- Email: [email protected]
Improved and residual functional abnormalities in major depressive disorder after electroconvulsive therapy
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
Contents lists available at ScienceDirect
Progress in Neuropsychopharmacology & Biological Psychiatry journal homepage: www.elsevier.com/locate/pnp
Improved and residual functional abnormalities in major depressive disorder after electroconvulsive therapy ⁎
T
⁎⁎
Jiaojian Wanga, ,1, Yang Jib,1, Xuemei Lic, Zhengyu Hec, Qiang Weib,d,e, Tongjian Baib,d,e, , ⁎⁎ Yanghua Tianb,d,e, , Kai Wangb,d,e,f a
Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen 518057, China Department of Neurology, the First Hospital of Anhui Medical University, Hefei 230022, China c Key Laboratory for Neurolnformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China d Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230022, China e Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230022, China f Department of Medical Psychology, Anhui Medical University, Hefei 230022, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Major depressive disorder ECT FcHo Resting-state functional connectivity Multivariate pattern classification
Electroconvulsive therapy (ECT) can induce fast remission of depression but still retain the residual functional impairments in major depressive disorder (MDD) patients. To delineate the different functional circuits of effective antidepressant treatment and residual functional impairments is able to better guide clinical therapy for depression. Herein, voxel-level whole brain functional connectivity homogeneity (FcHo), functional connectivity, multivariate pattern classification approaches were applied to reveal the specific circuits for treatment response and residual impairments in MDD patients after ECT. Increased FcHo values in right dorsomedial prefrontal cortex (dmPFC) and left angular gyrus (AG) and their corresponding functional connectivities between dmPFC and right AG, dorsolateral prefrontal cortex (dlPFC), superior frontal gyrus, precuneus (Pcu) and between left AG with dlPFC, bilateral AG, and left ventrolateral prefrontal cortex in MDD patients after ECT. Moreover, we found decreased FcHo values in left middle occipital gyrus (MOG) and lingual gyrus (LG) and decreased functional connectivities between MOG and dorsal postcentral gyrus (PCG) and between LG and middle PCG/anterior superior parietal lobule in MDD patients before and after ECT compared to healthy controls (HCs). The increased or normalized FcHo and functional connections may be related to effective antidepressant therapy, and the decreased FcHo and functional connectivities may account for the residual functional impairments in MDD patients after ECT. The different change patterns in MDD after ECT indicated a specific brain circuit supporting fast remission of depression, which was supported by the following multivariate pattern classification analyses. Finally, we found that the changed FcHo in dmPFC was correlated with changed depression scores. These results revealed a specific functional circuit supporting antidepressant effects of ECT and neuroanatomical basis for residual functional impairments. Our findings also highlighted the key role of dmPFC in antidepressant and will provide an important reference for depression treatment.
1. Introduction Abnormal structure and functional connectivity patterns contributing to the onset of major depressive disorder (MDD) has been well documented (Greicius et al., 2007; Sun et al., 2018; Wang et al., 2017a; Wu et al., 2016a). Electroconvulsive therapy (ECT) is an effective way for fast remission of depression symptoms by inducing structural
plasticity or reorganizing functional interactions between different large-scale brain networks or brain areas (Bai et al., 2018; Bai et al., 2019; Redlich et al., 2016; van Waarde et al., 2015; Wang et al., 2018a; Wang et al., 2017c; Wang et al., 2019b; Xu et al., 2019). In spite of numerous reports of depression remission after ECT, the residual functional impairments in MDD were also widely observed. Thus, to disentangle the specific circuits for fast remission of depression and
⁎
Corresponding author. Corresponding authors at: Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China. E-mail addresses: [email protected] (J. Wang), [email protected] (T. Bai), [email protected] (Y. Tian). 1 These authors equally contribute to this work. ⁎⁎
https://doi.org/10.1016/j.pnpbp.2020.109888 Received 30 October 2019; Received in revised form 3 February 2020; Accepted 11 February 2020 0278-5846/ © 2020 Elsevier Inc. All rights reserved.
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
of Anhui Medical University.
residual functional impairments will greatly advance the treatment and care for MDD patients. Emerging evidence has demonstrated that brain functions are determined by different connectivity patterns (Passingham et al., 2002; Wang et al., 2019a). Resting-state functional MRI (rs-fMRI) which mainly reflects spontaneous fluctuations has been widely applied to map the intrinsic functional patterns to investigate the functional organization and modules of the brain (Buckner et al., 2009; Cole et al., 2014; Fox et al., 2006; Mears and Pollard, 2016; Power et al., 2011; Wang et al., 2017d; Yeo et al., 2011). Thus, functional connectivity pattern analysis is an effective way to delineate the improved and residual functional abnormalities in MDD after ECT. Recently, we proposed a voxel-level method called functional connectivity pattern homogeneity (FcHo) to quantitatively measure similarity of the whole brain functional connectivity pattern. For a given voxel, FcHo was defined by calculating the whole brain functional connectivity patterns similarity of this voxel with those of its nearest 26 voxels, and the reliability of FcHo has been validated using different datasets, different fMRI pre-processing protocols, and intra-subjects reproducibility (Wang et al., 2018b). Compared to the regional homogeneity (ReHo) measuring the time-series similarity, FcHo can better identify the association cortex areas compared to ReHo (Wang et al., 2018b). This approach has been recently used to study the medication-free MDD patients and effectively identified the functional abnormalities which were consistent with the findings reported in previous studies (Wang et al., 2019c). FcHo is an ideal method to exactly identify the improved and residual functional alterations in MDD after ECT to facilitate understanding the mechanism of ECT and to identify the residually abnormal functional brain circuit. In this study, our goal was to identify the specific circuit for fast remission of depression and circuit for residual functional impairments in 23 MDD patients before and after ECT and 25 gender-, age, and education-level matched healthy controls using FcHo, functional connectivity, and multivariate patterns classification approaches. We first identified the brain areas with improved and residual FcHo abnormalities in MDD patients after ECT. Then, the changed functional connectivities of brain areas with changed FcHo were mapped. Finally, we used multivariable pattern classification to further validate the specificity of improved functional circuit for fast remission of depression and residual functional abnormalities.
2.2. ECT procedures A modified bi-frontal ECT protocol was used with a Thymatron System IV Integrated ECT System (Somatics, Lake Bluff, IL, USA) in Anhui Mental Health Center. The first three sessions were administered on consecutive days, and the following sessions were conducted every other day with a break over the weekends until patients reached symptom remission. All the patients were anesthetized using propofol During ECT. Succinylcholine and atropine were used to relax the musculature and suppress the secretion of glands, and seizure activity was monitor using electroencephalography. For ECT strategy, the initial percent energy was set according to the age of each participant (e.g., 50% for a 50-year-old patient), and the stimulation strength was evenly adjusted with an increment of 5% of the maximum charge (approximately 1000 millicoulombs) until seizure was visually observed (; Wang et al., 2017c; Wei et al., 2014a). 2.3. MRI data acquisition All MRI data were acquired on a 3.0 T GE MRI scanner at the First Affiliated Hospital of Anhui Medical University. Patients were scanned 12–24 h before the first ECT session and 24–72 h after the last ECT session. The healthy controls were scanned once for comparisons. During the MRI acquisition, participants were instructed to relax and to keep their eyes closed, to remain awake and not to think of anything. The resting-state functional images were acquired using a standard echo planar imaging (EPI) sequence. The scanning parameters were: repetition time (TR) = 2000 ms, echo time (TE) = 22.5 m, flip angle = 30o, matrix size = 64 × 64, voxel size = 3.4 × 3.4 × 4.6 mm3, 33 slices, 240 volumes. 2.4. Resting-state fMRI data pre-processing The resting-state fMRI data was pre-processed including discarding the first 10 volumes to facilitate magnetization equilibrium, realigning to the first volume to correct head motion, normalizing to the EPI template in MNI space, smoothing with a Gaussian kernel of 6 mm fullwidth at half maximum, regressing out Friston 24-parameter model of head motion, white matter, and cerebrospinal fluid mean signals, filtering with a temporal band-pass of 0.01–0.1 Hz. To exclude the head motion effects, the data was discarded if the head-movement exceeded one voxel of translation or rotation in any direction. Under this criterion, no subjects were excluded. Next, scrubbing was further used to eliminate the bad images (before 2 time points and after 1 time points) exceeding the pre-set criteria (frame displacement: FD, FD < 0.5) for excessive motion. The global signal was not regressed to ensure that the obtained results were reliable because the whole-brain signal regression will exaggerate anti-correlation (Wang et al., 2017b).
2. Materials and methods 2.1. Subjects 23 MDD patients diagnosed using the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria were recruited at Anhui Mental Health Center for ECT. Treatment-resistant or with acute suicidal tendencies MDD patients were entered for ECT. MDD Patients with pregnancy, life threatening somatic disease, substance dependence, neurological disorders, other comorbid mental disorders, MRIcontraindications, as well as previous ECT treatment were excluded. A total of 23 participants (11 males and 12 females, age range 18–55 years, mean age = 38.74, standard deviation = 11.02; mean education level = 8.83 years, standard deviation = 3.89) were used in the present study. The severity of depression assessed using the 17-item Hamilton Rating Scale for Depression (HRSD) was administered 12–24 h before the first ECT session and 24–72 h after the last ECT session (Hamilton, 1960). 25 sex, age, and education matched healthy controls (HC) (12 males and 13 females, range 26–51 years, mean = 39.52 years, standard deviation = 8.07; mean education level = 8.84 years, standard deviation = 3.05) were also included for comparisons to determine the ECT effects. All participants were righthanded and provided written informed consent. The study was conducted in accordance with the latest revision of the Declaration of Helsinki and had full ethical approval from the local ethics committees
2.5. Whole brain voxel-wise FcHo analyses The FcHo was measured using Kendall's coefficient concordance (KCC) (Kendall and Gibbons, 1990). To calculate FcHo of a given voxel, a KCC value was assigned to this voxel by computing the KCC of the whole brain functional connectivity of this voxel with those of its nearest 26 neighbors (see the following formula). An FcHo map for each subject of HC, MDD before and after was obtained. Then, a paired t-test was used to determine the regions that showed significantly changed FcHo in MDD patients after ECT compared to before ECT. Significance was determined using a cluster-level Monte Carlo simulation (5000 times) corrected threshold of P < .05 (cluster-forming threshold at voxel-level P < .001). 2
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al. −
KCC =
Table 1 Demographic and clinical variables.
