 t = 0.657 t = 0.601
0.856b 0.408c 0.513b 0.549b
The data are presented as the mean ± SD. Abbreviations: FD, frame-wise displacement; HC, healthy controls; HDRS, Hamilton Depression Rating Scale; MDD, major depressive disorder. a The data are available for 39 of 47 patients. b The P values were obtained by two-sample t-tests. c The P value was obtained by chi-square test.
2.4. Local FCS analysis We computed Pearson's correlation coefficients between the BOLD time courses of all pairs of voxels within the gray matter mask and obtained a whole gray matter functional connectivity matrix for each 81
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Fig. 1. Local FCS maps at different distance thresholds for healthy controls and patients with MDD. Local FCS maps were normalized to z scores and averaged across subjects. Abbreviations: FCS, functional connectivity strength; HC, healthy controls; L, left; MDD, major depressive disorder; R, right.
org/dpabi) was used to perform the correction with the following parameters: single voxel P = 0.001, 5000 simulations, cluster connection radius = 5 mm, voxels in a gray matter mask = 48539, voxel size = 3 mm × 3 mm × 3 mm, estimated smoothness of statistical map = 15.266 mm × 15.578 mm × 14.913 mm. This resulted in a cluster size of at least 74 voxels, which corresponded to a corrected threshold of P < 0.05.
participant. Because removal of the global signal may induce controversial negative correlations (Fox et al., 2009; Murphy et al., 2009), we restricted our analysis to positive correlations. To eliminate weak correlations possibly arising from background noise, we set a correlation threshold of 0.2 according to previous studies (Liu et al., 2015; Wang et al., 2014a, 2015, 2014b). This threshold was selected because lower thresholds may include false-positive connectivity and higher thresholds may exclude some meaningful connectivity. The entry was zero if a functional connectivity was smaller than the threshold. The local FCS of a voxel was computed as the sum of functional connectivity between this voxel and other voxels within a certain anatomical distance to the given voxel. The anatomical distance between two voxels referred to the Euclidean distance between their MNI coordinates. To examine the effects of anatomical distance on local FCS analysis, we employed a distance threshold range of 10 mm to 100 mm with an interval of 10 mm. The local FCS maps were spatially smoothed with an 8 mm × 8 mm × 8 mm full width at half maximum (FWHM) Gaussian kernel.
2.6. Validation analysis In the local FCS computation, we used a correlation coefficient threshold of 0.2 to eliminate weak correlations possibly arising from noise signals. To further evaluate the reproducibility of our main results, we re-calculated the local FCS maps using two other correlation thresholds (i.e., 0.1 and 0.3) and then repeated all of the analyses. 3. Results 3.1. Local FCS mapping
2.5. Statistical analysis Local FCS maps at different distance thresholds in healthy controls and MDD patients are illustrated in Fig. 1. Both groups exhibited similar local FCS spatial distributions. For the distribution of functional brain hubs with high local FCS, the overall changing trend from the distance thresholds of 10–100 mm was from lateral to medial. Specifically, for
We compared local FCS at different distance thresholds between patients with MDD and healthy controls in a voxel-wise fashion. Multiple comparisons were corrected using a Monte Carlo simulation. AlphaSim program in DPABI software (Yan et al., 2016) (http://rfmri. 82
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Fig. 2. Altered local FCS at different distance thresholds in patients with MDD. Abbreviations: FCS, functional connectivity strength; L, left; MDD, major depressive disorder; R, right.
showed increased local FCS in the right putamen, and decreased local FCS in the left paracentral lobule and right supplementary motor area. For the threshold of 100 mm, patients with MDD exhibited increased local FCS in the left inferior temporal gyrus and right putamen, and decreased local FCS in the left paracentral lobule and right supplementary motor area.
the distance thresholds of 10 mm and 20 mm, high local FCS was found primarily in the precuneus/posterior cingulate cortex, posterior parietal cortex, dorsolateral prefrontal cortex, ventromedial prefrontal cortex, and visual cortex. For the distance thresholds of 30 mm and 40 mm, the spatial extent of the lateral functional hubs became smaller, while that of the medial functional hubs became larger. In addition, insular cortex began to exhibit high local FCS at these two distance thresholds. For the distance thresholds from 50 mm to 100 mm, the medial part of the brain had widespread high local FCS, including the precuneus, cingulate cortex, thalamus, and medial visual cortex; the spatial extent of the lateral functional hubs gradually became larger again.
