磁共振弥散张量成像数据处理及临床应用研究
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摘要
磁共振弥散张量成像(diffusion tensor imaging , DTI)技术,是目前在生物活体上测量水分子空间弥散运动与成像的唯一方法,可以全面反映生物体内水分子的弥散情况从而了解人体器官组织随年龄变化的特点以及病变造成的组织受压、移位、变形与破坏,为病变的诊断与鉴别诊断提供更多信息,为手术方案的制定,术后随访提供依据。目前,这一新兴技术已受到神经科学、认知科学和临床诊断等领域前所未有的极大关注。
在对弥散张量成像所能提供的众多参数值图的处理中,大多数的研究者,由于方法所限,往往采用手动绘制感兴趣区(region-of-interest,ROI)的方法。然而ROI绘制者不可避免的主观性,绘制方法的不确定性和难以实现的可重复性决定了绘制ROI方法本身的风险。因此,为了弥补ROI的缺陷,有研究学者提出了基于体素分析(voxel-based analysis,VBA)的方法来检测人们不曾预料到的神经解剖学区域。本文在国外同行的研究基础上,结合教研室与合作医院的实际情况,提出了一种优化的基于体素分析的数据处理方法,该方法能够更好地利用磁共振弥散张量数据的空间信息。和ROI方法相比,新的数据分析方法不需要先验信息,和其它数据驱动方法不同的是:该方法对数据的具体特性没有太多要求,并且克服了传统VBA方法存在的处理流程复杂,临床医生难以掌握等缺点。
同时,结合临床问题,发现了女性精神分裂症患者的大脑异常区域,难治性部分癫痫患者的脑损伤区域,并首次研究了男性难治性抑郁症患者与正常对照的大脑异常表现区域。应用VBA方法进行的临床课题研究,不仅取了一定的科研成果,同时也印证了该方法在临床科研上的有效性和易用性。
综上所述,这种数据处理方法对于弥散张量影像数据的处理是一个非常有前景和应用价值的方法。
At present, diffusion tensor imaging is the only way to measure water molecule’s diffusion in-vivo without invasion. This powerful imaging technique could reflect the diffusion information of water molecule; thereby help to understand the human body’s organic aging and pathological changes. As one of the most efficient methods, it could provide more information for diagnosis, surgery and therapy. For the moment, it has attracted scientists’attention and has been used in many correlative research areas. There are two principal methods in diffusion tensor image analysis:
region-of-interest analysis and whole-brain, voxel-based analysis. The majority of studies, to date, have adopted the former one, manually defining region-of-interest on the raw images, which allows a powerful examination of the selected regions on the basis of prior information. However, the unavoidable subjective of region-of-interests’define, the unambiguous criteria of region-of-interests’depict methods and the deficiency of the reliability all devaluate the method of region-of-interesting. At the contrary, the voxel-based analysis could be more helpful in discovering unanticipated or unpredicted areas of neuro-anatomical deficits. In this paper, an optimized voxel-based analysis was introduced into diffusion tensor imaging data processing, which might extract more information from the diffusion tensor imaging data. Comparing to region-of-interesting methods, it doesn’t need prior information, as the symptom and so on. Furthermore, it outweighs the traditional voxel-based analysis method at lower operational complexity, less memory consumption, and smaller calculation time.
In this paper, the optimized voxel-based analysis was introduced to depict the brain white matter abnormalities of first-episode schizophrenia, refractory partial epilepsy and refractory depression, respectively. To our best knowledge, this is the first study to find out the brain anatomic difference between refractory depression patients and the normal controls. And significant differences have been found, which provided new imaging evidence for further investigation of these neuro-psychiatry diseases.
In sum, voxel-based analysis has been proved to be an important and effective data-driven method for diffusion tensor imaging data analysis, and is suitable for clinical investigation of the brain white matter deficits of all neuro-psychiatry diseases.
