Functional connectivity from the anterior cingulate cortex in major depressive disorder

Functional  connectivity  from  the  anterior  

cingulate  cortex  in  major  depressive  

disorder  

Abstract

Background: While one of the main characteristics of major depressive disorder (MDD) is a

negative processing bias, the underlying networks remain poorly understood. The primary goal of this study was to investigate functional connectivity (FC) patterns of the ventral (affective) and dorsal (cognitive control) network of emotion processing, from the seed regions subgenual anterior cingulate cortex (sgACC) and the pregenual anterior cingulate cortex (pgACC) respectively. Revealing the FC patterns from these two regions could gain more insight in underlying networks causing the negative bias in MDD. Methods: Twenty individuals with MDD and twenty healthy controls performed a passive viewing faces task with neutral, positive and negative emotional faces. During the task functional scans with a 3T scanner were conducted. Results: Compared to controls, MDD patients showed increased FC between the sgACC and several areas located in the ventral emotion-processing network (thalamus, amygdala, (para)hippocampus, caudate nucleus and orbital frontal cortex). Furthermore, controls showed increased FC between the pgACC and the hippocampus compare to MDD patients. Conclusion: This study suggests a primary source of dysregulation in the ventral emotion-processing network in MDD.

Introduction

Major depressive disorder (MDD) is a widely disabling mood disorder with a life time prevalence of about 16% in the United States (Kessler et al., 2003). One of the main characteristics of MDD is a negative bias for emotion

processing (Leppänen, 2006). This bias causes problems with facial recognition and emotion discrimination in MDD patients (Gur et al., 1992; Mikhailova, Vladimirova, Iznak, Tsusulkovskaya, & Sushko, 1996; Rubinow, 1992) . In healthy individuals the neural basis of emotion processing has been defined in two major networks (Adolphs, 2002; Phillips, 2003a, 2003b). A ventral network has been implicated for the identification of emotional stimuli and the production of affective states, including limbic structures as amygdala, insula, ventral striatum, ventral anterior cingulate cortex, and orbitofrontal cortex (OFC). A dorsal network has been implicated in the regulation of emotion, reflecting a form of cognitive control. This network includes the dorsal anterior cingulate cortex and more dorsal prefrontal cortex areas. Deficits in or between these networks may cause problems in emotion processing, like the negative bias in MDD patients. Therefore, investigating the functioning of these two networks may gain insight in the pathology of MDD.

One recently developed method to investigate brain network alterations is by looking at functional connectivity (FC). With the use of a seed-region FC analysis, the degree to which a given region of interest (ROI) co-activates with other regions, can be calculated. If the blood oxygen level dependent (BOLD) signal flucutations of two brain regions are similary modulated by a specific task, then they are likely to be functionally connected (Sun, Miller, & D’Esposito, 2004; van den Heuvel & Hulshoff Pol, 2010 ; Cordes et al., 2000).

Despite the fact that many researchers suggest that depression may arise from abnormal interactions between brain regions, only a few studies compared FC circuits in MDD (Anand et al., 2005; Anand, Li, Wang, Gardner, & Lowe, 2007; Chen et al., 2008; Hamilton & Gotlib, 2008; Matthews, Strigo, Simmons,

Yang, & Paulus, 2008). Between these few studies there is a great variety in methodology, which makes it difficult to draw solid conclusions with respect to FC differences in emotion-processing networks in MDD. Some studies used a task-paradigm (Anand et al., 2005, 2007; Chen et al., 2008; Hamilton & Gotlib, 2008; Matthews et al., 2008) while others used a resting-state paradigm (Greicius et al., 2007). And in some studies the amygdala was chosen as a region of interest (Chen et al., 2008; Hamilton & Gotlib, 2008; Matthews et al., 2008), while in others the ACC (Anand et al., 2005, 2007). Because of this widespread attention to different areas involved in emotion processing and the limited number of studies, FC results are inconclusive and more research is needed.

