Hyperactive frontolimbic and frontocentral resting-state gamma connectivity in major depressive disorder

Journal of Affective Disorders 257 (2019) 74–82

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Research paper

Hyperactive frontolimbic and frontocentral resting-state gamma connectivity in major depressive disorder ⁎⁎

Haiteng Jianga, Shui Tianb,c, Kun Bib,c, Qing Lub,c, , Zhijian Yaoa,d,

T

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a

Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China c Child Development and Learning Science, Key Laboratory of Ministry of Education, NanJing 210096, China d Medical College of Nanjing University, Nanjing 210093, China b

A B S T R A C T

Background: Major depressive disorder (MDD) is a system-level disorder affecting multiple functionally integrated cerebral networks. Nevertheless, their temporospatial organization and potential disturbance remain mostly unknown. The present report tested the hypothesis that deficient temporospatial network organization separates MDD and healthy controls (HC), and is linked to symptom severity of the disorder. Methods: Eyes-closed resting-state magnetoencephalographic (MEG) recordings were obtained from twenty-two MDD and twenty-two HC subjects. Beamforming source localization and functional connectivity analysis were applied to identify frequency-specific network interactions. Then, a novel virtual cortical resection approach was used to pinpoint putatively critical network controllers, accounting for aberrant cerebral connectivity patterns in MDD. Results: We found significantly elevated frontolimbic and frontocentral connectivity mediated by gamma (30–48 Hz) activity in MDD versus HC, and the right amygdala was the key differential network controller accounting for aberrant cerebral connectivity patterns in MDD. Furthermore, this frontolimbic and frontocentral gamma-band hyper-connectivity was positively correlated with depression severity. Limitations: The overall sample size was small, and we found significant effects in the deep limbic regions with resting-state MEG, the reliability of which was difficult to corroborate further. Conclusions: Overall, these findings support a notion that the right amygdala critically controls the exaggerated gamma-band frontolimbic and frontocentral connectivity in MDD during the resting-state condition, which potentially constitutes pre-established aberrant pathways during task processing and contributes to MDD pathology.

1. Introduction MDD is a prevalent condition associated with mood dysregulation, impaired cognition and increased suicide tendency (Mayberg, 2009). Accumulating neuroimaging studies have demonstrated disrupted large-scale network interactions in MDD (Fingelkurts and Fingelkurts, 2015; Lu et al., 2010; Mulders et al., 2015). Altered core brain networks such as default mode network (DMN), salience network (SN) and central executive network (CEN) are frequently reported in the depression literature (Hamilton et al., 2011; Menon, 2011; Seeley et al., 2007). The DMN, consisting of the medial prefrontal cortex, posterior cingulate cortex, and precuneus, is related to self-referential processing and emotion regulation (Hamilton et al., 2011). The CEN, including the lateral prefrontal cortex, posterior parietal cortex, frontal eye fields and part of the dorsomedial prefrontal cortex, is most active during cognitive tasks and is implicated in attention and working memory cognitive

functions (Seeley et al., 2007). The SN, which typically consists of the frontoinsular cortex, dorsal anterior cingulate cortex, amygdala, and temporal poles, is believed to reflect paralimbic emotional processing and plays a central role in emotional control through its extensive subcortical connectivity (Menon, 2011). However, it is unclear as to what extent these brain regions could in fact act as network “controllers” that potentially affect network topology and thereby relate to psychopathology. Brain networks are functionally coordinated by synchronized neuronal oscillations at multiple time scales (Buzsaki and Draguhn, 2004; Jiang et al., 2015b; Siegel et al., 2012). Many aberrant oscillatory patterns at different frequencies in MDD have been reported mostly with electroencephalography (EEG) (Alamian et al., 2017; Fingelkurts and Fingelkurts, 2015; Smart et al., 2015). Comparative analysis of resting-state neuroelectric and neuromagnetic recordings have consistently demonstrated an enhanced low frequency band (<30 Hz)