∑ (Ri )2 − K (R )2 1 N 2 (K 3 12
− K)
where: Ri is the sum rank of the i th voxel of the whole brain;−R = ((K + 1) × N)/2 is the mean of the Ri; N is the number of a given voxel and its nearest neighbors (N = 26); K is the number of whole brain voxels. To determine the residual impairments in MDD patients after ECT, two-sample t-tests were performed to compare the FcHo maps between MDD patients before and after ECT and HC groups to reveal the residually disrupted FcHo. The significance was determined using a cluster-level Monte Carlo simulation (5000 times) corrected threshold of P < .05 (cluster-forming threshold at voxel-level P < .001). 2.6. Functional connectivity analyses The whole brain functional connectivity analysis was performed to identify altered functional connectivity of the brain areas with changed FcHo. The strength of the functional connectivity was measured through Pearson's correlations between the averaged time series of the brain areas with changed FcHo and voxels in the rest of the brain. The Fisher's z transformation was applied to normalize the original correlation maps. Paired two-sample t-test was performed to determine areas with significantly different functional connectivities between MDD patients before and after ECT. To identify residual functional connectivity abnormalities, two-sample t-tests were performed to determine areas with significantly abnormal functional connectivities between MDD patients before and after ECT compared to HC. The significance was determined using a cluster-level Monte Carlo simulation (5000 times) corrected threshold of P < .05 (cluster-forming threshold at voxel-level P < .001).
Subjects
MDD
Healthy controls
P value
Number of subjects Age (mean ± SD) Gender (male/female) Education level (mean ± SD) HRSD scores (mean ± SD) Before ECT After ECT Number of Treatment (mean ± SD) Age of onset (years) Durations of illness (months) Episodes(n patients) First Recurrence Family history (n patients) Medication (n patients)
23 38.74 ± 11.02 11/12 8.83 ± 3.89
25 39.52 ± 8.07 12/13 8.84 ± 3.05
0.78 0.99 0.99
22.22 ± 4.74 3.83 ± 2.15 7.26 ± 2 33.90 ± 12.26 70.35 ± 83.27 8 15 2 23
Note: A Pearson chi-squared test was used for gender comparison. Two-sample t-tests were used for age, education comparisons. MDD, major depressive disorder; HRSD, Hamilton Rating Scale for Depression.
patients after ECT (P < .001).
3.2. Changed FcHo Statistical analyses identified significantly increased FcHo in dorsomedial prefrontal cortex (dmPFC) (peak MNI coordinate: [9, 42, 39]) and left angular gyrus (AG) (peak MNI coordinate: [−45, −54, 36]) in MDD patients after ECT compared to before ECT (Fig. 1). Compared to HC, significant decreases of FcHo in MDD patients before and after ECT were consistently found in the left middle occipital gyrus (MOG) and left lingual gyrus (LG) (Fig. 2).
2.7. Multivariate pattern analysis using support vector machine A linear support vector machine (SVM) approach was performed to explore whether the changed FcHo and functional connectivities between MDD before and after ECT, and changed FcHo and functional connectivities between HC and MDD before and after ECT as the features have different contributions for classification to validate the specific circuit hypothesis for fast remission and depression (Chang and Lin, 2011) A leave-one-out cross-validation strategy was used to estimate the generalization ability of the classifier because of limited number of samples. The performance of a classifier was assessed using classification accuracy based on the results of the cross-validation.
3.3. Altered functional connectivity Compared to MDD patients before ECT, MDD patients after ECT showed significantly increased functional connectivities between dmPFC and right AG, dorsolateral prefrontal cortex (dlPFC), superior frontal gyrus (SFG), and precuneus (Pcu) (Fig. 3, Table 2). Moreover, significantly increased functional connectivities between left AG and bilateral AG, dlPFC, and left ventrolateral prefrontal cortex (vlPFC) in MDD patients after ECT compared to before ECT (Fig. 3, Table 3). In MDD patients before and after ECT, consistently reduced functional connectivity with MOG was found in dorsal postcentral gyrus (dPCG) compared to HC. Compared to HC, LG showed consistent reduction in functional connectivity to middle PCG or anterior superior parietal lobule (aSPL) in MDD patients before and after ECT (Fig. 4).
2.8. Correlation analyses To determine the relationship between neuroimaging metrics and clinical characteristics, correlation analyses were performed between changed HRSD scores and changed FcHo, changed functional connections were performed. Moreover, correlation analyses between disease duration and mean FcHo, mean functional connections in MDD patients before ECT were also performed. The significance was set at P < .05 and corrected with Bonferroni correction.
3.4. Classification results Using the changed FcHo and functional connectivities in MDD patients after ECT compared to before ECT as features, the linear SVM achieved an accuracy of 54.17% to classify MDD patients before ECT from HC, an accuracy of 75% to classify MDD patients after ECT from HC, and an accuracy of 84.78% to classify MDD patients before and after ECT. Similarly, combined features of changed FcHo and functional connectivities in MDD patients before and after ECT compared to HC as features, the linear SVM classifier achieved an accuracy of 81.25% to classify MDD patients before ECT from HC, an accuracy of 81.25% to classify MDD patients after ECT from HC, and an accuracy of 58.7% to classify MDD patients before and after ECT. The area under curve (AUC) values for these classifications were consistent with the classification accuracy results (Fig. 5).
3. Results 3.1. Demographics and clinical characteristics The demographics and clinical characteristics of the subjects used in current study are presented in Table 1. No significant differences in education level (P = .99), gender (P = .99), and age (P = .78) were found between MDD and HC groups (Table 1). The anti-depressive ECT response was evaluated using a paired two-sided t-test on the HRSD scores, and significantly decreased HRSD scores were found in MDD 3
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Fig. 1. Increased whole brain functional connectivity pattern homogeneity (FcHo) of right dorsomedial prefrontal cortex (dmPFC) and left angular gyrus (AG) in MDD patients after ECT was identified.
patients after ECT. Consistent reductions of FcHo in left MOG and LG were observed in MDD patients before and after ECT compared to HC. The following functional connectivity analyses revealed increased functional connections between dmPFC and right AG, dlPFC, SFG, Pcu, and between left AG and bilateral AG, dlPFC, left vlPFC in MDD patients after ECT. Moreover, the disrupted functional connections between MOG and dPCG, and between LG and mPCG/aSPL were consistently identified in MDD patients before and after ECT. The decreased FcHo and functional connections may account for the residual functional impairments. These results suggested that the fast remission of depression may have specific brain circuit, which was supported by the following multivariate classification analysis. Also, the changed FcHo in dmPFC was positively associated with changed HRSD scores highlighting the importance of this area in treatment response of depression.
3.5. Correlation analyses Correlation analyses revealed that changed z scores of FcHo values in dmPFC were significantly correlated with the changed HRSD scores in MDD patients after ECT minus before ECT (R = 0.71, P < .001) (Fig. 6). 4. Discussion In this study, whole brain functional connectivity patterns homogeneity (FcHo), functional connectivity, and multivariate pattern analyses were used to identify circuits associated with ECT response and residual disruption of functions in MDD patients after ECT. Significantly increased whole brain FcHo in dmPFC and left AG were found in MDD
Fig. 2. Decreased functional connectivity pattern homogeneity (FcHo) in MDD patients before and after ECT compared to healthy controls (HC). The FcHo maps between HC and MDD patients before and after ECT were compared, and consistently decreased FcHo were observed in left middle occipital gyrus (MOG_L) and lingual gyrus (LG_L) in MDD patients before and after ECT compared to HC. 4
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Fig. 3. Increased functional connections in major depressive disorder (MDD) patients after ECT. Increased whole brain functional connectivities between dmPFC and right angular gyrus (AG_R), dorsolateral prefrontal cortex (dlPFC_R), superior frontal gyrus (SFG_R), precuneus (Pcu_R) and between left AG and bilateral AG (AG_L, AG_R), bilateral dlPFC (dlPFC_L, dlPFC_R), left ventrolateral prefrontal cortex (vlPFC_L) were found.
reported in depressive individuals in many previous neuroimaging studies (Fonseka et al., 2016; Nixon et al., 2013), and functional normalization of dmPFC is an biomarker of effective antidepressant treatment (Abdallah et al., 2017; Bai et al., 2019; Shiota et al., 2017). A recent review further pointed out that the dmPFC may be a new and effective target for rTMS therapy in depression (Downar and Daskalakis, 2013). In our study, we found increased FcHo value in dmPFC after ECT in MDD, and the increased FcHo in this area is positively correlated with the improvement of depressive symptoms. Thus, functional improvement, i.e. increased FcHo of dmPFC may be also a neural biomarker for treatment response of depression. Moreover, ECT also increased RSFCs between AG and dlPFC, left vlPFC, bilateral AG, and between dmPFC and right AG, dlPFC, SFG, Pcu in MDD patients. Interestingly, almost all these regions belong to DMN and central execution network (CEN). The DMN primarily participates in self-referential functioning and autobiographical memory, while CEN is crucial for the active maintenance and manipulation of information in working memory, and for judgement and decision making in the context of goal directed behaviour (Menon, 2011). Recent studies demonstrated that dysfunction of interactions between different networks of DMN, CCN, attention network, and salience network contributes to the neuropathology of depression (Gudayol-Ferre et al., 2015; Hamilton et al., 2011; Kaiser et al., 2015). Our current findings further demonstrated that functional reorganization of these networks contributed to the remission of depression, which is consistent with our previous study
4.1. Improved functional abnormalities Increased FcHo was found in left AG in MDD patients after ECT, which is in line with previous findings. AG is one of the main part of the default mode network (DMN) and plays an important role in attention, memory retrieval, and semantic processing (Bambini et al., 2011; Seghier, 2012; Wang et al., 2012; Wang et al., 2017d; Wang et al., 2016b). Lee et al. found decreased volume of AG in MDD with suicide attempts (Lee et al., 2016). Decreased functional connectivities of AG with other brain regions in MDD were also reported in the initial phase of depression, even in the longitudinal follow up (de Kwaasteniet et al., 2015; Strikwerda-Brown et al., 2014; Wang et al., 2019c; Wu et al., 2016b). The abnormalities in AG were associated with abnormal perception, attention and extensive processing of self-referential information (Lai et al., 2017). Recently, Wei et al. found that ECT can increase the functional connectivity strength of left angular gyrus in depression patients contributing to improved mood (Wei et al., 2018). All these findings underscore the importance of AG in the neuropathology of MDD and antidepressant effects of depression treatment. The dmPFC is involved in many cognitive and emotional processes including sustaining attention, working memory, and response inhibition (Garavan et al., 2002; Seeley et al., 2007). Sheline et al. (2010) et al. showed that dmPFC was a key nexus connecting with three core brain networks-the cognitive control network (CCN), DMN, and affective network (AN). Functional dysfunction of dmPFC has been widely
Table 2 Increased functional connectivities with dorsomedial prefrontal cortex (dmPFC) in MDD after ECT compared to before ECT. Brain regions
Superior frontal gyrus Angular gyrus Precuneus Dorsolateral prefrontal cortex
Abbreviation
SFG AG Pcu dlPFC
L/R
R R R R
Cluster size
348 347 305 165
Peak MNI Coordinates
t-values
X
Y
Z
18 48 6 42
51 −48 −63 15
18 27 48 24
MDD: major depressive disorder; ECT: electroconvulsive therapy; MNI: Montreal Neurological Institute; L: left hemisphere; R: right hemisphere. 5
6.81 5.41 5.02 5.25
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Table 3 Increased functional connectivities with angular gyrus (AG) in MDD after ECT compared to before ECT. Brain Regions
Ventrolateral prefrontal cortex Dorsolateral prefrontal cortex Dorsolateral prefrontal cortex Angular gyrus Angular gyrus
Abbreviation
vlPFC dlPFC dlPFC AG AG
L/R
L R L R L
Cluster size
230 246 552 365 610
Peak MNI Coordinates
t-values
X
Y
Z
−45 48 −30 45 −48
36 21 12 −69 −51
6 30 60 42 30
6.40 5.29 7.10 5.90 6.21
MDD: major depressive disorder; ECT: electroconvulsive therapy; MNI: Montreal Neurological Institute; L: left hemisphere; R: right hemisphere.