在对弥散张量成像所能提供的众多参数值图的处理中,大多数的研究者,由于方法所限,往往采用手动绘制感兴趣区(region-of-interest,ROI)的方法。然而ROI绘制者不可避免的主观性,绘制方法的不确定性和难以实现的可重复性决定了绘制ROI方法本身的风险。因此,为了弥补ROI的缺陷,有研究学者提出了基于体素分析(voxel-based analysis,VBA)的方法来检测人们不曾预料到的神经解剖学区域。本文在国外同行的研究基础上,结合教研室与合作医院的实际情况,提出了一种优化的基于体素分析的数据处理方法,该方法能够更好地利用磁共振弥散张量数据的空间信息。和ROI方法相比,新的数据分析方法不需要先验信息,和其它数据驱动方法不同的是:该方法对数据的具体特性没有太多要求,并且克服了传统VBA方法存在的处理流程复杂,临床医生难以掌握等缺点。
同时,结合临床问题,发现了女性精神分裂症患者的大脑异常区域,难治性部分癫痫患者的脑损伤区域,并首次研究了男性难治性抑郁症患者与正常对照的大脑异常表现区域。应用VBA方法进行的临床课题研究,不仅取了一定的科研成果,同时也印证了该方法在临床科研上的有效性和易用性。
综上所述,这种数据处理方法对于弥散张量影像数据的处理是一个非常有前景和应用价值的方法。
At present, diffusion tensor imaging is the only way to measure water molecule’s diffusion in-vivo without invasion. This powerful imaging technique could reflect the diffusion information of water molecule; thereby help to understand the human body’s organic aging and pathological changes. As one of the most efficient methods, it could provide more information for diagnosis, surgery and therapy. For the moment, it has attracted scientists’attention and has been used in many correlative research areas. There are two principal methods in diffusion tensor image analysis:
region-of-interest analysis and whole-brain, voxel-based analysis. The majority of studies, to date, have adopted the former one, manually defining region-of-interest on the raw images, which allows a powerful examination of the selected regions on the basis of prior information. However, the unavoidable subjective of region-of-interests’define, the unambiguous criteria of region-of-interests’depict methods and the deficiency of the reliability all devaluate the method of region-of-interesting. At the contrary, the voxel-based analysis could be more helpful in discovering unanticipated or unpredicted areas of neuro-anatomical deficits. In this paper, an optimized voxel-based analysis was introduced into diffusion tensor imaging data processing, which might extract more information from the diffusion tensor imaging data. Comparing to region-of-interesting methods, it doesn’t need prior information, as the symptom and so on. Furthermore, it outweighs the traditional voxel-based analysis method at lower operational complexity, less memory consumption, and smaller calculation time.
In this paper, the optimized voxel-based analysis was introduced to depict the brain white matter abnormalities of first-episode schizophrenia, refractory partial epilepsy and refractory depression, respectively. To our best knowledge, this is the first study to find out the brain anatomic difference between refractory depression patients and the normal controls. And significant differences have been found, which provided new imaging evidence for further investigation of these neuro-psychiatry diseases.
In sum, voxel-based analysis has been proved to be an important and effective data-driven method for diffusion tensor imaging data analysis, and is suitable for clinical investigation of the brain white matter deficits of all neuro-psychiatry diseases.
引文
[1] 唐孝威. 脑功能成像. 合肥: 中国科学技术大学出版社, 1999, 60-90
[2] 赵喜平. 磁共振成像. 北京: 科学出版社, 2004, 674-700
[3] 田捷,包尚联. 医学影像处理与分析. 北京:电子工业出版社,2004,172-187
[4] 俎栋林. 核磁共振成像学. 北京:高等教育出版社, 2004, 231-320
[5] Basser, P. J., Mattiello, J., LeBihan, D., MR diffusion tensor spectroscopy and imaging. Biophys. J. 1994, 66 (1), 259–267.
[6] Basser, P. J., Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. J. NMR Biomed. 1995, 8 (7–8), 333–344.
