Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder

Accepted Manuscript Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder

Zongling He, Wei Sheng, Fengmei Lu, Zhiliang Long, Shaoqiang Han, Yajing Pang, Yuyan Chen, Wei Luo, Yue Yu, Xiaoyu Nan, Qian Cui, Huafu Chen PII: DOI: Reference:

S0278-5846(18)30187-8 https://doi.org/10.1016/j.pnpbp.2018.11.009 PNP 9537

To appear in:

Progress in Neuropsychopharmacology & Biological Psychiatry

Received date: Revised date: Accepted date:

16 March 2018 26 August 2018 15 November 2018

Please cite this article as: Zongling He, Wei Sheng, Fengmei Lu, Zhiliang Long, Shaoqiang Han, Yajing Pang, Yuyan Chen, Wei Luo, Yue Yu, Xiaoyu Nan, Qian Cui, Huafu Chen , Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder. Pnp (2018), https://doi.org/ 10.1016/j.pnpbp.2018.11.009

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder

Zongling Hea,b,, Wei Shengb, Fengmei Lua,b, Zhiliang Longb,, Shaoqiang Hanb, Yajing Pangb,

PT

Yuyan Chenb, Wei Luoa, Yue Yua, Xiaoyu Nanb, Qian Cuic*, Huafu Chena,b*

a

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of

RI

Electronic Science and Technology of China, Chengdu, China b

SC

Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of

Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China c

* Corresponding authors: Huafu Chen: [email protected]

EP

TE

D

MA

NU

School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of

AC C

Electronic Science and Technology of China, Chengdu, 610054, China Or Qian Cui: [email protected]

School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China

Conflicts of interest None The authors declare that there are no conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

ACCEPTED MANUSCRIPT Abstract Background: Clinically distinguishing bipolar disorder (BD) from major depressive disorder (MDD) during depressive states is difficult. Neuroimaging findings suggested that patients with BD and those with MDD differed with respect to the gray matter volumes of their subcortical structures, especially in their striatum. However,

PT

whether these disorders have different effects on functionally striatal neuronal activity and connectivity is unclear. Methods: Arterial spin labeling and resting-state

RI

functional MRI was performed on 25 currently depressive patients with BD, 25

SC

depressive patients with MDD, and 34 healthy controls (HCs). The functional properties of striatal neuronal activity (cerebral blood flow, CBF) and its functional

NU

connectivity (FC) were analyzed, and the results from the three groups were compared. The result of the multiple comparisons was corrected on the basis of the

MA

Gaussian Random Field theory. Results: The patients with BD and those with MDD both had higher CBF values than the HCs in the right caudate and right putamen. The hyper-metabolism of right striatum in BD patients was associated with increased

D

average duration per depressive episode. The two disorders showed commonly

TE

increased FC between the striatum and dorsolateral prefrontal cortex, whereas the altered FC of the striatum with precuneus/cuneus was observed only in patients with

EP

BD. Conclusions: Patients with BD and those with MDD had a common deficit in

AC C

their prefrontal-limbic-striatal circuits. The altered striato-precuneus FC can be considered as a marker for the differentiation of patients with BD from those with MDD.

Keywords: bipolar disorder, major depressive disorder, cerebral blood flow, functional connectivity, striatum, receiver operating characteristic curves.

ACCEPTED MANUSCRIPT Introduction Bipolar disorder (BD) is a chronic and fluctuating disease and considered as one of the most debilitating psychiatric illness, affecting 1.5%~3.0% of the population worldwide (Ferrari et al. , 2016). BD causes various degrees of manic, hypomanic, and depressive states. According to the Diagnostic and Statistical Manual of Mental

PT

Disorders (DSM-IV), diagnosis is easy when patients with BD are in the manic and hypomanic state, whereas those mainly in depressive episodes and have no history of

RI

mania are often misdiagnosed (Cardoso de Almeida JR and ML., 2013) and often

SC

treated as having major depressive disorder (MDD). This misdiagnosis may lead to inappropriate medication and increase the risks of switching to mania and suicide

NU

(Hirschfeld et al. , 2003). Therefore, identifying robust biomarkers is helpful in correctly diagnosing the two disorders. Brain imaging techniques may be useful in

MA

revealing some critical changes in the brain of patients with BD and in assisting the distinctions between BD and MDD.

Subcortical neuroanatomic sites relevant to mood states including the striatum

D

and thalamus, and subcortical gray matter structures which were widely studied in

TE

MDD and BD are associated with cognitive process as well as emotion generation and regulation (Lindquist et al. , 2012, Ochsner et al. , 2012). Furthermore, the

EP

cortico-striatal projections are arranged into a number of parallel and integrative loops,

AC C

which subserve specific motor, cognitive, or affective processes and may be involved in the pathology of mood disorders (Jung et al. , 2014). The cortical-subcortical interactions have been implicated in the cognitive regulation of emotion, the neural pathway through striatum, which was associated with reward and reinforcement learning, might mediate this regulation ability of emotion (Kober H et al. , 2008). In several task-based functional magnetic resonance imaging (fMRI) studies, the greater reappraisal success can be predicted by the neural circuit through striatum, furthermore, the activity in the amygdala and the striatum were typically modulated by cortical regions, such as the prefrontal cortex, during reappraising affective stimuli (Ochsner, Silvers, 2012, Wager TD et al. , 2008).

ACCEPTED MANUSCRIPT In some structural neuroimaging studies, the disruption of frontal volume was observed in patients with MDD and BD, although the two disorders have different effects on subcortical volume (Bielau et al. , 2005, Grotegerd et al. , 2013, Konarski et al. , 2008, Redlich et al. , 2014, Strakowski et al. , 2002). Specifically, patients with BD consistently showed more enlargement in the basal ganglia, especially in their

PT

striatum, than healthy subjects (DelBello et al. , 2004a, Nugent et al. , 2006). By contrast, volumes of these subcortical structures were smaller in patients with MDD

RI

(Kempton et al. , 2011, Krishnan et al. , 1992, Liu et al. , 2012). These studies highlighted the crucial role of the striatal volume in differentiating BD from MDD

SC

(Redlich, Almeida, 2014, Sacchet et al. , 2015) and indicated that the two disorders are distinct with respect to the abnormalities in striatal neuronal activity.