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Corresponding author at: Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, No. 264 Guangzhou Road, Nanjing 210029, China. ⁎⁎ Corresponding author at: School of Biological Sciences & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China. E-mail addresses: [email protected] (Q. Lu), [email protected] (Z. Yao). https://doi.org/10.1016/j.jad.2019.06.066 Received 28 November 2018; Received in revised form 20 May 2019; Accepted 30 June 2019 Available online 02 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

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power in MDD in comparison to HC (Brenner et al., 1986; Fingelkurts et al., 2006; Jiang et al., 2016; Lu et al., 2014, 2013b; Nieber and Schlegel, 1992; Pollock and Schneider, 1990). For example, MDD patients exhibited stronger theta and alpha coherence in long-distance connections between frontopolar, temporal and parietooccipital regions (Fingelkurts et al., 2007; Keeser et al., 2014; Leuchter et al., 2012). It has been suggested that the overall increase in spectral power and functional connectivity at rest might indicate MDD patients’ inefficiency in neural processing. Effective treatments of MDD patients using antidepressant drugs, deep brain stimulation, repetitive transcranial magnetic stimulation, and electroconvulsive therapy have demonstrated the capability to normalize the relevant aberrant network (McCabe and Mishor, 2011; Pathak et al., 2016; Perrin et al., 2012; Quraan et al., 2014). Although it is conceivable that different frequencies mediating network organization could account for reported network disturbances in MDD, the frequency-specific network control mechanism disruptions still await demonstration. Recent advances in the field of network theory provide novel insights into the topographic organization of complex networks (Bullmore and Sporns, 2012). The ‘virtual cortical resection’ (VCR) can pinpoint putative control regions based on a network's response to an artificial lesion. This technique has been successfully applied in studying the dynamic of structural connectivity (Alstott et al., 2009), probabilistic tractography (Rafal et al., 2015), functional connectivity in epilepsy seizure propagation (Khambhati et al., 2016), and could also be potentially utilized to identify key controllers of aberrant network organization in MDD versus HC. From a neurological perspective, MDD is considered a result of maladaptive functional interactions among highly integrated cortical-limbic network regions, which are responsible for maintaining steady emotional control (Mayberg, 2003; Seminowicz et al., 2004). Consistent with this view, McCabe et al. showed antidepressant medications reduced resting-state connectivity between the amygdala seed region and the prefrontal cortex in healthy volunteers (McCabe and Mishor, 2011). Electroconvulsive therapy also attenuated global functional connectivity that was mainly restricted to the dorsolateral prefrontal cortex in severe depressive disorder (Perrin et al., 2012). Therefore, it is speculated that compromised interactions between the amygdala and frontal cortex could potentially contribute to key symptoms of MDD (Murray et al., 2011), with the amygdala hypothetically representing a key network ‘controller’ hub. To identify the aberrant frequency-specific network controllers influencing psychopathology in MDD, we used eyes-closed resting-state MEG recordings in a cohort of MDD and HC participants. Compared to functional magnetic resonance imaging (fMRI) and EEG, MEG seems to be a promising but still mostly unexplored technique to reveal the neurophysiological mechanisms underlying MDD. In particular, a significant advantage of MEG is its capability to provide reliable sourcespace estimations of neuromagnetic long-range oscillatory coupling at various frequency bands (Alamian et al., 2017; Dinga et al., 2018; Xiang et al., 2014). In combination with beamforming source reconstruction technique (Gross et al., 2001) and an appropriate interaction measure that is robust against spurious interaction due to signal mixing (Nolte et al., 2004), the whole brain resting-state connectivity at each frequency band (theta: 4–8 Hz; alpha: 8–12 Hz; beta: 13–30 Hz; gamma: 30–48 Hz) was investigated at the source level. Furthermore, group differences of network connectivity strength and network topography were further evaluated with the hypotheses that (1) Aberrant frequency-specific large-scale network organization is present in MDD and relevant to depression severity. (2) Key control regions regulate the network dynamics differently between MDD and HC participants.

Table 1 Demographic and clinical characteristics information of participants.

