Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2016 Nov 1.
Published in final edited form as: J Psychiatr Res. 2016 Feb 13;76:111–120. doi: 10.1016/j.jpsychires.2016.02.005

Enhanced default mode network connectivity with ventral striatum in Subthreshold depression individuals

JW Hwang a,b, SC Xin a, YM Ou c, WY Zhang a,f, YL Liang d,b, J Chen e, XQ Yang a,g, XY Chen b, TW Guo a,h, XJ Yang a, WH Ma a, J Li a, BC Zhao a, Y Tu a,*, J Kong b,*
PMCID: PMC4838997  NIHMSID: NIHMS775092  PMID: 26922247

Abstract

Subthreshold depression (StD) is a highly prevalent condition associated with increased service utilization and social morbidity. Nevertheless, due to limitations in current diagnostic systems that set the boundary for major depressive disorder (MDD), very few brain imaging studies on the neurobiology of StD have been carried out, and its underlying neurobiological mechanism remains unclear. In recent years, accumulating evidence suggests that the disruption of the default mode network (DMN), a network involved in self-referential processing, affective cognition, and emotion regulation, is involved in major depressive disorder. Using independent component analysis, we investigated resting-state default mode network (DMN) functional connectivity (FC) changes in two cohorts of StD patients with different age ranges (young and middle-aged, n= 57) as well as matched controls (n=79). We found significant FC increase between the DMN and ventral striatum (key region in the reward network), in both cohorts of StD patients in comparison with controls. In addition, we also found the FC between the DMN and ventral striatum was positively and significantly associated with scores on the Center for Epidemiologic Studies Depression Scale (CES-D), a measurement of depressive symptomatology. We speculate that this enhanced FC between the DMN and the ventral striatum may reflect a self-compensation to the lowered reward function.

Keywords: Subthreshold depression, major depressive disorder, resting-state functional connectivity, default mode network, independent component analysis, fMRI, ventral striatum, reward system, center of epidemiology studies depression scale

Introduction

Subthreshold depression (StD) refers to clinically relevant depressive symptoms without meeting the criteria for full-blown major depressive disorder (MDD) (Rodriguez et al., 2012). Previous studies have suggested that StD is a highly prevalent condition (Horwath et al., 1992) associated with increased service utilization and social morbidity. Thus, although the symptoms of StD are less severe than symptoms of major depressive disorder (MDD), they may be associated with a greater service burden and impairment compared with MDD or dysthymia on a population basis (Johnson et al., 1992). In addition, studies also suggested that StD is an important risk factor for MDD (de Graaf et al., 2010, Horwath, Johnson, 1992, Wesselhoeft et al., 2013). Individuals with StD have an odds ratio of more than 5 for having a first lifetime episode of MDD (Fogel et al., 2006).

Despite its high prevalence and significant social and economic impacts, the neurobiology of StD remains unclear. This is mainly due to a limitation of the current diagnostic systems that set the boundary for the disorder based on the presence of a certain number of symptoms. As a result, individuals that fall below the threshold are not recognized in primary care settings or community surveys and are often not included in biological (imaging and genetic) studies (Rodriguez, Nuevo, 2012). Intensive investigation of the neuropathology of StD is important because it will not only provide crucial information on brain response during the initial medication-free stage of the depressive symptom onset, but it will also help us elucidate a dynamic course of MDD brain function/connectivity changes, which is crucial for developing tailored treatments for patients at different stages of the disorder.

In the last few decades, with the aid of powerful brain imaging tools, our understanding of MDD has been significantly enhanced. We now know that MDD is associated with structural and functional abnormalities in brain circuits involved in emotional processing, self-representation, reward, and external stimulus (stress, distress) interactions (Davidson et al., 2002, Hasler and Northoff, 2011, Northoff et al., 2011, Pizzagalli, 2011). These brain regions include the ventromedial prefrontal cortex, dorsal medial prefrontal cortex, anterior cingulate cortex, hippocampus, and amygdala. Interestingly, most of these brain regions also fall within the default mode network (DMN) (Andrews-Hanna et al., 2010, Buckner et al., 2008), a network believed to be involved in self-referential processing, affective cognition, and emotion regulation (Berman et al., 2011, Buckner et al., 2009, Connolly et al., 2013, Etkin et al., 2011, Nejad et al., 2013).

Previous studies (Bluhm et al., 2009, Greicius et al., 2007, Hamilton et al., 2013, Ho et al., 2014, Li et al., 2013, Liston et al., 2014, Posner et al., 2013, Wang et al., 2012, Wu et al., 2013, Zhu et al., 2012) have found disrupted DMN functional connectivity (FC) in MDD patients, and these changes are associated with psychiatric measurements such as rumination score in MDD patients. Yet one question that remains unanswered is whether DMN FC changes can be observed in StD patients. The answer to this question will provide us with a complete picture of the association between FC changes in the brain and clinical depressive symptoms, further enhance our understanding of the development of depression, and provide a biological basis for diagnosis of depression.

A core characteristic of depressed patients is anhedonia, the loss of interest in pleasurable activities, and limitations in multiple dimensions of well-being (Bogdan et al., 2013, Hasler and Northoff, 2011, Naranjo et al., 2001, Russo and Nestler, 2013). Previous studies (Rodriguez, Nuevo, 2012) also suggested that the most common symptoms of StD patients are depressed mood and loss of interest. Thus, the reward system (Naranjo, Tremblay, 2001, Pizzagalli et al., 2009) may play an important role in the pathophysiology of StD.

One challenge in performing brain imaging studies of depression is considerable variation in the nature of the findings across studies (Leibenluft and Pine, 2013). Clearer conclusions might emerge more rapidly if two separate cohorts of patients can be investigated and compared in the same experiment. Thus, in this study, we investigated DMN FC changes in two separate cohorts of StD subjects (young and middle age) and corresponding healthy controls. We hypothesize that there is a dysfunction of MDD FC with a key region in reward network, the ventral striatum in StD subjects. We believe that the two cohorts of StD subjects (young and middle age) will show similar DMN FC differences as compared to healthy controls.

Method

We briefly describe the experimental procedures below. Please also see a previously published study for more details on the experimental procedure (Hwang et al., 2015). The data has been used in a previous study to investigate the functional connectivity of bilateral dorsal lateral prefrontal cortex changes between individuals with StD and healthy control. In this study, we used ICA to investigate the DMN FC difference between individuals with StD and controls. These results have not been reported before.