NU

As a reflection of neuronal activity, regional cerebral blood flow (CBF), which can be measured using functional neuroimaging techniques, such as single-photon

MA

emission computed tomography, positron emission tomography (PET), and arterial spin labeling (ASL), have been widely employed in BD and MDD studies. These

D

studies observed CBF alterations mainly within the prefrontal-limbic-subcortical

TE

circuits, including the prefrontal cortex (PFC), anterior cingulate cortex (ACC), amygdala, insula, caudate, putamen, and thalamus in depression patients (Brooks et

EP

al. , 2009, Chen et al. , 2015, Gabbay et al. , 2007, Lui et al. , 2009, Mah et al. , 2007, Monkul et al. , 2012, Vasic et al. , 2015). However, whether these brain areas showing

AC C

impaired neuronal activity in BD and MDD are interrelated is unknown. To investigate the interaction between brain areas, the resting-state functional connectivity (FC) method (Biswal B et al. , 1995) was employed to measure the temporal correlation between the spontaneous blood oxygenation level dependent fluctuations in distributed brain regions at resting-state. This method has been applied to BD and MDD research. Altered FC of striatum was found in patients with BD accompanied by ACC (Anand et al. , 2009), thalamus, default-mode network (Teng et al. , 2014), and ventrolateral PFC (Pompei et al. , 2011). Impaired striatal FC was also reported in patients with MDD, mainly in their PFC (including ventromedial and dorsal prefrontal areas), ACC, hippocampus, and thalamus (Anand, Li, 2009, Lui et

ACCEPTED MANUSCRIPT al. , 2011, Ma et al. , 2012). However, to the best of our knowledge, no study has directly investigated the striatal FC between patients with BD and those with MDD, especially when they are under a depressive state. Given the role of striatal volume in the differentiation between patients with BD and those with MDD, we aim to examine whether the two

PT

disorders differed with respect to striatal CBF and its FC with other brain areas. Depressive patients with BD and MDD and healthy controls (HCs) were recruited in

RI

the current study. The CBF and FC values were then estimated by using ASL imaging data and resting-state fMRI data respectively, and the results of the three groups were

SC

compared. Based on previous structural and functional neuroimaging evidence, we hypothesized that 1) depressive patients with BD and MDD have impaired CBF and

NU

FC of the striatum, and 2) the two types of depression differed in striatal dysfunction.

MA

Methods Participants

We recruited 25 BD patients and 25 MDD patients from the Clinical Hospital of

D

Chengdu Brain Science Institute, University of Electronic Science and Technology of

TE

China. Both groups were under a depressive state. The patients were interviewed by two experienced psychiatrists using the Structured Clinical Interview for

EP

DSM-IV-TR-Patient Edition (SCID-P, 2/2001 revision). The patients were diagnosed

AC C

according to the DSM-IV criteria. Exclusion criteria included schizophrenia, mental retardation or personality disorder, any history of loss of consciousness, substance abuse, and serious medical or neurological illness. The clinical states of the patients were evaluated with the 24-item Hamilton Depression Rating Scale (HAMD-24) and 14-item Hamilton Anxiety Rating Scale

(HAMA-14).

The

other

clinical

characteristics data of patients were collected, including age of first onset, number of depressive episode, number of manic/hypomanic episode, average duration per depressive episode, and medication load. Patients with MDD were treated with antidepressants,

including

selective

serotonin

reuptake

inhibitors

and

serotonin-norepinephrine reuptake inhibitors. Meanwhile, patients with BD were

ACCEPTED MANUSCRIPT treated with antidepressants, mood stabilizers, and antipsychotics. To measure total medication load, we used the strategy described previously. Each antidepressant, mood-stabilizer, and antipsychotic medication was firstly coded as absent (0), low (1), or high (2). For antidepressants and mood-stabilizers we converted each medication into low- or high-dose groupings by using a previously employed approach (Sackeim,

PT

2001). Individuals in the levels 1 and 2 of these criteria were coded as low dose, whereas those in levels 3 and 4 were coded as high dose. We added a no-dose subtype

RI

for those who did not taking these medications. Medicines (e.g., escitalopram, duloxetine, and valproate) that are not included by Sackeim were coded as 0, 1, or 2, to

the

midpoint

of

the

daily

dose

range

SC

according

recommended

by

Physician’s-Desk-Reference. For the antipsychotics, we converted antipsychotic doses

NU

into chlorpromazine dose equivalents, and coded as 0, 1, or 2, for no medication, equal or below the chlorpromazine equivalents dose, or above 300 mg/d, respectively,

MA

according to Davis and Chen (Davis and Chen, 2004). We then calculated a composite measure of total medication load for each individual by reflecting the doses and

TE

for each medication category.

D

varieties of different medications taken and summing all individual medication codes

Thirty-four HCs who were recruited from the community through poster

EP

advertisement in this study were also interviewed using the SCID (non-patient edition). None of the HCs presented a history of serious medical or neuropsychiatric

AC C

illness or a family history of major psychiatric or neurological illness in their first-degree relatives. HCs were closely matched with age, sex, and years of education in the patients group.

A written informed consent was obtained from each participant before experiment. The study was approved by the research ethical committee of University of Electronic Science and Technology of China. Scan acquisition The MRI data of all participants were acquired using a 3 T GE DISCOVERY MR750 scanner (General Electric, Fairfield Connecticut, USA) equipped with a high-speed gradient. An eight-channel prototype quadrature birdcage head coil fitted

ACCEPTED MANUSCRIPT with foam padding was used for the minimization of head movement. Participants were asked to remain motionless, keep their eyes closed, and not think of anything. Functional MRI images were obtained by using an echo-planar imaging sequence with the following parameters: repetition time (TR)/echo time (TE) = 2000/30ms, 43 slices, matrix size = 64 × 64, voxel size = 3.75 × 3.75 × 3.2 mm3, flip angle = 90°,

PT

slice thickness = 3.2 mm, no gap, and 255 volumes. The T1 structural image was scanned by using the following parameters: TR/TE = 5.92/1.956 ms, slice thickness =

RI

1 mm, no gap, flip angle = 12°, matrix size = 256 × 256, and 156 slices. The following parameters were used for the acquisition of pseudo-continuous ASL images:

SC

TR/TE = 4632/10.536 ms, slice thickness = 4 mm, no gap, flip angle = 111°, voxel size = 1.875 × 1.875 × 4 mm3, matrix size = 128 × 128, 36 slices, post-labeling delay

NU

= 1.525 s, label duration = 1.45 s, number of excitations = 3, and 269 seconds. ASL fMRI data processing

MA

The preprocessing of ASL fMRI data was performed using the SPM12 software toolbox (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/). For the ASL fMRI data,

D

the CBF images of each individual were first obtained using the Functool (version

TE

12.2.01), an automated image postprocessing tool embedded in the GE MR-750 scanner system. Generally, ASL imaging data consist of two images, a control image

EP

and a label image. Then, we used the value obtained by subtraction of the label image from the control image to get the absolute quantification maps (Liu et al. , 2016).

AC C

Subsequently, the CBF maps were normalized into the standard Montreal neurological Institute (MNI) space and resampled into 3 × 3 × 3 mm3 voxel size. First, the T1 image of each participant was coregistered to its own CBF map. Then, the coregistered T1 image was segmented into gray matter, white matter, and cerebrospinal fluid. This step resulted in transform parameters. Finally, each CBF map was normalized into MNI space based on the transform parameters that were estimated during nonlinear co-registration. To reduce the effect of inter-subject variations in the global CBF values for the group-level comparisons, the resulted CBF maps were normalized by subtracting the mean CBF map within the gray matter mask and divided by the standard deviation of the CBF. The normalized CBF images were

ACCEPTED MANUSCRIPT then smoothed with 6 mm full width at half maximum (FWHM). A subcortical mask was obtained through an automated anatomic labeling (AAL) altas, including bilateral caudate, putamen, pallidum and thalamus (Figure 1A). We performed one-way analysis of variance (ANOVA) on the CBF images within this subcortical mask to test the hypothesis that the CBF values of the patients with BD,

PT

patients with MDD, and HCs had no significant difference. The multiple comparisons were corrected by using the Gaussian random field (GRF) theory with a

RI

cluster-defining threshold (CDT) of P = 0.001 voxel Z>3.1, cluster level p<0.05) (Eklund et al. , 2016, Kessler et al. , 2017).

