J Clin Neurol.  2018 Apr;14(2):129-140. 10.3988/jcn.2018.14.2.129.

Current Clinical Applications of Diffusion-Tensor Imaging in Neurological Disorders

Affiliations
  • 1Brain Convergence Research Center, Korea University, Seoul, Korea. nukbj@korea.ac.kr
  • 2Department of Psychiatry, Korea University College of Medicine, Seoul, Korea.
  • 3Department of Physical Medicine and Rehabilitation, Korea University College of Medicine, Seoul, Korea.
  • 4Department of Neurosurgery, Korea University College of Medicine, Seoul, Korea.
  • 5Department of Neurology, Korea University College of Medicine, Seoul, Korea.

Abstract

Diffusion-tensor imaging (DTI) is a noninvasive medical imaging tool used to investigate the structure of white matter. The signal contrast in DTI is generated by differences in the Brownian motion of the water molecules in brain tissue. Postprocessed DTI scalars can be used to evaluate changes in the brain tissue caused by disease, disease progression, and treatment responses, which has led to an enormous amount of interest in DTI in clinical research. This review article provides insights into DTI scalars and the biological background of DTI as a relatively new neuroimaging modality. Further, it summarizes the clinical role of DTI in various disease processes such as amyotrophic lateral sclerosis, multiple sclerosis, Parkinson's disease, Alzheimer's dementia, epilepsy, ischemic stroke, stroke with motor or language impairment, traumatic brain injury, spinal cord injury, and depression. Valuable DTI postprocessing tools for clinical research are also introduced.

Keyword

diffusion-tensor imaging; diffusion-tensor imaging scalar; postprocessing; neurological disorders

MeSH Terms

Amyotrophic Lateral Sclerosis
Brain
Brain Injuries
Dementia
Depression
Diagnostic Imaging
Disease Progression
Epilepsy
Multiple Sclerosis
Nervous System Diseases*
Neuroimaging
Parkinson Disease
Spinal Cord Injuries
Stroke
Water
White Matter
Water

Figure

  • Fig. 1 DTI scalar images derived from diffusion-tensor images with 20 gradient directions. The FA is a DTI scalar that represents axonal integrity and is strongly related to fiber integrity. The AD is related to axonal damage. The RD is probably a DTI marker of myelin, with an increased RD value suggestive of myelin damage in white matter tissue. The MD is a measure of the average molecular motion. The size and integrity of cells affects the MD, which is known to be related to necrosis, edema, and cellularity. The MO is a probabilistic tractography measure for crossing white matter fibers. AD: axial diffusivity, DTI: diffusion-tensor imaging, FA: fractional anisotropy, MD: mean diffusivity, MO: mode, RD: radial diffusivity.

  • Fig. 2 Frontal-habenula-cerebellar and frontal-cerebellar tracts. The long fiber pathway connecting the frontal cortex via the habenula to the cerebellum (left) and the frontal-cerebellar tracts (right) were tracked using 3-T, 64-direction diffusion-tensor imaging data, analyzed using DSI Studio software.

  • Fig. 3 Diffusion-tensor tractography in a patient (a female aged 74 years) with left hemiparesis after suffering an infarction in the right corona radiata (arrow in lower left figure) and basal ganglia (arrow in upper right figure). The right CST exhibited marked decreases in the number of fibers [n=234 on the right (blue) and n=876 on the left (red)] and in fractional anisotropy (0.4267 and 0.5483, respectively), but the continuity of the right CST was preserved throughout its course. CST: corticospinal tract, L: left, R: right.

  • Fig. 4 Three levels of damage to the left AF. DTI of the AF (left column) and T2-weighted magnetic resonance images (right column) show three types of AF (blue, right AF; red, left AF) categorized according to the severity of damage. In type A, fibers of the AF are severely damaged and thus are not visualized in DTI reconstructions. In type B, the AF is disrupted between Wernicke's and Broca's areas. In type C, the AF is preserved around the brain lesion. The arrow indicates disruption of the left AF around the stroke lesion. AF: arcuate fasciculus, DTI: diffusion-tensor imaging.