Age (years) Gender (male/female) Educations (years) HAMD First episode Comorbidity with anxiety disorder

MDD (n = 22)

HC(n = 22)

33.3 ± 7.8 11/11 13.7 ± 1.6 26.4 ± 4.1 14 (63.4%) 9 (40.9%)

29.9 ± 7.1 12/10 14.7 ± 1.5

* There are no significant age and education differences between two groups at p = 0.05 level (Mann-Whitney U test). The ratio for the gender in both groups is not significantly different assessed by two-tailed chi-square test at p = 0.05 levels.

previously reported in Jiang et al. (2016). Briefly, twenty-two medication-free individuals diagnosed with major depressive disorder (MDD) and twenty-two age, gender, and education-level matched HC participants were recruited via advertisement. All MDD patients had been free of medications that could potentially affect neurological function for at least two weeks. Professional psychiatrists diagnosed MDD patients at the Nanjing Brain Hospital utilizing the Hamilton Depression Rating Scale (HAM-D) and the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV). The demographic and clinical information is illustrated in Table 1, and the study was approved by an Institutional Review Board of the Affiliated Brain Hospital of Nanjing Medical University. 2.2. Data acquisition Four minutes of eyes-closed resting-state MEG (CTF MEG 275 system) data with a 300 Hz sampling rate were collected. During the recording, participants were seated and instructed to relax with their eyes closed and not think about anything. Fiducial head coils were placed on the nasion and left/right periauricular points to localize head positions. To localize periauricular points precisely on the anatomical MRI, we used ear molds with a hole and inserted a small tube into the hole to attach the MEG localizer coil. For anatomical localization, structural T1 MRI images were acquired by a GE 1.5T system with a high-resolution, 3D gradient-echo pulse sequence. In the MRI scanner, we used the same ear molds but instead inserted a custom-made marker with a small drop of vitamin E into the hole. The position, thereby obtained with the MEG localizer coils, was reproduced as precisely as possible in the MRI given the movement that was allowed by the ear molds. During the MEG recording, participants were instructed to adjust their head position if the head movement exceeded 5 mm. There was no significant difference in head movement between two groups (MDD (4.3 ± 2.1 mm; HC: 3.9 ± 2.4 mm; t (42) = 0.8, p = 0.41). MEG coils and anatomical MRI were co-registered offline for later data analysis. 2.3. Data preprocessing Data were analyzed with the MATLAB-based FieldTrip toolbox in combination with custom written scripts (Oostenveld et al., 2011). A 50 Hz notch filter was applied to remove power line noise and the 4 min of continuous data were then segmented into 2 s non-overlapping epochs. Epochs with large jumps or variations were rejected by visual inspection. The number of remaining epochs were not significantly different (MDD: 111 ± 5.8; HC: 113.7 ± 4.9; t (42) = −1.7, p = 0.09). Independent component analysis (ICA) was then performed to deconstruct MEG signals into different components representing activity from the brain, or non-cortical physiological activity (e.g., muscle activity, eye blink, heartbeat) or even non-physiological activity (line noise and other environmental noise) (Jung et al., 2000). Artifact components not related to brain activity were removed based on their

2. Material and methods 2.1. Participants We did an independent analysis of the data set, which was 75

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temporal-spatial patterns. The number of removed ICA components was not significantly different between MDD and HC groups either (MDD: 2.77 ± 0.81; HC: 2.45 ± 0.86; t (42) = 1.26, p = 0.21).