identification circuit involved in the facial expressional and emotional procession (Sun et al., 2018; Tao et al., 2013). Studies have demonstrated hypoactivity in the LG may provide a neural basis for impaired face emotion processing and suicidal behaviour in depressed patients (Brakowski et al., 2017; Dutta et al., 2015; Ma et al., 2019). In addition, Wei et al. (2014b) revealed altered fALFF in the SPL and left LG in subclinical depression. The SPL is part of the attentional control network and participates in mediating voluntary orientation and reorientating attention (Wang et al., 2016a; Wang et al., 2015; Wu et al., 2016c; Yang et al., 2016). Depressive patients showed a decreased FC between the LG and aSPL may suggest impaired mediation of attention about face emotion processing. In summary, the brain areas with abnormal FcHo and FC were located in the occipital lobe processing of visual information and in the parietal lobe processing of the sensory and attention. Disruption of the connectivity patterns of these brain areas before and after ECT may lead to residual abnormal processing of emotional information and abnormal cognitive rigidity (poor attention flexibility), which are potential susceptibility factors of recurrence risk (Figueroa et al., 2019). Increased FcHo and functional connections contribute to the improved symptoms in MDD after ECT, while decreased FcHo and functional connections contribute to the residual abnormalities. This finding suggested that remission of depression symptoms may have a specific circuit, which was also validated by our multivariate pattern classification analyses. Thus, antidepressant effects of ECT may be through regulating certain brain circuit to alleviate rather than completely normalizes all the defects in MDD. Our findings were supported by a previous study that found 30% of the discriminative connections were normalized in the clinically recovered patients after antidepressant treatments (Qin et al., 2015). All the evidence underscores the importance to identify state-specific circuit for depression therapy. There are also some limitations in this study. First, the sample size of the MDD patients receiving ECT is still small, and the findings needed to be further tested. Second, all the MDD patients still took medications during ECT which may affect the final results. Third, our study is lack of the MDD controls that only took medications. These limitations need to be addressed in future studies.
Fig. 4. Decreased functional connections in major depressive disorder (MDD) patients before and after ECT. The whole brain functional connectivity maps of left middle occipital gyrus (MOG_L) and lingual gyrus (LG_L) were compared between HC and MDD patients before and after ECT. Consistent reduction in functional connectivities between MOG and dorsal postcentral gyrus (dPCG) and between LG and middle PCG (mPCG) and anterior superior parietal lobule (aSPL) were found in MDD patients before and after ECT.
(Wang et al., 2018a). 4.2. Residual functional abnormalities In this study, we also observed decreased FcHo in the left MOG and the disrupted functional connection between MOG and dPCG in MDD patients before and after ECT, which may account for the residual functional impairments in MDD after ECT. This finding was consistent with a recent meta-analysis which found decreased activity in MOG in MDD patients (Ma et al., 2019). The MOG plays an important role in the visual recognition network and the perception of emotional facial expression, and decreased FcHo in MDD may be associated with hypoactive emotional processing and encoding of positive self-related visual information (Quevedo et al., 2018; Wu et al., 2016a). Moreover, increased activation in the bilateral MOG was associated with a lower MDD risk score during the working memory task (Yueksel et al., 2017). dPCG is a primary somatosensory cortex and is involved in emotion experience (Zhang et al., 2017). The decreased functional connectivity between MOG and dPCG may be related to negative visual and somatization memory during rumination. Similarly, the LG showed consistent reduction in FcHo and functional connectivity to middle PCG/aSPL in MDD patients before and after ECT. The LG, along with the fusiform gyrus, is part of the visual
Author statement Kai Wang, Yanghua Tian, and Tongjian Bai: Conceptualization, experiment design, and data collection. Qiang Wei: data collection. Jiaojian Wang, Yang Ji, Xuemei Li, and Zhengyu He: data analysis, manuscript writing and editing.
Ethical statement All participants were provided written informed consent. The study was conducted in accordance with the latest revision of the Declaration of Helsinki and had full ethical approval from the local ethics committees of Anhui Medical University. 6
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Fig. 5. Multivariate pattern analysis using support vector machine (SVM) based on different features. Using the changed FcHo and functional connectives between MDD patients before and after ECT as features achieved a higher accuracy to classify MDD patients before and after ECT than to classify MDD patients before and after ECT from HC. Using the changed FcHo and functional connectives between HC and MDD patients before and after ECT as features achieved a higher accuracy to classify MDD patients before ECT and after ECT from HC that to classify MDD patients before and after ECT.
Research Schemes (JCYJ20170818110103216, 2019SHIBS0003, JCYJ20170412164413575), Guangdong Key Basic Research Scheme (2018B030332001), Guangdong Pearl River Talents Plan (2016ZT06S220), the National Natural Science Foundation of China (81871338, 81671354, 91732303, 31970979, 81601187), Anhui Provincial Science Fund for Distinguished Young Scholars (1808085J23), National Basic Research Program of China (2015CB856400), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAI13B01). References Abdallah, C.G., Averill, L.A., Collins, K.A., Geha, P., Schwartz, J., Averill, C., DeWilde, K.E., Wong, E., Anticevic, A., Tang, C.Y., Iosifescu, D.V., Charney, D.S., Murrough, J.W., 2017. Ketamine treatment and global brain connectivity in major depression. Neuropsychopharmacology 42, 1210–1219. Bai, T., Wei, Q., Xie, W., Wang, A., Wang, J., Ji, G.J., Wang, K., Tian, Y., 2018. Hippocampal-subregion functional alterations associated with antidepressant effects and cognitive impairments of electroconvulsive therapy. Psychol Med 1–8. Bai, T., Wei, Q., Zu, M., Xie, W., Wang, J., Gong-Jun, J., Yu, F., Tian, Y., Wang, K., 2019. Functional plasticity of the dorsomedial prefrontal cortex in depression reorganized by electroconvulsive therapy: validation in two independent samples. Hum. Brain Mapp. 40, 465–473. Bambini, V., Gentili, C., Ricciardi, E., Bertinetto, P.M., Pietrini, P., 2011. Decomposing metaphor processing at the cognitive and neural level through functional magnetic resonance imaging. Brain Res. Bull. 86, 203–216. Brakowski, J., Spinelli, S., Dorig, N., Bosch, O.G., Manoliu, A., Holtforth, M.G., Seifritz, E., 2017. Resting state brain network function in major depression - depression symptomatology, antidepressant treatment effects, future research. J. Psychiatr. Res. 92, 147–159. Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., AndrewsHanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J. Neurosci. 29, 1860–1873. Chang, C.-C., Lin, C.-J., 2011. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27. Cole, M.W., Bassett, D.S., Power, J.D., Braver, T.S., Petersen, S.E., 2014. Intrinsic and task-evoked network architectures of the human brain. Neuron 83, 238–251. de Kwaasteniet, B.P., Rive, M.M., Ruhe, H.G., Schene, A.H., Veltman, D.J., Fellinger, L.,
Fig. 6. Correlation analyses between the changed functional connectivity pattern homogeneity (FcHo) and changed Hamilton Rating Scale for Depression (HRSD) scores. Significant correlation between the changes of HRSD scores and changed FcHo in dmPFC was identified.
