Journal of Affective Disorders 160 (2014) 74–79
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Research report
Frontal-insula gray matter deficits in first-episode medication-naïve patients with major depressive disorder Chien-Han Lai a,b,n, Yu-Te Wu b,c a
Department of Psychiatry, Cheng Hsin General Hospital, Taipei City, Taiwan, ROC Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, ROC c Brain Research Center, National Yang-Ming University, Taipei, Taiwan, ROC b
art ic l e i nf o
a b s t r a c t
Article history: Received 15 October 2013 Received in revised form 17 December 2013 Accepted 17 December 2013 Available online 3 January 2014
Objective: This study is designed to investigate the gray matter volume (GMV) deficits in patients with firstepisode medication-naïve major depressive disorder (MDD). Methods: We enrolled 38 patients with first-episode medication-naïve MDD and 27 controls in this project. Voxel-based morphometry was used to compare GMV differences between two groups. Besides, the relationship between GMV of patients and the severity of clinical symptoms was estimated to confirm the role of GMV deficits in clinical symptoms. The correlation between total GMV and illness duration was also performed to elucidate the impacts of untreated duration on the GMV. Results: We found that first-episode medication-naïve MDD patients had significant GMV deficits in bilateral superior frontal gyri, left middle frontal gyrus, left medial frontal gyrus and left insula. The GMV of patient group was negatively correlated with the severity of clinical symptoms and the illness duration. Conclusion: A pattern of GMV deficits in fronto-insula might represent the biomarker for first-episode medication-naïve MDD. & 2014 Elsevier B.V. All rights reserved.
Keywords: Major depressive disorder Gray matter volume Voxel-based morphometry Superior frontal gyrus
1. Introduction Major depressive disorder (MDD) is a kind of mental illness with a relatively high prevalence. It will cause deteriorations in social and occupational functions. The biomarker of brain structure in MDD is an intriguing issue and might reveal some important meanings for clinical symptoms and functions. Many scientists focus their interest in the pathophysiology of MDD. In the structural aspect, gray matter is an important factor to establish the pathological model for MDD. Apart from traditional manual-based parcellation method in magnetic resonance imaging (MRI) studies, several semiautomatic methods are developed to avoid manual biases. A new methodology, optimized voxel-based morphometry (VBM), shows the stable quality for the study in gray matter volume (GMV) of many neuropsychiatric illnesses (Lai and Hsu, 2011; Lai et al., 2010; Lai and Wu, 2012; Sobanski et al., 2010). Recently several studies of voxel-based morphometry (VBM) showed fronto-limbic deficits in gray matter volume (GMV) of MDD patients (Abe et al., 2010; Lai and Hsu, 2011; Lai et al., 2010; Li et al., 2010; van Tol et al., 2010; Yuan et al., 2008). Abe et al. also found that GMV reductions in bilateral middle frontal gyri (MFG) of MDD patients and they suggested the existence of GMV deficits might constitute the fronto-limbic circuit n Correspondence to: Department of Psychiatry, Cheng Hsin General Hospital, No. 45, Cheng Hsin St., Pai-Tou District, Taipei City, Taiwan, ROC. Tel.: þ 886 2 28264400x3502; fax: þ 886 2 28264570. E-mail address: [email protected] (C.-H. Lai).
0165-0327/$ - see front matter & 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.12.036
model for pathophysiology (Abe et al., 2010). The gray matter reductions in superior frontal gyrus (SFG) would be associated with attention biases toward negative stimuli and predispose MDD patients to respond inappropriately to negative stress (Leung et al., 2009). Frontal-related GMV deficits in the MDD patients might contribute to neuropsychological impairments (Abe et al., 2010; Ballmaier et al., 2004b; Li et al., 2010; van Tol et al., 2010). The VBM study of van Tol et al. suggested that inferior frontal cortex may reflect specific symptom clusters for MDD (van Tol et al., 2010). The gray matter deficits of right medial frontal gyrus (MeFG) are also associated with depressive psychopathology and might be a part of structural deficit model of MDD (Vasic et al., 2008). Serro-Blasco et al. suggested that GMV deficits in SFG and MeFG would be associated with duration of illness (Serra-Blasco et al., 2013) These studies supported the existence of frontal-specific pattern of neuroanatomical deficits in patients with MDD. Apart from frontal-specific GMV deficits, several other brain regions with GMV alterations or even volume increases have been reported in MDD. Frodl et al. reported that hippocampal GMV reductions would be an important marker of first-episode MDD patients (Frodl et al., 2002b) and would be associated with different genotypes of serotonin transporter polymorphism (Frodl et al., 2004; Frodl et al., 2008b). Even paradoxically, larger volumes of amygdala were found in first-episode patients with MDD (Frodl et al., 2002a; Frodl et al., 2003). In this study, we planned to enroll first-episode medicationnaïve MDD patients into our VBM study to clarify the GMV deficit
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pattern in MDD. According to the above VBM studies, we hypothesized that MDD patients might have GMV deficits in multiple frontal regions, such as SFG, MFG, MeFG, inferior frontal gyrus, and hippocampus or amygdala. We would also investigate the correlation between total GMV and MDD symptom severity. We hypothesized that there was a negative correlation between total GMV and depressive severity. The correlation between the illness duration and total GMV was also estimated. We hypothesized that a negative correlation would also exist between two parameters.