[7] Schaefer PW., Grant P.E, Gonzales RG. Diffusion-weighted MR imaging of the brain. Radiology, 2000, 217: 331-345
[8] Hasan KM., Parker DL, Alexander AL. Comparison of gradient encoding schemes for diffusion-tensor MRI. Journal of Magnetic Resonance Image, 2001, 13: 769-780
[9] Zivan JA. Diffusion-weighted MRI for diagnosis and treatment of ischemic stroke. Ann Neurol,1997, 41: 567-568
[10] Douek P, Turner R, Pekar J, et al. MR color mapping of myelin fiber orientation.J Comput Assist Tomogr, 1991, 15: 923-929
[11] Basser, P. J., Pierpaoli C. Microstructural and physiological feature of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson, 1996, B111:209-219
[12] Pierpaoli C, Basser, P. J., Towards a quantitative assessment of diffusion anisotropy.Magn Reson Med, 1996, 36: 893-906
[13] Frank LR. Anistropy in high angular resolution diffusion-weighted MRI. Magn Reson Med, 2001, 45: 935-939
[14] Sorensen A.G., Gonzalez R.G., Weisskoff R.M., et al. In vivo full diffusion tensor measurement in normal and pathologic human brain. ISMRM Fourth Science Meeting. New York, NY, Berkeley, CA, 1996, 1327
[15] Jones D.K., Williams S.c.r., Simmons A., et al. The nature of chronic ischemic demage andlacunar infarction demonstrated by diffusion tensor MRI. ISMRM Fifth Science Meeting., Vancouver, BC, Canada, Berkeley, CA, 1997, 1712
[16] Oedkerk M, Edelman RR. High-power gradient MR-imaging advances in MRI ii. Blackwell Science, 1997,157-188
[17] Lim KO, Helper J A. Neuropsychiatric applications of DTI [J]. NMR Biomed, 2002, 15(7-8):587-593
[18] Ashburner J., Friston K. J. Voxel-based morphometry--the methods[J]. Neuroimage. 2000, 11(6 Pt 1): 805-821.
[19] Ashburner J., Friston K. J. Why voxel-based morphometry should be used[J]. Neuroimage. 2001, 14(6): 1238-1243.
[20] Eriksson, S.H., Rugg-Gunn, F.J., et al. Diffusion tensor imaging in patients with epilepsy and malformations of cortical development. Brain 2001, 124 (Pt 3): 617–626.
[21] Michael Moseley,a, Roland Bammer, and Judy Illesa, Diffusion-tensor imaging of cognitive performance. Brain and Cognition. 2002, 50: 396-413
[22] Hae-Jeong Park, Carl-Fredrik Westin, Marek Kubicki, et al. White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study. NeuroImage .2004, 23 : 213–223
[23] Richard B. Schwartz. Apparent diffusion coefficient mapping in patients with alzheimer disease or mild cognitive impairment and in normally aging control subjects: present and future. Radiology, 2001, 219: 8-9
[24] Rugg-Gunn F. J., Eriksson S. H., Symms M. R. et al. Diffusion tensor imaging of cryptogenic and acquired partial epilepsies[J]. Brain. 2001, 124(Pt 3): 627-636.
[25] Eriksson S. H., Rugg-Gunn F. J., Symms M. R. et al. Diffusion tensor imaging in patients with epilepsy and malformations of cortical development[J]. Brain. 2001, 124(Pt 3): 617-626.
[26] Thivard L., Adam C., Hasboun D. et al. Interictal diffusion MRI in partial epilepsies explored with intracerebral electrodes[J]. Brain. 2006, 129(Pt 2): 375-385.
[27] Thivard L., Lehericy S., Krainik A. Et al. Diffusion tensor imaging in medial temporal lobe epilepsy with hippocampal sclerosis[J]. Neuroimage. 2005, 28(3): 682-690.
[28] Friston. Assessing the significance of focal activations using their spatial extent.[J]. Hum Brain Mapping. 1994, 1: 210-220
[29] Frith C. The neural basis of hallucinations and delusions. C R Biol, 2005, 328 (2) : 169.
[30] Buchsbaum MS, Tang CY, Peled S, et al. MRI white matter diffusion anisotropy and PETmetabolic rate in schizophrenia. Neuroreport, 1998, 9 (3): 425.
[31] Lim KO, Hedehus M, Moseley M, et al. Compromised white matter tract integrity in schizophrenia inferred from diffusion tensor imaging. Arch Gen Psychiatry, 1999, 56 (4): 367.
[32] Minami T, Nobuhara K, Okugawa G, et al. Diffusion tensor magnetic resonance imaging of disruption of regional white matter in schizophrenia. Neuropsychobiology, 2003, 47 (3) : 141.
[33] Buchsbaum MS, Tang CY, Peled S, et al. MRI white matter diffusion anisotropy and PET metabolic rate in schizophrenia [J]. Neuroreport, 1998, 9(3): 425-430.
[34] Lim KO, Helper J A. Neuropsychiatric applications of DTI [J]. NMR Biomed, 2002, 15(7-8):587-593.