Declaration of Competing Interest All the authors declared that there are no conflicts of interest. Acknowledgements This work was supported by grants from Shenzhen Key Basic 7
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
van Wingen, G.A., Denys, D., 2015. Decreased resting-state connectivity between neurocognitive networks in treatment resistant depression. Frontiers in Psychiatry 6. Downar, J., Daskalakis, Z.J., 2013. New targets for rTMS in depression: a review of convergent evidence. Brain Stimulation 6, 231–240. Dutta, A., McKie, S., Deakin, J.F.W., 2015. Resting state networks in major depressive disorder (vol 224, pg 139, 2014). Psychiatry Research-Neuroimaging 234, 392–397. Figueroa, C.A., DeJong, H., Mocking, R.J.T., Fox, E., Rive, M.M., Schene, A.H., Stein, A., Ruhe, H.G., 2019. Attentional control, rumination and recurrence of depression. J. Affect. Disord. 256, 364–372. Fonseka, B.A., Jaworska, N., Courtright, A., MacMaster, F.P., MacQueen, G.M., 2016. Cortical thickness and emotion processing in young adults with mild to moderate depression: a preliminary study. Bmc Psychiatry 16. Fox, M.D., Corbetta, M., Snyder, A.Z., Vincent, J.L., Raichle, M.E., 2006. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl. Acad. Sci. U. S. A. 103, 10046–10051. Garavan, H., Ross, T.J., Murphy, K., Roche, R.A.P., Stein, E.A., 2002. Dissociable executive functions in the dynamic control of behavior: inhibition, error detection, and correction. NeuroImage 17, 1820–1829. Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Kenna, H., Reiss, A.L., Schatzberg, A.F., 2007. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62, 429–437. Gudayol-Ferre, E., Pero-Cebollero, M., Gonzalez-Garrido, A.A., Guardia-Olmos, J., 2015. Changes in brain connectivity related to the treatment of depression measured through fMRI: a systematic review. Front. Hum. Neurosci. 9, 582. Hamilton, M., 1960. A rating scale for depression. Journal Of Neurology Neurosurgery And Psychiatry 23, 56–62. Hamilton, J.P., Furman, D.J., Chang, C., Thomason, M.E., Dennis, E., Gotlib, I.H., 2011. Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol. Psychiatry 70, 327–333. Kaiser, R.H., Andrews-Hanna, J.R., Wager, T.D., Pizzagalli, D.A., 2015. Large-scale network dysfunction in major depressive disorder a meta-analysis of resting-state functional connectivity. Jama Psychiatry 72, 603–611. Kendall, M.G., Gibbons, J.D., 1990. Rank Correlation Methods. Oxford University Press. Lai, C.H., Wu, Y.T., Hou, Y.M., 2017. Functional network-based statistics in depression: theory of mind subnetwork and importance of parietal region. J. Affect. Disord. 217, 132–137. Lee, Y.J., Kim, S., Gwak, A.R., Kim, S.J., Kang, S.-G., Na, K.-S., Son, Y.-D., Park, J., 2016. Decreased regional gray matter volume in suicide attempters compared to suicide non-attempters with major depressive disorders. Compr. Psychiatry 67, 59–65. Ma, X., Liu, J., Liu, T., Ma, L., Wang, W., Shi, S., Wang, Y., Gong, Q., Wang, M., 2019. Altered resting-state functional activity in medication-naive patients with first-episode major depression disorder vs. healthy control: a quantitative meta-analysis. Frontiers in Behavioral Neuroscience 13. Mears, D., Pollard, H.B., 2016. Network science and the human brain: using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease. J. Neurosci. Res. 94, 590–605. Menon, V., 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15, 483–506. Nixon, N.L., Liddle, P.F., Worwood, G., Liotti, M., Nixon, E., 2013. Prefrontal cortex function in remitted major depressive disorder. Psychol. Med. 43, 1219–1230. Passingham, R.E., Stephan, K.E., Kotter, R., 2002. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3, 606–616. Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C., Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E., 2011. Functional network organization of the human brain. Neuron 72, 665–678. Qin, J., Shen, H., Zeng, L.-L., Jiang, W., Liu, L., Hu, D., 2015. Predicting clinical responses in major depression using intrinsic functional connectivity. Neuroreport 26, 675–680. Quevedo, K., Harms, M., Sauder, M., Scott, H., Mohamed, S., Thomas, K.M., Schallmo, M.P., Smyda, G., 2018. The neurobiology of self face recognition among depressed adolescents. J. Affect. Disord. 229, 22–31. Redlich, R., Opel, N., Grotegerd, D., Dohm, K., Zaremba, D., Burger, C., Munker, S., Muhlmann, L., Wahl, P., Heindel, W., Arolt, V., Alferink, J., Zwanzger, P., Zavorotnyy, M., Kugel, H., Dannlowski, U., 2016. Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry 73, 557–564. Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., Greicius, M.D., 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356. Seghier, M.L., 2012. The angular gyrus. Neuroscientist 19, 43–61. Sheline, Y.I., Price, J.L., Yan, Z., Mintun, M.A., 2010. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc. Natl. Acad. Sci. U. S. A. 107, 11020–11025. Shiota, S., Okamoto, Y., Okada, G., Takagaki, K., Takamura, M., Mori, A., Yokoyama, S., Nishiyama, Y., Jinnin, R., Hashimoto, R.I., Yamawaki, S., 2017. Effects of behavioural activation on the neural basis of other perspective self-referential processing in subthreshold depression: a functional magnetic resonance imaging study. Psychol. Med. 47, 877–888. Strikwerda-Brown, C., Davey, C.G., Whittle, S., Allen, N.B., Byrne, M.L., Schwartz, O.S., Simmons, J.G., Dwyer, D., Harrison, B.J., 2014. Mapping the relationship between subgenual cingulate cortex functional connectivity and depressive symptoms across adolescence. Soc. Cogn. Affect. Neurosci. 10, 961–968. Sun, H., Luo, L., Yuan, X., Zhang, L., He, Y., Yao, S., Wang, J., Xiao, J., 2018. Regional homogeneity and functional connectivity patterns in major depressive disorder, cognitive vulnerability to depression and healthy subjects. J. Affect. Disord. 235, 229–235.
Tao, H., Guo, S., Ge, T., Kendrick, K.M., Xue, Z., Liu, Z., Feng, J., 2013. Depression uncouples brain hate circuit. Mol. Psychiatry 18, 101–111. van Waarde, J.A., Scholte, H.S., van Oudheusden, L.J., Verwey, B., Denys, D., van Wingen, G.A., 2015. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol. Psychiatry 20, 609–614. Wang, J., Fan, L., Zhang, Y., Liu, Y., Jiang, D., Zhang, Y., Yu, C., Jiang, T., 2012. Tractography-based parcellation of the human left inferior parietal lobule. Neuroimage 63, 641–652. Wang, J., Yang, Y., Fan, L., Xu, J., Li, C., Liu, Y., Fox, P.T., Eickhoff, S.B., Yu, C., Jiang, T., 2015. Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approaches. Hum. Brain Mapp. 36, 238–257. Wang, J., Tian, Y., Wang, M., Cao, L., Wu, H., Zhang, Y., Wang, K., Jiang, T., 2016a. A lateralized top-down network for visuospatial attention and neglect. Brain Imaging Behav 10, 1029–1037. Wang, J., Zhang, J., Rong, M., Wei, X., Zheng, D., Fox, P.T., Eickhoff, S.B., Jiang, T., 2016b. Functional topography of the right inferior parietal lobule structured by anatomical connectivity profiles. Hum. Brain Mapp. 37, 4316–4332. Wang, C., Wu, H., Chen, F., Xu, J., Li, H., Li, H., Wang, J., 2017a. Disrupted functional connectivity patterns of the insula subregions in drug-free major depressive disorder. J. Affect. Disord. 234, 297–304. Wang, J., Wei, Q., Bai, T., Zhou, X., Sun, H., Becker, B., Tian, Y., Wang, K., Kendrick, K., 2017b. Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder. Soc. Cogn. Affect. Neurosci. 12, 1983–1992. Wang, J., Wei, Q., Yuan, X., Jiang, X., Xu, J., Zhou, X., Tian, Y., Wang, K., 2017c. Local functional connectivity density is closely associated with the response of electroconvulsive therapy in major depressive disorder. J. Affect. Disord. 225, 658–664. Wang, J., Xie, S., Guo, X., Becker, B., Fox, P.T., Eickhoff, S.B., Jiang, T., 2017d. Correspondent functional topography of the human left inferior parietal lobule at rest and under task revealed using resting-state fMRI and Coactivation based Parcellation. Hum. Brain Mapp. 38, 1659–1675. Wang, J., Wei, Q., Wang, L., Zhang, H., Bai, T., Cheng, L., Tian, Y., Wang, K., 2018a. Functional reorganization of intra- and internetwork connectivity in major depressive disorder after electroconvulsive therapy. Hum. Brain Mapp. 39, 1403–1411. Wang, L., Xu, J., Wang, C., Wang, J., 2018b. Whole brain functional connectivity pattern homogeneity mapping. Front. Hum. Neurosci. 12, 164. Wang, J., Becker, B., Wang, L., Li, H., Zhao, X., Jiang, T., 2019a. Corresponding anatomical and coactivation architecture of the human precuneus showing similar connectivity patterns with macaques. Neuroimage 200, 562–574. Wang, L., Wei, Q., Wang, C., Xu, J., Wang, K., Tian, Y., Wang, J., 2019b. Altered functional connectivity patterns of insular subregions in major depressive disorder after electroconvulsive therapy. Brain Imaging Behav. https://doi.org/10.1007/s11682018-0013-z. Wang, L., Yu, L., Wu, F., Wu, H., Wang, J., 2019c. Altered whole brain functional connectivity pattern homogeneity in medication-free major depressive disorder. J. Affect. Disord. 253, 18–25. Wei, Q., Tian, Y., Yu, Y., Zhang, F., Hu, X., Dong, Y., Chen, Y., Hu, P., Hu, X., Wang, K., 2014a. Modulation of interhemispheric functional coordination in electroconvulsive therapy for depression. Transl. Psychiatry 4, e453. Wei, X., Shen, H., Ren, J., Li, X., Xu, X., Yang, R., Lai, L., Chen, L., Hu, J., Liu, W., Jiang, X., 2014b. Altered resting-state connectivity in college students with nonclinical depressive symptoms. Plos One 9. Wei, Q., Bai, T., Chen, Y., Ji, G., Hu, X., Xie, W., Xiong, Z., Zhu, D., Wei, L., Hu, P., Yu, Y., Wang, K., Tian, Y., 2018. The changes of functional connectivity strength in electroconvulsive therapy for depression: a longitudinal study. Front. Neurosci. 12, 661. Wu, H., Sun, H., Xu, J., Wu, Y., Wang, C., Xiao, J., She, S., Huang, J., Zou, W., Peng, H., Lu, X., Huang, G., Jiang, T., Ning, Y., Wang, J., 2016a. Changed hub and corresponding functional connectivity of Subgenual anterior cingulate cortex in major depressive disorder. Front. Neuroanat. 10, 120. Wu, X., Lin, P., Yang, J., Song, H., Yang, R., Yang, J., 2016b. Dysfunction of the cinguloopercular network in first-episode medication-naive patients with major depressive disorder. J. Affect. Disord. 200, 275–283. Wu, Y., Wang, J., Zhang, Y., Zheng, D., Zhang, J., Rong, M., Wu, H., Wang, Y., Zhou, K., Jiang, T., 2016c. The neuroanatomical basis for posterior superior parietal lobule control lateralization of visuospatial attention. Front. Neuroanat. 10, 32. Xu, J., Wang, J., Bai, T., Zhang, X., Li, T., Hu, Q., Li, H., Zhang, L., Wei, Q., Tian, Y., Wang, K., 2019. Electroconvulsive therapy induces cortical morphological alterations in major depressive disorder revealed with surface-based Morphometry analysis. Int. J. Neural Syst. 29, 1950005. Yang, W., Chen, Q., Liu, P., Cheng, H., Cui, Q., Wei, D., Zhang, Q., Qiu, J., 2016. Abnormal brain activation during directed forgetting of negative memory in depressed patients. J. Affect. Disord. 190, 880–888. Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zollei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L., 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165. Yueksel, D., Dietsche, B., Forstner, A.J., Witt, S.H., Maier, R., Rietschel, M., Konrad, C., Noethen, M.M., Dannlowski, U., Baune, B.T., Kircher, T., Krug, A., 2017. Polygenic risk for depression and the neural correlates of working memory in healthy subjects. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 79, 67–76. Zhang, L., Shi, L., Zhang, B., Zhao, L., Dong, Y., Liu, J., Lian, Z., Liang, L., Chen, W., Luo, X., Pei, S., Mo, X., Huang, W., Ouyang, F., Guo, B., Liang, C., Zhang, S., 2017. Probabilistic entity-relationship diagram: a correlation between functional connectivity and spontaneous brain activity during resting state in major depressive disorder. PLoS One 12.