Participants

We screened 981 subjects from three universities (young cohort) and 383 subjects from twelve Beijing residence community centers (middle aged cohort) through advertisements and flyers. All participants received a health lecture from investigators followed by a survey using the Center for Epidemiologic Studies depression scale (CES-D, Chinese version) (Radloff, 1977). The surveys were evaluated by a trained clinician. Potentially depressed participants were further assessed by a licensed psychiatrist using a 17-item Hamilton rating of depression scale (HAMD) to confirm study qualifications.

Inclusion criteria for StD participants included: (1) age between 18–60 years; (2) CES-D score ≥ 16; (3) 17-item HAMD score between 7–17. Exclusion criteria included: (1) abnormal or impaired judgment abilities (Wechsler Adult Intelligence Scale (WAIS) score ≥ 90); (2) diagnosis of severe depression based on ICD-10 (first-episode; (3) prior use of psychiatric medications; (4) any suicidal tendencies posing immediate threat to the subject’s life; (5) history of addictive disorders such as substance abuse and dependence and alcoholism; and (5) any fMRI exclusion criteria including any major medical, neurological or psychological disorders, pregnancy or intent to become pregnant, and history of head trauma.

Healthy control (HC) participants were recruited from the same sources as StD participants based on the age and gender status of selected StD participants. All HC participants have a CES-D score of less than 16, and satisfied the same exclusion criteria as StD participants. All participants were given a description of the study and provided with written informed consent forms. All subjects signed the consent forms before the fMRI scans. The study was approved by the Committee on the Use of Human Subjects in Research at Beijing University of Chinese Medicine.

MRI data acquisition

Images were acquired on a 3-axis gradient head coil in a 3-Tesla Siemens MRI system equipped for echo planar imaging (EPI) at the Research Institute of the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University. T1-weighted sagittal localizing (T1) structures sequence was followed by an 8-minute resting state scan. The T1 scanning parameters included TR of 2000 ms, echo time of 3.39 ms, flip angle of 70°, slices thickness of 1.33 mm and a field of view of 256 mm2. For the resting state, the scan acquisition included 32 slices with a thickness of 4.8 mm, a TR of 2000 ms, a TE of 30 ms, flip angle of 90 degrees, field of view of 240 mm2 and a 3×3mm in-plane spatial resolution. During Resting-State (RS) fMRI data acquisition, participants were instructed to remain still with their eyes closed and let their minds wander freely. After every scan, we asked the subjects whether they had fallen asleep during the scan and we received no positive responses.

Independent Component Analysis for resting state fMRI data

We analyzed the resting-state data of StD patients and healthy control subjects using Independent Component Analysis in the FMRIB Software Library (FSL) (Smith et al., 2004), following similar processing steps as those described in previous studies (Biswal et al., 2010, Fang et al., 2015, Kong et al., 2013). We first applied a band pass filter between 0.01 and 0.1 Hz to the functional time series corrected for motion using MCFLIRT and slice timing, skull stripped using the Brain Extraction Tool (BET).

We then registered the data to its respective skull stripped anatomical volume, and further registered the data to the MNI152 template using linear affine transformations with 12 degrees of freedom, and applied smoothing (full width at half-maximum=5mm). After that, we concatenated the functional data into 4D data and performed a probabilistic independent component analysis using MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) (Beckmann and Smith, 2004) on the data set to identify 20 resting state networks. The negative loading on ICs was ignored in our study. We used an algorithm (fslcc in the fsl program) to search for similarities between our group-level networks and the template networks derived from 1414 healthy subjects to identify the DMN (Biswal, Mennes, 2010).

To perform group-level analyses of the association between StD patients and healthy control subjects and resting-state networks derived from ICA, we used a dual-regression technique (Biswal, Mennes, 2010). We used DMN as spatial regressors in a general linear model (GLM) to extract temporal dynamics associated with each spatial map. The resulting time courses (averages across voxels) served as temporal regressors in a GLM to generate subject-specific maps of the whole brain for each subject.

Finally, group analyses were performed using whole-brain subject-specific network maps from the second GLM. The results represent the strength of FC for each voxel with the DMN.

To explore the main difference between healthy and StD patients, we combined the young and middle age cohorts and performed a two-sample t-test to compare the DMN FC of healthy and StD subjects, including age as a non-interest covariate. To further explore the difference between healthy and StD patients between different age ranges (young or middle age), we performed a two-sample t-test comparing the FC of healthy and StD subjects in the young and middle age groups separately, including age and gender as non-interest covariates..

To explore the association between psychiatric measurements and FC changes, we performed regression analyses using the DMN FC of all participants and the CES-D scores (total CES-D score; and four factors of CES-D, i.e. depressive affect (DA), positive affect (PA), somatic affect (SA) and interpersonal affect (IA)) (Wang et al., 2013), including age as a non-interest covariate. In addition, we also performed regression analyses using the network connectivity maps of the StD group and the HAMD clinical scores, including age as a non-interest covariate.

We performed all resting state analyses with a voxel-wise cluster forming threshold of Z > 2.3 and a cluster significance threshold of P < 0.05 using “easythresh” tool in FSL. In addition, we conservatively omitted small significant clusters of less than 30 voxels

Results

Of the 981 young subjects and 383 middle aged subjects we screened, 57 subjects fit the criteria for StD. We also recruited 80 healthy controls from same population of StD subjects. All 137 subjects participated in the fMRI scan. The fMRI data from one healthy control was discarded due to excessive head motion during image acquisition, resulting in a total of 136 participants (79 healthy controls, 57 StD subjects) for the reported analyses.

The demographics of the two groups of participants are shown in Table 1. StD patients and healthy controls groups did not significantly differ in terms of age, gender, or years of education. CES-D scores did not differ between the young and middle age groups. However, StD groups had significantly higher CES-D scores compared to healthy controls. Middle age StD groups had non-significantly higher CES-D scores compared to the young StD group (p = 0.08), but there is a significant difference on the HAMD between the two StD groups (p<0.0001). There was also a significant positive correlation between CES-D and HAMD scores across all groups (p < 0.0001, r = 0.47).

Table 1.

The demographic variables of the subjects with subthreshold depression (StD) and demographically matched healthy comparison subjects.