SC

The brain regions which survived the correction were served as regions of interest (ROIs) for the subsequent analyses. First, we performed a post-hoc analysis of

NU

two-tailed two-sample t-test on the ROIs to determine the direction of the CBF changes. The statistical level of p<0.05/3 were considered as significant. Second, we

MA

employed the Pearson correlation analysis to determine the relationship between the CBF values as well as FCs of the ROIs and clinical characteristics, including age of

D

first onset, number of depressive episodes, average duration per depressive episode,

TE

medication load, score of HAMD and HAMA. The statistical level of p<0.05/6 (Bonferroni corrected) was considered as significant.

EP

BOLD fMRI data processing

The preprocessing of BOLD fMRI data was conducted with the SPM12. After

AC C

the discarding of the first five time points, the images were corrected for the time-delay between slices and for the motion movement between volumes. Participants with head motion parameters exceeding 3 mm in the x, y, or z directions or 3° rotation of each axis were discarded for further analysis. No participant was excluded on the basis of this criterion. Normalization was then performed on the resulting images by using the unified segmentation of anatomical images, each was resampled into a 3-mm isotropic voxel. The effect of covariance of no interests, including 24 motion parameters and the first top five components obtained from the white matter signals and cerebrospinal fluid signals, were removed by using a multiple regression model (Behzadi et al. , 2007, Muschelli et al. , 2014). The resulted

ACCEPTED MANUSCRIPT images were then smoothed with 6 mm FWHM and then linearly detrended and filtered at 0.01-0.08 Hz. Head motion analysis Given that resting-state FC is sensitive to minor head movements (Liu et al. , 2015, Lu et al. , 2017a, Lu et al. , 2017b, Power et al. , 2012), we computed the

PT

frame-wise displacement (FD) (Power, Barnes, 2012) at time point i , which is defined as:

RI

FDi  dix  diy  diz  r i  r i  r  i

,

SC

where dix  d(i 1) x  dix , and is similar with other parameters of diy , diz ,  i , i , and  i . The radius r is 50 mm, which is the approximate mean distance from the

NU

cortex to the center of the head. The mean FD across time points was calculated for each participant. No significant difference was observed among the mean FD values

MA

of the patients with BD (0.1±0.08; mean±SD), patients with MDD (0.08±0.03), and HCs (0.09±0.046; p=0.61, ANOVA). “Bad” time points and their 1-back and

D

2-forward time points were removed from the time series through a “scrubbing”

TE

method (Power, Barnes, 2012) and by using an FD threshold of 0.5 mm. Participants

further analysis.

EP

retaining more than 80% of the original signals after scrubbing were included for

Resting-state FC analysis

AC C

Resting-state FC was computed as the Pearson correlation between spontaneous low-frequency fluctuations of seed regions (ROIs) and the entire brain. The seed regions were the brain areas in which the CBF values of the three groups were significantly different from one another. The correlation maps were then Fisher r-to-z transformed (Liu et al. , 2017). The differences among the numbers of time points of the participants after scrubbing were accounted by transforming the z-maps into Z score maps. That is, the square root of the theoretical variance were divided (1

n 3 ,

where n is degrees of freedom) (Vincent et al. , 2007). The differences among the FC values of the patients with BD, patients with MDD, and HCs were tested by using

ACCEPTED MANUSCRIPT ANOVA with FD as covariate. The multiple comparisons were corrected using GRF theory with a CDT of P = 0.001 (voxel Z>3.1, cluster level p<0.05) (Eklund, Nichols, 2016, Kessler, Angstadt, 2017). Brain areas that survived the GRF correction were used for the post-hoc analysis of two-tailed two-sample t-test. Statistical level of p<0.05/3 was considered significant.

PT

Reproducibility analysis To test the reproducibility of our results, a split-half analysis was carried out

RI

(Zhang Z et al. , 2011). HCs were divided into two subgroups, matched for age and sex (HC1: 17 participants, 10 females, age: 34.22±11.77 years; HC2: 17 participants,

SC

7 females, age: 34.05±10.45 years). Meanwhile, patients were also divided into two matched subgroups (MDD1: 12 participants, 6 females, age: 14.45±3.78 years;

NU

MDD2: 13 participants, 7 females, age: 13.63±2.69 years. BD1: 14 participants, 7 females, age: 14.08±4.03 years; BD2: 11 participants, 5 females, age: 13.33±3.47

MA

years). Then, the same analysis procedures was did in these two sub-dataset (A: among HC1, MDD1 and BD1; B: among HC2, MDD2 and BD2).

D

Receiver operating characteristic curves

TE

The availability of seed-based resting-state FC, which is a potential neuroimaging difference between depressive patients with BD and those with MDD,

EP

was determined by employing the receiver-operating characteristic curves (ROCs). ROC analysis was performed, and the FC values of the brain regions surviving the

AC C

GRF correction were used. The area under the curve (AUC) of ROCs was computed (Liu et al. , 2013).

The statistical significance of the AUCs was tested through a nonparametric permutation test. The group labels (MDD and BD) were permuted for 5000 times, and the AUC was calculated for each permutation. The statistical significance level was determined by calculating the number of permutations whose AUCs were greater than the original AUC divided by the total number of permutations. Multiple comparisons were corrected through the Bonferroni method with p<0.05/n, where n is the number of brain areas.

ACCEPTED MANUSCRIPT Results and statistical analyses Demographics A total of 84 patients, 25 patients with BD, 25 patients with MDD, and 34 HCs, completed the study. Demographic information and clinical characteristics are presented in Table 1. No significant difference in age (F(2,81)=0.1877, p>0.05), sex

PT

(Chi-square=0.51, df=2, p>0.05), years of education (F(2,81)=0.1623, p>0.05), FD (F(2,81)=0.6792, p>0.05) and handedness (Chi-square=0.8647, df=2, p>0.05) was

RI

observed among the three groups. The differences among the patient groups with

SC

respect to age of first onset (t=0.36, df=48, p>0.05), average duration per depressive episode (t=0.95, df=48, p>0.05), HAMD score (t=1.94, df=48, p>0.05) and

NU

medication load (t=-1.12, df=48, p>0.05) were not significant. The BD group had more depressive episodes (t=-3.72, df=48, p<0.05) and had a higher HAMA score

MA

(t=3.54, df=48, p<0.05) than the MDD group.