One key brain region, connected to both the dorsal as well as the ventral network is the ACC. The subgenual anterior cingulate cortex (sgACC) is a subregion of the ACC that has been a region of interest in many neuroimaging studies in MDD. Increased activation of the sgACC has been found in MDD patients repeatedly (Bush, Luu, & Posner, 2000; Margulies et al., 2007; Mohanty et al., 2007). Furthermore, the sgACC has been indicated to predict clinical treatment outcome (W.C. Drevets et al., 1997; Wayne C Drevets, Price, & Furey, 2008; Gotlib et al., 2005; H S Mayberg et al., 1999), and activity of the sgACC was reversed following successful treatment (Delaveau et al., 2011; Mayberg et al., 2000; Mayberg, 2009). Based on this key role of the sgACC in MDD and since this region is anatomically strong connected with regions of the ventral emotion processing network, such as the nucleus accumbens, amygdala, hypothalamus and OFC (Johansen-Berg et al., 2008), it was chosen as a region-of-interest (ROI) in our study.

The pregenual anterior cingulate cortex (pgACC) is the second brain area that served as a ROI in this study. The function of the pgACC in MDD is less clear, since studies investigating pgACC activity are inconsistent. The pgACC has been indicated to be involved in the regulation of emotions (Drevets, 1998). Furthermore, increased resting state glucose metabolism in the pgACC was

found before treatment in MDD patients (Kennedy et al., 2001), and pgACC activity predicted treatment response (Mayberg, 1997). The pgACC is

anatomically strong connected to the medial prefrontal cortex and dorsal ACC regions (Johansen-Berg et al., 2008), which suggests that the pgACC may has a role in cognitive control.

Because the sgACC is thought to be located in the ventral, and the

pgACC in the dorsal network, investigating FC patterns from these two subareas of the ACC will provide insight in the FC patterns within and between the dorsal and ventral network. More specifically, we hypothesize that the negative bias during emotion processing in MDD patients is be related to an increased FC within the ventral network and a decreased FC between the dorsal and ventral network. Our questions are: 1) is there an increased FC from the sgACC to other areas of the ventral network during emotion processing in MDD? 2) Is there a decreased FC between the pgACC and other areas of the ventral network during emotion processing in MDD?

Methods

Participants

Two groups, MDD and healhty controls, were included, each consisting of 20 participants. The patients were recruited at the psychiatry department of the AMC. The inclusion criteria for MDD patients were a MDD diagnosis with the Structured Clinical Interview from DSM-IV Axis 1 disorders (SCID) and a minimum score of 15 on the Hamilton Depression Rating Scale (HDRS). Patients were medication free or used selective serotonin reuptake inhibitors/ selective noradrenaline reuptake inhibitors only. The inclusion criterion for the controls was no current or past psychiatric disorder.

Exclusion criteria for both groups were: a past or present substance dependence/abuse, except cigarettes, the use of stimulants (tobacco/alcohol/coffee/tea or chocolate) 24 hours before the scanning procedure,

a history of neurological diseases that affects the CNS, left-handedness and standard MRI scan exclusion criteria (e.g. pregnancy, pacemaker and metals contraindicated for MRI). After screening, all participants signed a written informed consent for participation in the study. The study was approved by the intstitutional ethical committee.

Experimental Design

The face task used in this study was a passive viewing face task with a blocked design. The stimuli were black-and white pictures obtained from the NIMSTIN data base (www.macbrain.org). Three types of negative faces (fearful, angry, sad), neutral and happy faces were included. Additionally, blurred faces not showing emotions, and a fixation cross were included. Each block consisted of 4 faces displayed for 4 seconds. For every emotion 6 blocks were included.

The subjects were instructed to evaluate the gender of the face by pressing a left or right button. This method is to make sure the subjects keep paying attention to task and proved to be essential because emotional processing requires attention (Pessoa, Kastner, & Ungerleider, 2002). In the blurred faces condition the subjects had to evaluate the direction of the presented arrow, because no face can be detected in this condition. No feedback was provided. To familiarize participants, the task was explained outside the scanner.