Items Healthy group StD group P value
All N(male) 79(25) 57(15) 0.570
Age(y) (mean ± SD) 29.52±14.32 32.25±15.62 0.294
Education (y) (mean ± SD) 14.82±3.80 15.35±3.00 0.367
CES-D (mean ± SD) 6.08±4.44 25.72±6.02 0.000
HAMD(17items) (mean ± SD) N/A 10.65±2.69 -
Young-age N(male) 54(20) 34(12) 0.527
Age(y) (mean ± SD) 20.65±1.90 20.29±1.40 0.320
Education(y) (mean ± SD) 15.98±2.94 16.32±2.40 0.553
CES-D (mean ± SD) 6.85±4.61 24.56±6.66 0.000
HAMD(17items) N/A 9.56±2.02 -
Middle-age N(male) 25(5) 23(3) 0.400
Age(y) (mean ± SD) 49.20±10.25 49.91±8.44 0.795
Education(y) (mean ± SD) 12.32±4.27 13.91±3.27 0.156
CES-D (mean ± SD) 4.40±3.58 27.43±4.55 0.000
HAMD(17items) (mean ± SD) N/A 12.04±2.84 -
Open in a new tab

CES-D, Center for Demonological Studies Depression Scale; HAMD, Hamilton rating of depression scale; N/A, Non-applicable

fMRI results

The DMN derived from independent component analysis includes all subjects (patients and healthy controls) and is shown in Figure 1.

Figure 1.

Figure 1

Open in a new tab

Default-mode Network identified by Independent Component Analysis includes all subjects (patients and healthy controls).

Comparison between healthy controls and StD patients

Two sample t-tests indicated that there were significant DMN FC differences between healthy control and StD patient groups (Table 2, Figure 2A). StD subjects showed increased FC between the DMN and the bilateral middle frontal gyrus, ventral striatum/caudate, thalamus, left superior frontal gyrus, precentral gyrus, parahippocampus, superior occipital gyrus, insula, lentiform nucleus, cerebellum, right inferior frontal gyrus, superior temporal gyrus, postcentral gyrus, inferior parietal lobule cuneus, and middle/superior occipital gyrus. Control subjects showed increased FC in the right superior and middle frontal gyrus, and the medial frontal gyrus.

Table 2.

Brain regions showed significant default mode network functional connectivity differences between all StD patients (young and middle age) and healthy controls.

Region Coordinates (x,y,z) Peak z-Score Cluster size
HC>StD R superior/middle frontal gyrus 32,28,48 4.17 346
R medial orbito-frontal gyrus 4,60,−24 3.72 74
StD>HC L superior/middle frontal gyrus −20,64,−2 3.14 55
R middle/inferior frontal gyrus 50,18,24 3.2 111
R inferior frontal gyrus 62,18,12 3,74 42
L precentral gyrus −36,−4,62 4.62 100
R superior temporal gyrus 50,6,−18 3.42 64
R inferior parietal lobule/postcentral gyrus 38,−30,36 3.98 175
R poscentral gyrus/paracentral lobule 26,−34,50 3.28 46
R cuneus, superior occipital gyrus 36,−80,34 3.02 32
R cuneus/middle occipital gyrus 22,−92,34 3.56 105
bilateral ventral striatum/caudate, thalamus 6,6,2 5.07 789
L ventral tegment area −6,−26,−6 3.29 128
L parahippocampus −20,−28,−6 3.38
L insula −42,−18,18 3.21 44
L lentiform nucleus −20,8,−16 3.08 44
L cerebellum −10,−72,−50 4.04 493
Open in a new tab

R, right; L, left; HC, Healthy Control; StD, Subthreshold Depression.

Figure 2.

Figure 2

Open in a new tab

Brain regions showed significant functional connectivity differences with the DMN (StD>HC) between StD patients and healthy controls; (A) indicates results from all-subjects; bar indicates the peak and averaged Z values of StD and HC groups; (B) and (C) indicate overlapping brain region from young age (red) and middle age (blue) separately.

In young StD subjects, increased DMN FC was observed in the bilateral caudate and thalamus, middle frontal gyrus, precentral gyrus, left superior frontal gyrus, superior temporal gyrus, cuneus, supramarginal gyrus, parahippocampus, insula, ligual gyrus, lentiform nucleus, claustrum, right inferior frontal gyrus, pre/postcentral gyrus, post cingulate cortex, and inferior parietal lobule. In contrast, young healthy control subjects showed increased FC in the left superior/middle frontal gyrus, inferior temporal gyrus, left uncus, and right dorsal lateral prefrontal gyrus (Table 3 and Figure 2B&2C).

Table 3.

Brain regions showed significant default mode network functional connectivity difference between StD patients and controls in young-age and middle-age group separately.

Contrasts Regions Coordinates (x,y,z) Peak z-Score Cluster size
HC>StD (Young-age) L superior/middle frontal gyrus −36,52,26 3.23 40
R dorsal lateral prefrontal gyrus 32,28,46 2.97 33
L inferior temporal gyrus −32,−4,−38 3.85 190
L uncus −32,−16,−36 3.3 51
StD>HC (Young-age) R inferior frontal gyrus 62,20,12 4.69 231
R inferior frontal gyrus 48,34,−12 3.52 60
L superior/middle frontal gyrus −18,64,−2 3.49 152
L middle frontal gyrus, precentral gyrus −34,20,30 3.75 250
R precentral gyrus, middle frontal gyrus 38,−6,66 3.56 139
L precentral gyrus −36,−6,64 4.07 87
R postcentral gyrus, inferior parietal lobule 50,−28,32 4.18 275
L supramarginal gyrus/superior temporal gyrus −58,−58,36 3.16 41
L cuneus, lingual gyrus −6,−86,10 3.45 68
R post cingulate cortex 8,−58,8 3.86 103
L thalamus −22,−26,−2 3.39 111
bilateral ventral striatum/caudate, thalamus 20,24,−4 4.29 1134
R ventral striatum/caudate 18,16,18 3.07 35
L parahippocampus −18,2,−24 3.17 34
L insula −46,−20,10 2.97 69
L lentiform nucleus −22,8,18 3.56 58
L lentiform nucleus, claustrum −30,4,−2 2.97 55
HC>StD (Middle-age) bilateral medial/superior frontal gyrus −2,58,−14 5.19 590
R superior/medial frontal gyrus 14,16,62 3.6 60
StD>HC (Middle-age) L inferior frontal gyrus −40,18,−20 3.07 34
R posterior MPFC/dorsal ACC 10,46,22 3.75 157
R dorsal anterior cingulate cortex 8,24,34 3.05 42
R middle frontal gyrus 34,22,54 4.24 558
L middle frontal gyrus −32,28,32 3.97 107
L superior parietal lobule −12,−66,62 3.26 40
R thalamus 22,−12,12 3.13 38
L caudate −16,4,14 3.89 188
R lingual gyrus 8,−74,−2 3.53 111
R middle frontal gyrus, precentral gyrus 44,−2,44 3.82 221
R inferior/middle temporal/occipital gyrus 62,−60,−4 3.66 97
R cuneus 12,−86,42 3.99 435
R ventral striatum/caudate 6,8,0 3.57 112
bilateral VTA/red nucleus/PAG −6,−28,−4 3.87 112
R middle frontal gyrus, precentral gyrus 44,−2,44 3.82 221
Open in a new tab

R, right; L, left; HC, Healthy Control; StD, Subthreshold Depression.