Table 2 Abbreviations for 90 regions of interest (ROIs) used in this study. Regions

Abbreviations Left hemisphere

Right hemisphere

PreCG.L SFGdor.L ORBsup.L MFG.L ORBsup.L IFGoperc.L IFGtriang.L ORBinf.L ROL.L SMA.L OLF.L SFGmed.L ORBsupmed.L REC.L INS.L ACG.L MCG.L PCG.L HIP.L PHG.L AMYG.L CAL.L CUN.L LING.L SOG.L MOG.L IOG.L FFG.L PoCG.L SPG.L IPL.L

PreCG.R SFGdor.R ORBsup.R MFG.R ORBsup.R IFGoperc.R IFGtriang.R ORBinf.R ROL.R SMA.R OLF.R SFGmed.R ORBsupmed.R REC.R INS.R ACG.R MCG.R PCG.R HIP.R PHG.R AMYG.R CAL.R CUN.R LING.R SOG.R MOG.R IOG.R FFG.R PoCG.R SPG.R IPL.R

SMG.L ANG.L PCUN.L PCL.L CAU.L PUT.L PAL.L THA.L HES.L STG.L TPOsup.L MTG.L TPOmid.L ITG.L

SMG.R ANG.R PCUN.R PCL.R CAU.R PUT.R PAL.R THA.R HES.R STG.R TPOsup.R MTG.R TPOmid.R ITG.R

2.4. Source analysis Precentral gyrus Superior frontal gyrus, dorsolateral Superior frontal gyrus, orbital part Middle frontal gyrus Middle frontal gyrus, orbital part Inferior frontal gyrus, opercular part Inferior frontal gyrus, triangular part Inferior frontal gyrus, orbital part Rolandic operculum Supplementary motor area Olfactory cortex Superior frontal gyrus, medial part Superior frontal gyrus, medial orbital Gyrus rectus Insula Anterior cingulate and paracingulate gyri Median cingulate and paracingulate gyri Posterior cingulate gyrus Hippocampus Parahippocampal gyrus Amygdala Calcarine fissure and surrounding cortex Cuneus Lingual gyrus Superior occipital gyrus Middle occipital gyrus Inferior occipital gyrus Fusiform gyrus Postcentral gyrus Superior parietal gyrus Inferior parietal, but supramarginal and angular gyri Supramarginal gyrus Angular gyrus Precuneus Paracentral lobule Caudate nucleus Lenticular nucleus, putamen Lenticular nucleus, pallidum Thalamus Heschl gyrus Superior temporal gyrus Temporal pole: superior temporal gyrus Middle temporal gyrus Temporal pole: middle temporal gyrus Inferior temporal gyrus

Reconstruction of neural activity in the source space was performed using dynamic imaging of coherent sources (DICS) approach (Gross et al., 2001). A single-shell head model was constructed from the individual MRI (Nolte, 2003). Then, a regular 3D grid with 4 mm spacing was used as the source model and warped to the MNI152 template brain in the SPM8 toolbox (http://www.fil.ion.ucl.ac.uk/spm). Using this source model and sensor locations in relation to the individual head position within the MEG dewar, the leadfield was computed for every grid point, i.e., the location of the brain. To reduce the depth bias, we further normalized the leadfield by the sum of squares of the elements in the leadfield matrix (Fuchs et al., 1999). Next, the DICS spatial filter was estimated from the leadfield and cross-spectral density matrix for each brain location to maximize the activity of interest at that specific location, while suppressing the contribution of all other locations. To obtain the source level activity at each grid point, the DICS spatial filter was multiplied by the sensor level Fourier-transformed data. 2.5. Functional connectivity analysis All-to-all connectivity analysis at the source level was performed after dividing the whole brain volume into 90 regions of interest (ROI) based on the automated anatomical labeling (AAL) template atlas (Tzourio-Mazoyer et al., 2002). This atlas consists of all cortical and subcortical regions except the cerebellum (Table 2). The centroid of each ROI, defined by the minimum Euclidean distance to all other grid points inside the ROI, was selected as representative of that ROI to avoid size differences bias (Hillebrand et al., 2016b). After DICS beamforming source reconstruction, the imaginary part of coherency, which minimizes the effect of spurious connectivity arising from volume conduction (Nolte et al., 2004), was used to compute the functional connectivity between ROI at each frequency band (theta: 4–8 Hz; alpha: 8–12 Hz; beta: 13–30 Hz; gamma: 30–48 Hz). Together, the functional connectivity networks at each frequency band constitute (90 × 89/2=) 4005 pairs of connections 2.6. Network synchronizability Network topology was addressed by estimating network synchronizability (Barahona and Pecora, 2002). This method characterizes how easily neural activity can synchronize or diffuse over the network. Network synchronizability was calculated in three steps. First, a wholebrain functional connectivity adjacency matrix A (90 × 90) can be obtained by computing the imaginary part of coherency between 90 ROI. Next, the Laplacian matrix L of the raw functional connectivity adjacency matrix A was calculated as L = D-A, where D is the diagonal matrix of the node strength of A (D = diag (sum (A, 2))). Lastly, Laplacian matrix L was eigen decomposed (eig (L)), and network synchronizability s was calculated as the ratio between the second smallest eigenvalue λ2 and the biggest eigenvalue λmax: s = λ2/λmax. Intuitively, the larger network synchronizability indicates a lower gradient between eigenvalues and greater ease for the neural network to synchronize (Barahona and Pecora, 2002).