Functional MRI Data Acquisition

Imaging data were acquired on a 3.0 Tesla MRI scanner (Philips Intera, Philips Medical Systems, Best, the Netherlands) with body coil excitation and 8-channel SENSE head coil. The head was held in place with a headphone and padding in between the head coil. Both a functional scan and structural scan were acquired. For the structural scan a T1-weighted structural image was acquired for anatomical registration purposes. For the functional scan the following parameters were used: echo time 25 ms, repetition time 2300 ms, flip angle 80°, matrix 96x95, number of slices 40, slice gap 0, slice thickness 3mm, slice order;

ascending, field of view 220x220x120, voxel size 2.29/2.29/3 mm, scan duration 2.3 seconds per 40-slice volume. In total 314 dynamics were acquired with a total duration of 12 minutes and 11 seconds.

Data Analysis

For all fMRI data-analyses, SPM8 (Statistical Parametric Mapping; Wellcome Department of Cognitive Neurology, London, UK; (M Phillips, 2003a), operating under Matlab version 7.8.0.347 (R2009a; the Mathworks, Natick, Massachusetts, USA) was used. All the data analyses were performed anonymously.

Standard preprocessing of scans consisted of correcting for slice-timing differences and head movement by realignment, coregistration to the structural scan, segmentation, and normalisation to MNI standard space and smoothing (8 mm FWHM Gaussian Kernel). Statistical parametric maps were calculated based on a voxel-by-voxel method, using a general linear model (Friston, Frith, Liddle, & Frackowiak, 1993).

To test our hypothesis, two a priori defined region of interest (ROI), the subgenual anterior cingulate cortex (sgACC) and the pregenual anterior cingulate cortex (pgACC), were used. In order to get a reliable location for the ROIs previous activation studies in MDD were reviewed. For the sgACC, a 6mm sphere was drawn around a coordinate [1, 32, -6], which showed a high co-activation with the amygdala, during an emotional face matching task (Matthews et al., 2008). For the location of the pgACC, the coordinate [5, 45, 9] was choosen, which was positively correlated with response to anti-depressant treatment in the study of Salvadore and colleagues (2009). Both ROIs are displayed in figure 1.

For each subject the mean times-series of the ROIs were extracted with the use of the Marsbar-Toolbox (Brett, Anton, Valabregue, & Poline, 2002). These mean voxel time-series were included as a regressor in the general linear

model (GLM), next to realignment parameters to control for movement artefacts. To correct for medication use, the antidepressant treatment history form (ATHF) described by Sackeim (2001) was used. The medication was scored by type and duration individually, and these medication loads were put in the analysis as a covariate.

All first level images per subject were then analyzed to investigate the group-related correlation effects between the ROI and all other voxels in second level analyses. Effects were identified at FWE (Family wise error) p <. 05 and small volume corrected.

Figure 1: ROI placement. The red circle: sgACC; Montreal Neurological Institute (MNI) coördinates x=1, y=32, z =-6. The green circle: pgACC; MNI coördinates x=5, y= 45, z= 9.

Results

Twenty MDD patients and twenty healthy controls were included in this study and the results presented are for all 40 participants. Subject characteristics are detailed in table 1. MDD patients had significantly higher depression (p <0.001) and anxiety scores (p <0.001) than controls. No significant differences between MDD patients and healthy controls on age (p = 0.855) and IQ (p = 0.588) were found.

Table 1. Demographic and clinical characteristics of major depressive disorder patients and healthy controls. Characteristic MDD patients (N = 20) Healthy Controls (N=20) Statistics

Age (mean years ± SD) 45.0 (10.2) 40.9 (10.5) t(37)=1.22, p= .23 Gender (male/female) 6/14 7/13 X^(2)=.46, p= .80

Medication (yes/no) 10/10 0/20

HDRS score (mean ± SD) a 18.9 (3.88) 0.89 (0.99) _{t(37)= 19.47, p>.001*}

IQ-score (mean ± SD) 106.35 (6.88) 111.70 (7.05) t(35)= -2.33, p= .14 Anxiety score (mean ± SD) a 13.25 (4.99) 0.63 (0.83) t(37)= 10.87, p> .001*

a _{= measured with the Beck Depression Inventory – II}

sgACC ROI correlation

A full list of functionally connected areas with the sgACC and their correspondeing p-values is displayed in tabele 2. MDD patients showed increased FC between the sgACC and limbic structures as the amygdala, caudate nucleus, hippocampus, parahippocampus, and thalamus relative to healthy controls. Second, MDD patients showed increased FC between the sgACC and the OFC, relative to healthy controls. Third, MDD patients showed increased FC between the sgACC and several occipital, temporal and parietal cortices in MDD patients, relative to healhty controls. There was one cluster close to the ROI itself that showed significantly greater FC in healthy controls compared to MDD patients.