In middle-aged StD subjects, significant DMN FC increases were observed in the bilateral ventral striatum/caudate, ventral tegment area/red nucleus/periagueduct gray, right inferior/middle temporal gyrus, middle frontal gyrus, precentral gyrus, and cuneus. Middle-aged healthy control subjects showed increased DMN FC in the bilateral medial/superior frontal gyrus, middle frontal gyrus, left inferior frontal gyrus, caudate, superior parietal lobule, right dorsal anterior cingulate cortex, lingual gyrus, and thalamus (Table 3, Figure 2B&2C).

Further comparison between the two StD groups (young and middle age) found that young StD subjects showed significant DMN FC increases in the bilateral middle cingulate gyrus, left superior temporal gyrus, right middle frontal gyrus, post cingulated gyrus, precuneus, cuneus, and cerebellum. The middle-aged StD subjects showed increased DMN FC in the bilateral anterior medial frontal gyrus, inferior/middle frontal gyrus, insula, and superior temporal gyrus (Table 4).

Table 4.

Brain regions showed significant default mode network functional connectivity difference between young-age and middle-age StD subjects.

Region Coordinates (x,y,z) Peak z-Score Cluster size
Young > Middle age R middle frontal gyrus 46,18,30 2.89 156
L superior temporal gyrus −36,10,−30 3.54 127
Bilateral middle cingulate cortex 14,−6,26 3.83 433
R post cingulate gyrus 4,−50,18 2.79 61
R precuneus/cuneus 12,−78,40 3.86 209
R cerebellum 16,−36,−20 3.83 275
Middle > Young age L anterior medial frontal gyrus −4,70,8 3.89 376
L fusiform gyrus −48,−60,−22 3.38 70
L inferior/middle frontal gyrus −54,16,32 3.46 55
L insula −46,0,6 3.13 54
L superior temporal gyrus −42,−10,56 3.15 33
Open in a new tab

R, right; L, left; StD, Subthreshold Depression.

Association between CES-D/HAMD and DMN FC

A regression analysis between CES-D and DMN FC in all subjects showed a positive association between CES-D scores and FC between the DMN and bilateral precentral gyrus, ventral striatum, thalamus, and cerebellum; left parahippocampus, inferior/middle frontal gyrus, and superior occipital gyrus; right postcentral gyrus, precuneus, inferior parietnal lobule, and precentral lobule. A negative association was observed in the bilateral uncus, left precuneus, parahippocampus, superior parietal lobule, and right middle/superior frontal gyrus (Table 5 and Figure 3A).

Table 5.

Significant clusters of default mode network (DMN) functional connectivity associated with CES-D scores (across all participants and all StD patients separately)/HAMD scores (across all StD patients) respectively.

Region Coordinates (x,y,z) Peak z- Score Cluster size
Positive association with CES-D in all subjects R inferior frontal gyrus 62,18,14 3.17 143
L precentral gyrus −36, −6,62 4.48 105
R middle frontal gyrus, precentral gyrus 36, −2,60 3.13 104
R postcentral gyrus, inferior parietal lobule 36, −30,36 4.5 142
R paracentral lobule, postcentral gyrus 26, −34,50 3.31 55
R precuneus, superior occipital gyrus 42, −76,40 3.08 30
R cuneus 16, −90,40 3.91 228
L parahippocampus, thalamus −22, −30, −6 3.29 38
bilateral ventral striatum/caudate, thalamus 8,8, −4 5.36 659
bilateral cerebellum 6, −72, −42 3.65 315
Negative association With CES-D in all subjects R middle/superior frontal gyrus 32,28,46 3.98 286
L precuneus, superior parietal lobule −4, −58,68 4.02 211
L parahippocampus, uncus −24, −10, −32 3.21 155
R uncus 24, −8, −42 3.4 32
Positive association with CES-D in all StD subjects Bilateral medial frontal gyrus 6,66, −16 3.09 53
R claustrum 34,8,10 3.01 32
L cuneus −2, −94,20 3.19 61
L inferior frontal gyrus −30,26, −24 3.17 58
Negative association With CES-D in all StD subjects R middle frontal gyrus 28,60,12 3.14 42
R culmen 24, −32, −30 3.17 35
R dorsal anterior cingulated gyrus 0,30, −8 3.45 55
L inferior frontal gyrus −54,14,16 3.34 48
L poscentral gyrus, inferior parietal lobule −46, −16,26 3.59 440
L superior temporal gyrus −26,12, −44 3.99 111
L parahippocampus −36, −34, −10 3.17 32
L inferior frontal gyrus −26,24, −28 3.72 37
Positive association with HAMD in StD subjects L inferior frontal gyrus −26,24, −28 3.72 37
Bilateral mid-cingulate cortex/posterior-medial prefrontal cortex −6,6,40 2.92 38
L pregenual/subgenual anterior cingulate- cortex −10,36, −6 3.35 39
R superior parietal lobule/precuneus 22, −48,62 2.95 65
L precuneus −32, −80,46 3.15 43
R middle temporal gyrus 62,0, −28 3.42 84
R cerebellum 36, −52, −36 3.36 39
Negative association with HAMD in StD subjects L superior/middle frontal gyrus −32,56,2 3.1 64
R middle frontal gyrus, precentral gyrus 44,20,30 2,87 53
bilateral ventral medial prefrontal gyrus −2,38, −20 3.03 37
L postcentral gyrus −40, −26,54 3.63 319
R inferior parietal lobule/postcentral- gyrus 68, −32,36 3.92 76
R superior temporal gyrus 56, −52,12 3.09 76
bilateral precuneus −4, −54,72 4.66 278
R lingual gyrus 24, −64,2 3.4 104
bilateral paracentral lobule −2, −30,74 3.24 96
R parahippocampus/amygdala −34, −10, −20 3.28 129
Open in a new tab

R, right; L, left; HC, Healthy Control; StD, Subthreshold Depression.

Figure 3.