Reference

1. Acosta-Cabronero J, Williams GB, Pengas G, Nestor PJ. Absolute diffusivities define the landscape of white matter degeneration in Alzheimer's disease. Brain. 2010; 133:529–539.
Article
2. Zeineh MM, Holdsworth S, Skare S, Atlas SW, Bammer R. Ultra-high resolution diffusion tensor imaging of the microscopic pathways of the medial temporal lobe. Neuroimage. 2012; 62:2065–2082.
Article
3. Abhinav K, Yeh FC, Pathak S, Suski V, Lacomis D, Friedlander RM, et al. Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: a review. Biochim Biophys Acta. 2014; 1842:2286–2297.
Article
4. Dong Q, Welsh RC, Chenevert TL, Carlos RC, Maly-Sundgren P, Gomez-Hassan DM, et al. Clinical applications of diffusion tensor imaging. J Magn Reson Imaging. 2004; 19:6–18.
Article
5. Cauley KA, Filippi CG. Diffusion-tensor imaging of small nerve bundles: cranial nerves, peripheral nerves, distal spinal cord, and lumbar nerve roots--clinical applications. AJR Am J Roentgenol. 2013; 201:W326–W335.
6. Jiang Q, Zhang ZG, Chopp M. MRI evaluation of white matter recovery after brain injury. Stroke. 2010; 41:S112–S113.
Article
7. Maller JJ, Thomson RH, Lewis PM, Rose SE, Pannek K, Fitzgerald PB. Traumatic brain injury, major depression, and diffusion tensor imaging: making connections. Brain Res Rev. 2010; 64:213–240.
Article
8. Schlaug G, Renga V, Nair D. Transcranial direct current stimulation in stroke recovery. Arch Neurol. 2008; 65:1571–1576.
Article
9. Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics. 2006; 26:Suppl 1. S205–S223.
Article
10. Douaud G, Jbabdi S, Behrens TE, Menke RA, Gass A, Monsch AU, et al. DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. Neuroimage. 2011; 55:880–890.
Article
11. Yoncheva YN, Somandepalli K, Reiss PT, Kelly C, Di Martino A, Lazar M, et al. Mode of anisotropy reveals global diffusion alterations in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2016; 55:137–145.
Article
12. Squarcina L, Bellani M, Rossetti MG, Perlini C, Delvecchio G, Dusi N, et al. Similar white matter changes in schizophrenia and bipolar disorder: a tract-based spatial statistics study. PLoS One. 2017; 12:e0178089.
Article
13. Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003; 20:1714–1722.
Article
14. Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of the brain. Neurotherapeutics. 2007; 4:316–329.
Article
15. Zhang J, Aggarwal M, Mori S. Structural insights into the rodent CNS via diffusion tensor imaging. Trends Neurosci. 2012; 35:412–421.
Article
16. 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; 81:106–116.
Article
17. Yeh FC, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI, et al. Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints. PLoS Comput Biol. 2016; 12:e1005203.
Article
18. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006; 31:1487–1505.
Article
19. Jbabdi S, Behrens TE, Smith SM. Crossing fibres in tract-based spatial statistics. Neuroimage. 2010; 49:249–256.
Article
20. Yendiki A, Panneck P, Srinivasan P, Stevens A, Zöllei L, Augustinack J, et al. Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front Neuroinform. 2011; 5:23.
Article
21. Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage. 2004; 23:1176–1185.
Article
22. Foerster BR, Dwamena BA, Petrou M, Carlos RC, Callaghan BC, Churchill CL, et al. Diagnostic accuracy of diffusion tensor imaging in amyotrophic lateral sclerosis: a systematic review and individual patient data meta-analysis. Acad Radiol. 2013; 20:1099–1106.