Table 2. ROI correlation from the sgACC

Area BA MNI coordinates z-score p-value

(FWE corrected at p<o.o5) X Y Z MDD > healthy controls Amygdala 26 0 -26 3.62 0.008* Angular gyrus 39 42 -72 38 3.83 0.018 Anterior cingulate cortex -10 28 -10 5.28 0.000* Caudate nucleus 25 -6 10 -6 4.45 0.001* Fusiform gyrus 20 38 -28 -22 4.49 0.002* Hippocampus 26 -8 -26 3.84 0.010

Inferior parietal gyrus 40 58 -48 46 4.42 0.002*

Inferior temporal gyrus 20 50 -44 -22 4.55 0.001*

Middle frontal gyrus 8 46 24 46 4.11 0.018

Occipital gyrus 18 38 -84 -14 4.18 0.002*

Olfactory gyrus 32 -6 24 -10 6.06 0.000*

Orbital frontal cortex 11 -34 40 -18 4.57 0.000*

Parahippocampus 28 34 -28 -18 3.99 0.005*

Superior temporal pole 38 -54 16 -14 3.55 0.025

Thalamus -18 -28 10 3.53 0.038

Healthy controls > MDD Anterior cingulate

cortex 2 32 -2 4.09 0.033

Scores are small volume corrected with a p FWE < 0.05; highest peak value per region is presented.

* = p-value significant at FWE < 0.01

Abbreviations: BA, Brodmann’s area; MNI, Montreal Neurological Institute

pgACC ROI correlation

The results of the pgACC analysis are shown in table 3. MDD patients showed no significant increased FC from the pgACC with other regions, compared to healthy controls. However, there was a small increased FC between the pgACC and the hippocampus in healhty controls compared to MDD patients (table 2).

Table 3. ROI correlation from the pgACC

Area BA MNI coordinates z-score p-value

(FWE corrected, at p<0.05) X Y Z MDD > Healhty controls No significant clusters

Healhty controls > MDD

Hippocampus 38 -16 -14 3.76 0.020

Scores are small volume corrected with a p FWE < 0.05; highest peak value per region is presented.

* = p-value significant at FWE < 0.01

Abbreviations: BA, Brodmann’s area; MNI, Montreal Neurological Institute

Discussion

The aim of this study was to investigate the FC patterns in MDD patients within and between the two networks that are thought to be involved in emotion processing. As predicted, the MDD patients showed increased FC between sgACC and a wide-distributed ventral emotion processing network including OFC, amygdala, (para)hippocampus, nucleus caudatus and thalamus, during exposure to emotional faces relative to healthy controls. This is in line with previous studies showing that MDD patients have increased FC between the sgACC and amygdala and thalamus during resting-state and emotional face-matching task respectively (Greicius et al., 2007; Matthews et al., 2008).

Furthermore, a decreased FC was found between to pgACC and the hippocampus in MDD patients. As a core region in the limbic system, the hippocampus is involved in regulation of motivation and emotion (Phillips, 2003b; Seminowicz et al., 2004). However, while functional impairment of the hippocampus in MDD was already seen in fMRI studies (Milne, MacQueen, & Hall, 2012) , negative FC of the hippocampus has, to our knowledge, never been reported, making it difficult to compare this with the findings of others. Thus, the role of the hippocampus in the neural networks of emotion processing needs to be clarified in the future.

Last, while a decreased FC between the pgACC and areas of the ventral network work predicted, there were no significant differences between MDD patients and controls. A possible explanation for this null finding is that the passive viewing face task was insufficient to induce cognitive control.

This is the first study to find increased FC between a wide-distributed network, from the sgACC to several areas of the ventral network, during emotion processing. This suggests that dysfunctional emotion processing in MDD may be mainly due to an enhanced ventral network connectivity. Furthermore, this study adds to the converging lines that the sgACC is an important area in MDD. Because no FC differences have been found between the dorsal and ventral network, it cannot be concluded that cognitive control is reduced in MDD.