following resection of node k from the network, node k control centrality ck is defined as the percent change in network synchronizability: ck=(sk−s)/s, where s is the original network synchronizability, and sk is the network synchronizability after removing node k. The magnitude of ck represents the overall ability of node k to control the network synchronizability. If ck is positive, then node k is a desynchronizing node because network synchronizability increases after node k removal. Conversely, if ck is negative, then node k is a synchronizing node because network synchronizability decreases after node k removal.

2.8. Statistical analysis The network-based statistic (NBS) (Zalesky et al., 2010) was conducted to test the functional connectivity network statistical significance while controlling for multiple comparison corrections. When comparing the functional connectivity network strength difference between two groups, an independent two-sample t-test was performed on every connectivity value. Connections exceeding the predefined threshold (e.g., p < 0.05) constituted a set of suprathreshold links. Then, connecting graph components formed topological clusters, and the number of connections was defined as a cluster score. The

2.7. Virtual cortical resection To evaluate the controllability of brain regions in the functional network organization, we applied a VCR technique (Khambhati et al., 2016). In brief, VCR can investigate how much the network topology might change by taking out one or more brain areas (i.e., nodes in the network), which is quantified by control centrality. Mathematically, 76

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area and left precuneus (p < 0.05, uncorrected). Most of these control regions were localized in limbic and frontocentral brain areas. However, only the right amygdala remained significant after false discovery rate correction (Benjamini and Yekutieli, 2001). To look into the control centrality of the right amygdala specifically, we virtually resected the right amygdala, which decreased network synchronization in MDD (control centrality: −0.0038 ± 0.0016), and increased network synchronization in HC (control centrality: 0.0040 ± 0.0019). Thus, the right amygdala was a synchronizing node in MDD while it was a desynchronizing node in HC, revealing its crucial network control difference in the MDD pathology.

maximum number of connections across clusters was used as the test statistic. By randomizing the data across groups and recalculating the test statistic 5000 times, a reference distribution of maximum cluster values was obtained to evaluate the statistic of the observed cluster scores. Observed cluster scores higher than the 97.5th percentile or lower than 2.5th percentile were considered significant at p < 0.05 level. Statistical evaluation of the correlation between functional connectivity strength and depression severity followed a similar procedure. The differences were as follows: we used Spearman correlation as the statistical metric instead and shuffled HAMD values across subjects during each permutation. These statistics were performed with the open source NBS toolbox (http://www.nitrc.org/projects/nbs), and brain network visualization was done by Circlize package in R (Gu et al., 2014) and BrainNet Viewer toolbox (Xia et al., 2013). Moreover, when comparing significant VCR difference between two groups of 90 ROIs with two sample independent t-test, false discovery rate correction was used to control the multiple comparisons (Benjamini and Yekutieli, 2001).

