вход по аккаунту



код для вставкиСкачать
Psychiatry Research: Neuroimaging 270 (2017) 80–85
Contents lists available at ScienceDirect
Psychiatry Research: Neuroimaging
journal homepage:
Distance-dependent alterations in local functional connectivity in drugnaive major depressive disorder
Jiajia Zhua, Xiaodong Linb, Chongguang Linb, Chuanjun Zhuob,c,
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
Major depressive disorder
Functional magnetic resonance imaging
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).
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
J. Zhu et al.
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
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. 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.
Number of subjects
Age (years)
Sex (female/male)
Education (years)
HDRS score
Illness duration
Onset age (years)a
Episode numbera
Current episode
duration (months)
46.4 ± 13.5
11.2 ± 3.8
0.141 ± 0.066
30.3 ± 7.1
23.7 ± 36.1
47.0 ± 17.9
11.7 ± 4.1
0.149 ± 0.073
43.4 ± 12.4
1.3 ± 0.7
5.0 ± 6.3
P value
t = 0.182
χ2 = 0.684
t = 0.657
t = 0.601
The data are presented as the mean ± SD. Abbreviations: FD, frame-wise displacement;
HC, healthy controls; HDRS, Hamilton Depression Rating Scale; MDD, major depressive
The data are available for 39 of 47 patients.
The P values were obtained by two-sample t-tests.
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
Psychiatry Research: Neuroimaging 270 (2017) 80–85
J. Zhu et al.
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
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.
Psychiatry Research: Neuroimaging 270 (2017) 80–85
J. Zhu et al.
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
Psychiatry Research: Neuroimaging 270 (2017) 80–85
J. Zhu et al.
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.,
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.
10 mm
Left postcentral gyrus
Right postcentral
20 mm
Left inferior temporal
30 mm
Left inferior temporal
Left paracentral lobule
40 mm
Left paracentral lobule
50 mm
Left paracentral lobule
60 mm
Left paracentral lobule
Right supplementary
motor area
70 mm
Left paracentral lobule
Right supplementary
motor area
80 mm
Right putamen
Left paracentral lobule
Right supplementary
motor area
90 mm
Right putamen
Left paracentral lobule
Right supplementary
motor area
100 mm
Left inferior temporal
Right putamen
Left paracentral lobule
Right supplementary
motor area
Peak t
Coordinates in
MNI (x, y, z)
−51, −6, 33
51, −18, 33
20, 37
−42, −18, −21
−39, −18, −24
−15, −21, 78
−15, −21, 78
−15, −21, 78
−15, −18, 78
12, −21, 78
−15, −18, 78
12, −21, 78
27, 21, −6
−15, −18, 78
12, −21, 78
24, 18, −3
−15, −18, 78
12, 3, 75
−36, −18, −24
24, 18, −3
−15, −18, 78
12, 3, 75
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
Psychiatry Research: Neuroimaging 270 (2017) 80–85
J. Zhu et al.
human brain. Proc. Natl. Acad. Sci. USA 110, 1929–1934.
Liu, F., Zhu, C., Wang, Y., Guo, W., Li, M., Wang, W., Long, Z., Meng, Y., Cui, Q., Zeng, L.,
Gong, Q., Zhang, W., Chen, H., 2015. Disrupted cortical hubs in functional brain
networks in social anxiety disorder. Clin. Neurophysiol. 126, 1711–1716.
Lu, Y., Liang, H., Han, D., Mo, Y., Li, Z., Cheng, Y., Xu, X., Shen, Z., Tan, C., Zhao, W., Zhu,
Y., Sun, X., 2016. The volumetric and shape changes of the putamen and thalamus in
first episode, untreated major depressive disorder. Neuroimage Clin. 11, 658–666.
Mennes, M., Zuo, X.N., Kelly, C., Di Martino, A., Zang, Y.F., Biswal, B., Castellanos, F.X.,
Milham, M.P., 2011. Linking inter-individual differences in neural activation and
behavior to intrinsic brain dynamics. Neuroimage 54, 2950–2959.
Meyer, J.H., McNeely, H.E., Sagrati, S., Boovariwala, A., Martin, K., Verhoeff, N.P.,
Wilson, A.A., Houle, S., 2006. Elevated putamen D(2) receptor binding potential in
major depression with motor retardation: an [11C]raclopride positron emission tomography study. Am. J. Psychiatry 163, 1594–1602.
Michel, T.M., Camara, S., Tatschner, T., Frangou, S., Sheldrick, A.J., Riederer, P.,
Grunblatt, E., 2010. Increased xanthine oxidase in the thalamus and putamen in
depression. World J. Biol. Psychiatry 11, 314–320.
Mulders, P.C., van Eijndhoven, P.F., Schene, A.H., Beckmann, C.F., Tendolkar, I., 2015.
Resting-state functional connectivity in major depressive disorder: a review.
Neurosci. Biobehav. Rev. 56, 330–344.
Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., Bandettini, P.A., 2009. The impact
of global signal regression on resting state correlations: are anti-correlated networks
introduced? Neuroimage 44, 893–905.
Murphy, K., Birn, R.M., Bandettini, P.A., 2013. Resting-state fMRI confounds and cleanup.
Neuroimage 80, 349–359.