*** Table 1 about here*** Altered CBF in patients with BD or MDD

D

In contrast to HCs, patients with BD and MDD showed increased CBF values in

TE

their right caudate and right putamen (Figure 1B). Furthermore, the average duration per depressive episode in the patients with BD was positively correlated with CBF

EP

value of right putamen (r=0.55, p=0.0044) and right caudate (r=0.61, p=0.0011)

AC C

(Figure 2). In addition, BD patients also showed significantly positively relationships between the average duration per depressive episode and the CBF value in right putamen (r=0.46, p=0.0290) and right caudate (r=0.50, p=0.0133) after the number of depressive episode was regressed out (Figure S1). *** Figure 1 about here*** *** Figure 2 about here*** Altered striatal FC between the three groups Compared with HCs, BD and MDD patients had increased FC between right caudate and right dorsolateral prefrontal cortex (DLPFC) (Figure 3). Only the patients with BD showed increased FC between right caudate and right precuneus (Figure 4A),

ACCEPTED MANUSCRIPT between the right putamen and left cuneus, as well as bilateral precuneus (Figure 4B). Table 2. No significant correlations between aberrant FC and clinical symptoms or characteristics were observed. *** Figure 3 about here*** *** Figure 4 about here***

PT

*** Table 2 about here*** Reproducibility analysis

RI

To test the reproducibility of results, our results were tested in two sub-dataset (see detailed information in Method part). The split-half analysis results showed that

SC

the abnormal brain function was located in similar brain regions as our prior analysis. These reproducibility results demonstrated that the main findings can be reproduced

NU

and our results were stable (see Table S1 and Figures S2-S3 in Supplementary materials).

MA

ROC analysis

ROC analysis results confirmed that patients with BD can be distinguished from

D

those with MDD on the basis of the FC values between the right caudate and right

TE

precunus, between the right putamen and bilateral precunus, and between the right putamen and left cuneus. The AUCs of these FCs were significantly higher than those

EP

expected by chance (Bonferroni-corrected with p<0.05/4 for four ROIs). Optimal performance was achieved in the FC between the right putamen and bilateral

AC C

precuneus at an AUC of 0.8087 (sensitivity of 88%, and specificity of 65.2% for left precuneus; sensitivity of 76%, and specificity of 82.6% for right precuneus; Figure 5).

Discussion

We investigated striatal CBF and its FC in patients with BD and those with MDD. The alteration in functional striato-prefrontal circuits was observed in both types of patients. Functional striato-precuneus/cuneus connectivity was observed impaired only in patients with BD, which could be served as a potential biomarker for differentiating the two disorders. In this study, both MDD and BD patients showed increased CBF values in the

ACCEPTED MANUSCRIPT striatal regions (right caudate and putamen) and had increased resting-state FC between the right caudate and right DLPFC. That is to say, the brain regions which exhibited depression-related FC increases also had increased CBF in MDD and BD as compared with HCs. This was consistent with previous studies suggesting that FC has a metabolic basis which is coupled with CBF and rates of metabolism, i.e. the highly

PT

connected hubs will show more CBF and higher metabolism rates (Liang et al. , 2013, Tomasi et al. , 2013). The striatum plays an important role in mood and cognitive

RI

processings and also form several neuroanatomic circuits that are associated with mood regulation (Tekin and Cummings, 2002). Previous studies of depressed

SC

individuals have found abnormalities in striatal function associated with anhedonia and activation in postive stimuli (Epstein et al. , 2006, Keedwell et al. , 2005).

NU

Recently, Vasic et al. applying the CBF and voxel-based morphometry method in patients with MDD have found higher CBF in patients than controls in right striatal

MA

areas including the caudate and lentiform nucleus (Vasic, Wolf, 2015). Additionally, using ASL fMRI imaging, Lui et al., reported increased rCBF in limbic-striatal areas

D

in MDD (Lui, Parkes, 2009). Moreover, Monkul and his colleagues utilizing H215O

TE

PET to measure the rCBF in unmedicated MDD patients have demonstrated an increased blood flow in right caudate (Monkul, Silva, 2012), Brody et al. using

18

F

EP

fluorodeoxyglucose PET also showed increased glucose metabolism in the caudate and thalamus in MDD patients (Brody et al. , 2001). Furthermore, increased striatal

AC C

(caudate) activity were found in BD patients in several PET studies (Blumberg et al. , 2000, Ketter et al. , 2001, Mah, Zarate, 2007). Hyperperfusion in the striatal regions revealed in our sample is in accordance with the results of prior perfusion or metabolism reports, which might be part of the overall increased caudate and putamen activity/responsiveness according to currently hypothesized memory biases for negative stimuli in depression models (Disner et al. , 2011). The convergence results of increased striatum CBF in MDD and BD in this study manifested the possibility of a common and stable pathway disruptions to depressive symptoms independent of depression subtype (unipolar vs. bipolar) to some extent, shedding light on that hyperperfusion in this brain structure may be pathophysiologically critical in

ACCEPTED MANUSCRIPT depression. Specifically, the increased FC between right caudate and right DLPFC in both types of depression patients suggested a common dysfunction of frontal-striatal neural circuit in these two disorders. Our findings supported the hypotheses proposing that the dysfunction of frontal-striatal circuit within the right hemisphere is related more to

PT

the pathophysiology of depression (Robinson et al. , 1988). Patients with MDD or BD shared some common clinical symptoms, such as depressed mood and diminished

RI

pleasure, which have been speculated to be closely associated with disrupted fronto-limbic-striatal circuit (Maletic and Raison, 2014, Palazidou, 2012). In previous

SC

task-based studies, patients with BD showed abnormally activated prefronto-striatal system during performing the tasks (McIntosh et al. , 2008, Townsend et al. , 2013).

NU

The microstructural and macrostructural deficits of the prefronto-striatal pathways were also found in these patients (Haznedar et al. , 2005, Ong et al. , 2012). Likewise,

MA

desynchronization of prefrontal-striatal circuits was reported in MDD (Furman et al. , 2011, Leaver et al. , 2016). Additionally, Heller et al., had observed increased

D

activation in the fronto-striatal system of MDD patients during performing an emotion

TE

regulation task after they received antidepressant treatment (Heller et al. , 2013). Accordant with previous studies, the current results might suggest the aberrant

EP

emotional processing underlying a common neural mechanisms of MDD and BD. Interestingly, only patients with BD showed increased FC between striatum

AC C

(right caudate and putamen) and bilateral precuneus as well as left cuneus, compared to patients with MDD and HCs. Moreover, AUCs of those connectivity were up to 80%, which could be considered as markers for the differentiation of BD from MDD. The altered FC of striato-cortical midline structures (including precuneus) observed in BD in our study agreed with prior findings (Marchand et al. , 2011). The precuneus is a core area of default-mode network (DMN), which is implicated in emotional regulation (Johnston et al. , 2010). Caudate’s connectivity is preferentially linked with areas of the DMN (such as the medial prefrontal and posterior cingulate cortex), whereas Putamen’s intrinsic FC pattern links more with the insula and anterior cingulate cortex which are the core regions of the salience network (SN) (Di Martino

ACCEPTED MANUSCRIPT et al. , 2008). Both SN and DMN are strongly involved in major depression (Hamilton et al. , 2013) and the DMN’s neural correlates with emotion regulation was confirmed in Xie’s study by comparing its activity at rest with their brain activations during emotional self-regulation tasks (Xie et al. , 2016). Although both patients with BD and MDD are characterized by emotion dysregulation (Gotlib and Joormann, 2010, Price

PT

and Drevets, 2010), differences in emotional regulation between the two disorders were observed in several neuroimaging studies. For example, Rodrigo et al. found that

RI

the abilities of emotion regulation in people with BD were less compromised compared to those in MDD regarding emotional awareness, acceptance of emotions

SC

and understanding of emotions (Rodrigo et al. , 2013). Besides, using emotional regulation fMRI task, Rive and his colleagues observed that patients with BD and

NU

MDD differed with regard to happy and sad emotional regulation processing (Rive et al. , 2015). Consistent with previous findings, the altered striatal connectivity

MA

associated with self-motion regulation found in the current study may be helpful in distinguishing patients with BD from those with MDD.