2. Method 2.1. Participants The selection criteria for patients were as follows: (1) firstepisode, medication-naïve patients with MDD diagnosis (DSM-IV criteria) made by the Structured Clinical Interview for DSM-IV; (2) no co-morbid psychiatric illnesses or medical illnesses; (3) severity of MDD was at least moderate: Clinician Global Impression of Severity 44, Hamilton Rating Scales for Depression (HDRS) score 420, Hamilton Rating Scales for Anxiety (HARS) score o5, (4) no previous cognitive behavioral therapy or other psychotherapies; (5) medication-naïve (6) no abuse of or dependence on alcohol or other substances; and (7) no past history of claustrophobia or discomfort while receiving MR scanning. The healthy controls had no psychiatric illnesses or significant medical illnesses. They were staff volunteers of Buddhist Tzu Chi Hospital, Taipei Branch. All participants signed the inform consents approved by the Institute of Review Board, Buddhist Tzu Chi Hospital, Taipei Branch. At the time of the MR imaging, none of the participants received psychotropic treatment of any kind. Handedness was determined by using the Edinburgh Inventory of Handedness (Oldfield, 1971). 2.2. MR imaging procedure 2.2.1. Data acquisition: The structural MR imaging brain scans were obtained using the 3T Siemens version scanners housed in the MR Center at the National Yang Ming University. Scans with three-dimensional fast spoiled gradient-echo recovery (3D-FSPGR) T1W1 (TR 25.30 ms; TE 3.03 ms; slice thickness¼ 1 mm (no gap); 192 slices; matrix¼224 256; field of view: 256 mm; number of excitation¼1; voxel size: 1 1 1) were performed on the patients and controls at baseline. 2.3. VBM processing and statistical analysis: After manually reorienting and centering the images on the anterior commissure, the processing of data was performed based on the optimized VBM approach. Structural MR images were also preprocessed with FSLVBM (http://www.fmrib.ox.ac.uk/fsl/fslvbm/, version 1.1) function of FSL (FMRIB Software Library; version 4.1.1) to compare the differences of GMV between patients and healthy controls. The theory of FSLVBM method consists of 4 following major steps. First, brain skull or other non-brain tissue was removed by “Brain Extraction Tool” to discard the confounding factors of nonbrain tissues in subsequent steps for analyzing. Second, FSL Automated Segmentaion Tool v4 performed tissue-specific segmentation to produce partial volume images of gray matter with more uniform intensity values with softer edges (Thomas et al., 2009). Then the images were aligned to Montreal Neurological Institute 152 template through the affine registration. The registered images were averaged and concatenated to establish a 4D self template of gray matter from all the participants in this study. Third, brain would be non-linearly registered to the study-specific template and all the registered images were visually inspected by Dr. Lai to check the quality of registration.
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After Jacobian modulation.of the warp field to compensate the nonlinear transformation induced contraction or enlargement, all the modulated and segmented gray matter images were concatenated into a 4D multi-subject concatenated image. The modulated 4D image was smoothed by Gaussian kernels (sigma 3 mm in FSLVBM protocol, which approximately equal to Full Width at Half Maximum 7.5 mm) (Seidman et al., 2011). Besides, a gray matter mask was created by unsmoothed segmentations and unmodulated normalized segmentations. The FSL-VBM mask included voxels which met following criteria: minimum of gray matter probability larger than minimal threshold (0) and maximum of gray matter probability larger than maximal threshold (100 for FSL images). Smoothing 4D modulated image and gray matter mask were necessary for the following step of permutations. Fourth, a permutation-based non-parametric inference (Randomise function of FSL; http://www.fmrib.ox.ac.uk/fsl/randomise, version 2.1) was performed with gray matter mask and 4D image by Threshold-Free Cluster Enhancement (TFCE) method to compare two groups0 GMV. Non-parametric computations were used due to the relatively small sample size and the method is comparable to multiple comparisons in random field theory (Nichols and Holmes, 2002). The randomise function used general linear model for permutations and we included global brain volume, age, gender, agoraphobia and duration of illness as covariates to control possible confounding factors. TFCE is a new method for finding clusters in data without having to define clusters in a binary way, which can avoid the bias related to the arbitrary threshold. Cluster-like structures were enhanced but the image remained fundamentally voxel-wise. This procedure would produce test statistic images and sets of P-value images. The neighborhood-connectivity parameters have been optimized and should be left unchanged to avoid edge effects of the border between gray matter and white matter. TFCE solved multiple comparisons by using a multi-threshold meta-analysis of random field theory cluster P-values. We used family wise error (FWE) to obtain results for continuous random processes to find P-values. “FWE-corrected” means that family-wise error rate is controlled. For the control of co-morbid agoraphobia in these patients, we included the agoraphobia as a covariate in the design matrix for TFCE analysis. Besides, we included global brain volume, age, gender and duration of illness as covariates in the design matrix to control these possible confounding factors. Statistical image after multiple comparisons was explored to find regions of GMV deficits. The statistics was performed by the TFCE method of FSL and the threshold was set as FWE corrected p value o0.05 with 5000 times of permutations for multiple comparisons (df¼ 63) due to that we wanted to find the most significant regions with GMV differences. The statistical comparisons were performed in two-way style (patients vs. controls and controls vs. patients) to see the increments and decrements in GMV of patients with MDD. A correlation between the scores of clinical rating scales (HDRS) and GMV in the general lineal model considering the voxel wise matrix with global brain volume, age and gender as covariates in design matrix of FSL correlation analysis would be performed (threshold: corrected p o0.05, multiple comparisons). This step could help us confirm which brain regions correlate with depression and which regions may be important in the physiopathology of the disorder. Besides, the correlation between untreated duration of illness and GMV with the same covariates (age, gender and global brain volume) was analyzed to elucidate the key characteristics of untreated duration in MDD. Demographic data of patients and controls, such as age, HDRS scores and HARS scores, would be compared by nonparametric independent 2 sample t test (Mann–Whitney U test) with statistical threshold as po0.05. The use of non-parametric comparison test was due to limited sample size of two groups. The genders and handedness of two groups were compared by Chi-Square test with po 0.05.