Murrough, J.W., Abdallah, C.G., Anticevic, A., Collins, K.A., Geha, P., Averill, L.A.,
Schwartz, J., DeWilde, K.E., Averill, C., Jia-Wei Yang, G., Wong, E., Tang, C.Y.,
Krystal, J.H., Iosifescu, D.V., Charney, D.S., 2016. Reduced global functional connectivity of the medial prefrontal cortex in major depressive disorder. Hum. Brain
Mapp. 37, 3214–3223.
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2012. Spurious but
systematic correlations in functional connectivity MRI networks arise from subject
motion. Neuroimage 59, 2142–2154.
Sacchet, M.D., Camacho, M.C., Livermore, E.E., Thomas, E.A., Gotlib, I.H., 2017.
Accelerated aging of the putamen in patients with major depressive disorder. J.
Psychiatry Neurosci. 42, 160010.
Sporns, O., 2011. The human connectome: a complex network. Ann. N. Y. Acad. Sci.
1224, 109–125.
Sporns, O., Zwi, J.D., 2004. The small world of the cerebral cortex. Neuroinformatics 2,
Tomasi, D., Volkow, N.D., 2010. Functional connectivity density mapping. Proc. Natl.
Acad. Sci. USA 107, 9885–9890.
Tomasi, D., Volkow, N.D., 2011a. Association between functional connectivity hubs and
brain networks. Cereb. Cortex 21, 2003–2013.
Tomasi, D., Volkow, N.D., 2011b. Functional connectivity hubs in the human brain.
Neuroimage 57, 908–917.
Vargas, C., Lopez-Jaramillo, C., Vieta, E., 2013. A systematic literature review of resting
state network–functional MRI in bipolar disorder. J. Affect. Disord. 150, 727–735.
Wang, L., Dai, Z., Peng, H., Tan, L., Ding, Y., He, Z., Zhang, Y., Xia, M., Li, Z., Li, W., Cai,
Y., Lu, S., Liao, M., Zhang, L., Wu, W., He, Y., Li, L., 2014a. Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect. Hum. Brain Mapp. 35, 1154–1166.
Wang, L., Xia, M., Li, K., Zeng, Y., Su, Y., Dai, W., Zhang, Q., Jin, Z., Mitchell, P.B., Yu, X.,
He, Y., Si, T., 2015. The effects of antidepressant treatment on resting-state functional
brain networks in patients with major depressive disorder. Hum. Brain Mapp. 36,
Wang, X., Xia, M., Lai, Y., Dai, Z., Cao, Q., Cheng, Z., Han, X., Yang, L., Yuan, Y., Zhang,
Y., Li, K., Ma, H., Shi, C., Hong, N., Szeszko, P., Yu, X., He, Y., 2014b. Disrupted
resting-state functional connectivity in minimally treated chronic schizophrenia.
Schizophr. Res. 156, 150–156.
Williams, J.B., 1988. A structured interview guide for the Hamilton Depression Rating
Scale. Arch. Gen. Psychiatry 45, 742–747.
Wu, H., Sun, H., Xu, J., Wu, Y., Wang, C., Xiao, J., She, S., Huang, J., Zou, W., Peng, H.,
Lu, X., Huang, G., Jiang, T., Ning, Y., Wang, J., 2016. Changed hub and corresponding functional connectivity of subgenual anterior cingulate cortex in major
depressive disorder. Front Neuroanat. 10, 120.
Yan, C.G., Wang, X.D., Zuo, X.N., Zang, Y.F., 2016. DPABI: data processing & analysis for
(resting-state) brain imaging. Neuroinformatics 14, 339–351.
Zang, Y., Jiang, T., Lu, Y., He, Y., Tian, L., 2004. Regional homogeneity approach to fMRI
data analysis. Neuroimage 22, 394–400.
Zang, Y.F., He, Y., Zhu, C.Z., Cao, Q.J., Sui, M.Q., Liang, M., Tian, L.X., Jiang, T.Z., Wang,
Y.F., 2007. Altered baseline brain activity in children with ADHD revealed by restingstate functional MRI. Brain Dev. 29, 83–91.
Zhang, B., Li, M., Qin, W., Demenescu, L.R., Metzger, C.D., Bogerts, B., Yu, C., Walter, M.,
2016a. Altered functional connectivity density in major depressive disorder at rest.
Eur. Arch. Psychiatry Clin. Neurosci. 266, 239–248.
Zhang, K., Zhu, Y., Wu, S., Liu, H., Zhang, W., Xu, C., Zhang, H., Hayashi, T., Tian, M.,
2016b. Molecular, functional, and structural imaging of major depressive disorder.
Neurosci. Bull. 32, 273–285.
Zhou, M., Hu, X., Lu, L., Zhang, L., Chen, L., Gong, Q., Huang, X., 2017. Intrinsic cerebral
activity at resting state in adults with major depressive disorder: a meta-analysis.
Prog. Neuropsychopharmacol. Biol. Psychiatry 75, 157–164.
Zhuo, C., Zhu, J., Wang, C., Qu, H., Ma, X., Qin, W., 2016. Different spatial patterns of
brain atrophy and global functional connectivity impairments in major depressive
disorder. Brain Imaging Behav.
Zuo, X.N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F.X., Sporns, O., Milham,
M.P., 2012. Network centrality in the human functional connectome. Cereb. Cortex
22, 1862–1875.
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
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,
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,
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
Без категории
Размер файла
730 Кб
2017, pscychresns, 009
Пожаловаться на содержимое документа