Figure 3

Open in a new tab

(A) functional connectivity between the DMN and ventral striatum (VST) is significantly associated with total CES-D score (B) functional connectivity between the DMN and ventral striatum is significantly associated with anhedonia factor of CES-D score (yellow color), depressive affect factor(green color), positive affect factor (blue color), and the somatic affect factor (red color). Scatter plots indicate the significant association between the peak/averaged z values and CES-D general score or factor scores.

A regression analysis between CES-D scores in only StD subjects and DMN FC showed a positive association between CES-D scores and DMN FC at the bilateral medial frontal gyrus, left cuneus and inferior gyrus, and right claustrum. Negative association was observed in the left inferior frontal gyrus, postcentral gyrus, inferior parietal lobule, superior temporal gyrus, parahippocampus and inferior frontal gyrus, right middle frontal gyrus, and dorsal anterior cingulated gyrus (Table 5).

A regression analysis between HAMD scores and DMN FC showed a positive association between the HAMD scores and the DMN FC at the bilateral middle cingulate cortex/posterior MPFC, left inferior frontal gyrus, precuneus, and pregenual/subgenual anterior cingulate cortex; right inferior frontal gyrus, middle temporal gyrus, precuneus, superior parietal lobule, and cerebellum. The negative association was observed in the bilateral superior/middle frontal gyrus, ventral medial prefrontal gyrus, postcentral gyrus, bilateral precuneus, paracentral lobule, right superior temporal gyrus, precentral gyrus, parahippocampus, and inferior parietal lobule (Table 5).

Finally, the exploratory analysis of the four factors of CES-D, i.e., depressive affect, positive affect, somatic affect, and interpersonal affect as well as the anhedonia related items (question 8 and 10) showed that three factors: depressive, positive and somatic affect scores, as well as anhedonia measurement, showed significant positive association with DMN ventral striatum FC across all participants (Figure 3B).

Discussion

In this study, we recruited two cohorts of StD patients and matched controls to investigate DMN FC changes in StD patients. We found significantly increased resting state FC between the DMN and the ventral striatum, a key region in the reward network in both young and middle aged cohorts of StD patients. This FC between the DMN and the ventral striatum is also positively associated with the CES-D scores of StD patients. In addition, we also found positive associations of FC between the DMN and pregenual/subgenual ACC with HAMD score in StD patients, which demonstrated enhanced DMN FC in StD patients.

Interestingly, we found remarkable DMN FC increases in both cohorts of StD patients as compared with matched controls. This result is consistent with results from a recent meta-analysis, in which the authors found that MDD patients are associated with increased resting state functional connectivity within the DMN network, as well as between the frontoparietal network and DMN (Kaiser et al., 2015). Previous studies suggested that the DMN is associated with the self-referential system, affective cognition, and emotion regulation (Berman, Peltier, 2011, Connolly, Wu, 2013, Nejad, Fossati, 2013). We speculate this increased FC may reflect depressive bias toward the internal thoughts.

Previous studies (Berridge and Robinson, 2003, Bogdan, Nikolova, 2013, Richard et al., 2013) suggest that reward is a complicated construct, and there are many different types of reward. A complicated brain network, including the ventral striatum, tegmental area (VTA), caudate, putamen, thalamus, orbitofrontal cortex, anterior insula, anterior cingulate cortex and posterior cingulate cortex, inferior parietal lobule, and prefrontal cortex is involved in the reward process (Haber and Knutson, 2010, Liu et al., 2011). Based on self-stimulation, pharmacological, physiological, and behavioral studies, the ventral striatum and the ventral tegmental areas are at the center of the reward network (Haber and Knutson, 2010).

In our study, the most consistent finding across two cohorts of StD patients is increased FC between the DMN and ventral striatum, and dorsal lateral prefrontal cortex. In addition, we also found significant FC increases between the DMN and VTA in the middle-aged cohort. At a less conservative threshold (voxel wise z > 1.96, p<0.05 at cluster level), an increased FC between the DMN and VTA is also observed in the young cohort. Most importantly, increased FC between the DMN and the ventral striatum is also positively associated with CES-D scores. Both the ventral striatum and VTA are key regions in the reward network (Haber and Knutson, 2010). Our results demonstrated an enhanced FC between the DMN and the VS in StD subjects. StD patients are characterized by anhedonia and limitations in multiple dimensions of well-being (Rodriguez, Nuevo, 2012). We speculate that this increased FC between the DMN and the ventral striatum may reflect a compensation to the disrupted reward function observed in StD patients.

Our results are indirectly supported by previous studies (Epstein et al., 2006, Keedwell et al., 2005, Kumar et al., 2008), which found reward dysfunction in depression patients. In a previous study, Pizzagalli and colleagues (Pizzagalli, Holmes, 2009) found that MDD patients showed significantly weaker responses to gains in the left nucleus accumbens and the caudate bilaterally. Furman and colleagues (Furman et al., 2011) found that depressed participants exhibited attenuated functional connectivity between the ventral striatum and the ventromedial prefrontal cortex and subgenual anterior cingulate cortex. Vrieze and colleagues (Vrieze et al., 2013) found that compared to controls, MDD patients showed reduced reward learning. Patients with high anhedonia showed diminished reward learning compared to patients with low anhedonia. Taken together, these studies may imply a weakened reward system in depression patients. We speculate that our findings of enhanced FC between the DMN and ventral striatum may reflect a self-compensation to the lowered reward function.

In a previous study, Bluhm and colleagues (Bluhm, Williamson, 2009) investigated resting-state DMN in the early stages of treatment-seeking for depression. They found that compared with controls, depressed subjects showed decreased connectivity between the precuneus/PCC and the bilateral caudate, a brain region that is believed to be involved in motivation and reward processing. This result may not necessarily indicate that the two results are contradictory. Our results are observed in StD patients, while the Bluhm et al study was applied to depression patients. In addition, the DMN is more notable at the MPFC in our study, the Bluhm et al study used precunus/PCC as seed. The two sub-DMN networks may be associated with different functions (Ho, Connolly, 2014, Li, Liu, 2013). Thus, we speculate that the difference may represent different neuropathologies at different stages of depression. Nevertheless, both studies demonstrated deficits in functional connectivity between the DMN and reward system.

We observed different DMN FC changes between the two cohorts of patients. This result suggests that DMN FC changes in StD patients may vary at different ages. We speculate this difference may reflect different reward processes associated with young and middle-aged StD patients (Kerestes et al., 2014). For instance, in a previous study (Jimura et al., 2011), Jimura and colleagues found that discounting rates for different types of reward varied between younger and older individuals.