D

Furthermore, only in the BD patients, we observed the significantly positive

TE

correlations between CBF values in the right caudate and putamen and the average duration per depressive episode, i.e. the longer the duration per depressive episode,

EP

the higher the striatum’s CBF value, suggesting that subtle abnormalities in striatum structures over the course of illness could reflect potential pathopyhsiological

AC C

mechanisms in BD. An inverse correlation was found between disease duration and total gray matter volume (GMV) of striatum in patients with BD (Frey et al. , 2008), which may reflect a functional compensation to the structural impairment in these regions in BD and may also indicate that more metabolic supply is needed to maintain its connections with the rest of the whole brain. Lisy et al. illustrated that length of illness has a significant influence on striatum structures (the caudate and putamen) in patients with BD in a longitudinal study that BD patients had increased GMV in the caudate (Lisy et al. , 2011). Additionally, several studies have reported caudate and putamen enlargement in bipolar patients as compared to healthy subjects (Aylward et al. , 1994, DelBello et al. , 2004b, Noga et al. , 2001, Strakowski et al. , 1999). These

ACCEPTED MANUSCRIPT results suggested that striatal enlargement may serve as a heritable vulnerability factor for developing bipolar disorder. The aberrant striatal CBF associated with length of bipolar illeness in our study might suggest a potential role of striatum in emotional regulation and expression in chronicity or cerebrovascular disease of BD. Moreover, the absence of correlation between striatal CBF and number of depressive episodes

PT

indicate that the progressive changes in striatum is independent of the recurrence of depressive episodes. Overall, we realized that the CBF and the FC abnormalities

RI

observed in this study was not all specific to MDD or BD, it is possible that CBF alterations in our patient sample and the associations between CBF and depressive

SC

durations could to some extent reflect relationships between neural activity and psychopathology that the striatum develops distinctively in MDD and BD.

NU

There were several limitations that warrant discussion in this study. Firstly, all of the patients in our study were on antidepressant treatment, and it is now

MA

well-recognized that drug treatment can modulate activity in several regions of the depressed brain, most markedly in lateral prefrontal and anterior cingulate regions

D

(Chen et al. , 2011, Savitz and Drevets, 2009), thus the current results should be

TE

interpreted with caution. However, in our study, we have measured the total medication load of each patient according to previous study (Almeida et al. , 2009).

EP

Each antidepressant, mood-stabilizer, and antipsychotic medication was firstly coded as 0, 1 or 2, denoting the absence, low dose or high dose of drug, respectively. In our

AC C

further statistical analysis, the medication load was found no relationships with the CBF values as well as FCs of the ROIs. Further studies is needed to recruit larger samples of unmedicated depression patients to explore the medication effects on perfusion in more detail. In addition, we caution against overgeneralization of our results because of our relatively small sample size, which may limit the statistical power. For this reason, further studies need to be performed using a larger cohort of patients to confirm our exploratory findings. Furthermore, all participants in our study were adults. Thus, how the striatal dysfunction varies with age in these diseases is unclear. Future research could help clarify this issue with more patients with wider age ranges. Finally, the brain function was largely constrained by anatomical

ACCEPTED MANUSCRIPT pathways. A combined analysis of multimodal imaging data would provide more information on the interaction between function and structure.

Conclusion Keeping the limitations of our study in mind, we investigated the resting-state CBF and FC of the striatum of patients with BD and those with MDD, our findings

PT

demonstrated the abnormal striato-prefrontal functional circuits in depression patients during conditions of absent experimental stimulation. Two disorders differed in

RI

functional striato-precuneus connectivity, which is associated with the self-regulation

SC

of emotion. Overall, this study highlighted the crucial role of striatum in the pathology of patients with BD or MDD.

NU

Acknowledgments

We thank the 863 project (2015AA020505), the 973 project (2012CB517901),

MA

the Natural Science Foundation of China (61533006, 81771919 and 31400901) which provided the funding to buy computer and software for data processing, the Scientific

D

research project of Sichuan Medical Association (S15012) and the Science

TE

Foundation of Ministry of Education of China (14XJC190003) which provided the funding for collecting data, paying remuneration and traffic expenditures fee to

EP

participants. Thank the Youth Innovation Project of Sichuan Provincial Medical Association (Q14014) and the Fundamental Research Funds for the Central

AC C

Universities (ZYGX2014J104, ZYGX2016KYQD120 and ZYGX2015J141) which provided the funding for data analyzing and English proofing.

ACCEPTED MANUSCRIPT References Almeida JRC, Akkal D, Hassel S, Travis MJ, Banihashemi L, Kerr N, et al. Reduced gray matter volume in ventral prefrontal cortex but not amygdala in bipolar disorder: Significant effects of gender and trait anxiety. Psychiatry Research: Neuroimaging. 2009;171:54-68. Anand A, Li Y, Wang Y, Lowe M, Dzemidzic M. Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression. Psychiatry research. 2009;171:189-98. Aylward EH, Roberts-Twillie JV, Barta PE, Kumar AJ. Basal ganglia volumes and white matter hyperintensities in patients with bipolar disorder. The American journal of psychiatry. 1994;151:687.

PT

Behzadi Y, Restom K, Liau J, Liu T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007;37:90-101.

RI

Bielau H, Trubner K, Krell D, Agelink M, Bernstein H, Stauch R. Volume deficits of subcortical nuclei in mood disorders A postmortem study. European archives of psychiatry and clinical neuroscience. 2005;255:401-12.

SC

Biswal B, Yetkin FZ, Haughton VM, JS. H. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537-41.

NU

Blumberg HP, Stern E, Martinez D, Ricketts S, De Asis J, White T, et al. Increased anterior cingulate and caudate activity in bipolar mania. Biological psychiatry. 2000;48:1045-52. Brody AL, Saxena S, Stoessel P, Gillies LA, Fairbanks LA, Alborzian S, et al. Regional Brain Metabolic Arch Gen Psychiatry. 2001;58:631-40.

MA

Changes in Patients With Major Depression Treated With Either Paroxetine or Interpersonal Therapy. Brooks J, Wang P, Bonner J, Rosen A, Hoblyn J, Hill S. Decreased prefrontal, anterior cingulate, insula, and ventral striatal metabolism in medication-free depressed outpatients with bipolar disorder.