8
Contents lists available at ScienceDirect
Progress in Neuropsychopharmacology & Biological Psychiatry journal homepage: www.elsevier.com/locate/pnp
Improved and residual functional abnormalities in major depressive disorder after electroconvulsive therapy ⁎
T
⁎⁎
Jiaojian Wanga, ,1, Yang Jib,1, Xuemei Lic, Zhengyu Hec, Qiang Weib,d,e, Tongjian Baib,d,e, , ⁎⁎ Yanghua Tianb,d,e, , Kai Wangb,d,e,f a
Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen 518057, China Department of Neurology, the First Hospital of Anhui Medical University, Hefei 230022, China c Key Laboratory for Neurolnformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China d Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230022, China e Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230022, China f Department of Medical Psychology, Anhui Medical University, Hefei 230022, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Major depressive disorder ECT FcHo Resting-state functional connectivity Multivariate pattern classification
Electroconvulsive therapy (ECT) can induce fast remission of depression but still retain the residual functional impairments in major depressive disorder (MDD) patients. To delineate the different functional circuits of effective antidepressant treatment and residual functional impairments is able to better guide clinical therapy for depression. Herein, voxel-level whole brain functional connectivity homogeneity (FcHo), functional connectivity, multivariate pattern classification approaches were applied to reveal the specific circuits for treatment response and residual impairments in MDD patients after ECT. Increased FcHo values in right dorsomedial prefrontal cortex (dmPFC) and left angular gyrus (AG) and their corresponding functional connectivities between dmPFC and right AG, dorsolateral prefrontal cortex (dlPFC), superior frontal gyrus, precuneus (Pcu) and between left AG with dlPFC, bilateral AG, and left ventrolateral prefrontal cortex in MDD patients after ECT. Moreover, we found decreased FcHo values in left middle occipital gyrus (MOG) and lingual gyrus (LG) and decreased functional connectivities between MOG and dorsal postcentral gyrus (PCG) and between LG and middle PCG/anterior superior parietal lobule in MDD patients before and after ECT compared to healthy controls (HCs). The increased or normalized FcHo and functional connections may be related to effective antidepressant therapy, and the decreased FcHo and functional connectivities may account for the residual functional impairments in MDD patients after ECT. The different change patterns in MDD after ECT indicated a specific brain circuit supporting fast remission of depression, which was supported by the following multivariate pattern classification analyses. Finally, we found that the changed FcHo in dmPFC was correlated with changed depression scores. These results revealed a specific functional circuit supporting antidepressant effects of ECT and neuroanatomical basis for residual functional impairments. Our findings also highlighted the key role of dmPFC in antidepressant and will provide an important reference for depression treatment.
1. Introduction Abnormal structure and functional connectivity patterns contributing to the onset of major depressive disorder (MDD) has been well documented (Greicius et al., 2007; Sun et al., 2018; Wang et al., 2017a; Wu et al., 2016a). Electroconvulsive therapy (ECT) is an effective way for fast remission of depression symptoms by inducing structural
plasticity or reorganizing functional interactions between different large-scale brain networks or brain areas (Bai et al., 2018; Bai et al., 2019; Redlich et al., 2016; van Waarde et al., 2015; Wang et al., 2018a; Wang et al., 2017c; Wang et al., 2019b; Xu et al., 2019). In spite of numerous reports of depression remission after ECT, the residual functional impairments in MDD were also widely observed. Thus, to disentangle the specific circuits for fast remission of depression and
⁎
Corresponding author. Corresponding authors at: Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, China. E-mail addresses: [email protected] (J. Wang), [email protected] (T. Bai), [email protected] (Y. Tian). 1 These authors equally contribute to this work. ⁎⁎
https://doi.org/10.1016/j.pnpbp.2020.109888 Received 30 October 2019; Received in revised form 3 February 2020; Accepted 11 February 2020 0278-5846/ © 2020 Elsevier Inc. All rights reserved.
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
of Anhui Medical University.
residual functional impairments will greatly advance the treatment and care for MDD patients. Emerging evidence has demonstrated that brain functions are determined by different connectivity patterns (Passingham et al., 2002; Wang et al., 2019a). Resting-state functional MRI (rs-fMRI) which mainly reflects spontaneous fluctuations has been widely applied to map the intrinsic functional patterns to investigate the functional organization and modules of the brain (Buckner et al., 2009; Cole et al., 2014; Fox et al., 2006; Mears and Pollard, 2016; Power et al., 2011; Wang et al., 2017d; Yeo et al., 2011). Thus, functional connectivity pattern analysis is an effective way to delineate the improved and residual functional abnormalities in MDD after ECT. Recently, we proposed a voxel-level method called functional connectivity pattern homogeneity (FcHo) to quantitatively measure similarity of the whole brain functional connectivity pattern. For a given voxel, FcHo was defined by calculating the whole brain functional connectivity patterns similarity of this voxel with those of its nearest 26 voxels, and the reliability of FcHo has been validated using different datasets, different fMRI pre-processing protocols, and intra-subjects reproducibility (Wang et al., 2018b). Compared to the regional homogeneity (ReHo) measuring the time-series similarity, FcHo can better identify the association cortex areas compared to ReHo (Wang et al., 2018b). This approach has been recently used to study the medication-free MDD patients and effectively identified the functional abnormalities which were consistent with the findings reported in previous studies (Wang et al., 2019c). FcHo is an ideal method to exactly identify the improved and residual functional alterations in MDD after ECT to facilitate understanding the mechanism of ECT and to identify the residually abnormal functional brain circuit. In this study, our goal was to identify the specific circuit for fast remission of depression and circuit for residual functional impairments in 23 MDD patients before and after ECT and 25 gender-, age, and education-level matched healthy controls using FcHo, functional connectivity, and multivariate patterns classification approaches. We first identified the brain areas with improved and residual FcHo abnormalities in MDD patients after ECT. Then, the changed functional connectivities of brain areas with changed FcHo were mapped. Finally, we used multivariable pattern classification to further validate the specificity of improved functional circuit for fast remission of depression and residual functional abnormalities.
2.2. ECT procedures A modified bi-frontal ECT protocol was used with a Thymatron System IV Integrated ECT System (Somatics, Lake Bluff, IL, USA) in Anhui Mental Health Center. The first three sessions were administered on consecutive days, and the following sessions were conducted every other day with a break over the weekends until patients reached symptom remission. All the patients were anesthetized using propofol During ECT. Succinylcholine and atropine were used to relax the musculature and suppress the secretion of glands, and seizure activity was monitor using electroencephalography. For ECT strategy, the initial percent energy was set according to the age of each participant (e.g., 50% for a 50-year-old patient), and the stimulation strength was evenly adjusted with an increment of 5% of the maximum charge (approximately 1000 millicoulombs) until seizure was visually observed (; Wang et al., 2017c; Wei et al., 2014a). 2.3. MRI data acquisition All MRI data were acquired on a 3.0 T GE MRI scanner at the First Affiliated Hospital of Anhui Medical University. Patients were scanned 12–24 h before the first ECT session and 24–72 h after the last ECT session. The healthy controls were scanned once for comparisons. During the MRI acquisition, participants were instructed to relax and to keep their eyes closed, to remain awake and not to think of anything. The resting-state functional images were acquired using a standard echo planar imaging (EPI) sequence. The scanning parameters were: repetition time (TR) = 2000 ms, echo time (TE) = 22.5 m, flip angle = 30o, matrix size = 64 × 64, voxel size = 3.4 × 3.4 × 4.6 mm3, 33 slices, 240 volumes. 2.4. Resting-state fMRI data pre-processing The resting-state fMRI data was pre-processed including discarding the first 10 volumes to facilitate magnetization equilibrium, realigning to the first volume to correct head motion, normalizing to the EPI template in MNI space, smoothing with a Gaussian kernel of 6 mm fullwidth at half maximum, regressing out Friston 24-parameter model of head motion, white matter, and cerebrospinal fluid mean signals, filtering with a temporal band-pass of 0.01–0.1 Hz. To exclude the head motion effects, the data was discarded if the head-movement exceeded one voxel of translation or rotation in any direction. Under this criterion, no subjects were excluded. Next, scrubbing was further used to eliminate the bad images (before 2 time points and after 1 time points) exceeding the pre-set criteria (frame displacement: FD, FD < 0.5) for excessive motion. The global signal was not regressed to ensure that the obtained results were reliable because the whole-brain signal regression will exaggerate anti-correlation (Wang et al., 2017b).
2. Materials and methods 2.1. Subjects 23 MDD patients diagnosed using the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria were recruited at Anhui Mental Health Center for ECT. Treatment-resistant or with acute suicidal tendencies MDD patients were entered for ECT. MDD Patients with pregnancy, life threatening somatic disease, substance dependence, neurological disorders, other comorbid mental disorders, MRIcontraindications, as well as previous ECT treatment were excluded. A total of 23 participants (11 males and 12 females, age range 18–55 years, mean age = 38.74, standard deviation = 11.02; mean education level = 8.83 years, standard deviation = 3.89) were used in the present study. The severity of depression assessed using the 17-item Hamilton Rating Scale for Depression (HRSD) was administered 12–24 h before the first ECT session and 24–72 h after the last ECT session (Hamilton, 1960). 25 sex, age, and education matched healthy controls (HC) (12 males and 13 females, range 26–51 years, mean = 39.52 years, standard deviation = 8.07; mean education level = 8.84 years, standard deviation = 3.05) were also included for comparisons to determine the ECT effects. All participants were righthanded and provided written informed consent. The study was conducted in accordance with the latest revision of the Declaration of Helsinki and had full ethical approval from the local ethics committees
2.5. Whole brain voxel-wise FcHo analyses The FcHo was measured using Kendall's coefficient concordance (KCC) (Kendall and Gibbons, 1990). To calculate FcHo of a given voxel, a KCC value was assigned to this voxel by computing the KCC of the whole brain functional connectivity of this voxel with those of its nearest 26 neighbors (see the following formula). An FcHo map for each subject of HC, MDD before and after was obtained. Then, a paired t-test was used to determine the regions that showed significantly changed FcHo in MDD patients after ECT compared to before ECT. Significance was determined using a cluster-level Monte Carlo simulation (5000 times) corrected threshold of P < .05 (cluster-forming threshold at voxel-level P < .001). 2
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al. −
KCC =
Table 1 Demographic and clinical variables.