Article
23. Li J, Pan P, Song W, Huang R, Chen K, Shang H. A meta-analysis of diffusion tensor imaging studies in amyotrophic lateral sclerosis. Neurobiol Aging. 2012; 33:1833–1838.
Article
24. Müller HP, Turner MR, Grosskreutz J, Abrahams S, Bede P, Govind V, et al. A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2016; 87:570–579.
Article
25. Filippini N, Douaud G, Mackay CE, Knight S, Talbot K, Turner MR. Corpus callosum involvement is a consistent feature of amyotrophic lateral sclerosis. Neurology. 2010; 75:1645–1652.
Article
26. Metwalli NS, Benatar M, Nair G, Usher S, Hu X, Carew JD. Utility of axial and radial diffusivity from diffusion tensor MRI as markers of neurodegeneration in amyotrophic lateral sclerosis. Brain Res. 2010; 1348:156–164.
Article
27. Sasaki S, Maruyama S. Ultrastructural study of skein-like inclusions in anterior horn neurons of patients with motor neuron disease. Neurosci Lett. 1992; 147:121–124.
Article
28. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002; 17:1429–1436.
Article
29. Rovaris M, Filippi M. Diffusion tensor MRI in multiple sclerosis. J Neuroimaging. 2007; 17:Suppl 1. 27S–30S.
Article
30. Klistorner A, Vootakuru N, Wang C, Yiannikas C, Graham SL, Parratt J, et al. Decoding diffusivity in multiple sclerosis: analysis of optic radiation lesional and non-lesional white matter. PLoS One. 2015; 10:e0122114.
Article
31. Glisson CC, Galetta SL. Nonconventional optic nerve imaging in multiple sclerosis. Neuroimaging Clin N Am. 2009; 19:71–79.
Article
32. van der Walt A, Kolbe SC, Wang YE, Klistorner A, Shuey N, Ahmadi G, et al. Optic nerve diffusion tensor imaging after acute optic neuritis predicts axonal and visual outcomes. PLoS One. 2013; 8:e83825.
Article
33. Kolbe SC, van der Walt A, Butzkueven H, Klistorner A, Egan GF, Kilpatrick TJ. Serial diffusion tensor imaging of the optic radiations after acute optic neuritis. J Ophthalmol. 2016; 2016:2764538.
Article
34. Chen J, Zhou C, Zhu L, Yan X, Wang Y, Chen X, et al. Magnetic resonance diffusion tensor imaging for occult lesion detection in multiple sclerosis. Exp Ther Med. 2017; 13:91–96.
Article
35. Stamile C, Kocevar G, Cotton F, Durand-Dubief F, Hannoun S, Frindel C, et al. A sensitive and automatic white matter fiber tracts model for longitudinal analysis of diffusion tensor images in multiple sclerosis. PLoS One. 2016; 11:e0156405.
Article
36. Pardini M, Yaldizli Ö, Sethi V, Muhlert N, Liu Z, Samson RS, et al. Motor network efficiency and disability in multiple sclerosis. Neurology. 2015; 85:1115–1122.
Article
37. Zheng Z, Shemmassian S, Wijekoon C, Kim W, Bookheimer SY, Pouratian N. DTI correlates of distinct cognitive impairments in Parkinson's disease. Hum Brain Mapp. 2014; 35:1325–1333.
Article
38. Cochrane CJ, Ebmeier KP. Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology. 2013; 80:857–864.
Article
39. Ofori E, Pasternak O, Planetta PJ, Li H, Burciu RG, Snyder AF, et al. Longitudinal changes in free-water within the substantia nigra of Parkinson's disease. Brain. 2015; 138:2322–2331.
Article
40. Lenfeldt N, Hansson W, Larsson A, Nyberg L, Birgander R, Forsgren L. Diffusion tensor imaging and correlations to Parkinson rating scales. J Neurol. 2013; 260:2823–2830.
Article
41. Mayo CD, Mazerolle EL, Ritchie L, Fisk JD, Gawryluk JR;. Alzheimer's Disease Neuroimaging Initiative. Longitudinal changes in microstructural white matter metrics in Alzheimer's disease. Neuroimage Clin. 2016; 13:330–338.