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3. Results 3.1. Demographic data Thirty-eight first-episode, medication-naïve patients with MDD (18 males, 20 females, age: 36.57 75.46 years old) and twentyseven healthy controls (male: 12; female: 15; age: 38.29 711.80 years old) were recruited from Department of Psychiatry, Buddhist Tzu Chi General Hospital Taipei Branch, Taiwan. The patient group and control group had no significant differences in age, gender, handedness, educational level and HARS scores. There were significant differences in HDRS scores between patients and controls (Table 1). 3.2. Brain structural MRI and FSLVBM results: The patients with MDD had decreased GMV in the frontal areas, such as bilateral superior frontal gyri, left middle frontal gyrus and left medial frontal gyrus. Besides, the reductions in GMV were found in left insula (corrected po 0.05, cluster size 4 20 voxels). (Table 2 and Fig. 1). The resulting t-test images were one-tailed. The mean and distribution of clusters were as follows: mean: 0.956127, minimal and maximal intensity: 0.950000 and 0.959 000, standard deviation: 0.003301. No significant increments in GMV were observed in patients with MDD when compared with controls. Besides, the total GMV was negatively correlated with the scores of HDRS (r: 0.587; 2 tailed p ¼0.001) with correction of global brain volumes, age and gender. At last, a negative correlation between total GMV and untreated duration of illness was also observed (r: 0.403; 2 tailed p¼ 0.001).
4. Discussion In this study, we found that first-episode medication-naïve MDD patients had significant GMV reductions in fronto-insula regions, such as bilateral SFG, left MFG, left MeFG and left insula.
Besides, a negative correlation between total GMV and depression severity existed. At last, a negative correlation between illness duration and total GMV was also observed. The findings of several frontal regions were compatible with our hypothesis. However, the reductions of GMV in the insula were unexpected for the hypothesis of this study. The relatively long untreated duration of illness (4.68 months) also might underlie the GMV deficits in our patients. Sheline et al. ever reported that longer untreated duration of illness would be associated with more reductions in GMV of hippocampus (Sheline et al., 2003). Shortening untreated duration of would predict better response and remission for the medication treatment in MDD (Ghio et al., 2014). Therefore the impacts of a relatively long untreated duration on the GMV should be considered in this study. Our results of the negative correlation between total GMV and illness duration also supported the impacts of untreated duration on the GMV. Our findings in fronto-insula regions also correspond to the “hate circuit uncoupling” hypothesis, which included the SFG and insula (Tao et al., 2013). The reduced GMV in fronto-insula regions might represent that MDD patients might have reductions in cognitive control over negative feelings toward both self and others (Tao et al., 2013). In addition, the SFG would be involved in emotional processing. The degree of SFG hypoactivation would be associated with the severity of rumination, which is also important for emotional processing and modulation (Schiller et al., 2013). Frodl et al. reported the failure of MDD patients to deactivate SFG while patients received emotional stimulus (Frodl et al., 2009). The GMV reductions in SFG and other frontal regions have also reported in patients with MDD (Serra-Blasco et al., 2013). The SFG is also a part of visuospatial attention circuit (Kushnir et al., 2013) and the reductions in SFG might influence the ability of visuospatial attention in MDD. The GMV deficits in SFG would be correlated with attention and neuropsychological impairments in MDD (Li et al., 2010). Yuan et al. also suggested that GMV reductions in SFG would influence cognitive function and play a role in the psychopathology of cognitive impairments in MDD (Yuan et al., 2008). The severity of depression is also correlated
Table 1 Demographic data of participating subjects.
Age, mean (SD), years old Gender (number) Duration of illness, mean (SD), months Educational years, mean (SD) Handedness HDRS, mean (SD) HARS, mean (SD)
Patients (N ¼ 38)
Controls (N¼ 27)
Sig p (2-tailed), Z df ¼63
36.57 (5.46) F(20), M(18) 4.68 (1.50) 15.68 (0.84) R (38) 22.26 (2.39) 2.36 (1.07)
38.29 (11.80) F(15), M(12) 0 (0) 15.92 (0.67) R (27) 1.37 (0.88) 2.03 (1.01)
0.836, –0.207 0.535 N/A 0.198, –1.139 N/A o0.001, –6.87 0.255, –1.139
N: number; SD: standard deviation; F: female, M: male; HDRS: Hamilton rating scales for depression; HARS: Hamilton rating scales for anxiety; N/A: not applicable; Sig p (significance of p value) was from Mann–Whitney U test for nonparametric independent 2 sample t test; df: degree of freedom.
Table 2 Regions of GMV deficits in first-episode medication-naïve patients with MDD. Region
Right SFG Left SFG Left MFG Left MeFG Left insula
MNI coordinate (voxels) X
Y
Z
14 14 26 18 42
12 16 18 6 6
54 68 58 54 7
Number of contiguous voxels
Statistical significance (one-tailed) and T value
698 42 28 21 23
Corrected Corrected Corrected Corrected Corrected
p p p p p
o0.05, o0.05, o0.05, o0.05, o0.05,
df ¼63, df ¼63, df ¼63, df ¼63, df ¼63,
T: 6.99 T: 6.84 T:6.76 T: 6.73 T: 6.75
The mean and distribution of clusters: mean: 0.956127, minimal and maximal intensity: 0.950000 and 0.959000; standard deviation: 0.003301. (SFG: superior frontal gyrus; MFG: middle frontal gyrus; MeFG: medial frontal gyrus).