[35] J. Burns, D. Job, M. E. Bastin, et al. Structural disconnectivity in schizophrenia: a diffusion tensor magnetic resonance imaging study. British Journal of Psychiatry .2003, 182,439-443
[36] Dumas de la Roque A., Oppenheim C., Chassoux F. et al . Diffusion tensor imaging of partial intractable epilepsy[J]. Eur Radiol. 2005, 15(2): 279-285.
[37] Assaf B. A., Mohamed F. B., Abou-Khaled K. J. et al. Diffusion tensor imaging of the hippocampus formation in temporal lobe epilepsy[J]. AJNR Am J Neuroradiol. 2003, 24(9): 1857-1862.
[38] Gupta R. K., Saksena S., Agarwal A. et al. Diffusion tensor imaging in late posttraumatic epilepsy[J]. Epilepsia. 2005, 46(9): 1465-1471.
[39] Malhi GS, Bartlett JR.Depression:a role for neurosurgery .BrJNeurosurg,2000,14:4158.
[40] Souery D, Amsterdam J, de Montigny C, et al. Treatment resistant depression: Methodological overview and operational criteria. Eur Neuropsychopharmacol, 1999, 9:83-91.
[41] Coffey CE, Figiel GS, Djang WT, Saunders WB, Weiner RD. White matter hyperintensity on magnetic resonance imaging: clinical and neuroanatomic correlates in the depressed elderly. J. Neuropsychiat. Clin. Neurosci. 1989,1: 135–144.
[42] MacFall JR, Payne ME, Provenzale JE, Krishnan KR. Medial orbital frontal lesions in late-onset depression. Biol. Psychiat. 2001, 49: 803–806.
[43] Mayberg HS. Frontal lobe dysfunction in secondary depression. J. Neuropsychiat. Clin. Neurosci. 1994, 6: 428–442.
[44] Taylor WD, MacFall JR, et al. Late-life depression and microstructural abnormalities in dorsolateral prefrontal cortex white matter. Am J Psychiatry 2004, 161:1293–6.
[45] Alexopoulos GS, Kiosses DN, Choi SJ, et al. Frontal white matter microstructure and treatment response of late-life depression: a preliminary study. Am J Psychiatry 2002, 159:1929–32.
[46] Botteron KN, Figiel GS, et al. The neuromorphometry of affective disorders. In Krishnan KRR, Doraiswamy M (eds): Brain Imaging in Clinical Psychiatry. New York: Marcel Dekker Press, 1997, pp 145–184.
[47] Cummings JL, et al. Frontal-subcortical circuits and human behavior. Arch Neurol. 1993, 50:873–880.
[48] Swerdlow NR, Koob GF, et al. Dopamine, schizophrenia, mania and depression: Toward a unified hypothesis of cortico-stiatopallido-thalamic function.Behav Brain Sci.1987,10:197–245.
[49] Drevets WC, Videen TO, Price JL, et al. A functional anatomical study of unipolar depression. J Neurosci.1992, 12:3628–3641.
[50] Drevets WC, Botteron K et al. Neuroimaging in Psychiatry. In Guze S(Ed): Washington University Adult Psychiatry. St. Louis: Mosby Year Book. 1996, pp 53–81.
[51] George MS, Ketter TA, et al. Post RM. Prefrontal cortex dysfunction in clinical depression. Depression 1994, 2:59–72.
[52] Drevets WC, Price JL, Simpson JR Jr, Todd RD, Reich T, Vannier M, Raichle ME. Subgenual prefrontal cortex abnormalities in mood disorders. Nature 1997, 386: 824–827.
[53] Lockwood KA, Alexopoulos GS, Kakuma T, Van Gorp WG. Subtypes of cognitive impairment in depressed older adults. Am. J. Geriatr. Psychiat. 2000, 8: 201–208.
[2] 赵喜平. 磁共振成像. 北京: 科学出版社, 2004, 674-700
[3] 田捷,包尚联. 医学影像处理与分析. 北京:电子工业出版社,2004,172-187
[4] 俎栋林. 核磁共振成像学. 北京:高等教育出版社, 2004, 231-320
[5] Basser, P. J., Mattiello, J., LeBihan, D., MR diffusion tensor spectroscopy and imaging. Biophys. J. 1994, 66 (1), 259–267.
[6] Basser, P. J., Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. J. NMR Biomed. 1995, 8 (7–8), 333–344.
