East Asian Arch Psychiatry 2011;21:5-9

ORIGINAL ARTICLE

从未接受治疗抑郁症患者之脑白质完整性缺损

Dr Xuan Ouyang, MD, Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China.
Dr Haojuan Tao, MD, Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China.
Dr Haihong Liu, MD, Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China.
Dr Qijian Deng, MD, Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China.
Dr Zihao Sun, MD, Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China.
Dr Lin Xu, MD, PhD, Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology of Chinese Academy of Science, Kunming, China.
Prof Zhening Liu, MD, Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China.
Dr Zhimin Xue, MD, Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China.

Address for correspondence: Dr Zhimin Xue / Prof Zhening Liu, Institute of Mental Health, Second Xiangya Hospital of Central South University, 139
Renmin Middle Road, Changsha, 410011, China.
Tel: (86) 13808486752 / (86-731) 5292470; Fax: (86-731) 5360086; Emails: xuezhimin@hotmail.com / zningl@163.com

Submitted: 15 November 2010; Accepted: 11 January 2011

pdf Full Paper in PDF

Introduction

It is well documented that the fronto-limbic system is composed of the frontal cortex, cingulate, and medial temporal cortex, which play critical roles in cognitive functions,^1-4 such as memory, decision-making, inference, executive control, attention, language, and emotion. Major depressive disorder (MDD) is a mood disorder characterised by depressed mood and loss of interest or pleasure. Recently, 2 studies^5,6 using diffusion tensor imaging (DTI) found that adult patients with MDD had a deficiency in white matter integrity in their fronto-limbic systems. Nine other studies ^7-15 reported similar findings when the same protocol was used in elderly patients with depression. Two of these reports ^13,15 found significant correlations between white matter integrity deficit in the fronto-limbic system and cognitive performance of Trail Making Test or Stroop Color-Word Test. However, to date there have been few studies reporting significant correlations between fractional anisotropy (FA), a measure often used in diffusion imaging in which it is thought to reflect fibre density, axonal diameter, and myelination in white matter in abnormal white matter brain regions and cognitive functioning in treatment-naïve adult patients with MDD. It is hypothesised that white matter FA changes may be associated with severity of cognitive impairment in patients with MDD. This study examined white matter integrity in treatment-naïve adult patients with MDD using DTI. A correlation analysis between mean white matter FA values in the abnormal brain regions with cognitive performance of Trail Making, Wisconsin Card Sorting, Stroop Color-Word, and Verbal Fluency tests was performed.

Methods

Participants

Eighteen treatment-naïve adult patients with MDD were recruited from the inpatient and outpatient departments at the Second Xiangya Hospital of Central South University, Changsha, Hunan, China. Eighteen healthy controls were enrolled. All patients met the inclusion criteria of current major depressive episode as assessed by 2 experienced psychiatrists using the Structural Clinical Interview for the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders¹⁶; first episode without any medication; aged between 18 and 45 years; were right-handed Han Chinese; and had a Hamilton Rating Scale for Depression (HAMD) score of ≥ 17. Patients and healthy controls were excluded if they had a history of neurological diseases or other serious physical diseases; history of electroconvulsive therapy; history of substance (drugs, alcohol, other psychoactive substance) abuse; and contra-indications for magnetic resonance imaging (MRI).

Neuropsychological assessments, including the Trail Making, Wisconsin Card Sorting, Stroop Color-Word, and Verbal Fluency tests, were performed by an experienced psychiatrist for all patients with MDD. The study was approved by the Hospital Ethics Committee. Written informed consent was obtained from all participants.

Imaging Acquisition

Scans were performed on a 1.5-Tesla GE Signa Twinspeed MRI scanner (General Electric Medical System, Milwaukee [WI], US) equipped with high-speed gradients. A birdcage head coil was used for radiofrequency transmission and reception of nuclear magnetic resonance signal. Participants were placed in the supine position in the scanner, and their head was snugly fixed with foam pads provided by the manufacturer to minimise head motion. Their bilateral external auditory meatuses were plugged with earplugs to minimise the noise influence. Diffusion-weighted images were acquired with a single-shot echo planar imaging sequence aligned to the straight axial plane. Diffusion sensitising gradients were applied along 13 non-collinear directions (b = 1000 s/mm²). Images with no diffusion weighting (b0 images) were acquired. Thirty contiguous axial slices were acquired with the following parameters: repetition time, 12,000 ms; echo time, 107 ms; acquisition matrix, 128 x 128; field of view, 24 × 24 cm; number of excitations, 5; slice thickness, 4 mm, and no gap.

Image Processing

Diffusion-weighted images were pre-processed with the soft DtiStudio software, version 2.40 (Johns Hopkins University [MD], US).¹⁷ Diffusion tensor matrix was calculated according to the Stejskal and Tanner equation.¹⁸ Three pairs of eigenvalues (λ¹, λ², λ³) and eigenvectors (ε¹, ε², ε³) can be obtained by diagonalisation of the tensor matrix. The largest eigenvalue (λ¹) corresponds to the principal eigenvector (ε¹), which demonstrates the main diffusion direction within the voxel. The FA value¹⁹ was calculated according to the following formula:

After image pre-processing on DtiStudio software, the original DTI images were transformed into parametric images of b0 and FA. For each participant, the b0 image and FA image were first normalised to standard Montreal Neurological Institute space by implementing the Statistical Parametric Mapping software package (SPM2, Wellcome Department of Cognitive Neurology, London, UK) on MATLAB 6.5 (Mathworks, Inc., Natick [MA], US). All images were re-sampled with a final voxel size of 2 × 2 × 2 mm³. Each normalised FA map was spatially smoothed with an 8-mm full-width half-maximum Gaussian kernel to decrease spatial noise and to compensate for the inexact nature of normalisation. No correction for eddy current distortions was applied.

Statistical Analyses

Voxel-based Analysis

The Voxel-based analysis strategy^20,21 used in this study was the same as that used in previous DTI studies.^6,22,23 Comparisons of normalised and smoothed FA images between the 2 groups were performed using 2-sample t test in a voxel-by-voxel manner. Clusters with p < 0.001 (uncorrected) between groups and size > 30 voxels (i.e. k > 30) were identified and superimposed onto the SPM2’s spatially normalised template brain for easy visualisation.

Fractional Anisotropy Value Analysis

Mean FA values of identified clusters were calculated for each participant using a self-developed program.²³ The two-sample t test implemented on the Statistical Package for the Social Sciences version 11.5 (SPSS, Inc., Chicago [IL], US) was used to confirm the statistical significance of the identified clusters. Correlation analysis was conducted between mean FA values in each of the identified clusters and cognitive performances. The data were presented as means (standard deviations) [SDs]. The significance level was set at p < 0.05.

Results

There were 9 women and 9 men with a mean (SD) age of 27.4 (6.4) years, and a mean (SD) duration of education of 11.8 (3.2) years. The mean (SD) illness duration was 15.1 (3.9) months, and the mean (SD) 17-item HAMD score was 24.2 (4.3). In the control group, there were 9 women and 9 men, with a mean (SD) age of 27.0 (6.8) years and a mean (SD) duration of education of 11.9 (2.7) years. The 2 groups were well matched for sex, age, ethnicity, and education (p > 0.05).

White matter integrity deficits indicated by lower FA values in the bilateral medial frontal gyri, right subgyral frontal and temporal lobes, and left middle frontal and cingulate gyri were found (Fig). The mean FA values of each of the clusters showed a significant difference between the MDD patients and the controls (p < 0.001) [Table]. The FA values had no correlation with performance of cognitive tests.

Discussion

The major finding of this study was that treatment-naïve adult patients with MDD showed white matter integrity deficit in the fronto-limbic system compared with the healthy controls. Although the locations of some clusters were somewhat different, the main findings in the frontal, temporal, and cingulate areas are basically consistent with previous reports of treatment-naïve adult patients with MDD^5,6 and elderly people with depression.^7-12,14,15 Characteristics of illness duration and sample size, and methodological differences including image data acquisition and pre-processing may have contributed to these slight differences. However, all the findings suggest that disruption of white matter integrity is a common feature in MDD patients, with different extent and content for each individual. Microstructural changes in the white matter of the fronto-limbic system may result in disconnection of cortical and subcortical regions. Accordingly, patients with MDD may exhibit a series of related syndromes, including disorder of mood regulation and executive dysfunctioning. Thus, white matter deficits of the fronto-limbic system, as a common feature, may contribute to the pathogenesis of major depression.

The other findings suggest no correlation between white matter integrity deficits expressed as FA values and scores of cognitive function tests, although 2 previous studies^13,15 of geriatric depression reported significant correlations between FA or FA values and scores of the Trail Making Test or Stroop Color-Word Test. This could be attributed to the age difference or other possible non-linear correlation. Another possibility could be that many factors contribute to the pathogenesis of MDD, and thus one of the cognitive tests may not correlate to FA values.

There were some certain limitations to this study. For example, the sample size was small, and cognitive assessment was relatively limited. Despite these limitations, the major finding is robust and independent of age, sex, and medication. However, future studies are needed to examine fibre tract changes in the specific regions of the fronto-limbic system using fibre tractography based on DTI. In addition, this study was a cross-sectional research. To further understand the relationship between FA value and cognitive performance, follow-up study with a larger sample is needed in the future.

Acknowledgements

This work was supported by a grant from National Programs for Fundamental Research and Development of China (973 Program, 2007CB512308) and The 11th Five Year Key Program for Science and Technology Development of China (2007BAI17B05). The authors would like to express their sincere appreciation to Drs Zhong He, Yihui Hao, Yan Zhang, Yu Zhang, Haojuan Tao, Xiangjuan Kong, Li Li, and Huiran Zhang for their technical assistance in this study.

References

1. LaBar KS, Cabeza R. Cognitive neuroscience of emotional memory. Nat Rev Neurosci 2006;7:54-64.
2. Miller EK. The prefrontal cortex and cognitive control. Nat Rev Neurosci 2000;1:59-65.
3. Simons JS, Spiers HJ. Prefrontal and medial temporal lobe interactions in long-term memory. Nat Rev Neurosci 2003;4:637-48.
4. Goldman-Rakic PS, Selemon LD. Functional and anatomical aspects of prefrontal pathology in schizophrenia. Schizophr Bull 1997;23:437- 58.
5. Li L, Ma N, Li Z, Tan L, Liu J, Gong G, et al. Prefrontal white matter abnormalities in young adult with major depressive disorder: a diffusion tensor imaging study. Brain Res 2007;1168:124-8.
6. Ma N, Li L, Shu N, Liu J, Gong G, He Z, et al. White matter abnormalities in first-episode, treatment-naive young adults with major depressive disorder. Am J Psychiatry 2007;164:823-6.
7. Bae JN, MacFall JR, Krishnan KR, Payne ME, Steffens DC, Taylor WD. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 2006;60:1356-63.
8. Nobuhara K, Okugawa G, Minami T, Takase K, Yoshida T, Yagyu T, et al. Effects of electroconvulsive therapy on frontal white matter in late- life depression: a diffusion tensor imaging study. Neuropsychobiology 2004;50:48-53.
9. Nobuhara K, Okugawa G, Sugimoto T, Minami T, Tamagaki C, Takase K, et al. Frontal white matter anisotropy and symptom severity of late- life depression: a magnetic resonance diffusion tensor imaging study. J Neurol Neurosurg Psychiatry 2006;77:120-2.
10. Taylor WD, MacFall JR, Payne ME, McQuoid DR, Provenzale JM, Steffens DC, et al. Late-life depression and microstructural abnormalities in dorsolateral prefrontal cortex white matter. Am J Psychiatry 2004;161:1293-6.
11. Alexopoulos GS, Kiosses DN, Choi SJ, Murphy CF, Lim KO. Frontal white matter microstructure and treatment response of late-life depression: a preliminary study. Am J Psychiatry 2002;159:1929-32.
12. Alexopoulos GS, Murphy CF, Gunning-Dixon FM, Latoussakis V, Kanellopoulos D, Klimstra S, et al. Microstructural white matter abnormalities and remission of geriatric depression. Am J Psychiatry 2008;165:238-44.
13. Murphy CF, Gunning-Dixon FM, Hoptman MJ, Lim KO, Ardekani B, Shields JK, et al. White-matter integrity predicts stroop performance in patients with geriatric depression. Biol Psychiatry 2007;61:1007-10.
14. Yang Q, Huang X, Hong N, Yu X. White matter microstructural abnormalities in late-life depression. Int Psychogeriatr 2007;19:757- 66.
15. Yuan Y, Zhang Z, Bai F, Yu H, Shi Y, Qian Y, et al. White matter integrity of the whole brain is disrupted in first-episode remitted geriatric depression. Neuroreport 2007;18:1845-9.
16. First MB, Spitzer RL, Gibbon M, Williams JB, editors. Structured Clinical Interview for DSM-IV Axis I Disorders — Patient Edition (SCID-I/P). New York: Biometrics Research Department, New York State Psychiatric Institute; 1996.
17. Jiang H, van Zijl PC, Kim J, Pearlson GD, Mori S. DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 2006;81:106-16.
18. Stejskal EO, Tanner JE. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 1965;42:288-92.
19. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B 1996;111:209-19.
20. Ashburner J, Friston KJ. Voxel-based morphometry — the methods. Neuroimage 2000;11(6 Pt 1):805-21.
21. Giuliani NR, Calhoun VD, Pearlson GD, Francis A, Buchanan RW. Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophr Res 2005;74:135-47.
22. Hao Y, Liu Z, Jiang T, Gong G, Liu H, Tan L, et al. White matter integrity of the whole brain is disrupted in first-episode schizophrenia. Neuroreport 2006;17:23-6.
23. Liu H, Li L, Hao Y, Cao D, Xu L, Rohrbaugh R, et al. Disrupted white matter integrity in heroin dependence: a controlled study utilizing diffusion tensor imaging. Am J Drug Alcohol Abuse 2008;34:562-75.