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Fig. 1. MDD patients had significant GMV reductions in bilateral SFG, left MFG, left MeFG and left insula when compared with healthy controls (corrected p o 0.05, cluster size 420 voxels). (SFG: superior frontal gyrus; MFG: middle frontal gyrus; MeFG: medial frontal gyrus).
with GMV deficits in SFG of depressive patients, which also support the important role of SFG for MDD symptoms (Goveas et al., 2011). The abnormal neural activities in SFG would be prone for the formation of MDD symptoms. Guo et al. found that abnormal neural activities in SFG could play a role in differentiating early-onset from late-onset depression, which also supported that the importance of SFG in the pathophysiology of MDD (Guo et al., 2013). Apart from SFG, reductions in GMV of MeFG and MFG were also important for the pathophysiology of MDD. The GMV of MeFC seemed to be associated with the untreated duration of MDD (Serra-Blasco et al., 2013). The reductions in GMV of MeFG would predict more severe depression and cognitive impairments in MDD (Vasic et al., 2008). Our past study of patients with MDD comorbid with panic disorder also found GMV deficits in MeFG (Lai and Hsu, 2011; Lai et al., 2010). Ansell et al. suggested that cumulative stress and life trauma might be associated with reduced GMV in MeFG. The increasing cumulative exposures to adverse events will make patients predispose to the onset of MDD (Ansell et al., 2012). Abe et al. mentioned that the reductions in GMV of MFG might play a role in fronto-limbic model for MDD (Abe et al., 2010). The structural deficits in MFG also correspond to significant correlation between implicit learning deficits and reduced GMV in MFG of patient with MDD (Naismith et al., 2010). According to the above studies, we can find the evidences to support the GMV deficits in SFG, MeFG and MFG. The GMV deficits are related to depression severity. It also corresponds to our results of a negative correlation between total GMV and HDRS scores. The decreases in GMV of frontal regions might represent the possible biomarker for MDD and might also be responsible for various deficits in the cognitive and emotional domains, such as neuropsychology, attention, emotion regulation, implicit learning, selfhate and other-hate ruminations. The GMV deficit in insula is an interesting result. The above studies find that insula might interact with frontal regions to form the “hate circuit” (Tao et al., 2013), the possible biomarker and state marker for treatment (Fitzgerald et al., 2008) in MDD. The reductions in gray matter density of insula was also reported in patients with MDD (Peng et al., 2011). Soriano-Mas et al. reported that melancholic MDD patients had GMV reductions in left insula. (Soriano-Mas et al., 2011). The differences in insula could also be recognized as part of a cluster in inferior frontal gyrus (Xie et al., 2012). The above results can support our findings of GMV reductions in insula. The coexistence of frontal and insula structural deficits might represent the biomarker for the pathophysiology of
MDD. However, we still need further functional MRI studies to explore functional connectivity and support our hypothesis. The lack of findings in limbic system, such as hippocampus, amygdala, anterior cingulate, and temporal lobes will be the limitation of this study. It is the major difference between our study and others. Several studies revealed GMV reductions in hippocampus for MDD (Cardoner et al., 2013; Chen et al., 2010; Dannlowski et al., 2012; Egger et al., 2008; Frodl et al., 2008a; Sexton et al., 2013; Zou et al., 2010). Besides, reduced GMV in amygdala has been recognized as the important factor for the psychopathology of MDD (Alemany et al., 2013; Canli et al., 2006; Dannlowski et al., 2012; Egger et al., 2008; Frodl et al., 2008a; Yoshikawa et al., 2006). The hippocampus and amygdala will connect with each other to influence negative thoughts and pessimistic ruminations in MDD (Canli et al., 2006). However, there are also various MDD studies without significant GMV reductions in hippocampus (Avila et al., 2011; Ballmaier et al., 2004a) and amygdala (Goveas et al., 2011). One postmortem study of MDD even showed larger amygdala volume (Monkul et al., 2007). Several studies also showed GMV deficits in anterior cingulate of MDD patients (Bora et al., 2012; Gunning et al., 2009; Lai, 2013; Sacher et al., 2012; van Tol et al., 2010). In addition, the GMV abnormalities of temporal lobes have also been reported in several MDD studies (Chen et al., 2008; Lim et al., 2013; Mackin et al., 2013; Peng et al., 2011; van Tol et al., 2010; Vasic et al., 2008). The absence of GMV deficits in limbic and temporal lobes can be explained by the following reasons: first, a first-episode medication-naïve MDD subgroup might limit the finding of our VBM study. Second, fronto-insula pattern of GMV deficits might represent a structural biomarker specific for first-episode medication-naïve MDD patients. However, it still needs several other groups of patients, such as treatment-resistant or multiple-episode or chronic subgroups, to confirm our hypothesis. Apart from the above-mentioned lack of limbic GMV deficits, there are several limitations in this study. (1) The relatively small sample size might limit the interpretation of our results. The results must be seen as preliminary. (2) We focused on brain structural analysis of MR images, not a functional study. This might limit the connection between FSLVBM results and functional connectivity between frontal regions and insula. (3) The lack of some adjusted analyses (such as small volume correction) due to the limitation of FSLVBM analysis would be a possible confounding factor for our results. However, there were several strength points as follows: first episode, medication-naïve and a FSLVBM automated analysis with less manual errors while performing the analysis.
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5. Conclusion We found specific GMV deficits in bilateral SFG, left MFG, MeFG and insula of first-episode medication-naïve MDD patients. A pattern of GMV deficits in fronto-insula might represent the biomarker for this subgroup of patients.
Role of funding source Grants of Buddhist Tzu Chi General Hospital Taipei Branch Hospital Project TCRD-TPE-100-02.
Conflict of interest The authors have no financial relationship or special conflict of interest to disclose.
Acknowledgments We want to thank Mr. YF Chen for the transportation help, Miss Wang (MR Center, National Yang Ming University) for MRI acquisition help and grant support from Buddhist Tzu-Chi General Hospital, Taipei Branch Hospital Project TCRD-TPE99-02. We also acknowledge MR support from National Yang-Ming University, Taiwan, which is in part supported by the MOE plan for the top university.