Article
42. Arfanakis K, Hermann BP, Rogers BP, Carew JD, Seidenberg M, Meyerand ME. Diffusion tensor MRI in temporal lobe epilepsy. Magn Reson Imaging. 2002; 20:511–519.
Article
43. Rugg-Gunn FJ, Eriksson SH, Symms MR, Barker GJ, Duncan JS. Diffusion tensor imaging of cryptogenic and acquired partial epilepsies. Brain. 2001; 124:627–636.
Article
44. Gross DW. Diffusion tensor imaging in temporal lobe epilepsy. Epilepsia. 2011; 52:Suppl 4. 32–34.
Article
45. Kim CH, Chung CK, Koo BB, Lee JM, Kim JS, Lee SK. Changes in language pathways in patients with temporal lobe epilepsy: diffusion tensor imaging analysis of the uncinate and arcuate fasciculi. World Neurosurg. 2011; 75:509–516.
Article
46. Otte WM, van Eijsden P, Sander JW, Duncan JS, Dijkhuizen RM, Braun KP. A meta-analysis of white matter changes in temporal lobe epilepsy as studied with diffusion tensor imaging. Epilepsia. 2012; 53:659–667.
Article
47. Yang Q, Tress BM, Barber PA, Desmond PM, Darby DG, Gerraty RP, et al. Serial study of apparent diffusion coefficient and anisotropy in patients with acute stroke. Stroke. 1999; 30:2382–2390.
Article
48. Jones DK, Lythgoe D, Horsfield MA, Simmons A, Williams SC, Markus HS. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke. 1999; 30:393–397.
Article
49. Cho SH, Kim DG, Kim DS, Kim YH, Lee CH, Jang SH. Motor outcome according to the integrity of the corticospinal tract determined by diffusion tensor tractography in the early stage of corona radiata infarct. Neurosci Lett. 2007; 426:123–127.
Article
50. Jang SH. The corticospinal tract from the viewpoint of brain rehabilitation. J Rehabil Med. 2014; 46:193–199.
Article
51. Jang SH. A review of diffusion tensor imaging studies on motor recovery mechanisms in stroke patients. NeuroRehabilitation. 2011; 28:345–352.
Article
52. Kumar P, Kathuria P, Nair P, Prasad K. Prediction of upper limb motor recovery after subacute ischemic stroke using diffusion tensor imaging: a systematic review and meta-analysis. J Stroke. 2016; 18:50–59.
Article
53. Kumar P, Yadav AK, Misra S, Kumar A, Chakravarty K, Prasad K. Prediction of upper extremity motor recovery after subacute intracerebral hemorrhage through diffusion tensor imaging: a systematic review and meta-analysis. Neuroradiology. 2016; 58:1043–1050.
Article
54. Eliassen JC, Boespflug EL, Lamy M, Allendorfer J, Chu WJ, Szaflarski JP. Brain-mapping techniques for evaluating poststroke recovery and rehabilitation: a review. Top Stroke Rehabil. 2008; 15:427–450.
Article
55. Maeshima S, Osawa A, Nishio D, Hirano Y, Kigawa H, Takeda H. Diffusion tensor MR imaging of the pyramidal tract can predict the need for orthosis in hemiplegic patients with hemorrhagic stroke. Neurol Sci. 2013; 34:1765–1770.
Article
56. Rickards T, Sterling C, Taub E, Perkins-Hu C, Gauthier L, Graham M, et al. Diffusion tensor imaging study of the response to constraint-induced movement therapy of children with hemiparetic cerebral palsy and adults with chronic stroke. Arch Phys Med Rehabil. 2014; 95:506–514.e1.
Article
57. Lazaridou A, Astrakas L, Mintzopoulos D, Khanicheh A, Singhal AB, Moskowitz MA, et al. Diffusion tensor and volumetric magnetic resonance imaging using an MR-compatible hand-induced robotic device suggests training-induced neuroplasticity in patients with chronic stroke. Int J Mol Med. 2013; 32:995–1000.
Article
58. Dick AS, Tremblay P. Beyond the arcuate fasciculus: consensus and controversy in the connectional anatomy of language. Brain. 2012; 135:3529–3550.