In a previous study, investigators (Bukh et al., 2011) compared the clinical presentation of early-onset adult depression (18–30 years) with late-onset adult depression (31–70 years). They found that early-onset depression in patients was associated with higher prevalence of co-morbid personality disorders, higher levels of neuroticism, and a lower prevalence of stressful life events preceding onset compared to patients with late–onset depression. Although the exact cause of the early-onset and late-onset depression remains unknown, investigators (Sneed and Culang-Reinlieb, 2011, Taylor et al., 2013) believe that cerebrovascular disease causes focal vascular damage and white matter lesion locations may be an underlying cause of late-onset depression. In our study, the average age of the middle-age group was 49, and we thus speculate that potential cerebrovascular disease in the middle-age group may also contribute to the difference between the two StD groups.

In addition, the average HAMD score in the middle-aged group is higher than that of young group, and the gender ratio in the two cohorts of patients is also different. These factors may contribute to the difference we observed. Future research should focus on the source of these variable FC changes in StD patients with different age ranges.

In our study, we found significant FC changes between the DMN and the dorsal lateral prefrontal cortex, orbitopreforntal cortex, post-central gyrus, temporal gyrus, and thalamus. This result is consistent with previous resting state FC studies in StD patients (Hwang, Egorova, 2015, Li et al., 2014b, Ma et al., 2013). In a previous study, Ma and colleagues (Ma, Li, 2013) compared regional homogeneity (ReHo) changes of late-life individuals with StD to controls. They found that StD subjects display lower ReHo in the right orbitofrontal cortex, left dorsolateral prefrontal cortex, left postcentral gyrus, left middle frontal, and inferior temporal gyri, as well as higher ReHo in the bilateral insula and right DLPFC. In another study on the same cohort of StD patients, Li and colleagues (Li, Ma, 2014b) found the StD group showed increased regional amplitude of low-frequency fluctuation (ALFF) in the anterior portion of the dorsal ACC (adACC), which also displayed increased FC with the dorsolateral prefrontal cortex and supplementary motor area, and decreased FC with the anterior insula, thalamus, and putamen. In a more recent study (Hwang, Egorova, 2015), based on the same data set, we found a significant resting state FC decrease between the DLPFC and the temporo-parietal junction (TPJ)/precuneus (the brain regions associated with the representation of self and other mental states) and the insula in StD subjects.

We found that the subgenual ACC is positively associated with the severity of StD as indicated by HAMD scores. This is consistent with previous studies (Berman, Peltier, 2011, Greicius, Flores, 2007), which found that the FC between DMN and subgenual ACC is significantly enhanced in MDD patients. Our results extend this previous finding by providing evidence that, in StD patients with mild depressive symptoms, the FC between the DMN and subgenual ACC and symptom severity are still significantly associated.

We found functional connectivity between the DMN and left insula is enhanced in StD patients. The insula belongs to a salience network, which is involved in detecting and orienting to both external and internal salient stimuli and events (Manoliu et al., 2013). Using the surface-based regional homogeneity (ReHo) method (Zuo et al., 2013), Li and colleagues (Li et al., 2014a) found that compared with healthy controls, first episode drug-naive MDD patients showed reduced surface-based ReHo in the left insula. In another meta-analysis, the authors (Kuhn and Gallinat, 2013) also found hypoactivity in depressed patients. Again, we speculated the enhanced DMN and left insula may reflect self-compensation for the lowered reward function of the left insula.

Previous studies have suggested that there are different subsets of the default mode network (Andrews-Hanna, 2012, Andrews-Hanna, Reidler, 2010). Li and colleagues (Li, Liu, 2013) found that after antidepressant treatment, differences in the posterior subnetwork were normalized after antidepressant treatment, while abnormal functional connectivity persisted within the anterior subnetwork. As a data-driven method, the DMN network identified in our study is more anterior dominated. Future study is needed to directly compare the subnetwork differences in StD patients.

One potential limitation of a resting state functional connectivity study in the clinical population is the reliability of the findings. Previous studies have suggested that ICA and the DMN are the most reliable functional connectivity method and network respectively (Zuo et al., 2010, Zuo and Xing, 2014). In addition, we included two cohorts of patients, each with age and gender matched controls, to explore the neuropathology of StD. We thus believe that our findings are valid.

In summary, we investigated DMN FC changes of two cohorts of StD patients with different age ranges. We found significant FC increases between the DMN and the ventral striatum in two cohorts of StD patients compared with matched controls, and the DMN and ventral striatum FC is also significantly associated with CES-D scores. We speculate that the findings may imply a self-compensation to the lowered reward function in StD patients.

Acknowledgments

This scientific work was supported by an International Collaboration Research Program at Science and Technology of China (2007DFA30780) Grant to Tuya Bao. Jian Kong is supported by R01AT006364 (NCCIH/NIH), R01AT008563 (NCCIH/NIH), R21AT008707 (NCCIH/NIH), and P01 AT006663 (NCCIH/NIH).