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Research report
Frontal-insula gray matter deficits in first-episode medication-naïve patients with major depressive disorder Chien-Han Lai a,b,n, Yu-Te Wu b,c a
Department of Psychiatry, Cheng Hsin General Hospital, Taipei City, Taiwan, ROC Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, ROC c Brain Research Center, National Yang-Ming University, Taipei, Taiwan, ROC b
art ic l e i nf o
a b s t r a c t
Article history: Received 15 October 2013 Received in revised form 17 December 2013 Accepted 17 December 2013 Available online 3 January 2014
Objective: This study is designed to investigate the gray matter volume (GMV) deficits in patients with firstepisode medication-naïve major depressive disorder (MDD). Methods: We enrolled 38 patients with first-episode medication-naïve MDD and 27 controls in this project. Voxel-based morphometry was used to compare GMV differences between two groups. Besides, the relationship between GMV of patients and the severity of clinical symptoms was estimated to confirm the role of GMV deficits in clinical symptoms. The correlation between total GMV and illness duration was also performed to elucidate the impacts of untreated duration on the GMV. Results: We found that first-episode medication-naïve MDD patients had significant GMV deficits in bilateral superior frontal gyri, left middle frontal gyrus, left medial frontal gyrus and left insula. The GMV of patient group was negatively correlated with the severity of clinical symptoms and the illness duration. Conclusion: A pattern of GMV deficits in fronto-insula might represent the biomarker for first-episode medication-naïve MDD. & 2014 Elsevier B.V. All rights reserved.
Keywords: Major depressive disorder Gray matter volume Voxel-based morphometry Superior frontal gyrus
1. Introduction Major depressive disorder (MDD) is a kind of mental illness with a relatively high prevalence. It will cause deteriorations in social and occupational functions. The biomarker of brain structure in MDD is an intriguing issue and might reveal some important meanings for clinical symptoms and functions. Many scientists focus their interest in the pathophysiology of MDD. In the structural aspect, gray matter is an important factor to establish the pathological model for MDD. Apart from traditional manual-based parcellation method in magnetic resonance imaging (MRI) studies, several semiautomatic methods are developed to avoid manual biases. A new methodology, optimized voxel-based morphometry (VBM), shows the stable quality for the study in gray matter volume (GMV) of many neuropsychiatric illnesses (Lai and Hsu, 2011; Lai et al., 2010; Lai and Wu, 2012; Sobanski et al., 2010). Recently several studies of voxel-based morphometry (VBM) showed fronto-limbic deficits in gray matter volume (GMV) of MDD patients (Abe et al., 2010; Lai and Hsu, 2011; Lai et al., 2010; Li et al., 2010; van Tol et al., 2010; Yuan et al., 2008). Abe et al. also found that GMV reductions in bilateral middle frontal gyri (MFG) of MDD patients and they suggested the existence of GMV deficits might constitute the fronto-limbic circuit n Correspondence to: Department of Psychiatry, Cheng Hsin General Hospital, No. 45, Cheng Hsin St., Pai-Tou District, Taipei City, Taiwan, ROC. Tel.: þ 886 2 28264400x3502; fax: þ 886 2 28264570. E-mail address: [email protected] (C.-H. Lai).
0165-0327/$ - see front matter & 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.12.036
model for pathophysiology (Abe et al., 2010). The gray matter reductions in superior frontal gyrus (SFG) would be associated with attention biases toward negative stimuli and predispose MDD patients to respond inappropriately to negative stress (Leung et al., 2009). Frontal-related GMV deficits in the MDD patients might contribute to neuropsychological impairments (Abe et al., 2010; Ballmaier et al., 2004b; Li et al., 2010; van Tol et al., 2010). The VBM study of van Tol et al. suggested that inferior frontal cortex may reflect specific symptom clusters for MDD (van Tol et al., 2010). The gray matter deficits of right medial frontal gyrus (MeFG) are also associated with depressive psychopathology and might be a part of structural deficit model of MDD (Vasic et al., 2008). Serro-Blasco et al. suggested that GMV deficits in SFG and MeFG would be associated with duration of illness (Serra-Blasco et al., 2013) These studies supported the existence of frontal-specific pattern of neuroanatomical deficits in patients with MDD. Apart from frontal-specific GMV deficits, several other brain regions with GMV alterations or even volume increases have been reported in MDD. Frodl et al. reported that hippocampal GMV reductions would be an important marker of first-episode MDD patients (Frodl et al., 2002b) and would be associated with different genotypes of serotonin transporter polymorphism (Frodl et al., 2004; Frodl et al., 2008b). Even paradoxically, larger volumes of amygdala were found in first-episode patients with MDD (Frodl et al., 2002a; Frodl et al., 2003). In this study, we planned to enroll first-episode medicationnaïve MDD patients into our VBM study to clarify the GMV deficit
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pattern in MDD. According to the above VBM studies, we hypothesized that MDD patients might have GMV deficits in multiple frontal regions, such as SFG, MFG, MeFG, inferior frontal gyrus, and hippocampus or amygdala. We would also investigate the correlation between total GMV and MDD symptom severity. We hypothesized that there was a negative correlation between total GMV and depressive severity. The correlation between the illness duration and total GMV was also estimated. We hypothesized that a negative correlation would also exist between two parameters.