D

Journal of psychiatric research. 2009;43:181-8.

Cardoso de Almeida JR, ML. P. Distinguishing between Unipolar Depression and Bipolar Depression:

TE

Current and Future Clinical and Neuroimaging Perspectives. Biol Psychiatry. 2013;73:111 -8. Chen Y, Wan H, O'Reardon J, Wang D, Wang Z, Korczykowski M, et al. Quantification of cerebral blood

EP

flow as biomarker of drug effect: arterial spin labeling phMRI after a single dose of oral citalopram. Clinical Pharmacology & Therapeutics. 2011;89:251-8. Chen Z-Q, Du M-Y, Zhao Y-J, Huang X-Q, Li J, Lui S, et al. Voxel-wise meta-analyses of brain blood flow

AC C

and local synchrony abnormalities in medication-free patients with major depressive disorder. Journal of psychiatry & neuroscience : JPN. 2015;40:401-11. Davis J, Chen N. Dose response and dose equivalence of antipsychotics. J Clin Psychopharmacol. 2004;24:192-208.

DelBello M, Zimmerman M, Mills N, Getz G, Strakowski S. Magnetic resonance imaging analysis of amygdala and other subcortical brain regions in adolescents with bipolar disorder. Bipolar disorders. 2004a;6:43-52. DelBello MP, Zimmerman ME, Mills NP, Getz GE, Strakowski SM. Magnetic resonance imaging analysis of amygdala and other subcortical brain regions in adolescents with bipolar disorder. Bipolar disorders. 2004b;6:43-52. Di Martino A, Scheres A, Margulies DS, Kelly A, Uddin LQ, Shehzad Z, et al. Functional connectivity of human striatum: a resting state FMRI study. Cerebral cortex. 2008;18:2735-47. Disner SG, Beevers CG, Haigh EA, Beck AT. Neural mechanisms of the cognitive model of depression. Nature Reviews Neuroscience. 2011;12:467.

ACCEPTED MANUSCRIPT Eklund A, Nichols TE, Knutsson H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences. 2016:201602413. 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. American Journal of Psychiatry. 2006;163:1784-90. Ferrari A, Stockings E, Khoo J, Erskine H, Degenhardt L, Vos T, et al. The prevalence and burden of bipolar disorder: findings from the Global Burden of Disease Study Bipolar disorders. 2016;18:440 -50. Frey B, Zunta-Soares G, Caetano S, Nicoletti M, Hatch J, Brambilla P, et al. Illness duration and total brain

gray

matter

in

bipolar

disorder:

evidence

for

neurodegeneration?

European

neuropsychopharmacology. the journal of the European College of Neuropsychopharmacology.

PT

2008;18:717-22.

Furman D, Hamilton J, Gotlib I. Frontostriatal functional connectivity in major depressive disorder.

RI

Biology of mood & anxiety disorders. 2011;1:11-9.

Gabbay V, Hess D, Liu S, Babb J, Klein R, Gonen O. Lateralized caudate metabolic abnormalities in adolescent major depressive disorder: a proton MR spectroscopy study. The American journal of

SC

psychiatry. 2007;164:1881-9.

Gotlib I, Joormann J. Cognition and depression: current status and future directions. Annual review of

NU

clinical psychology. 2010;6:285-312.

Grotegerd D, Suslow T, Bauer J, Ohrmann P, Arolt V, Stuhrmann A, et al. Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study. European archives of

MA

psychiatry and clinical neuroscience. 2013;263:119-31.

Hamilton JP, Chen MC, Gotlib IH. Neural systems approaches to understanding major depressive disorder: an intrinsic functional organization perspective. Neurobiology of disease. 2013;52:4-11. Haznedar M, Roversi F, Pallanti S, Baldini-Rossi N, Schnur D, Licalzi E, et al. Fronto-thalamo-striatal gray

D

and white matter volumes and anisotropy of their connections in bipolar spectrum illnesses. Biological psychiatry. 2005;57:733-42.

TE

Heller A, Johnstone T, Light S, Peterson M, Kolden G, Kalin N, et al. Relationships between changes in sustained fronto-striatal connectivity and positive affect in major depression resulting from

EP

antidepressant treatment. The American journal of psychiatry. 2013;170:197-206. Hirschfeld R, Lewis L, Vornik L. Perceptions and impact of bipolar disorder: how far have we really come? Results of the national depressive and manic-depressive association 2000 survey of individuals

AC C

with bipolar disorder. The Journal of clinical psychiatry. 2003;64:161-74. Johnston S, Boehm S, Healy D, Goebel R, Linden D. Neurofeedback: A promising tool for the self-regulation of emotion networks. NeuroImage. 2010;49:1066-72. Jung W, Jang J, Park J, Kim E, Goo E, Im O, et al. Unravelling the intrinsic functional organization of the human striatum: a parcellation and connectivity study based on resting-state FMRI. PloS one. 2014;9:e106768.

Keedwell PA, Andrew C, Williams SC, Brammer MJ, Phillips ML. The neural correlates of anhedonia in major depressive disorder. Biological psychiatry. 2005;58:843-53. Kempton M, Salvador Z, Munafo M, Geddes J, Simmons A, Frangou S. Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder. Archives of general psychiatry. 2011;68:675-90. Kessler D, Angstadt M, Sripada CS. Reevaluating “cluster failure” in fMRI using nonparametric control of the false discovery rate. Proceedings of the National Academy of Sciences. 2017;114:E3372-E3. Ketter TA, Kimbrell TA, George MS, Dunn RT, Speer AM, Benson BE, et al. Effects of mood and subtype

ACCEPTED MANUSCRIPT on cerebral glucose metabolism in treatment-resistant bipolar disorder. Biological Psychiatry. 2001;49:97-109. Kober H, Barrett LF, Joseph J, Bliss-Moreau E, Lindquist K, TD. W. Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies. NeuroImage. 2008;42:998-1031. Konarski J, McIntyre R, Kennedy S, Rafi-Tari S, Soczynska J, Ketter T. Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder. Bipolar disorders. 2008;10:1-37. Krishnan K, McDonald W, Escalona P, Doraiswamy P, Na C, Husain M. Magnetic resonance imaging of

PT

the caudate nuclei in depression. Preliminary observations. Archives of general psychiatry. 1992;49:553-7.

RI

Leaver AM, Espinoza R, Joshi SH, Vasavada M, Njau S, Woods RP, et al. Desynchronization and Plasticity of Striato-frontal Connectivity in Major Depressive Disorder. Cerebral cortex (New York, NY : 1991). 2016;26:4337–46.

SC

Liang X, Zou Q, He Y, Yang Y. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proceedings of the National

NU

Academy of Sciences. 2013;110:1929-34.

Lindquist K, Wager T, Kober H, Bliss-Moreau E, Barrett L. The brain basis of emotion: a meta-analytic review. The Behavioral and brain sciences. 2012;35:121-43.