∑ (Ri )2 − K (R )2 1 N 2 (K 3 12
− K)
where: Ri is the sum rank of the i th voxel of the whole brain;−R = ((K + 1) × N)/2 is the mean of the Ri; N is the number of a given voxel and its nearest neighbors (N = 26); K is the number of whole brain voxels. To determine the residual impairments in MDD patients after ECT, two-sample t-tests were performed to compare the FcHo maps between MDD patients before and after ECT and HC groups to reveal the residually disrupted FcHo. The significance was determined using a cluster-level Monte Carlo simulation (5000 times) corrected threshold of P < .05 (cluster-forming threshold at voxel-level P < .001). 2.6. Functional connectivity analyses The whole brain functional connectivity analysis was performed to identify altered functional connectivity of the brain areas with changed FcHo. The strength of the functional connectivity was measured through Pearson's correlations between the averaged time series of the brain areas with changed FcHo and voxels in the rest of the brain. The Fisher's z transformation was applied to normalize the original correlation maps. Paired two-sample t-test was performed to determine areas with significantly different functional connectivities between MDD patients before and after ECT. To identify residual functional connectivity abnormalities, two-sample t-tests were performed to determine areas with significantly abnormal functional connectivities between MDD patients before and after ECT compared to HC. The significance was determined using a cluster-level Monte Carlo simulation (5000 times) corrected threshold of P < .05 (cluster-forming threshold at voxel-level P < .001).
Subjects
MDD
Healthy controls
P value
Number of subjects Age (mean ± SD) Gender (male/female) Education level (mean ± SD) HRSD scores (mean ± SD) Before ECT After ECT Number of Treatment (mean ± SD) Age of onset (years) Durations of illness (months) Episodes(n patients) First Recurrence Family history (n patients) Medication (n patients)
23 38.74 ± 11.02 11/12 8.83 ± 3.89
25 39.52 ± 8.07 12/13 8.84 ± 3.05
0.78 0.99 0.99
22.22 ± 4.74 3.83 ± 2.15 7.26 ± 2 33.90 ± 12.26 70.35 ± 83.27 8 15 2 23
Note: A Pearson chi-squared test was used for gender comparison. Two-sample t-tests were used for age, education comparisons. MDD, major depressive disorder; HRSD, Hamilton Rating Scale for Depression.
patients after ECT (P < .001).
3.2. Changed FcHo Statistical analyses identified significantly increased FcHo in dorsomedial prefrontal cortex (dmPFC) (peak MNI coordinate: [9, 42, 39]) and left angular gyrus (AG) (peak MNI coordinate: [−45, −54, 36]) in MDD patients after ECT compared to before ECT (Fig. 1). Compared to HC, significant decreases of FcHo in MDD patients before and after ECT were consistently found in the left middle occipital gyrus (MOG) and left lingual gyrus (LG) (Fig. 2).
2.7. Multivariate pattern analysis using support vector machine A linear support vector machine (SVM) approach was performed to explore whether the changed FcHo and functional connectivities between MDD before and after ECT, and changed FcHo and functional connectivities between HC and MDD before and after ECT as the features have different contributions for classification to validate the specific circuit hypothesis for fast remission and depression (Chang and Lin, 2011) A leave-one-out cross-validation strategy was used to estimate the generalization ability of the classifier because of limited number of samples. The performance of a classifier was assessed using classification accuracy based on the results of the cross-validation.
3.3. Altered functional connectivity Compared to MDD patients before ECT, MDD patients after ECT showed significantly increased functional connectivities between dmPFC and right AG, dorsolateral prefrontal cortex (dlPFC), superior frontal gyrus (SFG), and precuneus (Pcu) (Fig. 3, Table 2). Moreover, significantly increased functional connectivities between left AG and bilateral AG, dlPFC, and left ventrolateral prefrontal cortex (vlPFC) in MDD patients after ECT compared to before ECT (Fig. 3, Table 3). In MDD patients before and after ECT, consistently reduced functional connectivity with MOG was found in dorsal postcentral gyrus (dPCG) compared to HC. Compared to HC, LG showed consistent reduction in functional connectivity to middle PCG or anterior superior parietal lobule (aSPL) in MDD patients before and after ECT (Fig. 4).
2.8. Correlation analyses To determine the relationship between neuroimaging metrics and clinical characteristics, correlation analyses were performed between changed HRSD scores and changed FcHo, changed functional connections were performed. Moreover, correlation analyses between disease duration and mean FcHo, mean functional connections in MDD patients before ECT were also performed. The significance was set at P < .05 and corrected with Bonferroni correction.
3.4. Classification results Using the changed FcHo and functional connectivities in MDD patients after ECT compared to before ECT as features, the linear SVM achieved an accuracy of 54.17% to classify MDD patients before ECT from HC, an accuracy of 75% to classify MDD patients after ECT from HC, and an accuracy of 84.78% to classify MDD patients before and after ECT. Similarly, combined features of changed FcHo and functional connectivities in MDD patients before and after ECT compared to HC as features, the linear SVM classifier achieved an accuracy of 81.25% to classify MDD patients before ECT from HC, an accuracy of 81.25% to classify MDD patients after ECT from HC, and an accuracy of 58.7% to classify MDD patients before and after ECT. The area under curve (AUC) values for these classifications were consistent with the classification accuracy results (Fig. 5).
3. Results 3.1. Demographics and clinical characteristics The demographics and clinical characteristics of the subjects used in current study are presented in Table 1. No significant differences in education level (P = .99), gender (P = .99), and age (P = .78) were found between MDD and HC groups (Table 1). The anti-depressive ECT response was evaluated using a paired two-sided t-test on the HRSD scores, and significantly decreased HRSD scores were found in MDD 3
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Fig. 1. Increased whole brain functional connectivity pattern homogeneity (FcHo) of right dorsomedial prefrontal cortex (dmPFC) and left angular gyrus (AG) in MDD patients after ECT was identified.
patients after ECT. Consistent reductions of FcHo in left MOG and LG were observed in MDD patients before and after ECT compared to HC. The following functional connectivity analyses revealed increased functional connections between dmPFC and right AG, dlPFC, SFG, Pcu, and between left AG and bilateral AG, dlPFC, left vlPFC in MDD patients after ECT. Moreover, the disrupted functional connections between MOG and dPCG, and between LG and mPCG/aSPL were consistently identified in MDD patients before and after ECT. The decreased FcHo and functional connections may account for the residual functional impairments. These results suggested that the fast remission of depression may have specific brain circuit, which was supported by the following multivariate classification analysis. Also, the changed FcHo in dmPFC was positively associated with changed HRSD scores highlighting the importance of this area in treatment response of depression.
3.5. Correlation analyses Correlation analyses revealed that changed z scores of FcHo values in dmPFC were significantly correlated with the changed HRSD scores in MDD patients after ECT minus before ECT (R = 0.71, P < .001) (Fig. 6). 4. Discussion In this study, whole brain functional connectivity patterns homogeneity (FcHo), functional connectivity, and multivariate pattern analyses were used to identify circuits associated with ECT response and residual disruption of functions in MDD patients after ECT. Significantly increased whole brain FcHo in dmPFC and left AG were found in MDD
Fig. 2. Decreased functional connectivity pattern homogeneity (FcHo) in MDD patients before and after ECT compared to healthy controls (HC). The FcHo maps between HC and MDD patients before and after ECT were compared, and consistently decreased FcHo were observed in left middle occipital gyrus (MOG_L) and lingual gyrus (LG_L) in MDD patients before and after ECT compared to HC. 4
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Fig. 3. Increased functional connections in major depressive disorder (MDD) patients after ECT. Increased whole brain functional connectivities between dmPFC and right angular gyrus (AG_R), dorsolateral prefrontal cortex (dlPFC_R), superior frontal gyrus (SFG_R), precuneus (Pcu_R) and between left AG and bilateral AG (AG_L, AG_R), bilateral dlPFC (dlPFC_L, dlPFC_R), left ventrolateral prefrontal cortex (vlPFC_L) were found.
reported in depressive individuals in many previous neuroimaging studies (Fonseka et al., 2016; Nixon et al., 2013), and functional normalization of dmPFC is an biomarker of effective antidepressant treatment (Abdallah et al., 2017; Bai et al., 2019; Shiota et al., 2017). A recent review further pointed out that the dmPFC may be a new and effective target for rTMS therapy in depression (Downar and Daskalakis, 2013). In our study, we found increased FcHo value in dmPFC after ECT in MDD, and the increased FcHo in this area is positively correlated with the improvement of depressive symptoms. Thus, functional improvement, i.e. increased FcHo of dmPFC may be also a neural biomarker for treatment response of depression. Moreover, ECT also increased RSFCs between AG and dlPFC, left vlPFC, bilateral AG, and between dmPFC and right AG, dlPFC, SFG, Pcu in MDD patients. Interestingly, almost all these regions belong to DMN and central execution network (CEN). The DMN primarily participates in self-referential functioning and autobiographical memory, while CEN is crucial for the active maintenance and manipulation of information in working memory, and for judgement and decision making in the context of goal directed behaviour (Menon, 2011). Recent studies demonstrated that dysfunction of interactions between different networks of DMN, CCN, attention network, and salience network contributes to the neuropathology of depression (Gudayol-Ferre et al., 2015; Hamilton et al., 2011; Kaiser et al., 2015). Our current findings further demonstrated that functional reorganization of these networks contributed to the remission of depression, which is consistent with our previous study
4.1. Improved functional abnormalities Increased FcHo was found in left AG in MDD patients after ECT, which is in line with previous findings. AG is one of the main part of the default mode network (DMN) and plays an important role in attention, memory retrieval, and semantic processing (Bambini et al., 2011; Seghier, 2012; Wang et al., 2012; Wang et al., 2017d; Wang et al., 2016b). Lee et al. found decreased volume of AG in MDD with suicide attempts (Lee et al., 2016). Decreased functional connectivities of AG with other brain regions in MDD were also reported in the initial phase of depression, even in the longitudinal follow up (de Kwaasteniet et al., 2015; Strikwerda-Brown et al., 2014; Wang et al., 2019c; Wu et al., 2016b). The abnormalities in AG were associated with abnormal perception, attention and extensive processing of self-referential information (Lai et al., 2017). Recently, Wei et al. found that ECT can increase the functional connectivity strength of left angular gyrus in depression patients contributing to improved mood (Wei et al., 2018). All these findings underscore the importance of AG in the neuropathology of MDD and antidepressant effects of depression treatment. The dmPFC is involved in many cognitive and emotional processes including sustaining attention, working memory, and response inhibition (Garavan et al., 2002; Seeley et al., 2007). Sheline et al. (2010) et al. showed that dmPFC was a key nexus connecting with three core brain networks-the cognitive control network (CCN), DMN, and affective network (AN). Functional dysfunction of dmPFC has been widely
Table 2 Increased functional connectivities with dorsomedial prefrontal cortex (dmPFC) in MDD after ECT compared to before ECT. Brain regions
Superior frontal gyrus Angular gyrus Precuneus Dorsolateral prefrontal cortex
Abbreviation
SFG AG Pcu dlPFC
L/R
R R R R
Cluster size
348 347 305 165
Peak MNI Coordinates
t-values
X
Y
Z
18 48 6 42
51 −48 −63 15
18 27 48 24
MDD: major depressive disorder; ECT: electroconvulsive therapy; MNI: Montreal Neurological Institute; L: left hemisphere; R: right hemisphere. 5
6.81 5.41 5.02 5.25
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Table 3 Increased functional connectivities with angular gyrus (AG) in MDD after ECT compared to before ECT. Brain Regions
Ventrolateral prefrontal cortex Dorsolateral prefrontal cortex Dorsolateral prefrontal cortex Angular gyrus Angular gyrus
Abbreviation
vlPFC dlPFC dlPFC AG AG
L/R
L R L R L
Cluster size
230 246 552 365 610
Peak MNI Coordinates
t-values
X
Y
Z
−45 48 −30 45 −48
36 21 12 −69 −51
6 30 60 42 30
6.40 5.29 7.10 5.90 6.21
MDD: major depressive disorder; ECT: electroconvulsive therapy; MNI: Montreal Neurological Institute; L: left hemisphere; R: right hemisphere.