2. Method 2.1. Participants The selection criteria for patients were as follows: (1) firstepisode, medication-naïve patients with MDD diagnosis (DSM-IV criteria) made by the Structured Clinical Interview for DSM-IV; (2) no co-morbid psychiatric illnesses or medical illnesses; (3) severity of MDD was at least moderate: Clinician Global Impression of Severity 44, Hamilton Rating Scales for Depression (HDRS) score 420, Hamilton Rating Scales for Anxiety (HARS) score o5, (4) no previous cognitive behavioral therapy or other psychotherapies; (5) medication-naïve (6) no abuse of or dependence on alcohol or other substances; and (7) no past history of claustrophobia or discomfort while receiving MR scanning. The healthy controls had no psychiatric illnesses or significant medical illnesses. They were staff volunteers of Buddhist Tzu Chi Hospital, Taipei Branch. All participants signed the inform consents approved by the Institute of Review Board, Buddhist Tzu Chi Hospital, Taipei Branch. At the time of the MR imaging, none of the participants received psychotropic treatment of any kind. Handedness was determined by using the Edinburgh Inventory of Handedness (Oldfield, 1971). 2.2. MR imaging procedure 2.2.1. Data acquisition: The structural MR imaging brain scans were obtained using the 3T Siemens version scanners housed in the MR Center at the National Yang Ming University. Scans with three-dimensional fast spoiled gradient-echo recovery (3D-FSPGR) T1W1 (TR 25.30 ms; TE 3.03 ms; slice thickness¼ 1 mm (no gap); 192 slices; matrix¼224 256; field of view: 256 mm; number of excitation¼1; voxel size: 1 1 1) were performed on the patients and controls at baseline. 2.3. VBM processing and statistical analysis: After manually reorienting and centering the images on the anterior commissure, the processing of data was performed based on the optimized VBM approach. Structural MR images were also preprocessed with FSLVBM (http://www.fmrib.ox.ac.uk/fsl/fslvbm/, version 1.1) function of FSL (FMRIB Software Library; version 4.1.1) to compare the differences of GMV between patients and healthy controls. The theory of FSLVBM method consists of 4 following major steps. First, brain skull or other non-brain tissue was removed by “Brain Extraction Tool” to discard the confounding factors of nonbrain tissues in subsequent steps for analyzing. Second, FSL Automated Segmentaion Tool v4 performed tissue-specific segmentation to produce partial volume images of gray matter with more uniform intensity values with softer edges (Thomas et al., 2009). Then the images were aligned to Montreal Neurological Institute 152 template through the affine registration. The registered images were averaged and concatenated to establish a 4D self template of gray matter from all the participants in this study. Third, brain would be non-linearly registered to the study-specific template and all the registered images were visually inspected by Dr. Lai to check the quality of registration.
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After Jacobian modulation.of the warp field to compensate the nonlinear transformation induced contraction or enlargement, all the modulated and segmented gray matter images were concatenated into a 4D multi-subject concatenated image. The modulated 4D image was smoothed by Gaussian kernels (sigma 3 mm in FSLVBM protocol, which approximately equal to Full Width at Half Maximum 7.5 mm) (Seidman et al., 2011). Besides, a gray matter mask was created by unsmoothed segmentations and unmodulated normalized segmentations. The FSL-VBM mask included voxels which met following criteria: minimum of gray matter probability larger than minimal threshold (0) and maximum of gray matter probability larger than maximal threshold (100 for FSL images). Smoothing 4D modulated image and gray matter mask were necessary for the following step of permutations. Fourth, a permutation-based non-parametric inference (Randomise function of FSL; http://www.fmrib.ox.ac.uk/fsl/randomise, version 2.1) was performed with gray matter mask and 4D image by Threshold-Free Cluster Enhancement (TFCE) method to compare two groups0 GMV. Non-parametric computations were used due to the relatively small sample size and the method is comparable to multiple comparisons in random field theory (Nichols and Holmes, 2002). The randomise function used general linear model for permutations and we included global brain volume, age, gender, agoraphobia and duration of illness as covariates to control possible confounding factors. TFCE is a new method for finding clusters in data without having to define clusters in a binary way, which can avoid the bias related to the arbitrary threshold. Cluster-like structures were enhanced but the image remained fundamentally voxel-wise. This procedure would produce test statistic images and sets of P-value images. The neighborhood-connectivity parameters have been optimized and should be left unchanged to avoid edge effects of the border between gray matter and white matter. TFCE solved multiple comparisons by using a multi-threshold meta-analysis of random field theory cluster P-values. We used family wise error (FWE) to obtain results for continuous random processes to find P-values. “FWE-corrected” means that family-wise error rate is controlled. For the control of co-morbid agoraphobia in these patients, we included the agoraphobia as a covariate in the design matrix for TFCE analysis. Besides, we included global brain volume, age, gender and duration of illness as covariates in the design matrix to control these possible confounding factors. Statistical image after multiple comparisons was explored to find regions of GMV deficits. The statistics was performed by the TFCE method of FSL and the threshold was set as FWE corrected p value o0.05 with 5000 times of permutations for multiple comparisons (df¼ 63) due to that we wanted to find the most significant regions with GMV differences. The statistical comparisons were performed in two-way style (patients vs. controls and controls vs. patients) to see the increments and decrements in GMV of patients with MDD. A correlation between the scores of clinical rating scales (HDRS) and GMV in the general lineal model considering the voxel wise matrix with global brain volume, age and gender as covariates in design matrix of FSL correlation analysis would be performed (threshold: corrected p o0.05, multiple comparisons). This step could help us confirm which brain regions correlate with depression and which regions may be important in the physiopathology of the disorder. Besides, the correlation between untreated duration of illness and GMV with the same covariates (age, gender and global brain volume) was analyzed to elucidate the key characteristics of untreated duration in MDD. Demographic data of patients and controls, such as age, HDRS scores and HARS scores, would be compared by nonparametric independent 2 sample t test (Mann–Whitney U test) with statistical threshold as po0.05. The use of non-parametric comparison test was due to limited sample size of two groups. The genders and handedness of two groups were compared by Chi-Square test with po 0.05.