3.3. Frontocentral and frontolimbic gamma-band connectivity positively correlates with depression severity After noting the increased resting-state gamma-band connectivity in MDD versus HC, we next asked whether the degree of increased brain network connectivity in the gamma band covaried with depression severity assessed by HAMD. As shown in Fig. 3, NBS statistic demonstrated a significant positive correlation between the frontocentral and frontolimbic gamma-band connectivity and HAMD. This network consisted of 44 connections and involved 39 regions, including precentral gyrus, left superior frontal gyrus (medial part), bilateral anterior cingulate and paracingulate gyri, right postcentral gyrus (Fig. 3A). To demonstrate the most engaged regions, the top 13 most connected regions within the significant network (degrees>=3) were shown in Fig. 3B. Note that the correlation displayed in Fig. 3C was obtained by correlating averaged network connectivity strength in Fig. 3A and HAMD (r = 0.91, p < 10−8). In summary, the stronger the frontocentral and frontolimbic resting-state gamma-band connectivity, the more depression severity in MDD.

3. Results 3.1. Exaggerated frontolimbic and frontocentral gamma-band connectivity in MDD First, we examined the functional connectivity network strength differences at each frequency between MDD and HC. After DICS beamforming source reconstruction, functional connectivity at each frequency band was assessed by the imaginary part of coherency (Nolte et al., 2004) and a frequency-specific whole-brain functional connectivity network was constructed. When comparing MDD to HC, NBS revealed significantly increased frontolimbic and frontocentral connectivity in the gamma frequency range (30–48 Hz) (Fig. 1A and C). This gamma-band network consisted of 53 regions and 69 connections, mainly involving the left gyrus rectus, left fusiform gyrus, right rolandic operculum, right amygdala, right superior frontal gyrus (medial part), right superior frontal gyrus (medial orbital), right anterior cingulate and paracingulate gyri (Fig. 1B). Of note, no significant differences were identified in the other three frequency bands (4–8 Hz theta, 8–12 Hz alpha, and 13–30 Hz beta). Therefore, the disrupted hyperactive frontolimbic and frontocentral circuit interactions in MDD might be specific in the gamma band. In the following analysis, we thus focused on the gamma band.

4. Discussion The present study investigated MEG frequency-specific whole-brain functional connectivity network and the critical network controller in MDD versus HC during the resting state. In exploring source level connectivity, we observed hyperactive frontolimbic and frontocentral gamma-band interactions in MDD compared to HC. Then, VCR demonstrated different network synchronization regulations in the right amygdala between MDD and HC, revealing differentiated network control mechanisms. Additionally, frontocentral and frontolimbic gamma-band connectivity was positively correlated with symptom severity as measured by HAMD. Overall, the present findings showed compromised temporospatial organization of the frontolimbic and frontocentral system in MDD, which was also related to depression severity. In our previous study with the same participants, we reported less frontal theta and posterior alpha power, whereas we found more frontal beta power in MDD patients compared to HC at rest (Jiang et al., 2016). Here, we only found significantly increased frontolimbic and frontocentral connectivity in the gamma band (30–48 Hz) but not in other frequency bands. Gamma-band oscillations are suggested to be involved in both local and large-scale neuronal synchronization underlying a broad range of perceptual and higher-order cognitive functions, such as those typically impaired in MDD (Engel et al., 2001). Importantly, it should be pointed out that the gamma-band connectivity difference is not due to the power difference since gamma power was not significantly different between two groups as demonstrated in our previous study (Jiang et al., 2016). Therefore, this suggests that modulation of power and functional connectivity reflect two different perspectives. Puzzlingly, most resting-state EEG studies found altered functional connectivity in the delta, theta, alpha, and beta band but not in the gamma band. The disparities between previously reported EEG findings and our MEG results might be attributed to EEG/MEG modality differences. In an MEG resting-state connectivity study, Nugent et al.