Several limitations of this study need to be considered. First, the paradigm used has some limitations. 1) No behavioral data were obtained (e.g. rating the emotional expression of the pictures) making it speculative if the results are due to the negative emotion processing bias or not. 2) Because the task was a passive viewing face task, instead of an emotion regulation task, the lack of results from the pgACC FC analysis may be explained by the needlessness to recruit cognitive control areas. Future studies should combine a face task with some emotional distracters during some form of cognitive performance, as used by Fales and colleagues (2008). This way the ventral as well as the dorsal network of emotion processing will be recruited.

Second, the seed-based correlation method that was used could only showed FC measurements, areas that showed temporal dependencies with the ROIs. This way, no conclusions can be drawn with respect to causility. A potential refinement of this study would be to include newer techniques, such as effective connectivity methods like Dynamic Causal Modelling (K.J. Friston, Harrison, & Penny, 2003) or a granger causality analysis (Sato et al., 2010).

Last, medication load was considered in our analysis as a covariate, however it is conceivable that network FC differences can be (partly) driven by the effect of psychotropic medication. Previous research showed that successful antidepressant medication treatment is related to reverse brain abnormalities like increased sgACC metabolism (Mayberg et al., 2000). Thus FC networks may also

be influenced by medication exposure. How FC networks are influenced by antidepressant treatment is therefore a valuable topic for future research.

Keeping the above caveats in mind, the results of this study were robust by showing significant increased FC patterns within many areas of the ventral network (amygdala, caudate nucleus, hippocampus, thalamus) during emotion processing in MDD. This suggest that the dysfunctional emotional processing bias in MDD may mainly be the result of an inappropriate engagement of the ventral network, making the ventral network one of the key factors underlying the pathophysiology of major depression.

References  

Adolphs, R. (2002). Neural systems for recognizing emotion. Current opinion in

neurobiology, 12(2), 169-77.

Anand, A., Li, Y., Wang, Y., Gardner, K., & Lowe, M. J. (2007). Reciprocal effects of antidepressant treatment and connectivity of the mood regulating circuit: an fMRI study. Journal Of Neuropsychiatry and Clinical Neuroscience, 19, 274-282.

Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., Mathews, V. P., et al. (2005). Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biological psychiatry, 57(10), 1079-88. doi:10.1016/j.biopsych.2005.02.021

Brett, M., Anton, J. L., Valabregue, R., & Poline, J. B. (2002). Region of interest analysis using an SPM toolbox. Neuroimage, 16(2), 9. Academic Press. Retrieved from http://www.mendeley.com/research/region-of-interest-based-analysis-of-functional-imaging-data/

Bush, G., Luu, P., & Posner, M. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in cognitive sciences, 4(6), 215-222. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10827444

Chen, C.-H., Suckling, J., Ooi, C., Fu, C. H. Y., Williams, S. C. R., Walsh, N. D., Mitterschiffthaler, M. T., et al. (2008). Functional coupling of the amygdala in depressed patients treated with antidepressant medication.

Neuropsychopharmacology  : official publication of the American College of Neuropsychopharmacology, 33(8), 1909-18. doi:10.1038/sj.npp.1301593

Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. a, Moritz, C. H., Quigley, M. a, et al. (2000). Mapping functionally related regions of brain with

functional connectivity MR imaging. AJNR. American journal of

neuroradiology, 21(9), 1636-44. Retrieved from

http://www.ncbi.nlm.nih.gov/pubmed/11039342

Delaveau, P., Jabourian, M., Lemogne, C., Guionnet, S., Bergouignan, L., & Fossati, P. (2011). Brain effects of antidepressants in major depression: a meta-analysis of emotional processing studies. Journal of affective disorders, 130(1-2), 66-74. doi:10.1016/j.jad.2010.09.032

Drevets, W C. (1998). Functional neuroimaging studies of depression: the anatomy of melancholia. Annual review of medicine, 49, 341-61.