Article
59. Forkel SJ, Thiebaut de, Dell'Acqua F, Kalra L, Murphy DG, Williams SC, et al. Anatomical predictors of aphasia recovery: a tractography study of bilateral perisylvian language networks. Brain. 2014; 137:2027–2039.
Article
60. Kim SH, Lee DG, You H, Son SM, Cho YW, Chang MC, et al. The clinical application of the arcuate fasciculus for stroke patients with aphasia: a diffusion tensor tractography study. NeuroRehabilitation. 2011; 29:305–310.
Article
61. Jang SH. Diffusion tensor imaging studies on arcuate fasciculus in stroke patients: a review. Front Hum Neurosci. 2013; 7:749.
Article
62. Schlaug G, Marchina S, Norton A. Evidence for plasticity in whitematter tracts of patients with chronic Broca’s aphasia undergoing intense intonation-based speech therapy. Ann N Y Acad Sci. 2009; 1169:385–394.
Article
63. Hosomi A, Nagakane Y, Yamada K, Kuriyama N, Mizuno T, Nishimura T, et al. Assessment of arcuate fasciculus with diffusion-tensor tractography may predict the prognosis of aphasia in patients with left middle cerebral artery infarcts. Neuroradiology. 2009; 51:549–555.
Article
64. Kim SH, Jang SH. Prediction of aphasia outcome using diffusion tensor tractography for arcuate fasciculus in stroke. AJNR Am J Neuroradiol. 2013; 34:785–790.
Article
65. Geva S, Correia M, Warburton EA. Diffusion tensor imaging in the study of language and aphasia. Aphasiology. 2011; 25:543–558.
Article
66. Arfanakis K, Haughton VM, Carew JD, Rogers BP, Dempsey RJ, Meyerand ME. Diffusion tensor MR imaging in diffuse axonal injury. AJNR Am J Neuroradiol. 2002; 23:794–802.
67. Strauss S, Hulkower M, Gulko E, Zampolin RL, Gutman D, Chitkara M, et al. Current clinical applications and future potential of diffusion tensor imaging in traumatic brain injury. Top Magn Reson Imaging. 2015; 24:353–362.
Article
68. Aoki Y, Inokuchi R. A voxel-based meta-analysis of diffusion tensor imaging in mild traumatic brain injury. Neurosci Biobehav Rev. 2016; 66:119–126.
Article
69. Niogi SN, Mukherjee P. Diffusion tensor imaging of mild traumatic brain injury. J Head Trauma Rehabil. 2010; 25:241–255.
Article
70. Aoki Y, Inokuchi R, Gunshin M, Yahagi N, Suwa H. Diffusion tensor imaging studies of mild traumatic brain injury: a meta-analysis. J Neurol Neurosurg Psychiatry. 2012; 83:870–876.
Article
71. Kraus MF, Susmaras T, Caughlin BP, Walker CJ, Sweeney JA, Little DM. White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain. 2007; 130:2508–2519.
Article
72. Ellingson BM, Ulmer JL, Kurpad SN, Schmit BD. Diffusion tensor MR imaging in chronic spinal cord injury. AJNR Am J Neuroradiol. 2008; 29:1976–1982.
Article
73. Facon D, Ozanne A, Fillard P, Lepeintre JF, Tournoux-Facon C, Ducreux D. MR diffusion tensor imaging and fiber tracking in spinal cord compression. AJNR Am J Neuroradiol. 2005; 26:1587–1594.
74. Vargas MI, Delavelle J, Jlassi H, Rilliet B, Viallon M, Becker CD, et al. Clinical applications of diffusion tensor tractography of the spinal cord. Neuroradiology. 2008; 50:25–29.
Article
75. Ducreux D, Fillard P, Facon D, Ozanne A, Lepeintre JF, Renoux J, et al. Diffusion tensor magnetic resonance imaging and fiber tracking in spinal cord lesions: current and future indications. Neuroimaging Clin N Am. 2007; 17:137–147.
Article
76. Kelley BJ, Harel NY, Kim CY, Papademetris X, Coman D, Wang X, et al. Diffusion tensor imaging as a predictor of locomotor function after experimental spinal cord injury and recovery. J Neurotrauma. 2014; 31:1362–1373.