[7] Schaefer PW., Grant P.E, Gonzales RG. Diffusion-weighted MR imaging of the brain. Radiology, 2000, 217: 331-345
[8] Hasan KM., Parker DL, Alexander AL. Comparison of gradient encoding schemes for diffusion-tensor MRI. Journal of Magnetic Resonance Image, 2001, 13: 769-780
[9] Zivan JA. Diffusion-weighted MRI for diagnosis and treatment of ischemic stroke. Ann Neurol,1997, 41: 567-568
[10] Douek P, Turner R, Pekar J, et al. MR color mapping of myelin fiber orientation.J Comput Assist Tomogr, 1991, 15: 923-929
[11] Basser, P. J., Pierpaoli C. Microstructural and physiological feature of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson, 1996, B111:209-219
[12] Pierpaoli C, Basser, P. J., Towards a quantitative assessment of diffusion anisotropy.Magn Reson Med, 1996, 36: 893-906
[13] Frank LR. Anistropy in high angular resolution diffusion-weighted MRI. Magn Reson Med, 2001, 45: 935-939
[14] Sorensen A.G., Gonzalez R.G., Weisskoff R.M., et al. In vivo full diffusion tensor measurement in normal and pathologic human brain. ISMRM Fourth Science Meeting. New York, NY, Berkeley, CA, 1996, 1327
[15] Jones D.K., Williams S.c.r., Simmons A., et al. The nature of chronic ischemic demage andlacunar infarction demonstrated by diffusion tensor MRI. ISMRM Fifth Science Meeting., Vancouver, BC, Canada, Berkeley, CA, 1997, 1712
[16] Oedkerk M, Edelman RR. High-power gradient MR-imaging advances in MRI ii. Blackwell Science, 1997,157-188
[17] Lim KO, Helper J A. Neuropsychiatric applications of DTI [J]. NMR Biomed, 2002, 15(7-8):587-593
[18] Ashburner J., Friston K. J. Voxel-based morphometry--the methods[J]. Neuroimage. 2000, 11(6 Pt 1): 805-821.
[19] Ashburner J., Friston K. J. Why voxel-based morphometry should be used[J]. Neuroimage. 2001, 14(6): 1238-1243.
[20] Eriksson, S.H., Rugg-Gunn, F.J., et al. Diffusion tensor imaging in patients with epilepsy and malformations of cortical development. Brain 2001, 124 (Pt 3): 617–626.
[21] Michael Moseley,a, Roland Bammer, and Judy Illesa, Diffusion-tensor imaging of cognitive performance. Brain and Cognition. 2002, 50: 396-413
[22] Hae-Jeong Park, Carl-Fredrik Westin, Marek Kubicki, et al. White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study. NeuroImage .2004, 23 : 213–223
[23] Richard B. Schwartz. Apparent diffusion coefficient mapping in patients with alzheimer disease or mild cognitive impairment and in normally aging control subjects: present and future. Radiology, 2001, 219: 8-9
[24] Rugg-Gunn F. J., Eriksson S. H., Symms M. R. et al. Diffusion tensor imaging of cryptogenic and acquired partial epilepsies[J]. Brain. 2001, 124(Pt 3): 627-636.
[25] Eriksson S. H., Rugg-Gunn F. J., Symms M. R. et al. Diffusion tensor imaging in patients with epilepsy and malformations of cortical development[J]. Brain. 2001, 124(Pt 3): 617-626.
[26] Thivard L., Adam C., Hasboun D. et al. Interictal diffusion MRI in partial epilepsies explored with intracerebral electrodes[J]. Brain. 2006, 129(Pt 2): 375-385.
[27] Thivard L., Lehericy S., Krainik A. Et al. Diffusion tensor imaging in medial temporal lobe epilepsy with hippocampal sclerosis[J]. Neuroimage. 2005, 28(3): 682-690.
[28] Friston. Assessing the significance of focal activations using their spatial extent.[J]. Hum Brain Mapping. 1994, 1: 210-220
[29] Frith C. The neural basis of hallucinations and delusions. C R Biol, 2005, 328 (2) : 169.
[30] Buchsbaum MS, Tang CY, Peled S, et al. MRI white matter diffusion anisotropy and PETmetabolic rate in schizophrenia. Neuroreport, 1998, 9 (3): 425.
[31] Lim KO, Hedehus M, Moseley M, et al. Compromised white matter tract integrity in schizophrenia inferred from diffusion tensor imaging. Arch Gen Psychiatry, 1999, 56 (4): 367.
[32] Minami T, Nobuhara K, Okugawa G, et al. Diffusion tensor magnetic resonance imaging of disruption of regional white matter in schizophrenia. Neuropsychobiology, 2003, 47 (3) : 141.
[33] Buchsbaum MS, Tang CY, Peled S, et al. MRI white matter diffusion anisotropy and PET metabolic rate in schizophrenia [J]. Neuroreport, 1998, 9(3): 425-430.
[34] Lim KO, Helper J A. Neuropsychiatric applications of DTI [J]. NMR Biomed, 2002, 15(7-8):587-593.
[35] J. Burns, D. Job, M. E. Bastin, et al. Structural disconnectivity in schizophrenia: a diffusion tensor magnetic resonance imaging study. British Journal of Psychiatry .2003, 182,439-443
[36] Dumas de la Roque A., Oppenheim C., Chassoux F. et al . Diffusion tensor imaging of partial intractable epilepsy[J]. Eur Radiol. 2005, 15(2): 279-285.
[37] Assaf B. A., Mohamed F. B., Abou-Khaled K. J. et al. Diffusion tensor imaging of the hippocampus formation in temporal lobe epilepsy[J]. AJNR Am J Neuroradiol. 2003, 24(9): 1857-1862.
[38] Gupta R. K., Saksena S., Agarwal A. et al. Diffusion tensor imaging in late posttraumatic epilepsy[J]. Epilepsia. 2005, 46(9): 1465-1471.
[39] Malhi GS, Bartlett JR.Depression:a role for neurosurgery .BrJNeurosurg,2000,14:4158.
[40] Souery D, Amsterdam J, de Montigny C, et al. Treatment resistant depression: Methodological overview and operational criteria. Eur Neuropsychopharmacol, 1999, 9:83-91.
[41] Coffey CE, Figiel GS, Djang WT, Saunders WB, Weiner RD. White matter hyperintensity on magnetic resonance imaging: clinical and neuroanatomic correlates in the depressed elderly. J. Neuropsychiat. Clin. Neurosci. 1989,1: 135–144.
[42] MacFall JR, Payne ME, Provenzale JE, Krishnan KR. Medial orbital frontal lesions in late-onset depression. Biol. Psychiat. 2001, 49: 803–806.
[43] Mayberg HS. Frontal lobe dysfunction in secondary depression. J. Neuropsychiat. Clin. Neurosci. 1994, 6: 428–442.
[44] Taylor WD, MacFall JR, et al. Late-life depression and microstructural abnormalities in dorsolateral prefrontal cortex white matter. Am J Psychiatry 2004, 161:1293–6.
[45] Alexopoulos GS, Kiosses DN, Choi SJ, et al. Frontal white matter microstructure and treatment response of late-life depression: a preliminary study. Am J Psychiatry 2002, 159:1929–32.
[46] Botteron KN, Figiel GS, et al. The neuromorphometry of affective disorders. In Krishnan KRR, Doraiswamy M (eds): Brain Imaging in Clinical Psychiatry. New York: Marcel Dekker Press, 1997, pp 145–184.
[47] Cummings JL, et al. Frontal-subcortical circuits and human behavior. Arch Neurol. 1993, 50:873–880.
[48] Swerdlow NR, Koob GF, et al. Dopamine, schizophrenia, mania and depression: Toward a unified hypothesis of cortico-stiatopallido-thalamic function.Behav Brain Sci.1987,10:197–245.
[49] Drevets WC, Videen TO, Price JL, et al. A functional anatomical study of unipolar depression. J Neurosci.1992, 12:3628–3641.
[50] Drevets WC, Botteron K et al. Neuroimaging in Psychiatry. In Guze S(Ed): Washington University Adult Psychiatry. St. Louis: Mosby Year Book. 1996, pp 53–81.
[51] George MS, Ketter TA, et al. Post RM. Prefrontal cortex dysfunction in clinical depression. Depression 1994, 2:59–72.
[52] Drevets WC, Price JL, Simpson JR Jr, Todd RD, Reich T, Vannier M, Raichle ME. Subgenual prefrontal cortex abnormalities in mood disorders. Nature 1997, 386: 824–827.
[53] Lockwood KA, Alexopoulos GS, Kakuma T, Van Gorp WG. Subtypes of cognitive impairment in depressed older adults. Am. J. Geriatr. Psychiat. 2000, 8: 201–208.