doi:10.1146/annurev.med.49.1.341

Drevets, W.C., Price, J. L., Simpson, J. R., Todd, R. D., Reich, T., Vannier, M., & Raichle, M. E. (1997). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386(6627), 824–827. Retrieved from

http://dionysus.psych.wisc.edu/lit/articles/DrevetsW1997a.pdf

Drevets, Wayne C, Price, J. L., & Furey, M. L. (2008). Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain structure & function, 213(1-2), 93-118. doi:10.1007/s00429-008-0189-x

Fales, C. L., Barch, D. M., Rundle, M. M., Mintun, M. a, Snyder, A. Z., Cohen, J. D., Mathews, J., et al. (2008). Altered emotional interference processing in affective and cognitive-control brain circuitry in major depression. Biological psychiatry,

63(4), 377-84. doi:10.1016/j.biopsych.2007.06.012

Gotlib, I. H., Sivers, H., Gabrieli, J. D. E., Whitfield-Gabrieli, S., Goldin, P., Minor, K. L., & Canli, T. (2005). Subgenual anterior cingulate activation to valenced emotional stimuli in major depression. Neuroreport, 16(16), 1731-4. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/16237317

Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., Reiss, A. L., et al. (2007). Resting-State Functional Connectivity in Major Depression  : Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus. Biological Psychiatry, 62, 429-437.

doi:10.1016/j.biopsych.2006.09.020

Gur, R. C., Erwin, R. J., Gur, R. E., Zwil, A. S., Heimberg, C., & Kraemer, H. C. (1992). Facial Emotion Depression Discrimination  : II . Behavioral Findings in.

Psychiatry research, 42, 241-251.

Hamilton, J., & Gotlib, I. H. (2008). Neural substrates of increased memory sensitivity for negative stimuli in major depression. Biological psychiatry,

63(12), 1155-1162. Retrieved from

http://www.sciencedirect.com/science/article/pii/s0006-3223(08)00038-3 Hariri, A. R., Mattay, V. S., Tessitore, A., Fera, F., Weinberger, D. R., James, W.,

Fearful Stimuli. Biological psychiatry, 53, 494-501. doi:10.1016/S0002-3223(03)01786-9

Johansen-Berg, H., Gutman, D. a, Behrens, T. E. J., Matthews, P. M., Rushworth, M. F. S., Katz, E., Lozano, a M., et al. (2008). Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression. Cerebral cortex (New York, N.Y.  : 1991), 18(6), 1374-83. doi:10.1093/cercor/bhm167

Keedwell, P. a, Drapier, D., Surguladze, S., Giampietro, V., Brammer, M., & Phillips, M. (2010). Subgenual cingulate and visual cortex responses to sad faces predict clinical outcome during antidepressant treatment for depression. Journal of

affective disorders, 120(1-3), 120-5. Elsevier B.V. doi:10.1016/j.jad.2009.04.031

Kennedy, S. H., Evans, K. R., Krüger, S., Mayberg, H. S., Meyer, J. H., McCann, S., Arifuzzman, a I., et al. (2001). Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. The American journal of psychiatry, 158(6), 899-905. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11384897

Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., et al. (2003). The Epidemiology of Major. Health Care, 289(23), 3095-3105.

Leppänen, J. M. (2006). Emotional information processing in mood disorders: a review of behavioral and neuroimaging findings. Current opinion in psychiatry,

19(1), 34-9. doi:10.1097/01.yco.0000191500.46411.00

Margulies, D. S., Kelly, a M. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2007). Mapping the functional connectivity of anterior cingulate cortex. NeuroImage, 37(2), 579-88. doi:10.1016/j.neuroimage.2007.05.019 Matthews, S. C., Strigo, I. a, Simmons, A. N., Yang, T. T., & Paulus, M. P. (2008).