Article
77. Rive MM, van Rooijen G, Veltman DJ, Phillips ML, Schene AH, Ruhé HG. Neural correlates of dysfunctional emotion regulation in major depressive disorder. A systematic review of neuroimaging studies. Neurosci Biobehav Rev. 2013; 37:2529–2553.
Article
78. Elliott R, Zahn R, Deakin JF, Anderson IM. Affective cognition and its disruption in mood disorders. Neuropsychopharmacology. 2011; 36:153–182.
Article
79. Liao Y, Huang X, Wu Q, Yang C, Kuang W, Du M, et al. Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. J Psychiatry Neurosci. 2013; 38:49–56.
Article
80. Wise T, Radua J, Nortje G, Cleare AJ, Young AH, Arnone D. Voxel-based meta-analytical evidence of structural disconnectivity in major depression and bipolar disorder. Biol Psychiatry. 2016; 79:293–302.
Article
81. Han KM, Choi S, Jung J, Na KS, Yoon HK, Lee MS, et al. Cortical thickness, cortical and subcortical volume, and white matter integrity in patients with their first episode of major depression. J Affect Disord. 2014; 155:42–48.
Article
82. Choi S, Han KM, Won E, Yoon BJ, Lee MS, Ham BJ. Association of brain-derived neurotrophic factor DNA methylation and reduced white matter integrity in the anterior corona radiata in major depression. J Affect Disord. 2015; 172:74–80.
Article
83. Olvet DM, Peruzzo D, Thapa-Chhetry B, Sublette ME, Sullivan GM, Oquendo MA, et al. A diffusion tensor imaging study of suicide attempters. J Psychiatr Res. 2014; 51:60–67.
Article
84. de Diego-Adeliño J, Pires P, Gómez-Ansón B, Serra-Blasco M, Vives-Gilabert Y, Puigdemont D, et al. Microstructural white-matter abnormalities associated with treatment resistance, severity and duration of illness in major depression. Psychol Med. 2014; 44:1171–1182.
Article
85. Murphy ML, Carballedo A, Fagan AJ, Morris D, Fahey C, Meaney J, et al. Neurotrophic tyrosine kinase polymorphism impacts white matter connections in patients with major depressive disorder. Biol Psychiatry. 2012; 72:663–670.
Article
86. Seok JH, Choi S, Lim HK, Lee SH, Kim I, Ham BJ. Effect of the COMT val158met polymorphism on white matter connectivity in patients with major depressive disorder. Neurosci Lett. 2013; 545:35–39.
Article
87. Zhu X, Wang X, Xiao J, Zhong M, Liao J, Yao S. Altered white matter integrity in first-episode, treatment-naive young adults with major depressive disorder: a tract-based spatial statistics study. Brain Res. 2011; 1369:223–229.
Article
88. Zuo N, Fang J, Lv X, Zhou Y, Hong Y, Li T, et al. White matter abnormalities in major depression: a tract-based spatial statistics and rumination study. PLoS One. 2012; 7:e37561.
Article
89. Versace A, Almeida JR, Quevedo K, Thompson WK, Terwilliger RA, Hassel S, et al. Right orbitofrontal corticolimbic and left corticocortical white matter connectivity differentiate bipolar and unipolar depression. Biol Psychiatry. 2010; 68:560–567.
Article
90. Blood AJ, Iosifescu DV, Makris N, Perlis RH, Kennedy DN, Dougherty DD, et al. ; Phenotype Genotype Project on Addiction and Mood Disorders. Microstructural abnormalities in subcortical reward circuitry of subjects with major depressive disorder. PLoS One. 2010; 5:e13945.
91. 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; 164:823–826.
Article
92. Srivastava S, Bhatia MS, Bhargava SK, Kumari R, Chandra S. A Diffusion tensor imaging study using a voxel-based analysis, region-of-interest method to analyze white matter abnormalities in first-episode, treatment-naïve major depressive disorder. J Neuropsychiatry Clin Neurosci. 2016; 28:131–137.