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3. Results 3.1. Demographic data Thirty-eight first-episode, medication-naïve patients with MDD (18 males, 20 females, age: 36.57 75.46 years old) and twentyseven healthy controls (male: 12; female: 15; age: 38.29 711.80 years old) were recruited from Department of Psychiatry, Buddhist Tzu Chi General Hospital Taipei Branch, Taiwan. The patient group and control group had no significant differences in age, gender, handedness, educational level and HARS scores. There were significant differences in HDRS scores between patients and controls (Table 1). 3.2. Brain structural MRI and FSLVBM results: The patients with MDD had decreased GMV in the frontal areas, such as bilateral superior frontal gyri, left middle frontal gyrus and left medial frontal gyrus. Besides, the reductions in GMV were found in left insula (corrected po 0.05, cluster size 4 20 voxels). (Table 2 and Fig. 1). The resulting t-test images were one-tailed. The mean and distribution of clusters were as follows: mean: 0.956127, minimal and maximal intensity: 0.950000 and 0.959 000, standard deviation: 0.003301. No significant increments in GMV were observed in patients with MDD when compared with controls. Besides, the total GMV was negatively correlated with the scores of HDRS (r: 0.587; 2 tailed p ¼0.001) with correction of global brain volumes, age and gender. At last, a negative correlation between total GMV and untreated duration of illness was also observed (r: 0.403; 2 tailed p¼ 0.001).
4. Discussion In this study, we found that first-episode medication-naïve MDD patients had significant GMV reductions in fronto-insula regions, such as bilateral SFG, left MFG, left MeFG and left insula.
Besides, a negative correlation between total GMV and depression severity existed. At last, a negative correlation between illness duration and total GMV was also observed. The findings of several frontal regions were compatible with our hypothesis. However, the reductions of GMV in the insula were unexpected for the hypothesis of this study. The relatively long untreated duration of illness (4.68 months) also might underlie the GMV deficits in our patients. Sheline et al. ever reported that longer untreated duration of illness would be associated with more reductions in GMV of hippocampus (Sheline et al., 2003). Shortening untreated duration of would predict better response and remission for the medication treatment in MDD (Ghio et al., 2014). Therefore the impacts of a relatively long untreated duration on the GMV should be considered in this study. Our results of the negative correlation between total GMV and illness duration also supported the impacts of untreated duration on the GMV. Our findings in fronto-insula regions also correspond to the “hate circuit uncoupling” hypothesis, which included the SFG and insula (Tao et al., 2013). The reduced GMV in fronto-insula regions might represent that MDD patients might have reductions in cognitive control over negative feelings toward both self and others (Tao et al., 2013). In addition, the SFG would be involved in emotional processing. The degree of SFG hypoactivation would be associated with the severity of rumination, which is also important for emotional processing and modulation (Schiller et al., 2013). Frodl et al. reported the failure of MDD patients to deactivate SFG while patients received emotional stimulus (Frodl et al., 2009). The GMV reductions in SFG and other frontal regions have also reported in patients with MDD (Serra-Blasco et al., 2013). The SFG is also a part of visuospatial attention circuit (Kushnir et al., 2013) and the reductions in SFG might influence the ability of visuospatial attention in MDD. The GMV deficits in SFG would be correlated with attention and neuropsychological impairments in MDD (Li et al., 2010). Yuan et al. also suggested that GMV reductions in SFG would influence cognitive function and play a role in the psychopathology of cognitive impairments in MDD (Yuan et al., 2008). The severity of depression is also correlated
Table 1 Demographic data of participating subjects.
Age, mean (SD), years old Gender (number) Duration of illness, mean (SD), months Educational years, mean (SD) Handedness HDRS, mean (SD) HARS, mean (SD)
Patients (N ¼ 38)
Controls (N¼ 27)
Sig p (2-tailed), Z df ¼63
36.57 (5.46) F(20), M(18) 4.68 (1.50) 15.68 (0.84) R (38) 22.26 (2.39) 2.36 (1.07)
38.29 (11.80) F(15), M(12) 0 (0) 15.92 (0.67) R (27) 1.37 (0.88) 2.03 (1.01)
0.836, –0.207 0.535 N/A 0.198, –1.139 N/A o0.001, –6.87 0.255, –1.139
N: number; SD: standard deviation; F: female, M: male; HDRS: Hamilton rating scales for depression; HARS: Hamilton rating scales for anxiety; N/A: not applicable; Sig p (significance of p value) was from Mann–Whitney U test for nonparametric independent 2 sample t test; df: degree of freedom.
Table 2 Regions of GMV deficits in first-episode medication-naïve patients with MDD. Region
Right SFG Left SFG Left MFG Left MeFG Left insula
MNI coordinate (voxels) X
Y
Z
14 14 26 18 42
12 16 18 6 6
54 68 58 54 7
Number of contiguous voxels
Statistical significance (one-tailed) and T value
698 42 28 21 23
Corrected Corrected Corrected Corrected Corrected
p p p p p
o0.05, o0.05, o0.05, o0.05, o0.05,
df ¼63, df ¼63, df ¼63, df ¼63, df ¼63,
T: 6.99 T: 6.84 T:6.76 T: 6.73 T: 6.75
The mean and distribution of clusters: mean: 0.956127, minimal and maximal intensity: 0.950000 and 0.959000; standard deviation: 0.003301. (SFG: superior frontal gyrus; MFG: middle frontal gyrus; MeFG: medial frontal gyrus).