Decreased functional coupling of the amygdala and supragenual cingulate is related to increased depression in unmedicated individuals with current major depressive disorder. Journal of affective disorders, 111(1), 13-20.

doi:10.1016/j.jad.2008.05.022

Mayberg, H S, Brannan, S. K., Tekell, J. L., Silva, J. a, Mahurin, R. K., McGinnis, S., & Jerabek, P. a. (2000). Regional metabolic effects of fluoxetine in major

depression: serial changes and relationship to clinical response. Biological

psychiatry, 48(8), 830-43. Retrieved from

http://www.ncbi.nlm.nih.gov/pubmed/11063978

Mayberg, H S, Liotti, M., Brannan, S. K., McGinnis, S., Mahurin, R. K., Jerabek, P. a, Silva, J. a, et al. (1999). Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. The

American journal of psychiatry, 156(5), 675-82. Retrieved from

Mayberg, H.S. (2009). Targeted electrode-based modulation of neural circuits for depression. The Journal of clinical investigation, 119(4), 717. American Society for Clinical Investigation. doi:10.1172/JCI38454.The

Mayberg, S. (1997). Limb ic-Cortical Dysregulation  : Depression. Neuropsychiatry, 471-481.

Mikhailova, E. S., Vladimirova, T. V., Iznak, a F., Tsusulkovskaya, E. J., & Sushko, N. V. (1996). Abnormal recognition of facial expression of emotions in

depressed patients with major depression disorder and schizotypal personality disorder. Biological psychiatry, 40(8), 697-705.

doi:10.1016/0006-3223(96)00032-7

Milne, A. M. B., MacQueen, G. M., & Hall, G. B. C. (2012). Abnormal hippocampal activation in patients with extensive history of major depression: an fMRI study.

Journal of psychiatry & neuroscience  : JPN, 37(1), 28-36.

doi:10.1503/jpn.110004

Mohanty, A., Engels, A. S., Herrington, J. D., Heller, W., Ho, M.-H. R., Banich, M. T., Webb, A. G., et al. (2007). Differential engagement of anterior cingulate cortex subdivisions for cognitive and emotional function. Psychophysiology,

44(3), 343-51. doi:10.1111/j.1469-8986.2007.00515.x

Pessoa, L., Kastner, S., & Ungerleider, L. G. (2002). A ttentional control of the processing of neutral and emotional stimuli. Cognitive Brain Research, 15, 31-45.

Phillips, M. (2003a). Neurobiology of emotion perception II: implications for major psychiatric disorders. Biological Psychiatry, 54(5), 515-528. doi:10.1016/S0006-3223(03)00171-9

Phillips, M. (2003b). Neurobiology of emotion perception I: the neural basis of normal emotion perception. Biological Psychiatry, 54(5), 504-514.

doi:10.1016/S0006-3223(03)00168-9

Rubinow, D. (1992). Impaired recognition of affect in facial expression in depressed patients. Biological psychiatry, 31, 947- 953. Retrieved from

http://www.sciencedirect.com/science/article/pii/000632239290120O

Sackeim, H. A. (2001). The definition and meaning of treatment-resistent depression.

Journal of clinical psychiatry, 62(Suppl 16), 10-17.

Salvadore, G., Cornwell, B. R., Colon-Rosario, V., Coppola, R., Grillon, C., Zarate, C. a, & Manji, H. K. (2009). Increased anterior cingulate cortical activity in response to fearful faces: a neurophysiological biomarker that predicts rapid antidepressant response to ketamine. Biological psychiatry, 65(4), 289-95. doi:10.1016/j.biopsych.2008.08.014

Sato, J. R., Fujita, A., Cardoso, E. F., Thomaz, C. E., Brammer, M. J., & Amaro, E. (2010). NeuroImage Analyzing the connectivity between regions of interest  : An

approach based on cluster Granger causality for fMRI data analysis.

NeuroImage, 52, 1444-1455. doi:10.1016/j.neuroimage.2010.05.022

Seminowicz, D. a, Mayberg, H. S., McIntosh, a R., Goldapple, K., Kennedy, S., Segal, Z., & Rafi-Tari, S. (2004). Limbic-frontal circuitry in major depression: a path modeling metanalysis. NeuroImage, 22(1), 409-18.

doi:10.1016/j.neuroimage.2004.01.015

Sun, F. T., Miller, L. M., & D’Esposito, M. (2004). Measuring interregional

functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage, 21(2), 647-58. doi:10.1016/j.neuroimage.2003.09.056 van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: a

review on resting-state fMRI functional connectivity. European

neuropsychopharmacology  : the journal of the European College of Neuropsychopharmacology, 20(8), 519-34. Elsevier B.V.

doi:10.1016/j.euroneuro.2010.03.008