MA

Lisy M, Jarvis K, DelBello M, Mills N, Weber W, Fleck D, et al. Progressive neurostructural changes in adolescent and adult patients with bipolar disorder. Bipolar disorders. 2011;13:396-405. Liu F, Guo W, Fouche J-P, Wang Y, Wang W, Ding J, et al. Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Structure and Function. 2015;220:101-15.

D

Liu F, Guo W, Yu D, Gao Q, Gao K, Xue Z, et al. Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PloS one.

TE

2012;7:e40968.

Liu F, Guo WB, Liu L, Long ZL, Ma CQ, Xue ZM, et al. Abnormal amplitude low-frequency oscillations in

EP

medication-naive, first-episode patients with major depressive disorder: A resting-state fMRI study. Journal of Affective Disorders. 2013;146:401-6. Liu F, Wang Y, Li M, Wang W, Li R, Zhang Z, et al. Dynamic functional network connectivity in idiopathic

AC C

generalized epilepsy with generalized tonic–clonic seizure. Human brain mapping. 2017;38:957-73. Liu F, Zhuo C, Yu C. Altered cerebral blood flow covariance network in schizophrenia. Front Neurosci-Switz. 2016;10:308. Lu FM, Liu CH, Lu SL, Tang LR, Tie CL, Zhang J, et al. Disrupted Topology of Frontostriatal Circuits Is Linked to the Severity of Insomnia. Front Neurosci-Switz. 2017a;11. Lu FM, Zhou JS, Wang XP, Xiang YT, Yuan Z. Short- and Long-Range Functional Connectivity Density Alterations in Adolescents with Pure Conduct Disorder at Resting-State. Neuroscience. 2017b;351:96-107. Lui S, Parkes LM, Huang X, Zou K, Chan RC, Yang H, et al. Depressive disorders: focally altered cerebral perfusion measured with arterial spin-labeling MR imaging. Radiology. 2009;251:476-84. Lui S, Wu Q, Qiu L, Yang X, Kuang W, Chan R. Resting-state functional connectivity in treatment-resistant depression. The American journal of psychiatry. 2011;168:642-8. Ma C, Ding J, Li J, Guo W, Long Z, Liu, et al. Resting-state functional connectivity bias of middle temporal gyrus and caudate with altered gray matter volume in major depression. PloS one.

ACCEPTED MANUSCRIPT 2012;7:e45263. Mah L, Zarate CJ, Singh J, Duan Y, Luckenbaugh D, Manji H. Regional cerebral glucose metabolic abnormalities in bipolar II depression. Biological psychiatry. 2007;61:765-75. Maletic V, Raison C. Integrated Neurobiology of Bipolar Disorder. Frontiers in Psychiatry. 2014;5:98-122. Marchand W, Lee J, Garn C, Thatcher J, Gale P, Kreitschitz S, et al. Striatal and cortical midline activation and connectivity associated with suicidal ideation and depression in bipolar II disorder. Journal of affective disorders. 2011;133:638-45. McIntosh A, Whalley H, McKirdy J, Hall J, Sussmann J, Shankar P, et al. Prefrontal function and

PT

activation in bipolar disorder and schizophrenia. The American journal of psychiatry. 2008;165:378 -84. Monkul E, Silva L, Narayana S, Peluso M, Zamarripa F, Nery F. Abnormal resting state corticolimbic Human brain mapping. 2012;33:272-9.

RI

blood flow in depressed unmedicated patients with major depression: a (15)O-H(2)O PET study. Muschelli J, Nebel M, Caffo B, Barber A, Pekar J, Mostofsky S. Reduction of motion-related artifacts in

SC

resting state fMRI using aCompCor. NeuroImage. 2014;96:22-35.

Noga JT, Vladar K, Torrey EF. A volumetric magnetic resonance imaging study of monozygotic twins

NU

discordant for bipolar disorder. Psychiatry Research: Neuroimaging. 2001;106:25-34. Nugent A, Milham M, Bain E, Mah L, Cannon D, Marrett S. Cortical abnormalities in bipolar disorder investigated with MRI and voxel-based morphometry. NeuroImage. 2006;30:485-97.

MA

Ochsner KN, Silvers JA, Buhle JT. Functional imaging studies of emotion regulation: A synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences. 2012;1251:E1-24.

Ong D, Walterfang M, Malhi G, Styner M, Velakoulis D, Pantelis C. Size and shape of the caudate

D

nucleus in individuals with bipolar affective disorder. The Australian and New Zealand journal of psychiatry. 2012;46:340-51.

TE

Palazidou E. The neurobiology of depression. British Medical Bulletin. 2012;101:127-45. Pompei F, Dima D, Rubia K, Kumari V, Frangou S. Dissociable functional connectivity changes during 2011;57:576-82.

EP

the Stroop task relating to risk, resilience and disease expression in bipolar disorder. NeuroImage. Power J, Barnes K, Snyder A, Schlaggar B, Petersen S. Spurious but systematic correlations in functional

AC C

connectivity MRI networks arise from subject motion. NeuroImage. 2012;59:2142-54. Price J, Drevets W. Neurocircuitry of mood disorders. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. 2010;35:192-216. Redlich R, Almeida J, Grotegerd D, Opel N, Kugel H, Heindel W. Brain morphometric biomarkers distinguishing unipolar and bipolar depression. A voxel-based morphometry-pattern classification approach. JAMA psychiatry. 2014;71:1222-30. Rive M, Mocking R, Koeter M, van Wingen G, de Wit S, van den Heuvel O, et al. State-Dependent Differences in Emotion Regulation Between Unmedicated Bipolar Disorder and Major Depressive Disorder. JAMA psychiatry. 2015;72:687-96. Robinson RG, Boston JD, Starkstein SE, Price TR. Comparison of mania and depression after brain injury: causal factors. The American journal of psychiatry. 1988;145:172-8. Rodrigo B, Kate C, Greg M, Darryl B, Craig H, Alfred A. Emotion regulation in bipolar disorder: Are emotion regulation abilities less compromised in euthymic bipolar disorder than unipolar depressive or anxiety disorders? Open Journal of Psychiatry. 2013;3:1-7.

ACCEPTED MANUSCRIPT Sacchet M, Livermore E, Iglesias J, Glover G, Gotlib I. Subcortical volumes differentiate Major Depressive Disorder, Bipolar Disorder, and remitted Major Depressive Disorder. Journal of psychiatric research. 2015;68:91-8. Sackeim H. The definition and meaning of treatment-resistant depression. The Journal of clinical psychiatry. 2001;62:10-7. Savitz

J,

Drevets

WC.

Bipolar

and

major

depressive

disorder:

neuroimaging

the

developmental-degenerative divide. Neuroscience & Biobehavioral Reviews. 2009;33:699-771. Strakowski S, Adler C, DelBello M. Volumetric MRI studies of mood disorders: do they distinguish unipolar and bipolar disorder? . Bipolar disorders. 2002;4:80-8.

PT

Strakowski SM, DelBello MP, Sax KW, Zimmerman ME, Shear PK, Hawkins JM, et al. Brain magnetic resonance imaging of structural abnormalities in bipolar disorder. Archives of general psychiatry.

RI

1999;56:254-60.