References

  1. Andrews-Hanna JR. The brain’s default network and its adaptive role in internal mentation. Neuroscientist. 2012;18:251–70. doi: 10.1177/1073858411403316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron. 2010;65:550–62. doi: 10.1016/j.neuron.2010.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 2004;23:137–52. doi: 10.1109/TMI.2003.822821. [DOI] [PubMed] [Google Scholar]
  4. Berman MG, Peltier S, Nee DE, Kross E, Deldin PJ, Jonides J. Depression, rumination and the default network. Soc Cogn Affect Neurosci. 2011;6:548–55. doi: 10.1093/scan/nsq080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Berridge KC, Robinson TE. Parsing reward. Trends Neurosci. 2003;26:507–13. doi: 10.1016/S0166-2236(03)00233-9. [DOI] [PubMed] [Google Scholar]
  6. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107:4734–9. doi: 10.1073/pnas.0911855107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bluhm R, Williamson P, Lanius R, Theberge J, Densmore M, Bartha R, et al. Resting state default-mode network connectivity in early depression using a seed region-of-interest analysis: decreased connectivity with caudate nucleus. Psychiatry Clin Neurosci. 2009;63:754–61. doi: 10.1111/j.1440-1819.2009.02030.x. [DOI] [PubMed] [Google Scholar]
  8. Bogdan R, Nikolova YS, Pizzagalli DA. Neurogenetics of depression: a focus on reward processing and stress sensitivity. Neurobiol Dis. 2013;52:12–23. doi: 10.1016/j.nbd.2012.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38. doi: 10.1196/annals.1440.011. [DOI] [PubMed] [Google Scholar]
  10. Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci. 2009;29:1860–73. doi: 10.1523/JNEUROSCI.5062-08.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bukh JD, Bock C, Vinberg M, Getherb U, Kessinga LV. Differences Between Early and Late Onset Adult Depression. Clinical Practice & Epidemiology in Mental Health. 2011;7:140–7. doi: 10.2174/1745017901107010140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Connolly CG, Wu J, Ho TC, Hoeft F, Wolkowitz O, Eisendrath S, et al. Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biol Psychiatry. 2013;74:898–907. doi: 10.1016/j.biopsych.2013.05.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Davidson RJ, Pizzagalli D, Nitschke JB, Putnam K. Depression: perspectives from affective neuroscience. Annu Rev Psychol. 2002;53:545–74. doi: 10.1146/annurev.psych.53.100901.135148. [DOI] [PubMed] [Google Scholar]
  14. de Graaf LE, Huibers MJ, Cuijpers P, Arntz A. Minor and major depression in the general population: does dysfunctional thinking play a role? Compr Psychiatry. 2010;51:266–74. doi: 10.1016/j.comppsych.2009.08.006. [DOI] [PubMed] [Google Scholar]
  15. Epstein J, Pan H, Kocsis JH, Yang Y, Butler T, Chusid J, et al. Lack of ventral striatal response to positive stimuli in depressed versus normal subjects. Am J Psychiatry. 2006;163:1784–90. doi: 10.1176/ajp.2006.163.10.1784. [DOI] [PubMed] [Google Scholar]
  16. Etkin A, Egner T, Kalisch R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci. 2011;15:85–93. doi: 10.1016/j.tics.2010.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fang J, Rong P, Hong Y, Fan Y, Liu J, Wang H, et al. Transcutaneous Vagus Nerve Stimulation Modulates Default Mode Network in Major Depressive Disorder. Biol Psychiatry. 2015 doi: 10.1016/j.biopsych.2015.03.025. pii: S0006-3223(15)00274-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fogel J, Eaton WW, Ford DE. Minor depression as a predictor of the first onset of major depressive disorder over a 15-year follow-up. Acta Psychiatr Scand. 2006;113:36–43. doi: 10.1111/j.1600-0447.2005.00654.x. [DOI] [PubMed] [Google Scholar]
  19. Furman DJ, Hamilton JP, Gotlib IH. Frontostriatal functional connectivity in major depressive disorder. Biol Mood Anxiety Disord. 2011;1:11. doi: 10.1186/2045-5380-1-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 2007;62:429–37. doi: 10.1016/j.biopsych.2006.09.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Haber SN, Knutson B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology. 2010;35:4–26. doi: 10.1038/npp.2009.129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hamilton JP, Chen MC, Gotlib IH. Neural systems approaches to understanding major depressive disorder: an intrinsic functional organization perspective. Neurobiol Dis. 2013;52:4–11. doi: 10.1016/j.nbd.2012.01.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hasler G, Northoff G. Discovering imaging endophenotypes for major depression. Mol Psychiatry. 2011;16:604–19. doi: 10.1038/mp.2011.23. [DOI] [PubMed] [Google Scholar]
  24. Ho TC, Connolly CG, Henje Blom E, LeWinn KZ, Strigo IA, Paulus MP, et al. Emotion-Dependent Functional Connectivity of the Default Mode Network in Adolescent Depression. Biol Psychiatry. 2014 doi: 10.1016/j.biopsych.2014.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Horwath E, Johnson J, Klerman GL, Weissman MM. Depressive symptoms as relative and attributable risk factors for first-onset major depression. Arch Gen Psychiatry. 1992;49:817–23. doi: 10.1001/archpsyc.1992.01820100061011. [DOI] [PubMed] [Google Scholar]
  26. Hwang J, Egorova N, Yang XQ, Zhang WY, Chen J, Yang XY, et al. Subthreshold depression is associated with impaired resting state functional connectivity of the cognitive control network. Translational Psychiatry. 2015;5 doi: 10.1038/tp.2015.174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Jimura K, Myerson J, Hilgard J, Keighley J, Braver TS, Green L. Domain independence and stability in young and older adults’ discounting of delayed rewards. Behav Processes. 2011;87:253–9. doi: 10.1016/j.beproc.2011.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Johnson J, Weissman MM, Klerman GL. Service utilization and social morbidity associated with depressive symptoms in the community. Jama. 1992;267:1478–83. [PubMed] [Google Scholar]
  29. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiatry. 2015;72:603–11. doi: 10.1001/jamapsychiatry.2015.0071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Keedwell PA, Andrew C, Williams SC, Brammer MJ, Phillips ML. The neural correlates of anhedonia in major depressive disorder. Biol Psychiatry. 2005;58:843–53. doi: 10.1016/j.biopsych.2005.05.019. [DOI] [PubMed] [Google Scholar]
  31. Kerestes R, Davey CG, Stephanou K, Whittle S, Harrison BJ. Functional brain imaging studies of youth depression: A systematic review. Neuroimage Clin. 2014;4:209–31. doi: 10.1016/j.nicl.2013.11.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kong J, Jensen K, Loiotile R, Cheetham A, Wey HY, Tan T, et al. Functional connectivity of frontoparietal network predicts cognitive modulation of pain. Pain. 2013;154:459–67. doi: 10.1016/j.pain.2012.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kuhn S, Gallinat J. Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis. Schizophr Bull. 2013;39:358–65. doi: 10.1093/schbul/sbr151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kumar P, Waiter G, Ahearn T, Milders M, Reid I, Steele JD. Abnormal temporal difference reward-learning signals in major depression. Brain. 2008;131:2084–93. doi: 10.1093/brain/awn136. [DOI] [PubMed] [Google Scholar]