3.3. Validation analyses The spatial distributions of local FCS at the correlation thresholds of 0.1 (Fig. S1) and 0.3 (Fig. S2) were similar to those at the threshold of 0.2. Furthermore, the brain regions exhibiting significant inter-group differences in local FCS at the threshold of 0.2 were largely preserved at the thresholds of 0.1 (Fig. S3) and 0.3 (Fig. S4).
3.2. Local FCS Changes at Different Distance Thresholds in MDD Inter-group differences in local FCS at 10 distance thresholds are shown in Fig. 2 and Table 2 (P < 0.05, AlphaSim corrected). For the distance threshold of 10 mm, patients with MDD exhibited decreased local FCS in the bilateral postcentral gyrus relative to healthy controls. For the threshold of 20 mm, patients with MDD showed increased local FCS in the left inferior temporal gyrus compared to healthy controls. For the threshold of 30 mm, MDD patients exhibited increased local FCS in the left inferior temporal gyrus, and decreased local FCS in the left paracentral lobule. For the thresholds of 40 mm and 50 mm, patients showed decreased local FCS in the left paracentral lobule. For the thresholds of 60 mm and 70 mm, patients with MDD had decreased local FCS in the left paracentral lobule and right supplementary motor area. For the thresholds of 80 mm and 90 mm, patients with MDD
4. Discussion Previous fMRI studies have supported the hypothesis that the brain's repertoire of responses to the external world is effectively modulated by the brain's intrinsic functional activity at rest (Mennes et al., 2011). Apart from the rsFC that reflects simultaneity in neural activity between spatially distinct brain regions, local spontaneous neural activity is also worth exploring (Vargas et al., 2013). Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) are the most frequently used methods to characterize local neural activity in restingstate fMRI studies (Zang et al., 2004, 2007). ALFF measures the power spectrum of low-frequency (0.01–0.08 Hz) fluctuations in the BOLD 83
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healthy controls (Iwabuchi et al., 2015). Despite inconsistency, these findings provide evidence that alterations in local neural activity may be the underlying pathological process engaged in MDD. The human brain is organized into a parallel, segregated complex system, which reaches a balance between global integration and local specialization (Sporns, 2011; Sporns and Zwi, 2004). Based on graph theoretical approaches, FCD/FCS method has been proposed to characterize the complex system at the voxel level (Buckner et al., 2009; Liang et al., 2013; Tomasi and Volkow, 2010, 2011a, 2011b). The global FCD/FCS reflects a voxel's role in global integration and has been applied to MDD research. For instance, Zhang et al. found reduced global FCD in the mid-cingulate cortex and increased global FCD in the occipital cortex in MDD (Zhang et al., 2016a). Compared with healthy controls, MDD patients with and without childhood neglect showed overlapping reduced global FCS in the ventral medial prefrontal cortex/ ventral anterior cingulate cortex (Wang et al., 2014a). Wu et al. reported that patients with MDD had decreased global FCS in the subgenual anterior cingulate cortex, which was correlated with the depressive symptom severity (Wu et al., 2016). Murrough et al. observed reduced global FCS within the ventromedial prefrontal cortex, which was associated with the depressive symptoms in patients with MDD (Murrough et al., 2016). Zhuo et al. found that patients with MDD displayed decreased global FCD mainly in the sensory system including the sensorimotor and visual cortex (Zhuo et al., 2016). The local FCS reflects a voxel's role in local specialization. In this study, we found that the spatial distribution of the local FCS was dependent on the choice of anatomical distance. The functional brain hubs shifted from lateral to medial along with the distance threshold change from 10 to 100 mm, indicating that the lateral cortical hubs may have more relatively short connectivity while the medial hubs may have more relatively long connectivity. We speculate that U-shaped association fibers, which form the major local white matter connections arching through the cortical sulci to connect adjacent gyri, may contribute to the short-connectivity hubs in the lateral cortex; while long white matter fibers, such as the cingulum and thalamic radiation, may contribute to the long-connectivity hubs in the medial brain regions. Furthermore, the alteration patterns of local FCS in MDD were also different along with the distance threshold change. Firstly, we found that disconnection of the sensorimotor system in MDD was independent of distance threshold, although the affected regions were from postcentral gyrus to paracentral lobule and supplementary motor area. Previous studies have demonstrated that dysfunction of the sensorimotor system has modulatory effects on mood and depression; moreover, depression in turn affects sensorimotor processing, resulting in an interaction contributing to the aggravation of depressive symptoms (Canbeyli, 2010). Secondly, patients with MDD exhibited increased local FCS in the inferior temporal gyrus at two lower distance thresholds (20 mm and 30 mm) and a higher distance threshold (100 mm). This finding suggests that MDD-related increase and decrease in connectivity may reach a balance at the medium distance thresholds, which may lead to the seemingly normal local FCS in this region. If distance thresholds were set within this range, one may lose the opportunity to find positive results. Finally, we found increased local FCS in the putamen at higher distance thresholds (80–100 mm), implying a selective functional dysconnectivity of the putamen in the cortico-limbic-thalamic-striatal system in MDD patients. This finding is consistent with prior studies reporting putamen abnormality in MDD, such as elevated D(2) receptor binding potential (Meyer et al., 2006), increased xanthine oxidase (Michel et al., 2010), volumetric/shape changes (Lu et al., 2016), and greater age-related volumetric decreases (Sacchet et al., 2017). There are several limitations in the present study. First, some of the patients had chronic MDD, so we cannot determine whether our findings can be generalized to all stages of depression. In future studies, first-episode drug-naive patients with MDD should be included to validate our findings. Second, only ten distance thresholds (from 10 to
Table 2 Local FCS changes in patients with MDD. Regions
10 mm MDD < HC Left postcentral gyrus Right postcentral gyrus 20 mm MDD > HC Left inferior temporal gyrus 30 mm MDD > HC Left inferior temporal gyrus MDD < HC Left paracentral lobule 40 mm MDD < HC Left paracentral lobule 50 mm MDD < HC Left paracentral lobule 60 mm MDD < HC Left paracentral lobule Right supplementary motor area 70 mm MDD < HC Left paracentral lobule Right supplementary motor area 80 mm MDD > HC Right putamen MDD < HC Left paracentral lobule Right supplementary motor area 90 mm MDD > HC Right putamen MDD < HC Left paracentral lobule Right supplementary motor area 100 mm MDD > HC Left inferior temporal gyrus Right putamen MDD < HC Left paracentral lobule Right supplementary motor area
Brodmann areas
Cluster size (voxels)
Peak t values
Coordinates in MNI (x, y, z)
3 3
119 85
−4.2 −4.5
−51, −6, 33 51, −18, 33
20, 37
209
4.8
−42, −18, −21
20
79
4.7
−39, −18, −24
6
86
−5.7
−15, −21, 78
6
93
−5.9
−15, −21, 78
6
105
−5.9
−15, −21, 78
6 6
126 76
−5.7 −4.3
−15, −18, 78 12, −21, 78
6 6
145 107
−5.8 −4.5
−15, −18, 78 12, −21, 78
80
4.5
27, 21, −6
147 111
−5.7 −4.6
−15, −18, 78 12, −21, 78
97
4.5
24, 18, −3
6 6
144 105
−5.6 −4.6
−15, −18, 78 12, 3, 75
20
75
4.6
−36, −18, −24
103
4.6
24, 18, −3
141 99
−5.5 −4.6
−15, −18, 78 12, 3, 75
6 6
6 6
Abbreviations: HC, healthy controls; MDD, major depressive disorder; MNI, Montreal Neurological Institute; FCS, functional connectivity strength.
signal (Zang et al., 2007); ReHo reflects the neural coherence of a given voxel with its nearest voxels (Zang et al., 2004). Both approaches have been widely applied to explore the neural mechanisms of MDD. For example, two meta-analyses using ALFF method have revealed that MDD patients show increased ALFF in the anterior cingulate cortex, supplementary motor area, insula, striatum and middle frontal gyrus, and decreased ALFF in the cerebellum, superior temporal gyrus, middle temporal gyrus and calcarine fissure cortex (Li et al., 2017; Zhou et al., 2017). Another meta-analysis using ReHo method has identified increased ReHo in the medial superior frontal gyrus and fusiform gyrus, and decreased ReHo in the cerebellum, postcentral gyrus, rolandic operculum, cuneus, inferior parietal gyrus in MDD patients compared to 84
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