identification circuit involved in the facial expressional and emotional procession (Sun et al., 2018; Tao et al., 2013). Studies have demonstrated hypoactivity in the LG may provide a neural basis for impaired face emotion processing and suicidal behaviour in depressed patients (Brakowski et al., 2017; Dutta et al., 2015; Ma et al., 2019). In addition, Wei et al. (2014b) revealed altered fALFF in the SPL and left LG in subclinical depression. The SPL is part of the attentional control network and participates in mediating voluntary orientation and reorientating attention (Wang et al., 2016a; Wang et al., 2015; Wu et al., 2016c; Yang et al., 2016). Depressive patients showed a decreased FC between the LG and aSPL may suggest impaired mediation of attention about face emotion processing. In summary, the brain areas with abnormal FcHo and FC were located in the occipital lobe processing of visual information and in the parietal lobe processing of the sensory and attention. Disruption of the connectivity patterns of these brain areas before and after ECT may lead to residual abnormal processing of emotional information and abnormal cognitive rigidity (poor attention flexibility), which are potential susceptibility factors of recurrence risk (Figueroa et al., 2019). Increased FcHo and functional connections contribute to the improved symptoms in MDD after ECT, while decreased FcHo and functional connections contribute to the residual abnormalities. This finding suggested that remission of depression symptoms may have a specific circuit, which was also validated by our multivariate pattern classification analyses. Thus, antidepressant effects of ECT may be through regulating certain brain circuit to alleviate rather than completely normalizes all the defects in MDD. Our findings were supported by a previous study that found 30% of the discriminative connections were normalized in the clinically recovered patients after antidepressant treatments (Qin et al., 2015). All the evidence underscores the importance to identify state-specific circuit for depression therapy. There are also some limitations in this study. First, the sample size of the MDD patients receiving ECT is still small, and the findings needed to be further tested. Second, all the MDD patients still took medications during ECT which may affect the final results. Third, our study is lack of the MDD controls that only took medications. These limitations need to be addressed in future studies.
Fig. 4. Decreased functional connections in major depressive disorder (MDD) patients before and after ECT. The whole brain functional connectivity maps of left middle occipital gyrus (MOG_L) and lingual gyrus (LG_L) were compared between HC and MDD patients before and after ECT. Consistent reduction in functional connectivities between MOG and dorsal postcentral gyrus (dPCG) and between LG and middle PCG (mPCG) and anterior superior parietal lobule (aSPL) were found in MDD patients before and after ECT.
(Wang et al., 2018a). 4.2. Residual functional abnormalities In this study, we also observed decreased FcHo in the left MOG and the disrupted functional connection between MOG and dPCG in MDD patients before and after ECT, which may account for the residual functional impairments in MDD after ECT. This finding was consistent with a recent meta-analysis which found decreased activity in MOG in MDD patients (Ma et al., 2019). The MOG plays an important role in the visual recognition network and the perception of emotional facial expression, and decreased FcHo in MDD may be associated with hypoactive emotional processing and encoding of positive self-related visual information (Quevedo et al., 2018; Wu et al., 2016a). Moreover, increased activation in the bilateral MOG was associated with a lower MDD risk score during the working memory task (Yueksel et al., 2017). dPCG is a primary somatosensory cortex and is involved in emotion experience (Zhang et al., 2017). The decreased functional connectivity between MOG and dPCG may be related to negative visual and somatization memory during rumination. Similarly, the LG showed consistent reduction in FcHo and functional connectivity to middle PCG/aSPL in MDD patients before and after ECT. The LG, along with the fusiform gyrus, is part of the visual
Author statement Kai Wang, Yanghua Tian, and Tongjian Bai: Conceptualization, experiment design, and data collection. Qiang Wei: data collection. Jiaojian Wang, Yang Ji, Xuemei Li, and Zhengyu He: data analysis, manuscript writing and editing.
Ethical statement All participants were provided written informed consent. The study was conducted in accordance with the latest revision of the Declaration of Helsinki and had full ethical approval from the local ethics committees of Anhui Medical University. 6
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
Fig. 5. Multivariate pattern analysis using support vector machine (SVM) based on different features. Using the changed FcHo and functional connectives between MDD patients before and after ECT as features achieved a higher accuracy to classify MDD patients before and after ECT than to classify MDD patients before and after ECT from HC. Using the changed FcHo and functional connectives between HC and MDD patients before and after ECT as features achieved a higher accuracy to classify MDD patients before ECT and after ECT from HC that to classify MDD patients before and after ECT.
Research Schemes (JCYJ20170818110103216, 2019SHIBS0003, JCYJ20170412164413575), Guangdong Key Basic Research Scheme (2018B030332001), Guangdong Pearl River Talents Plan (2016ZT06S220), the National Natural Science Foundation of China (81871338, 81671354, 91732303, 31970979, 81601187), Anhui Provincial Science Fund for Distinguished Young Scholars (1808085J23), National Basic Research Program of China (2015CB856400), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAI13B01). References Abdallah, C.G., Averill, L.A., Collins, K.A., Geha, P., Schwartz, J., Averill, C., DeWilde, K.E., Wong, E., Anticevic, A., Tang, C.Y., Iosifescu, D.V., Charney, D.S., Murrough, J.W., 2017. Ketamine treatment and global brain connectivity in major depression. Neuropsychopharmacology 42, 1210–1219. Bai, T., Wei, Q., Xie, W., Wang, A., Wang, J., Ji, G.J., Wang, K., Tian, Y., 2018. Hippocampal-subregion functional alterations associated with antidepressant effects and cognitive impairments of electroconvulsive therapy. Psychol Med 1–8. Bai, T., Wei, Q., Zu, M., Xie, W., Wang, J., Gong-Jun, J., Yu, F., Tian, Y., Wang, K., 2019. Functional plasticity of the dorsomedial prefrontal cortex in depression reorganized by electroconvulsive therapy: validation in two independent samples. Hum. Brain Mapp. 40, 465–473. Bambini, V., Gentili, C., Ricciardi, E., Bertinetto, P.M., Pietrini, P., 2011. Decomposing metaphor processing at the cognitive and neural level through functional magnetic resonance imaging. Brain Res. Bull. 86, 203–216. Brakowski, J., Spinelli, S., Dorig, N., Bosch, O.G., Manoliu, A., Holtforth, M.G., Seifritz, E., 2017. Resting state brain network function in major depression - depression symptomatology, antidepressant treatment effects, future research. J. Psychiatr. Res. 92, 147–159. Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., AndrewsHanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J. Neurosci. 29, 1860–1873. Chang, C.-C., Lin, C.-J., 2011. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27. Cole, M.W., Bassett, D.S., Power, J.D., Braver, T.S., Petersen, S.E., 2014. Intrinsic and task-evoked network architectures of the human brain. Neuron 83, 238–251. de Kwaasteniet, B.P., Rive, M.M., Ruhe, H.G., Schene, A.H., Veltman, D.J., Fellinger, L.,
Fig. 6. Correlation analyses between the changed functional connectivity pattern homogeneity (FcHo) and changed Hamilton Rating Scale for Depression (HRSD) scores. Significant correlation between the changes of HRSD scores and changed FcHo in dmPFC was identified.
Declaration of Competing Interest All the authors declared that there are no conflicts of interest. Acknowledgements This work was supported by grants from Shenzhen Key Basic 7
Progress in Neuropsychopharmacology & Biological Psychiatry 100 (2020) 109888
J. Wang, et al.
van Wingen, G.A., Denys, D., 2015. Decreased resting-state connectivity between neurocognitive networks in treatment resistant depression. Frontiers in Psychiatry 6. Downar, J., Daskalakis, Z.J., 2013. New targets for rTMS in depression: a review of convergent evidence. Brain Stimulation 6, 231–240. Dutta, A., McKie, S., Deakin, J.F.W., 2015. Resting state networks in major depressive disorder (vol 224, pg 139, 2014). Psychiatry Research-Neuroimaging 234, 392–397. Figueroa, C.A., DeJong, H., Mocking, R.J.T., Fox, E., Rive, M.M., Schene, A.H., Stein, A., Ruhe, H.G., 2019. Attentional control, rumination and recurrence of depression. J. Affect. Disord. 256, 364–372. Fonseka, B.A., Jaworska, N., Courtright, A., MacMaster, F.P., MacQueen, G.M., 2016. Cortical thickness and emotion processing in young adults with mild to moderate depression: a preliminary study. Bmc Psychiatry 16. Fox, M.D., Corbetta, M., Snyder, A.Z., Vincent, J.L., Raichle, M.E., 2006. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl. Acad. Sci. U. S. A. 103, 10046–10051. Garavan, H., Ross, T.J., Murphy, K., Roche, R.A.P., Stein, E.A., 2002. Dissociable executive functions in the dynamic control of behavior: inhibition, error detection, and correction. NeuroImage 17, 1820–1829. Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Kenna, H., Reiss, A.L., Schatzberg, A.F., 2007. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62, 429–437. Gudayol-Ferre, E., Pero-Cebollero, M., Gonzalez-Garrido, A.A., Guardia-Olmos, J., 2015. Changes in brain connectivity related to the treatment of depression measured through fMRI: a systematic review. Front. Hum. Neurosci. 9, 582. Hamilton, M., 1960. A rating scale for depression. Journal Of Neurology Neurosurgery And Psychiatry 23, 56–62. Hamilton, J.P., Furman, D.J., Chang, C., Thomason, M.E., Dennis, E., Gotlib, I.H., 2011. Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol. Psychiatry 70, 327–333. Kaiser, R.H., Andrews-Hanna, J.R., Wager, T.D., Pizzagalli, D.A., 2015. Large-scale network dysfunction in major depressive disorder a meta-analysis of resting-state functional connectivity. Jama Psychiatry 72, 603–611. Kendall, M.G., Gibbons, J.D., 1990. Rank Correlation Methods. Oxford University Press. Lai, C.H., Wu, Y.T., Hou, Y.M., 2017. Functional network-based statistics in depression: theory of mind subnetwork and importance of parietal region. J. Affect. Disord. 217, 132–137. Lee, Y.J., Kim, S., Gwak, A.R., Kim, S.J., Kang, S.-G., Na, K.-S., Son, Y.-D., Park, J., 2016. Decreased regional gray matter volume in suicide attempters compared to suicide non-attempters with major depressive disorders. Compr. Psychiatry 67, 59–65. Ma, X., Liu, J., Liu, T., Ma, L., Wang, W., Shi, S., Wang, Y., Gong, Q., Wang, M., 2019. Altered resting-state functional activity in medication-naive patients with first-episode major depression disorder vs. healthy control: a quantitative meta-analysis. Frontiers in Behavioral Neuroscience 13. Mears, D., Pollard, H.B., 2016. Network science and the human brain: using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease. J. Neurosci. Res. 94, 590–605. Menon, V., 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15, 483–506. Nixon, N.L., Liddle, P.F., Worwood, G., Liotti, M., Nixon, E., 2013. Prefrontal cortex function in remitted major depressive disorder. Psychol. Med. 43, 1219–1230. Passingham, R.E., Stephan, K.E., Kotter, R., 2002. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3, 606–616. Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C., Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E., 2011. Functional network organization of the human brain. Neuron 72, 665–678. Qin, J., Shen, H., Zeng, L.-L., Jiang, W., Liu, L., Hu, D., 2015. Predicting clinical responses in major depression using intrinsic functional connectivity. Neuroreport 26, 675–680. Quevedo, K., Harms, M., Sauder, M., Scott, H., Mohamed, S., Thomas, K.M., Schallmo, M.P., Smyda, G., 2018. The neurobiology of self face recognition among depressed adolescents. J. Affect. Disord. 229, 22–31. Redlich, R., Opel, N., Grotegerd, D., Dohm, K., Zaremba, D., Burger, C., Munker, S., Muhlmann, L., Wahl, P., Heindel, W., Arolt, V., Alferink, J., Zwanzger, P., Zavorotnyy, M., Kugel, H., Dannlowski, U., 2016. Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry 73, 557–564. Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., Greicius, M.D., 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 27, 2349–2356. Seghier, M.L., 2012. The angular gyrus. Neuroscientist 19, 43–61. Sheline, Y.I., Price, J.L., Yan, Z., Mintun, M.A., 2010. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc. Natl. Acad. Sci. U. S. A. 107, 11020–11025. Shiota, S., Okamoto, Y., Okada, G., Takagaki, K., Takamura, M., Mori, A., Yokoyama, S., Nishiyama, Y., Jinnin, R., Hashimoto, R.I., Yamawaki, S., 2017. Effects of behavioural activation on the neural basis of other perspective self-referential processing in subthreshold depression: a functional magnetic resonance imaging study. Psychol. Med. 47, 877–888. Strikwerda-Brown, C., Davey, C.G., Whittle, S., Allen, N.B., Byrne, M.L., Schwartz, O.S., Simmons, J.G., Dwyer, D., Harrison, B.J., 2014. Mapping the relationship between subgenual cingulate cortex functional connectivity and depressive symptoms across adolescence. Soc. Cogn. Affect. Neurosci. 10, 961–968. Sun, H., Luo, L., Yuan, X., Zhang, L., He, Y., Yao, S., Wang, J., Xiao, J., 2018. Regional homogeneity and functional connectivity patterns in major depressive disorder, cognitive vulnerability to depression and healthy subjects. J. Affect. Disord. 235, 229–235.
Tao, H., Guo, S., Ge, T., Kendrick, K.M., Xue, Z., Liu, Z., Feng, J., 2013. Depression uncouples brain hate circuit. Mol. Psychiatry 18, 101–111. van Waarde, J.A., Scholte, H.S., van Oudheusden, L.J., Verwey, B., Denys, D., van Wingen, G.A., 2015. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol. Psychiatry 20, 609–614. Wang, J., Fan, L., Zhang, Y., Liu, Y., Jiang, D., Zhang, Y., Yu, C., Jiang, T., 2012. Tractography-based parcellation of the human left inferior parietal lobule. Neuroimage 63, 641–652. Wang, J., Yang, Y., Fan, L., Xu, J., Li, C., Liu, Y., Fox, P.T., Eickhoff, S.B., Yu, C., Jiang, T., 2015. Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approaches. Hum. Brain Mapp. 36, 238–257. Wang, J., Tian, Y., Wang, M., Cao, L., Wu, H., Zhang, Y., Wang, K., Jiang, T., 2016a. A lateralized top-down network for visuospatial attention and neglect. Brain Imaging Behav 10, 1029–1037. Wang, J., Zhang, J., Rong, M., Wei, X., Zheng, D., Fox, P.T., Eickhoff, S.B., Jiang, T., 2016b. Functional topography of the right inferior parietal lobule structured by anatomical connectivity profiles. Hum. Brain Mapp. 37, 4316–4332. Wang, C., Wu, H., Chen, F., Xu, J., Li, H., Li, H., Wang, J., 2017a. Disrupted functional connectivity patterns of the insula subregions in drug-free major depressive disorder. J. Affect. Disord. 234, 297–304. Wang, J., Wei, Q., Bai, T., Zhou, X., Sun, H., Becker, B., Tian, Y., Wang, K., Kendrick, K., 2017b. Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder. Soc. Cogn. Affect. Neurosci. 12, 1983–1992. Wang, J., Wei, Q., Yuan, X., Jiang, X., Xu, J., Zhou, X., Tian, Y., Wang, K., 2017c. Local functional connectivity density is closely associated with the response of electroconvulsive therapy in major depressive disorder. J. Affect. Disord. 225, 658–664. Wang, J., Xie, S., Guo, X., Becker, B., Fox, P.T., Eickhoff, S.B., Jiang, T., 2017d. Correspondent functional topography of the human left inferior parietal lobule at rest and under task revealed using resting-state fMRI and Coactivation based Parcellation. Hum. Brain Mapp. 38, 1659–1675. Wang, J., Wei, Q., Wang, L., Zhang, H., Bai, T., Cheng, L., Tian, Y., Wang, K., 2018a. Functional reorganization of intra- and internetwork connectivity in major depressive disorder after electroconvulsive therapy. Hum. Brain Mapp. 39, 1403–1411. Wang, L., Xu, J., Wang, C., Wang, J., 2018b. Whole brain functional connectivity pattern homogeneity mapping. Front. Hum. Neurosci. 12, 164. Wang, J., Becker, B., Wang, L., Li, H., Zhao, X., Jiang, T., 2019a. Corresponding anatomical and coactivation architecture of the human precuneus showing similar connectivity patterns with macaques. Neuroimage 200, 562–574. Wang, L., Wei, Q., Wang, C., Xu, J., Wang, K., Tian, Y., Wang, J., 2019b. Altered functional connectivity patterns of insular subregions in major depressive disorder after electroconvulsive therapy. Brain Imaging Behav. https://doi.org/10.1007/s11682018-0013-z. Wang, L., Yu, L., Wu, F., Wu, H., Wang, J., 2019c. Altered whole brain functional connectivity pattern homogeneity in medication-free major depressive disorder. J. Affect. Disord. 253, 18–25. Wei, Q., Tian, Y., Yu, Y., Zhang, F., Hu, X., Dong, Y., Chen, Y., Hu, P., Hu, X., Wang, K., 2014a. Modulation of interhemispheric functional coordination in electroconvulsive therapy for depression. Transl. Psychiatry 4, e453. Wei, X., Shen, H., Ren, J., Li, X., Xu, X., Yang, R., Lai, L., Chen, L., Hu, J., Liu, W., Jiang, X., 2014b. Altered resting-state connectivity in college students with nonclinical depressive symptoms. Plos One 9. Wei, Q., Bai, T., Chen, Y., Ji, G., Hu, X., Xie, W., Xiong, Z., Zhu, D., Wei, L., Hu, P., Yu, Y., Wang, K., Tian, Y., 2018. The changes of functional connectivity strength in electroconvulsive therapy for depression: a longitudinal study. Front. Neurosci. 12, 661. Wu, H., Sun, H., Xu, J., Wu, Y., Wang, C., Xiao, J., She, S., Huang, J., Zou, W., Peng, H., Lu, X., Huang, G., Jiang, T., Ning, Y., Wang, J., 2016a. Changed hub and corresponding functional connectivity of Subgenual anterior cingulate cortex in major depressive disorder. Front. Neuroanat. 10, 120. Wu, X., Lin, P., Yang, J., Song, H., Yang, R., Yang, J., 2016b. Dysfunction of the cinguloopercular network in first-episode medication-naive patients with major depressive disorder. J. Affect. Disord. 200, 275–283. Wu, Y., Wang, J., Zhang, Y., Zheng, D., Zhang, J., Rong, M., Wu, H., Wang, Y., Zhou, K., Jiang, T., 2016c. The neuroanatomical basis for posterior superior parietal lobule control lateralization of visuospatial attention. Front. Neuroanat. 10, 32. Xu, J., Wang, J., Bai, T., Zhang, X., Li, T., Hu, Q., Li, H., Zhang, L., Wei, Q., Tian, Y., Wang, K., 2019. Electroconvulsive therapy induces cortical morphological alterations in major depressive disorder revealed with surface-based Morphometry analysis. Int. J. Neural Syst. 29, 1950005. Yang, W., Chen, Q., Liu, P., Cheng, H., Cui, Q., Wei, D., Zhang, Q., Qiu, J., 2016. Abnormal brain activation during directed forgetting of negative memory in depressed patients. J. Affect. Disord. 190, 880–888. Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zollei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L., 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165. Yueksel, D., Dietsche, B., Forstner, A.J., Witt, S.H., Maier, R., Rietschel, M., Konrad, C., Noethen, M.M., Dannlowski, U., Baune, B.T., Kircher, T., Krug, A., 2017. Polygenic risk for depression and the neural correlates of working memory in healthy subjects. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 79, 67–76. Zhang, L., Shi, L., Zhang, B., Zhao, L., Dong, Y., Liu, J., Lian, Z., Liang, L., Chen, W., Luo, X., Pei, S., Mo, X., Huang, W., Ouyang, F., Guo, B., Liang, C., Zhang, S., 2017. Probabilistic entity-relationship diagram: a correlation between functional connectivity and spontaneous brain activity during resting state in major depressive disorder. PLoS One 12.
8