Tekin S, Cummings JL. Frontal–subcortical neuronal circuits and clinical neuropsychiatry: an update. Journal of psychosomatic research. 2002;53:647-54.

SC

Teng S, Chia-Feng Lu, Po-Shan Wang, Cheng-Ta Li, Pei-Chi Tu, Chih-I Hung, et al. Altered resting-state functional connectivity of striatal-thalamic circuit in bipolar disorder. PloS one. 2014;9:e96422.

NU

Tomasi D, Wang G-J, Volkow ND. Energetic cost of brain functional connectivity. Proceedings of the National Academy of Sciences. 2013;110:13642-7.

Townsend J, Sugar C, Walshaw P, Vasquez R, Foland-Ross L, Moody T, et al. Frontostriatal neuroimaging affective disorders. 2013;147:389-96.

MA

findings differ in patients with bipolar disorder who have or do not have ADHD comorbidity. Journal of Vasic N, Wolf N, Gron G, Sosic-Vasic Z, Connemann B, Sambataro F. Baseline brain perfusion and brain structure in patients with major depression: a multimodal magnetic resonance imaging study. Journal

D

of psychiatry & neuroscience : JPN. 2015;40:412-21.

Vincent J, Patel G, Fox M, Snyder A, Baker J, Van Essen D, et al. Intrinsic functional architecture in the

TE

anaesthetized monkey brain. Nature. 2007;447:83-6. Wager TD, Davidson ML, Hughes BL, Lindquist MA, KN. O. Prefrontal-subcortical pathways mediating

EP

successful emotion regulation. Neuron. 2008;59:1037-50. Xie X, Mulej Bratec S, Schmid G, Meng C, Doll A, Wohlschläger A, et al. How do you make me feel better? Social cognitive emotion regulation and the default mode network. NeuroImage.

AC C

2016;134:270-80.

Zhang Z, Liao W, Chen H, Mantini D, Ding JR, Xu Q, et al. Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain. 2011;134:2912-28.

ACCEPTED MANUSCRIPT Table 1. Demographic information and characteristics of patients with MDD or BD and HCs. Variables

Age (years)

HC

MDD(n=2

(n=34)

5)

33.53±11.

33±10.8

BD(n=25)

p-valu e

34.28±8.6

08

0.92a

5

16/18

12/13

13/12

0.84b

Years of education

13.79±4.8

13.48±3.6

13.6±3.39

0.96a

D

Number of manic/hypomanic episodes

TE

Average duration per depressive

EP

AC C

Score of HAMA

0.076±0.0

0.079±0.0

5

3

4

0/25

1/24

0.65b

28.39±8.9

26.72±7.5

0.49c

6

9

2.04±0.93

2.84±1.55

0.04c

0

1.20±0.50

0.00 c

5.26±3.15

4.24±3.22

0.27c

24.96±6.0

20.16±10.

0.06c

9

12

22.13±11.

16.08±8.8

52

7

23/2

21/4

MA

Number of depressive episodes

Score of HAMD

0.089±0.0

1/33

Age of first onset (years)

episode (month)

`

NU

Handness (Left/Right)

Medication state

RI

FD

9

SC

2

PT

Sex (Male/Female)

0.51 a

0.04c

0.91b

(treatment/treatment-naïve) 0.21 c

Medication load 2.28±0.61

2.68±1.43

Antidepressant

17

0

Antidepressant+antipsychotic

6

4

Combination of medicine

ACCEPTED MANUSCRIPT medication Antidepressant+mood stabilizer

0

4

Antidepressant+mood-stabilizer+antips

0

6

0

7

ychotic medication Antipsychotic

medication+

mood

stabilizer

PT

Note: Values are presented as mean±SD, aOne-way analysis of variance, bChi-square

RI

t-test, cTwo-tailed two-sample t-test. MDD, Major depressive disorder; BD, Bipolar disorder; HC, Healthy controls; FD: frame-wise displacement; HAMD, Hamilton

SC

Depression Rating Scale; HAMA, Hamilton Anxiety Rating Scale.

NU

Table 2. One-Way ANCOVA Contrast FC of striatal among BD, MDD and HCs. Cluster size

Statistic

(voxels)

value

at (MNI)

Peak

x

y

z

12.5

30

21

48

MA

Brain Regions

Peak Coordinates

D

Both BD and MDD showed increased FC

dorsolateral

cortex

prefrontal 69

EP

R.

TE

Seed: R caudate

BD showed increased FC

AC C

Seed: R putamen L. precuneus

165

15.3

-6

-66

45

R. precuneus

108

13.1

6

-75

39

L. cuneus

58

11.9

-12

-69

27

45

12.4

1

-63

45

Seed: R caudate R. precuneus

Note: MNI, Montreal Neurological Institute; L, left; R, right; FC, functional connectivity.

ACCEPTED MANUSCRIPT

PT

Figure 1. A: Subcortical mask based on the automated anatomic labeling (AAL) atlas, including bilateral caudate, putamen, pallidum and thalamus. B: Compared with HCs,

RI

both patients with BD and those with MDD showed increased CBF values in right

TE

D

MA

NU

SC

caudate and putamen.

Figure 2. CBF value of right caudate and right putamen were positively correlated

AC C

EP

with average duration per depressive episode in BD patients.

Figure 3. Compared to HCs, both BD and MDD patients had increased FC between right caudate and right dorsolateral prefrontal cortex (DLPFC).

ACCEPTED MANUSCRIPT

Figure 4. A: Compared with MDD and HCs, patients with BD showed increased FC

PT

between right caudate and right precuneus. B: Compared with MDD and HCs, patients with BD showed increased FC between right putamen and left cuneus and

MA

NU

SC

RI

bilateral precuneus.

Figure 5. The ROCs for discrimination between MDD and BD for variance of resting-state FC between right caudate and right precuneus (A), variance of

D

resting-state FC between right putamen and left cuneus and bilateral precuneus(B).

TE

The AUC of these ROIs were higher than those expected by chance (Bonferroni corrected). ROC, receiver operating characteristic; AUC, areas under the ROC curve;

AC C

EP

MDD, major depressive disorder; BD, bipolar disorder; ROI, region of interest.

ACCEPTED MANUSCRIPT Ethical statement A written informed consent was obtained from each participants before experiment. The study was approved by the research ethical committee of University of Electronic Science and

AC C

EP

TE

D

MA

NU

SC

RI

PT

Technology of China.

ACCEPTED MANUSCRIPT Highlights



Both patients with Bipolar disorder (BD) and Major depressive disorder (MDD) had higher cerebral blood flow (CBF) value in right caudate and putamen.



Both patients with BD and MDD showed commonly increased functional conectivity (FC)

PT

between striatum and dorsolateral prefrontal cortex.

The altered FC of striatum with precuneus/cuneus was observed only in patients with BD.



This study suggested a common deficits of prefrontal-limbic-striatal circuit in patients with

SC

RI



NU

BD and those with MDD. While the striato-precuneus FC could be considered as marker to

AC C

EP

TE

D

MA

differentiate BD patients from MDD patients.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5