3.2. Virtual cortical resection reveals right amygdala as a critical network controller Next, we addressed network topology group differences between MDD and HC. Reflecting the topology of a given graph, network synchronizability was introduced to measure the ability of neural populations to synchronize within the network. We observed no differences in network synchronizability between MDD and HC in the gamma band (MDD: 0.30 ± 0.07; HC: 0.31 ± 0.06; t (42) = −0.6, p = 0.55), which suggested similar network homogeneity or heterogeneity in these two groups. Despite no group differences in network synchronizability, network synchronizability can nevertheless be sensitive to specific network connection patterns between nodes (network geometry). It was still unclear how network geometry regulated network synchronizability, and whether each network node (brain region) contributed to network synchronizability differently between two groups. The VCR was applied to address this question. By virtually removing the node from the network, the importance of the node (control centrality) can be determined by computing network synchronization with or without its presence. Fig. 2 summarizes the VCR results over 90 ROIs. The group differences of control centrality were found in several brain regions, containing the right amygdala, bilateral median cingulate and paracingulate gyri, bilateral paracentral lobule, left supplementary motor 77

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Fig. 1. Gamma-band functional connectivity network differences when comparing MDD to HC at rest. (A) Significant differential network topographies identified by NBS at the p = 0.05 level. A link represents a significantly different connection. (B) Top 11 most connected brain regions within the identified network in A (degrees≥4). The size of each node represents the number of connections within the network (degrees). (C) Grand average of gamma functional connectivity strengths within the identified network in A across subjects for MDD and HC respectively. Error bar represents standard deviation.

MDD, the frontolimbic hyperconnectivity in the gamma band might be considered an overcompensation process that induces mental agitation and sustains negative moods. Moreover, MDD patients characterized by dysregulation in orbital frontal regions could have deficits in cognitive evaluation and reappraisal, leading to inefficiency in experiencing positive feedback (Murray et al., 2011). Consequently, excessive interactions between the amygdala and frontal regions in MDD might be linked to deficits in appraisal, implicating a compromised ability to use positive feedback. Although network synchronizability between MDD and HC was similar, different brain regions didn't contribute equally. To pinpoint the key network controllers accounting for the network regulation difference, VCR was used. VCR revealed significantly different control mechanisms in the right amygdala. Exaggerated amygdala activations are consistently reported when subjects are exposed to negative emotional stimuli in MDD (Fu et al., 2004; Siegle et al., 2002; Surguladze et al., 2005). Similar patterns were observed in unmedicated, remitted MDD patients (Neumeister et al., 2006; Victor et al., 2010), suggesting a somewhat trait-like abnormality. Based on VCR results, the right amygdala was a synchronizing controller in MDD, whereas it was a desynchronizing controller in HC. Framed as a push-pull control mechanism, synchronizing controllers pull the network towards a homogenous state, whereas desynchronizing controllers push the network

found an altered network, especially between the amygdala and cortical nodes in the beta band (14–30 Hz) in MDD as compared to HC (Nugent et al., 2015). In the present report, no significant group differences in the beta network were found, which was probably due to methodological differences. Nugent et al. applied ICA on the power envelope of band-pass filtered signals to derive the significant network difference. In contrast, we extracted a connectivity network on the original band-pass filtered signal and conducted statistical analysis via NBS. Notably, the exaggerated frontolimbic gamma-band connectivity pattern during the resting-state was also observed in the MEG task data. Lu et al. showed enhanced gamma-band connectivity between the anterior cingulate cortex and amygdala in MDD during an emotion recognition task using MEG (Lu et al., 2013c). Therefore, it is conceivable that increased frontolimbic gamma-band interactions in MDD might be a common feature of compromised connectivity in MDD during both rest and task conditions. The identified exaggeration of the gamma-band mediated cerebral network in MDD is mainly confined to the frontocentral and frontolimbic circuits, which overlaps with the most frequently reported DMN, CEN and SN dysregulations in MDD. It has been demonstrated that DMN reflects a representation of negative, self-referential information in depression (Hamilton et al., 2011), and anxiety ratings correlate with the intrinsic functional connectivity of the SN (Seeley et al., 2007). In 78