Article
93. Wu F, Tang Y, Xu K, Kong L, Sun W, Wang F, et al. Whiter matter abnormalities in medication-naive subjects with a single short-duration episode of major depressive disorder. Psychiatry Res. 2011; 191:80–83.
Article
94. Ouyang X, Tao HJ, Liu HH, Deng QJ, Sun ZH, Xu L, et al. White matter integrity deficit in treatment-naïve adult patients with major depressive disorder. East Asian Arch Psychiatry. 2011; 21:5–9.
95. Steele JD, Bastin ME, Wardlaw JM, Ebmeier KP. Possible structural abnormality of the brainstem in unipolar depressive illness: a tran scranial ultrasound and diffusion tensor magnetic resonance imaging study. J Neurol Neurosurg Psychiatry. 2005; 76:1510–1515.
Article
96. Jia Z, Huang X, Wu Q, Zhang T, Lui S, Zhang J, et al. High-field magnetic resonance imaging of suicidality in patients with major depressive disorder. Am J Psychiatry. 2010; 167:1381–1390.
Article
97. Tha KK, Terae S, Nakagawa S, Inoue T, Kitagawa N, Kako Y, et al. Impaired integrity of the brain parenchyma in non-geriatric patients with major depressive disorder revealed by diffusion tensor imaging. Psychiatry Res. 2013; 212:208–215.
Article
98. Lichenstein SD, Bishop JH, Verstynen TD, Yeh FC. Diffusion capillary phantom vs. human data: outcomes for reconstruction methods depend on evaluation medium. Front Neurosci. 2016; 10:407.
Article
99. Bracht T, Linden D, Keedwell P. A review of white matter microstructure alterations of pathways of the reward circuit in depression. J Affect Disord. 2015; 187:45–53.
Article
100. Steffens DC, Taylor WD, Denny KL, Bergman SR, Wang L. Structural integrity of the uncinate fasciculus and resting state functional connectivity of the ventral prefrontal cortex in late life depression. PLoS One. 2011; 6:e22697.
Article
101. Murphy ML, Frodl T. Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression. Biol Mood Anxiety Disord. 2011; 1:3.
Article
102. Jiang J, Zhao YJ, Hu XY, Du MY, Chen ZQ, Wu M, et al. Microstructural brain abnormalities in medication-free patients with major depressive disorder: a systematic review and meta-analysis of diffusion tensor imaging. J Psychiatry Neurosci. 2017; 42:150–163.
Article
103. Wen MC, Steffens DC, Chen MK, Zainal NH. Diffusion tensor imaging studies in late-life depression: systematic review and meta-analysis. Int J Geriatr Psychiatry. 2014; 29:1173–1184.
Article
104. Zhou Y, Qin LD, Chen J, Qian LJ, Tao J, Fang YR, et al. Brain microstructural abnormalities revealed by diffusion tensor images in patients with treatment-resistant depression compared with major depressive disorder before treatment. Eur J Radiol. 2011; 80:450–454.
Article
105. Vasavada MM, Leaver AM, Espinoza RT, Joshi SH, Njau SN, Woods RP, et al. Structural connectivity and response to ketamine therapy in major depression: a preliminary study. J Affect Disord. 2016; 190:836–841.
Article
106. Qin J, Wei M, Liu H, Chen J, Yan R, Yao Z, et al. Altered anatomical patterns of depression in relation to antidepressant treatment: evidence from a pattern recognition analysis on the topological organization of brain networks. J Affect Disord. 2015; 180:129–137.
Article
107. Fang P, Zeng LL, Shen H, Wang L, Li B, Liu L, et al. Increased corticallimbic anatomical network connectivity in major depression revealed by diffusion tensor imaging. PLoS One. 2012; 7:e45972.
Article
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