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Fig. 1. MDD patients had significant GMV reductions in bilateral SFG, left MFG, left MeFG and left insula when compared with healthy controls (corrected p o 0.05, cluster size 420 voxels). (SFG: superior frontal gyrus; MFG: middle frontal gyrus; MeFG: medial frontal gyrus).
with GMV deficits in SFG of depressive patients, which also support the important role of SFG for MDD symptoms (Goveas et al., 2011). The abnormal neural activities in SFG would be prone for the formation of MDD symptoms. Guo et al. found that abnormal neural activities in SFG could play a role in differentiating early-onset from late-onset depression, which also supported that the importance of SFG in the pathophysiology of MDD (Guo et al., 2013). Apart from SFG, reductions in GMV of MeFG and MFG were also important for the pathophysiology of MDD. The GMV of MeFC seemed to be associated with the untreated duration of MDD (Serra-Blasco et al., 2013). The reductions in GMV of MeFG would predict more severe depression and cognitive impairments in MDD (Vasic et al., 2008). Our past study of patients with MDD comorbid with panic disorder also found GMV deficits in MeFG (Lai and Hsu, 2011; Lai et al., 2010). Ansell et al. suggested that cumulative stress and life trauma might be associated with reduced GMV in MeFG. The increasing cumulative exposures to adverse events will make patients predispose to the onset of MDD (Ansell et al., 2012). Abe et al. mentioned that the reductions in GMV of MFG might play a role in fronto-limbic model for MDD (Abe et al., 2010). The structural deficits in MFG also correspond to significant correlation between implicit learning deficits and reduced GMV in MFG of patient with MDD (Naismith et al., 2010). According to the above studies, we can find the evidences to support the GMV deficits in SFG, MeFG and MFG. The GMV deficits are related to depression severity. It also corresponds to our results of a negative correlation between total GMV and HDRS scores. The decreases in GMV of frontal regions might represent the possible biomarker for MDD and might also be responsible for various deficits in the cognitive and emotional domains, such as neuropsychology, attention, emotion regulation, implicit learning, selfhate and other-hate ruminations. The GMV deficit in insula is an interesting result. The above studies find that insula might interact with frontal regions to form the “hate circuit” (Tao et al., 2013), the possible biomarker and state marker for treatment (Fitzgerald et al., 2008) in MDD. The reductions in gray matter density of insula was also reported in patients with MDD (Peng et al., 2011). Soriano-Mas et al. reported that melancholic MDD patients had GMV reductions in left insula. (Soriano-Mas et al., 2011). The differences in insula could also be recognized as part of a cluster in inferior frontal gyrus (Xie et al., 2012). The above results can support our findings of GMV reductions in insula. The coexistence of frontal and insula structural deficits might represent the biomarker for the pathophysiology of
MDD. However, we still need further functional MRI studies to explore functional connectivity and support our hypothesis. The lack of findings in limbic system, such as hippocampus, amygdala, anterior cingulate, and temporal lobes will be the limitation of this study. It is the major difference between our study and others. Several studies revealed GMV reductions in hippocampus for MDD (Cardoner et al., 2013; Chen et al., 2010; Dannlowski et al., 2012; Egger et al., 2008; Frodl et al., 2008a; Sexton et al., 2013; Zou et al., 2010). Besides, reduced GMV in amygdala has been recognized as the important factor for the psychopathology of MDD (Alemany et al., 2013; Canli et al., 2006; Dannlowski et al., 2012; Egger et al., 2008; Frodl et al., 2008a; Yoshikawa et al., 2006). The hippocampus and amygdala will connect with each other to influence negative thoughts and pessimistic ruminations in MDD (Canli et al., 2006). However, there are also various MDD studies without significant GMV reductions in hippocampus (Avila et al., 2011; Ballmaier et al., 2004a) and amygdala (Goveas et al., 2011). One postmortem study of MDD even showed larger amygdala volume (Monkul et al., 2007). Several studies also showed GMV deficits in anterior cingulate of MDD patients (Bora et al., 2012; Gunning et al., 2009; Lai, 2013; Sacher et al., 2012; van Tol et al., 2010). In addition, the GMV abnormalities of temporal lobes have also been reported in several MDD studies (Chen et al., 2008; Lim et al., 2013; Mackin et al., 2013; Peng et al., 2011; van Tol et al., 2010; Vasic et al., 2008). The absence of GMV deficits in limbic and temporal lobes can be explained by the following reasons: first, a first-episode medication-naïve MDD subgroup might limit the finding of our VBM study. Second, fronto-insula pattern of GMV deficits might represent a structural biomarker specific for first-episode medication-naïve MDD patients. However, it still needs several other groups of patients, such as treatment-resistant or multiple-episode or chronic subgroups, to confirm our hypothesis. Apart from the above-mentioned lack of limbic GMV deficits, there are several limitations in this study. (1) The relatively small sample size might limit the interpretation of our results. The results must be seen as preliminary. (2) We focused on brain structural analysis of MR images, not a functional study. This might limit the connection between FSLVBM results and functional connectivity between frontal regions and insula. (3) The lack of some adjusted analyses (such as small volume correction) due to the limitation of FSLVBM analysis would be a possible confounding factor for our results. However, there were several strength points as follows: first episode, medication-naïve and a FSLVBM automated analysis with less manual errors while performing the analysis.
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5. Conclusion We found specific GMV deficits in bilateral SFG, left MFG, MeFG and insula of first-episode medication-naïve MDD patients. A pattern of GMV deficits in fronto-insula might represent the biomarker for this subgroup of patients.
Role of funding source Grants of Buddhist Tzu Chi General Hospital Taipei Branch Hospital Project TCRD-TPE-100-02.
Conflict of interest The authors have no financial relationship or special conflict of interest to disclose.
Acknowledgments We want to thank Mr. YF Chen for the transportation help, Miss Wang (MR Center, National Yang Ming University) for MRI acquisition help and grant support from Buddhist Tzu-Chi General Hospital, Taipei Branch Hospital Project TCRD-TPE99-02. We also acknowledge MR support from National Yang-Ming University, Taiwan, which is in part supported by the MOE plan for the top university.
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