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Fig. 2. Virtual cortical resection. The effect of artificially removing a brain region on network synchronizability is quantified by control centrality. Control centrality was computed over 90 ROI regions individually. Highlighted dashed rectangular regions are uncorrected significant different regions between MDD and HC at p = 0.05 level including the right amygdala, bilateral median cingulate and paracingulate gyri, bilateral paracentral lobule, left supplementary motor area and left precuneus. Notably, only right amygdala survived after multiple comparison correction. Error bar represents standard error. * p < 0.05, ** p < 0.01, ***p < 0.005.

effects. Improving the reliability of MEG resting-state connectivity estimation would be valuable in the future (Colclough et al., 2016). Finally, we only focused on within-frequency functional connectivity networks here, whereas different neuronal oscillations interact with each other (Jensen and Colgin, 2007; Jiang et al., 2015a; Park et al., 2016). Future studies on cross-frequency coupling would be of great relevance in understanding the pathophysiology of MDD further (Chattun et al., 2018; Smart et al., 2015).

away from this state (Khambhati et al., 2016). In line with this view, the present findings suggest that the right amygdala pulls the gamma-band meditated network towards homogeneity, potentially accounting for dysregulated neural inhibition and excitation balance in the MDD's frontolimbic circuit (Lener et al., 2017). 4.1. Limitations There are also a few limitations in our study. First, the overall subject sample size was relatively small. Although we found an extremely high correlation between frontocentral and frontolimbic gamma-band connectivity and depression severity, interpretation should be cautious. Further study is required to test whether our current findings could be replicated in a large cohort. Second, 40.9% of MDD patients in our patient population had a comorbid anxiety disorder. It is still unclear whether the identified effects are due to depression or anxiety. Third, we investigated MEG resting-state whole-brain functional connectivity networks and found significant effects in the deep limbic regions, whereas the feasibility of MEG to detect deep brain source activity is still under debate. However, it should be pointed out that accumulating evidences have shown the possibility under different tasks (Backus et al., 2016; Krishnaswamy et al., 2017; Lu et al., 2013a, 2013c; Recasens et al., 2018), resting state (Bi et al., 2016; Nugent et al., 2015, 2016) and epileptic state (Hall et al., 2018; Hillebrand et al., 2016a). A recent review by Pu et al. also systematically discussed simulation studies and empirical evidence of the limitations and capacities of MEG “deep source imaging” in the human hippocampus (Pu et al., 2018). Although it is incredibly challenging to validate the reliability of MEG deep source signal during the resting state, we considered our study an exploratory investigation which might bring out new insights regarding the pathophysiology of MDD. Additionally, this was the first VCR application in MEG data, and the right amygdala was identified as the critical differential control region. Further validation with fMRI resting-state connectivity in a larger dataset could provide substantial support. Moreover, caution should be had when constructing the frequency-specific functional connectivity network at the source level. Even though DICS beamforming was applied to solve the inverse problem, the field spread still existed at the source level (Bastos and Schoffelen, 2015; Palva et al., 2018). Although we applied the imaginary part of coherence to minimize the effect of spurious connectivity due to field spread, the measure does not eliminate such

Author statement The work described has not been published previously or under consideration for publication elsewhere. The publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out. If accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright holder.

Role of funding source This study was supported by grants of the National Natural Science Foundation of China (81701784, 81871066, 81571639), Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology, and Education (CXTDC2016004) and Jiangsu Provincial key research and development program (BE2018609).

Conflict of interest All authors have no conflict of interest to declare.

CRediT authorship contribution statement Haiteng Jiang: Conceptualization, Investigation, Writing - original draft. Shui Tian: Investigation. Kun Bi: Investigation. Qing Lu: Conceptualization, Investigation, Supervision, Writing - original draft. Zhijian Yao: Conceptualization, Investigation, Supervision, Writing original draft. 79

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Fig. 3. Correlation between the resting-state gamma connectivity network in MDD and HAMD. (A) Significant network topographies in the gamma band correlated with HAMD. (B) Top 13 most connected regions within the significant network in A (degrees≥3). Each node represents a single brain region with the size indicating the number of connections within the network (degrees). (C) Correlation plot between mean network connectivity strength in A and HAMD (r = 0.91, p < 10−8).

Acknowledgments

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