  35. Leibenluft E, Pine DS. Resting state functional connectivity and depression: in search of a bottom line. Biol Psychiatry. 2013;74:868–9. doi: 10.1016/j.biopsych.2013.10.001. [DOI] [PubMed] [Google Scholar]
  36. Li B, Liu L, Friston KJ, Shen H, Wang L, Zeng LL, et al. A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry. 2013;74:48–54. doi: 10.1016/j.biopsych.2012.11.007. [DOI] [PubMed] [Google Scholar]
  37. Li HJ, Cao XH, Zhu XT, Zhang AX, Hou XH, Xu Y, et al. Surface-based regional homogeneity in first-episode, drug-naive major depression: a resting-state FMRI study. Biomed Res Int. 2014a;2014:374828. doi: 10.1155/2014/374828. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Li R, Ma Z, Yu J, He Y, Li J. Altered local activity and functional connectivity of the anterior cingulate cortex in elderly individuals with subthreshold depression. Psychiatry Res. 2014b;222:29–36. doi: 10.1016/j.pscychresns.2014.02.013. [DOI] [PubMed] [Google Scholar]
  39. Liston C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, et al. Default Mode Network Mechanisms of Transcranial Magnetic Stimulation in Depression. Biol Psychiatry. 2014 doi: 10.1016/j.biopsych.2014.01.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Liu X, Hairston J, Schrier M, Fan J. Common and distinct networks underlying reward valence and processing stages: a meta-analysis of functional neuroimaging studies. Neurosci Biobehav Rev. 2011;35:1219–36. doi: 10.1016/j.neubiorev.2010.12.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Ma Z, Li R, Yu J, He Y, Li J. Alterations in regional homogeneity of spontaneous brain activity in late-life subthreshold depression. PLoS One. 2013;8:e53148. doi: 10.1371/journal.pone.0053148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Manoliu A, Meng C, Brandl F, Doll A, Tahmasian M, Scherr M, et al. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Front Hum Neurosci. 2013;7:930. doi: 10.3389/fnhum.2013.00930. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Naranjo CA, Tremblay LK, Busto UE. The role of the brain reward system in depression. Prog Neuropsychopharmacol Biol Psychiatry. 2001;25:781–823. doi: 10.1016/s0278-5846(01)00156-7. [DOI] [PubMed] [Google Scholar]
  44. Nejad AB, Fossati P, Lemogne C. Self-referential processing, rumination, and cortical midline structures in major depression. Front Hum Neurosci. 2013;7:666. doi: 10.3389/fnhum.2013.00666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Northoff G, Wiebking C, Feinberg T, Panksepp J. The ‘resting-state hypothesis’ of major depressive disorder-a translational subcortical-cortical framework for a system disorder. Neurosci Biobehav Rev. 2011;35:1929–45. doi: 10.1016/j.neubiorev.2010.12.007. [DOI] [PubMed] [Google Scholar]
  46. Pizzagalli DA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology. 2011;36:183–206. doi: 10.1038/npp.2010.166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Pizzagalli DA, Holmes AJ, Dillon DG, Goetz EL, Birk JL, Bogdan R, et al. Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. Am J Psychiatry. 2009;166:702–10. doi: 10.1176/appi.ajp.2008.08081201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Posner J, Hellerstein DJ, Gat I, Mechling A, Klahr K, Wang Z, et al. Antidepressants normalize the default mode network in patients with dysthymia. JAMA Psychiatry. 2013;70:373–82. doi: 10.1001/jamapsychiatry.2013.455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Radloff LS. The CES-D scale: A self report depression scale for research in the general population. Applied Psychological Measurements. 1977;1:385–401. [Google Scholar]
  50. Richard JM, Castro DC, Difeliceantonio AG, Robinson MJ, Berridge KC. Mapping brain circuits of reward and motivation: in the footsteps of Ann Kelley. Neurosci Biobehav Rev. 2013;37:1919–31. doi: 10.1016/j.neubiorev.2012.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Rodriguez MR, Nuevo R, Chatterji S, Ayuso-Mateos JL. Definitions and factors associated with subthreshold depressive conditions: a systematic review. BMC Psychiatry. 2012;12:181. doi: 10.1186/1471-244X-12-181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Russo SJ, Nestler EJ. The brain reward circuitry in mood disorders. Nat Rev Neurosci. 2013;14:609–25. doi: 10.1038/nrn3381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(Suppl 1):S208–19. doi: 10.1016/j.neuroimage.2004.07.051. [DOI] [PubMed] [Google Scholar]
  54. Sneed JR, Culang-Reinlieb ME. The vascular depression hypothesis: an update. Am J Geriatr Psychiatry. 2011;19:99–103. doi: 10.1097/jgp.0b013e318202fc8a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Taylor WD, Aizenstein HJ, Alexopoulos GS. The vascular depression hypothesis: mechanisms linking vascular disease with depression. Mol Psychiatry. 2013;18:963–74. doi: 10.1038/mp.2013.20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Vrieze E, Pizzagalli DA, Demyttenaere K, Hompes T, Sienaert P, de Boer P, et al. Reduced reward learning predicts outcome in major depressive disorder. Biol Psychiatry. 2013;73:639–45. doi: 10.1016/j.biopsych.2012.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Wang L, Hermens DF, Hickies IB, Lagopoulos J. A systematic review of resting-state functional-MRI studies in major depression. J Affect Disord. 2012;142:6–12. doi: 10.1016/j.jad.2012.04.013. [DOI] [PubMed] [Google Scholar]
  58. Wang M, Armour C, Wu Y, Ren F, Zhu X, Yao S. Factor structure of the CES-D and measurement invariance across gender in Mainland Chinese adolescents. J Clin Psychol. 2013;69:966–79. doi: 10.1002/jclp.21978. [DOI] [PubMed] [Google Scholar]
  59. Wesselhoeft R, Sorensen MJ, Heiervang ER, Bilenberg N. Subthreshold depression in children and adolescents - a systematic review. J Affect Disord. 2013;151:7–22. doi: 10.1016/j.jad.2013.06.010. [DOI] [PubMed] [Google Scholar]
  60. Wu D, Yuan Y, Bai F, You J, Li L, Zhang Z. Abnormal functional connectivity of the default mode network in remitted late-onset depression. J Affect Disord. 2013;147:277–87. doi: 10.1016/j.jad.2012.11.019. [DOI] [PubMed] [Google Scholar]
  61. Zhu X, Wang X, Xiao J, Liao J, Zhong M, Wang W, et al. Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biol Psychiatry. 2012;71:611–7. doi: 10.1016/j.biopsych.2011.10.035. [DOI] [PubMed] [Google Scholar]
  62. Zuo XN, Kelly C, Adelstein JS, Klein DF, Castellanos FX, Milham MP. Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach. Neuroimage. 2010;49:2163–77. doi: 10.1016/j.neuroimage.2009.10.080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Zuo XN, Xing XX. Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. Neurosci Biobehav Rev. 2014;45C:100–18. doi: 10.1016/j.neubiorev.2014.05.009. [DOI] [PubMed] [Google Scholar]
  64. Zuo XN, Xu T, Jiang L, Yang Z, Cao XY, He Y, et al. Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage. 2013;65:374–86. doi: 10.1016/j.neuroimage.2012.10.017. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES