, Volume 55, Issue 5, pp 567–584

Towards automated detection of depression from brain structural magnetic resonance images

  • Kuryati Kipli
  • Abbas Z. Kouzani
  • Lana J. Williams
Diagnostic Neuroradiology



Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).


Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.


The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.


Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.


Depression Magnetic resonance imaging Pattern recognition Image processing Classification 


  1. 1.
    Sayers J (2001) The world health report 2001—mental health: new understanding, new hope. Bull World Health Organ 79:1085Google Scholar
  2. 2.
    Gruenberg AM, Goldstein RD, Pincus HA (2008) Classification of depression: research and diagnostic criteria: DSM-IV and ICD-10. In: Biology of depression. Wiley-VCH, Weinheim, pp 1–12. doi:10.1002/9783527619672.ch1
  3. 3.
    McDermott B, Baigent M, Chanen A, Fraser L, Graetz B, Hayman N, Newman L, Parikh N, Peirce B, Proimos J, Smalley T, Spence S (2011) Clinical practice guidelines: depression in adolescents and young adults. Beyond Blue Ltd., MelbourneGoogle Scholar
  4. 4.
    Bromet E, Andrade L, Hwang I, Sampson N, Alonso J, de Girolamo G, de Graaf R, Demyttenaere K, Hu C, Iwata N, Karam A, Kaur J, Kostyuchenko S, Lepine J-P, Levinson D, Matschinger H, Mora M, Browne M, Posada-Villa J, Viana M, Williams D, Kessler R (2011) Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine 9(1):90CrossRefPubMedGoogle Scholar
  5. 5.
    Twenge JM, Gentile B, DeWall CN, Ma D, Lacefield K, Schurtz DR (2010) Birth cohort increases in psychopathology among young Americans, 1938–2007: a cross-temporal meta-analysis of the MMPI. Clin Psychol Rev 30(2):145–154CrossRefPubMedGoogle Scholar
  6. 6.
    Aguirre AB (2008) Depression. Greenwood, WestportGoogle Scholar
  7. 7.
    Gelenberg AJ (2010) Depression symptomatology and neurobiology. J Clin Psychiat 71(1)Google Scholar
  8. 8.
    Gopal AA, Ropper AE, Tramontozzi LA (2008) Deja review: psychiatry. McGraw-Hill Medical, New YorkGoogle Scholar
  9. 9.
    Helén I (2011) The depression paradigm and beyond. Sci Stud 24(1):81–112Google Scholar
  10. 10.
    Both F, Hoogendoorn M, Michel Klein JT (2009) Design and analysis of an ambient intelligent system supporting depression therapy. Paper presented at the Proceedings of the Second International Conference on Health Informatics, Porto, PortugalGoogle Scholar
  11. 11.
    Marti J, Hine A (1998) The alternative health & medicine encyclopedia, 2nd edn. Gale Research, DetroitGoogle Scholar
  12. 12.
    Almeida OP, Alfonso H, Flicker L, Hankey GJ, Norman PE (2012) Cardiovascular disease, depression and mortality: the Health In Men Study. Am J Geriatric Psych 20(5):433–440CrossRefGoogle Scholar
  13. 13.
    Satin JR, Linden W, Phillips MJ (2009) Depression as a predictor of disease progression and mortality in cancer patients. Cancer 115(22):5349–5361CrossRefPubMedGoogle Scholar
  14. 14.
    O’Neil A, Williams E, Stevenson C, Oldenburg B, Berk M, Sanderson K (2012) Co-morbid cardiovascular disease and depression: sequence of disease onset is linked to mental but not physical self-rated health. Results from a cross-sectional, population-based study. Soc Psychiatry Psychiatr Epidemiol 47(7):1145–1151CrossRefPubMedGoogle Scholar
  15. 15.
    Covic T, Cumming S, Pallant J, Manolios N, Emery P, Conaghan P, Tennant A (2012) Depression and anxiety in patients with rheumatoid arthritis: prevalence rates based on a comparison of the Depression, Anxiety and Stress Scale (DASS) and the hospital, Anxiety and Depression Scale (HADS). BMC Psychiatry 12(1):6CrossRefPubMedGoogle Scholar
  16. 16.
    Katon WLCRPMMKAJHESWRA (2012) Association of depression with increased risk of dementia in patients with type 2 diabetes: the diabetes and aging study. Arch Gen Psychiatry 69(4):410–417CrossRefPubMedGoogle Scholar
  17. 17.
    Pan ALMSQ et al (2011) Increased mortality risk in women with depression and diabetes mellitus. Arch Gen Psychiatry 68(1):42–50CrossRefPubMedGoogle Scholar
  18. 18.
    Feinstein A (2011) Multiple sclerosis and depression. Multiple Sclerosis Journal 17(11):1276–1281CrossRefPubMedGoogle Scholar
  19. 19.
    Barlow DH, Durand VM (2009) Abnormal psychology: an integrative approach, 5th edn. Wadsworth Cengage Learning, BelmontGoogle Scholar
  20. 20.
    Venn HR, Watson S, Gallagher P, Young AH (2006) Facial expression perception: an objective outcome measure for treatment studies in mood disorders? Int J Neuropsychopharmacol 9(02):229–245CrossRefPubMedGoogle Scholar
  21. 21.
    US Department of Health & Human Services, National Institutes of Mental Health (2011) Depression. Available at Accessed 1 Nov 2012
  22. 22.
    Dale J, Sorour E, Milner G (2008) Do psychiatrists perform appropriate physical investigations for their patients? A review of current practices in a general psychiatric inpatient and outpatient setting. J Ment Heal 17(3):293–298CrossRefGoogle Scholar
  23. 23.
    Borgelt E, Buchman D, Illes J (2011) “This is why you’ve been suffering”: reflections of providers on neuroimaging in mental health care. Bioethical Inquiry 8(1):15–25CrossRefPubMedGoogle Scholar
  24. 24.
    Wright SL, Persad C (2007) Distinguishing between depression and dementia in older persons: neuropsychological and neuropathological correlates. J Geriatr Psych Neur 20(4):189–198CrossRefGoogle Scholar
  25. 25.
    Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23(1):56–62CrossRefPubMedGoogle Scholar
  26. 26.
    Robins LN, Helzer JE, Croughan J, Ratcliff KS (1981) National Institute of Mental Health Diagnostic Interview Schedule. Its history, characteristics, and validity. Arch Gen Psychiatry 38(4):381–389CrossRefPubMedGoogle Scholar
  27. 27.
    Zigmond A, Snaith R (1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67(6):361–370CrossRefPubMedGoogle Scholar
  28. 28.
    Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198CrossRefPubMedGoogle Scholar
  29. 29.
    Montgomery SA, Asberg M (1979) A new depression scale designed to be sensitive to change. Br J Psychiatry 134(4):382–389CrossRefPubMedGoogle Scholar
  30. 30.
    Rush AJ, Gullion CM, Basco MR, Jarrett RB, Trivedi MH (1996) The Inventory of Depressive Symptomatology (IDS): psychometric properties. Psychol Med 26(03):477–486CrossRefPubMedGoogle Scholar
  31. 31.
    John Rush A, Giles DE, Schlesser MA, Fulton CL, Weissenburger J, Burns C (1986) The inventory for depressive symptomatology (IDS): preliminary findings. J Psychiatr Res 18(1):65–87CrossRefGoogle Scholar
  32. 32.
    Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB (2003) The 16-Item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry 54(5):573–583CrossRefPubMedGoogle Scholar
  33. 33.
    Sen S, Sanacora G (2008) Major depression: emerging therapeutics. Mt Sinai J Med: J Transl Personalizes Med 75(3):204–225CrossRefGoogle Scholar
  34. 34.
    Rush AJ, Ryan ND (2002) Current and emerging therapeutics for depression. In: Charney D, Coyle J, Nemeroff C, Davis K (eds) Neuropsychopharmacology: the fifth generation of progress. Lippincott Williams & Wilkins, Philadelphia, pp 1081–1095Google Scholar
  35. 35.
    NIMH (2010) Neuroimaging and mental illness: a window into the brain. Frequently asked questions about brain scans. National Institutes of Health, US Department of Health and Human ServicesGoogle Scholar
  36. 36.
    Chen C-H, Ridler K, Suckling J, Williams S, Fu CHY, Merlo-Pich E, Bullmore E (2007) Brain imaging correlates of depressive symptom severity and predictors of symptom improvement after antidepressant treatment. Biol Psychiatry 62(5):407–414CrossRefPubMedGoogle Scholar
  37. 37.
    Symms M, Jäger HR, Schmierer K, Yousry TA (2004) A review of structural magnetic resonance neuroimaging. J Neurol Neurosurg Psychiatry 75(9):1235–1244CrossRefPubMedGoogle Scholar
  38. 38.
    Niida R, Niida A, Motomura M, Uechi A (2011) Diagnosis of depression by MRI scans with the use of VSRAD—a promising auxiliary means of diagnosis: a report of 10 years research. Int J Gen Med 4(1):377–387CrossRefPubMedGoogle Scholar
  39. 39.
    Arnone D, McIntosh AM, Ebmeier KP, Munafò MR, Anderson IM (2011) Magnetic resonance imaging studies in unipolar depression: Systematic review and meta-regression analyses. Eur Neuropsychopharmacol 22:1–16Google Scholar
  40. 40.
    Firbank MJ, Lloyd AJ, Ferrier N, O’Brien JT (2004) A volumetric study of MRI signal hyperintensities in late-life depression. Am J Geriat Psychiat 12(6):606–612Google Scholar
  41. 41.
    Janssen J, Hulshoff Pol HE, Lampe IK, Schnack HG, de Leeuw F-E, Kahn RS, Heeren TJ (2004) Hippocampal changes and white matter lesions in early-onset depression. Biol Psychiatry 56(11):825–831CrossRefPubMedGoogle Scholar
  42. 42.
    Konarski JZ, McIntyre RS, Kennedy SH, Rafi-Tari S, Soczynska JK, Ketter TA (2008) Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder. Bipolar Disord 10(1):1–37CrossRefPubMedGoogle Scholar
  43. 43.
    Li C-T, Lin C-P, Chou K-H, Chen IY, Hsieh J-C, Wu C-L, Lin W-C, Su T-P (2010) Structural and cognitive deficits in remitting and non-remitting recurrent depression: a voxel-based morphometric study. NeuroImage 50(1):347–356CrossRefPubMedGoogle Scholar
  44. 44.
    Meisenzahl E, Seifert D, Bottlender R, Teipel S, Zetzsche T, Jäger M, Koutsouleris N, Schmitt G, Scheuerecker J, Burgermeister B, Hampel H, Rupprecht T, Born C, Reiser M, Möller H-J, Frodl T (2010) Differences in hippocampal volume between major depression and schizophrenia: a comparative neuroimaging study. Eur Arch Psy Clin N 260(2):127–137CrossRefGoogle Scholar
  45. 45.
    Tamburo RJ, Siegle GJ, Stetten GD, Cois CA, Butters MA, Reynolds Iii CF, Aizenstein HJ (2009) Amygdalae morphometry in late-life depression. Int J Geriatr Psych 24(8):837–846CrossRefGoogle Scholar
  46. 46.
    Videbech P, Ravnkilde B (2004) Hippocampal volume and depression: a meta-analysis of MRI studies. Am J Psychiatry 161(11):1957–1966CrossRefPubMedGoogle Scholar
  47. 47.
    Lorenzetti V, Allen NB, Fornito A, Yücel M (2009) Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies. J Affect Disord 117(1–2):1–17CrossRefPubMedGoogle Scholar
  48. 48.
    Frisoni GB, Fox NC, Jack CR, Scheltens P, Thompson PM (2010) The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 6(2):67–77CrossRefPubMedGoogle Scholar
  49. 49.
    Petrella JR, Coleman RE, Doraiswamy PM (2003) Neuroimaging and early diagnosis of Alzheimer disease: a look to the future. Radiology 226(2):315–336CrossRefPubMedGoogle Scholar
  50. 50.
    Steffens DC, Krishnan KRR (1998) Structural neuroimaging and mood disorders: recent findings, implications for classification, and future directions. Biol Psychiatry 43(10):705–712CrossRefPubMedGoogle Scholar
  51. 51.
    Zeng L-L, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D (2012) Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135(5):1498–1507CrossRefPubMedGoogle Scholar
  52. 52.
    Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, Nelson SM, Wig GS, Vogel AC, Lessov-Schlaggar CN, Barnes KA, Dubis JW, Feczko E, Coalson RS, Pruett JR, Barch DM, Petersen SE, Schlaggar BL (2010) Prediction of individual brain maturity using fMRI. Science 329(5997):1358–1361CrossRefPubMedGoogle Scholar
  53. 53.
    Sheline YI (2003) Neuroimaging studies of mood disorder effects on the brain. Biol Psychiatry 54(3):338–352CrossRefPubMedGoogle Scholar
  54. 54.
    Wilke M, Kowatch RA, DelBello MP, Mills NP, Holland SK (2004) Voxel-based morphometry in adolescents with bipolar disorder: first results. Psychiat Res: Neuroimag 131(1):57–69CrossRefGoogle Scholar
  55. 55.
    Turner JA, Potkin SG, Brown GG, Keator DB, McCarthy G, Glover GH (2007) Neuroimaging for the diagnosis and study of psychiatric disorders [life sciences]. IEEE Signal Processing Magazine 24(4):112–117CrossRefGoogle Scholar
  56. 56.
    Gotlib IH, Hamilton JP (2008) Neuroimaging and depression. Curr Dir Psychol Sci 17(2):159–163CrossRefGoogle Scholar
  57. 57.
    Cannon DM (2010) Neuroimaging and the pathophysiology and treatment of depression: recent advances and future needs. Karger, BaselGoogle Scholar
  58. 58.
    Schmidt HD, Shelton RC, Duman RS (2011) Functional biomarkers of depression: diagnosis, treatment, and pathophysiology. Neuropsychopharmacology 36:2375–2394Google Scholar
  59. 59.
    Low LSA, Maddage NC, Lech M, Allen N (2009) Mel frequency cepstral feature and Gaussian mixtures for modeling clinical depression in adolescents. In: Cognitive Informatics, 2009, ICCI ‘09, 8th IEEE International Conference on 15–17 June, pp 346–350Google Scholar
  60. 60.
    Cohn JF, Kruez TS, Matthews I, Yang Y, Nguyen MH, Padilla MT, Zhou F, de la Torre F (2009) Detecting depression from facial actions and vocal prosody. Proceedings International Conference on Affective Computing and IntelligentGoogle Scholar
  61. 61.
    Yu-Hsun L, Yong-Sheng C, Li-Fen C (2009) Automated sleep staging using single EEG channel for REM sleep deprivation. In: Bioinformatics and BioEngineering, 2009, BIBE ’09, Ninth IEEE International Conference on 22–24 June, pp 439–442Google Scholar
  62. 62.
    Hahn T, Marquand AF, Ehlis A-C, Dresler T, Kittel-Schneider S, Jarczok TA, Lesch K-P, Jakob PM, Mourao-Miranda J, Brammer MJ, Fallgatter AJ (2011) Integrating neurobiological markers of depression. Arch Gen Psychiatry 68(4):361–368CrossRefPubMedGoogle Scholar
  63. 63.
    Fu CHY, Mourao-Miranda J, Costafreda SG, Khanna A, Marquand AF, Williams SCR, Brammer MJ (2008) Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 63(7):656–662CrossRefPubMedGoogle Scholar
  64. 64.
    Mundt JC, Snyder PJ, Cannizzaro MS, Chappie K, Geralts DS (2007) Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology. J Neurolinguist 20(1):50–64CrossRefGoogle Scholar
  65. 65.
    Low LSA, Maddage MC, Lech M, Sheeber LB, Allen NB (2011) Detection of clinical depression in adolescents’ speech during family interactions. IEEE T Bio-Med Eng 58(3):574–586CrossRefGoogle Scholar
  66. 66.
    Baltes C, Mueggler T, Rudin M (2010) Magnetic resonance imaging. In: Stolerman IP (ed) Encyclopedia of psychopharmacology, vol 2. Springer, Berlin, p 739Google Scholar
  67. 67.
    Wattjes MP (2011) Structural MRI. Int Psychogeriatr 23(SupplementS2):S13–S24CrossRefPubMedGoogle Scholar
  68. 68.
    Orrù G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A (2012) Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36(4):1140–1152CrossRefPubMedGoogle Scholar
  69. 69.
    Borgwardt S, Koutsouleris N, Aston J, Studerus E, Smieskova R, Riecher-Rössler A, Meisenzahl EM (2012) Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition. Schizophr Bull (in press)Google Scholar
  70. 70.
    Mwangi B, Ebmeier KP, Matthews K, Douglas Steele J (2012) Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 135(5):1508–1521CrossRefPubMedGoogle Scholar
  71. 71.
    Schneider B, Prvulovic D, Oertel-Knöchel V, Knöchel C, Reinke B, Grexa M, Weber B, Hampel H (2011) Biomarkers for major depression and its delineation from neurodegenerative disorders. Prog Neurobiol 95:703–717Google Scholar
  72. 72.
    Atkinson AJ, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, Hoth DF, Oates JA, Peck CC, Schooley RT, Spilker BA, Woodcock J, Zeger SL (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69(3):89–95CrossRefGoogle Scholar
  73. 73.
    Davidson RJ, Pizzagalli D, Nitschke JB, Putnam K (2002) Depression: perspectives from affective neuroscience. Annu Rev Psychol 53(1):545–574CrossRefPubMedGoogle Scholar
  74. 74.
    Masdeu J (2011) Neuroimaging in psychiatric disorders. Neurotherapeutics 8(1):93–102CrossRefPubMedGoogle Scholar
  75. 75.
    Mossner R, Mikova O, Koutsilieri E, Saoud M, Ehlis A-C, Muller N, Fallgatter AJ, Riederer P (2007) Consensus paper of the WFSBP Task Force on Biological Markers: biological markers in depression. World J Biol Psychiatr 8(3):141–174CrossRefGoogle Scholar
  76. 76.
    Kempton MJ, Salvador Z, Munafo MR, Geddes JR, Simmons A, Frangou S, Williams SCR (2011) Structural neuroimaging studies in major depressive disorder: meta-analysis and comparison with bipolar disorder. Arch Gen Psychiatry 68(7):675–690CrossRefPubMedGoogle Scholar
  77. 77.
    Gotlib IH, Joormann J (2010) Cognition and depression: current status and future directions. Annu Rev Clin Psychol 6(1):285–312CrossRefPubMedGoogle Scholar
  78. 78.
    Haller S, Badoud S, Nguyen D, Garibotto V, Lovblad KO, Burkhard PR (2012) Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results. ANJR Am J Neuroradiol 33:2123–2128Google Scholar
  79. 79.
    Haynes J-D, Rees G (2006) Decoding mental states from brain activity in humans. Nat Rev Neurosci 7(7):523–534CrossRefPubMedGoogle Scholar
  80. 80.
    Bergouignan L, Chupin M, Czechowska Y, Kinkingnéhun S, Lemogne C, Le Bastard G, Lepage M, Garnero L, Colliot O, Fossati P (2009) Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression? NeuroImage 45(1):29–37CrossRefPubMedGoogle Scholar
  81. 81.
    Tae WS, Kim S, Lee K, Nam E-C, Kim K (2008) Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder. Neuroradiology 50(7):569–581CrossRefPubMedGoogle Scholar
  82. 82.
    Abe O, Yamasue H, Kasai K, Yamada H, Aoki S, Inoue H, Takei K, Suga M, Matsuo K, Kato T, Masutani Y, Ohtomo K (2010) Voxel-based analyses of gray/white matter volume and diffusion tensor data in major depression. Psychiatr Res: Neuroimag 181(1):64–70CrossRefGoogle Scholar
  83. 83.
    Amico F, Meisenzahl E, Koutsouleris N, Reiser M, Möller H-J, Frodl T (2011) Structural MRI correlates for vulnerability and resilience to major depressive disorder. J Psychiatr Neurosci 36(1):15–22CrossRefGoogle Scholar
  84. 84.
    Egger K, Schocke M, Weiss E, Auffinger S, Esterhammer R, Goebel G, Walch T, Mechtcheriakov S, Marksteiner J (2008) Pattern of brain atrophy in elderly patients with depression revealed by voxel-based morphometry. Psychiatr Res: Neuroimag 164(3):237–244CrossRefGoogle Scholar
  85. 85.
    Soriano-Mas C, Hernández-Ribas R, Pujol J, Urretavizcaya M, Deus J, Harrison BJ, Ortiz H, López-Solà M, Menchón JM, Cardoner N (2011) Cross-sectional and longitudinal assessment of structural brain alterations in melancholic depression. Biol Psychiatry 69(4):318–325CrossRefPubMedGoogle Scholar
  86. 86.
    Chen PS, McQuoid DR, Payne ME, Steffens DC (2006) White matter and subcortical gray matter lesion volume changes and late-life depression outcome: a 4-year magnetic resonance imaging study. Int Psychogeriatr 18(3):445–456CrossRefPubMedGoogle Scholar
  87. 87.
    Taylor WD, Zhao Z, Ashley-Koch A, Payne ME, Steffens DC, Krishnan RR, Hauser E, Macfall JR (2011) Fiber tract-specific white matter lesion severity findings in late-life depression and by AGTR1 A1166C genotype. Hum Brain Mapp 34:295–303Google Scholar
  88. 88.
    Hannestad J, Taylor WD, McQuoid DR, Payne ME, Krishnan KRR, Steffens DC, MacFall JR (2006) White matter lesion volumes and caudate volumes in late-life depression. Int JGeriatr Psychiat 21(12):1193–1198CrossRefGoogle Scholar
  89. 89.
    Rosso IM, Cintron CM, Steingard RJ, Renshaw PF, Young AD, Yurgelun-Todd DA (2005) Amygdala and hippocampus volumes in pediatric major depression. Biol Psychiatry 57(1):21–26CrossRefPubMedGoogle Scholar
  90. 90.
    Zhao Z, Taylor WD, Styner M, Steffens DC, Krishnan KRR, MacFall JR (2008) Hippocampus shape analysis and late-life depression. PLoS One 3(3):e1837CrossRefPubMedGoogle Scholar
  91. 91.
    Frodl T, Stauber J, Schaaff N, Koutsouleris N, Scheuerecker J, Ewers M, Omerovic M, Opgen-Rhein M, Hampel H, Reiser M, Möller HJ, Meisenzahl E (2010) Amygdala reduction in patients with ADHD compared with major depression and healthy volunteers. Acta Psychiatr Scand 121(2):111–118CrossRefPubMedGoogle Scholar
  92. 92.
    Kronenberg G, Tebartz van Elst L, Regen F, Deuschle M, Heuser I, Colla M (2009) Reduced amygdala volume in newly admitted psychiatric in-patients with unipolar major depression. J Psychiatr Res 43(13):1112–1117CrossRefPubMedGoogle Scholar
  93. 93.
    Penttilä J, Paillère-Martino ML, Martinot JL, Ringuenet D, Wessa M, Houenou J, Gallarda T, Bellivier F, Galinowski A, Bruguière P, Pinabel F, Leboyer M, Olié JP, Duchesnay E, Artiges E, Mangin JF, Cachia A (2009) Cortical folding in patients with bipolar disorder or unipolar depression. J Psychiatr Neurosci 34(2):127–135Google Scholar
  94. 94.
    Mechelli A, Price CJ, Friston KJ, Ashburner J (2005) Voxel-based morphometry of the human brain: methods and applications. Current Med Imaging Rev 1(2):105–113CrossRefGoogle Scholar
  95. 95.
    Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. NeuroImage 11(6):805–821CrossRefPubMedGoogle Scholar
  96. 96.
    Ashburner J, Friston KJ (2005) Unified segmentation. NeuroImage 26(3):839–851CrossRefPubMedGoogle Scholar
  97. 97.
    Friston KJ, Ashburner J, Kiebel SJ, Nichols TE, Penny WD (2007) Statistical parametric mapping: the analysis of functional brain images. Academic, BostonGoogle Scholar
  98. 98.
    Chupin M, Hammers A, Liu RSN, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L (2009) Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. NeuroImage 46(3):749–761CrossRefPubMedGoogle Scholar
  99. 99.
    Morey RA, Petty CM, Xu Y, Pannu Hayes J, Wagner Ii HR, Lewis DV, LaBar KS, Styner M, McCarthy G (2009) A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes. NeuroImage 45(3):855–866CrossRefPubMedGoogle Scholar
  100. 100.
    Ambarki K, Wåhlin A, Birgander R, Eklund A, Malm J (2011) MR imaging of brain volumes: evaluation of a fully automatic software. Am J Neuroradiol 32(2):408–412CrossRefPubMedGoogle Scholar
  101. 101.
    Matsuo K, Kopecek M, Nicoletti MA, Hatch JP, Watanabe Y, Nery FG, Zunta-Soares G, Soares JC (2011) New structural brain imaging endophenotype in bipolar disorder. Mol Psychiatry 17:412–420Google Scholar
  102. 102.
    Worsley KJ (2003) Developments in random field theory. In: Frackowiak RSJ, Friston KJ, Frith C et al. (eds) Human brain function, 2nd edn. Academic, New YorkGoogle Scholar
  103. 103.
    Mühlau M, Wohlschläger AM, Gaser C, Valet M, Weindl A, Nunnemann S, Peinemann A, Etgen T, Ilg R (2009) Voxel-based morphometry in individual patients: a pilot study in early Huntington disease. Am J Neuroradiol 30(3):539–543CrossRefPubMedGoogle Scholar
  104. 104.
    Campbell S, Marriott M, Nahmias C, MacQueen GM (2004) Lower hippocampal volume in patients suffering from depression: a meta-analysis. Am J Psychiatry 161(4):598–607CrossRefPubMedGoogle Scholar
  105. 105.
    Koolschijn PCMP, van Haren NEM, Lensvelt-Mulders GJLM, Hulshoff Pol HE, Kahn RS (2009) Brain volume abnormalities in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Hum Brain Mapp 30(11):3719–3735CrossRefPubMedGoogle Scholar
  106. 106.
    Rayner L, Price A, Evans A, Valsraj K, Hotopf M, Higginson I (2011) Antidepressants for the treatment of depression in palliative care: systematic review and meta-analysis. Palliat Med 25(1):36–51CrossRefPubMedGoogle Scholar
  107. 107.
    Costafreda SG, Chu C, Ashburner J, Fu CHY (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 4(7):e6353CrossRefPubMedGoogle Scholar
  108. 108.
    Nouretdinov I, Costafreda SG, Gammerman A, Chervonenkis A, Vovk V, Vapnik V, Fu CHY (2011) Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage 56(2):809–813CrossRefPubMedGoogle Scholar
  109. 109.
    Gong Q, Wu Q, Scarpazza C, Lui S, Jia Z, Marquand A, Huang X, McGuire P, Mechelli A (2011) Prognostic prediction of therapeutic response in depression using high-field MR imaging. NeuroImage 55(4):1497–1503CrossRefPubMedGoogle Scholar
  110. 110.
    Mwangi B, Matthews K, Steele JD (2012) Prediction of illness severity in patients with major depression using structural MR brain scans. J Magn Reson Imaging 35(1):64–71CrossRefPubMedGoogle Scholar
  111. 111.
    Bao F, Ghosh S, Giard J, Parsey R, Klein A (2011) Brain shape analysis for predicting treatment remission in major depressive disorder. Paper presented at the 41st Annual Meeting for the Society for NeuroscienceGoogle Scholar
  112. 112.
    Arimura H, Magome T, Yamashita Y, Yamamoto D (2009) Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms 2(3):925–952CrossRefGoogle Scholar
  113. 113.
    Wellcome Trust Centre for Neuroimaging. SPM. Available at
  114. 114.
    Kikinis R, Shenton ME, Gerig G, Martin J, Anderson M, Metcalf D, Guttmann CRG, McCarley RW, Jolesz FA, Lorensen W, Cline H (1992) Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. J Magn Reson Imaging 2(6):619–629CrossRefPubMedGoogle Scholar
  115. 115.
    Steffens DC, Byrum CE, McQuoid DR, Greenberg DL, Payne ME, Blitchington TF, MacFall JR, Krishnan KRR (2000) Hippocampal volume in geriatric depression. Biol Psychiatry 48(4):301–309CrossRefPubMedGoogle Scholar
  116. 116.
    Andreasen NC, Cohen G, Harris G, Cizadlo T, Parkkinen J, Rezai K, Swayze Ii VW (1992) Image processing for the study of brain structure and function: problems and programs. J Neuropsych Clin N 4(2):125–133Google Scholar
  117. 117.
    Mayo Clinic’s Biomedical Imaging Resource (BIR). Analyze software system. Available at
  118. 118.
    FMRIB Software Library. Available at
  119. 119.
    BrainVISA. Available at
  120. 120.
    DrView software. Available at
  121. 121.
    Lacerda ALT, Brambilla P, Sassi RB, Nicoletti MA, Mallinger AG, Frank E, Kupfer DJ, Keshavan MS, Soares JC (2005) Anatomical MRI study of corpus callosum in unipolar depression. J Psychiatr Res 39(4):347–354CrossRefPubMedGoogle Scholar
  122. 122.
    Delbello MP, Zimmerman ME, Mills NP, Getz GE, Strakowski SM (2004) Magnetic resonance imaging analysis of amygdala and other subcortical brain regions in adolescents with bipolar disorder. Bipolar Disord 6(1):43–52CrossRefPubMedGoogle Scholar
  123. 123.
    van Eijndhoven P, van Wingen G, van Oijen K, Rijpkema M, Goraj B, Jan Verkes R, Oude Voshaar R, Fernández G, Buitelaar J, Tendolkar I (2009) Amygdala volume marks the acute state in the early course of depression. Biol Psychiatry 65(9):812–818CrossRefPubMedGoogle Scholar
  124. 124.
    Chen MC, Hamilton JP, Gotlib IH (2010) Decreased hippocampal volume in healthy girls at risk of depression. Arch Gen Psychiatry 67(3):270–276CrossRefPubMedGoogle Scholar
  125. 125.
    Lochhead RA, Parsey RV, Oquendo MA, Mann JJ (2004) Regional brain gray matter volume differences in patients with bipolar disorder as assessed by optimized voxel-based morphometry. Biol Psychiatry 55(12):1154–1162CrossRefPubMedGoogle Scholar
  126. 126.
    Frodl T, Meisenzahl EM, Zetzsche T, Born C, Jäger M, Groll C, Bottlender R, Leinsinger G, Möller H-J (2003) Larger amygdala volumes in first depressive episode as compared to recurrent major depression and healthy control subjects. Biol Psychiatry 53(4):338–344CrossRefPubMedGoogle Scholar
  127. 127.
    Kipli K, Kouzani AZ, Joordens M (2012) Computer-aided detection of depression from magnetic resonance images. Paper presented at the ICME International Conference on Complex Medical Engineering (CME 2012), Kobe, JapanGoogle Scholar
  128. 128.
    Wu M (2010) Registration and segmentation of brain MR images from elderly individuals. Dissertation, Universiy of PittsburghGoogle Scholar
  129. 129.
    NIRL imaging database. Available at
  130. 130.
    Baaré WFC, Vinberg M, Knudsen GM, Paulson OB, Langkilde AR, Jernigan TL, Kessing LV (2010) Hippocampal volume changes in healthy subjects at risk of unipolar depression. J Psychiatr Res 44(10):655–662CrossRefPubMedGoogle Scholar
  131. 131.
    Pizzagalli DA, Oakes TR, Fox AS, Chung MK, Larson CL, Abercrombie HC, Schaefer SM, Benca RM, Davidson RJ (2003) Functional but not structural subgenual prefrontal cortex abnormalities in melancholia. Mol Psychiatry 9(4):393–405CrossRefGoogle Scholar
  132. 132.
    Ballmaier M, Sowell ER, Thompson PM, Kumar A, Narr KL, Lavretsky H, Welcome SE, DeLuca H, Toga AW (2004) Mapping brain size and cortical gray matter changes in elderly depression. Biol Psychiatry 55(4):382–389CrossRefPubMedGoogle Scholar
  133. 133.
    Woods RP, Grafton ST, Holmes CJ, Cherry SR, Mazziotta JC (1998) Automated image registration: I. General methods and intrasubject, intramodality validation. J Comput Assist Tomogr 22(1):139–152CrossRefPubMedGoogle Scholar
  134. 134.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155CrossRefPubMedGoogle Scholar
  135. 135.
    MacFall JR, Taylor WD, Rex DE, Pieper S, Payne ME, McQuoid DR, Steffens DC, Kikinis R, Toga AW, Krishnan KRR (2005) Lobar distribution of lesion volumes in late-life depression: the Biomedical Informatics Research Network (BIRN). Neuropsychopharmacology 31(7):1500–1507CrossRefPubMedGoogle Scholar
  136. 136.
    Munn MA, Alexopoulos J, Nishino T, Babb CM, Flake LA, Singer T, Ratnanather JT, Huang H, Todd RD, Miller MI, Botteron KN (2007) Amygdala volume analysis in female twins with major depression. Biol Psychiatry 62(5):415–422CrossRefPubMedGoogle Scholar
  137. 137.
    Takahashi T, Yücel M, Lorenzetti V, Walterfang M, Kawasaki Y, Whittle S, Suzuki M, Pantelis C, Allen NB (2010) An MRI study of the superior temporal subregions in patients with current and past major depression. Progress Neuro-Psychopharmacol Biol Psychiatr 34(1):98–103CrossRefGoogle Scholar
  138. 138.
    Takahashi T, Yücel M, Lorenzetti V, Tanino R, Whittle S, Suzuki M, Walterfang M, Pantelis C, Allen NB (2010) Volumetric MRI study of the insular cortex in individuals with current and past major depression. J Affect Disord 121(3):231–238CrossRefPubMedGoogle Scholar
  139. 139.
    Vythilingam M, Charles HC, Tupler LA, Blitchington T, Kelly L, Krishnan KRR (2003) Focal and lateralized subcortical abnormalities in unipolar major depressive disorder: an automated multivoxel proton magnetic resonance spectroscopy study. Biol Psychiatry 54(7):744–750CrossRefPubMedGoogle Scholar
  140. 140.
    Zetzsche T, Frodl T, Preuss UW, Schmitt G, Seifert D, Leinsinger G, Born C, Reiser M, Möller H-J, Meisenzahl EM (2006) Amygdala volume and depressive symptoms in patients with borderline personality disorder. Biol Psychiatry 60(3):302–310CrossRefPubMedGoogle Scholar
  141. 141.
    Lorenzetti V, Allen NB, Whittle S, Yücel M (2010) Amygdala volumes in a sample of current depressed and remitted depressed patients and healthy controls. J Affect Disord 120(1–3):112–119CrossRefPubMedGoogle Scholar
  142. 142.
    Lorenzetti V, Allen NB, Fornito A, Pantelis C, De Plato G, Ang A, Yücel M (2009) Pituitary gland volume in currently depressed and remitted depressed patients. Psychiatr Res: Neuroimag 172(1):55–60CrossRefGoogle Scholar
  143. 143.
    Frodl T, Zill P, Baghai T, Schüle C, Rupprecht R, Zetzsche T, Bondy B, Reiser M, Möller H-J, Meisenzahl EM (2008) Reduced hippocampal volumes associated with the long variant of the tri- and diallelic serotonin transporter polymorphism in major depression. Am J Med Gen Part B: Neuropsychiatr Gen 147B(7):1003–1007CrossRefGoogle Scholar
  144. 144.
    Frodl T, Jäger M, Smajstrlova I, Born C, Bottlender R, Palladino T, Reiser M, Möller H-J, Meisenzahl E (2008) Effect of hippocampal and amygdala volumes on clinical outcomes in major depression: a 3-year prospective magnetic resonance imaging study. J Psychiatr Neurosci: JPN 33(5):423–430Google Scholar
  145. 145.
    Frodl T, Schaub A, Banac S, Charypar M, Markus J, Kümmler P, Bottlender R, Zetzsche T, Born C, Leinsinger G, Reiser M, Möller H-J, Meisenzahl EM (2006) Reduced hippocampal volume correlates with executive dysfunctioning in major depression. J Psychiatr Neurosci 31(5):316–323Google Scholar
  146. 146.
    Frodl T, Meisenzahl EM, Zetzsche T, Born C, Groll C, Jäger M, Leisinger G, Bottlender R, Hahn K, Möller H-J (2002) Hippocampal changes in patients with a first episode of major depression. Am J Psychiatry 159(7):1112CrossRefPubMedGoogle Scholar
  147. 147.
    Ashtari M, Greenwald BS, Kramer-Ginsberg E, Hu J, Wu H, Patel M, Aupperle P, Pollack S (1999) Hippocampal/amygdala volumes in geriatric depression. Psychol Med 29(03):629–638CrossRefPubMedGoogle Scholar
  148. 148.
    Posener JA, Wang L, Price JL, Gado MH, Province MA, Miller MI, Babb CM, Csernansky JG (2003) High-dimensional mapping of the hippocampus in depression. Am J Psychiatry 160(1):83–89CrossRefPubMedGoogle Scholar
  149. 149.
    Frodl T, Meisenzahl E, Zetzsche T, Bottlender R, Born C, Groll C, Jäger M, Leinsinger G, Hahn K, Möller H-J (2002) Enlargement of the amygdala in patients with a first episode of major depression. Biol Psychiatry 51(9):708–714CrossRefPubMedGoogle Scholar
  150. 150.
    Frodl T, Jäger M, Born C, Ritter S, Kraft E, Zetzsche T, Bottlender R, Leinsinger G, Reiser M, Möller H-J, Meisenzahl E (2008) Anterior cingulate cortex does not differ between patients with major depression and healthy controls, but relatively large anterior cingulate cortex predicts a good clinical course. Psychiatr Res: Neuroimag 163(1):76–83CrossRefGoogle Scholar
  151. 151.
    Walterfang M, Yücel M, Barton S, Reutens DC, Wood AG, Chen J, Lorenzetti V, Velakoulis D, Pantelis C, Allen NB (2009) Corpus callosum size and shape in individuals with current and past depression. J Affect Disord 115(3):411–420CrossRefPubMedGoogle Scholar
  152. 152.
    Hviid LB, Ravnkilde B, Ahdidan J, Rosenberg R, Stødkilde-Jørgensen H, Videbech P (2010) Hippocampal visuospatial function and volume in remitted depressed patients: an 8-year follow-up study. J Affect Disord 125(1–3):177–183CrossRefPubMedGoogle Scholar
  153. 153.
    Taylor WD, MacFall JR, Steffens DC, Payne ME, Provenzale JM, Krishnan KRR (2003) Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacol Biol Psychiatr 27(3):539–544CrossRefGoogle Scholar
  154. 154.
    Taylor WD, MacFall JR, Payne ME, McQuoid DR, Steffans DC, Provenzale JM, Krishnan KRR (2007) Orbitofrontal cortex volume in late life depression: influence of hyperintense lesions and genetic polymorphisms. Psychol Med 37(12):1763–1773CrossRefPubMedGoogle Scholar
  155. 155.
    Taylor WD, Steffens DC, McQuoid DR, Payne ME, Lee S-H, Lai T-J, Krishnan KRR (2003) Smaller orbital frontal cortex volumes associated with functional disability in depressed elders. Biol Psychiatry 53(2):144–149CrossRefPubMedGoogle Scholar
  156. 156.
    Taylor WD, Steffens DC, Payne ME, MacFall JR, Marchuk DA, Svenson IK, Krishnan KRR (2005) Influence of serotonin transporter promoter region polymorphisms on hippocampal volumes in late-life depression. Arch Gen Psychiatry 62(5):537–544CrossRefPubMedGoogle Scholar
  157. 157.
    Taylor WD, Zchner S, McQuoid DR, Payne ME, MacFall JR, Steffens DC, Speer MC, Krishnan KRR (2008) The brain-derived neurotrophic factor VAL66MET polymorphism and cerebral white matter hyperintensities in late-life depression. Am J Geriatric Psychiatr 16(4):263–271CrossRefGoogle Scholar
  158. 158.
    Pan C-C, McQuoid DR, Taylor WD, Payne ME, Ashley-Koch A, Steffens DC (2009) Association analysis of the COMT/MTHFR genes and geriatric depression: an MRI study of the putamen. Int J Geriatr Psychiatr 24(8):847–855CrossRefGoogle Scholar
  159. 159.
    Greenberg DL, Payne ME, MacFall JR, Steffens DC, Krishnan RR (2008) Hippocampal volumes and depression subtypes. Psychiatr Res: Neuroimag 163(2):126–132CrossRefGoogle Scholar
  160. 160.
    Potter GG, Blackwell AD, McQuoid DR, Payne ME, Steffens DC, Sahakian BJ, Welsh-Bohmer KA, Krishnan KRR (2007) Prefrontal white matter lesions and prefrontal task impersistence in depressed and nondepressed elders. Neuropsychopharmacology 32(10):2135–2142CrossRefPubMedGoogle Scholar
  161. 161.
    Bae JN, MacFall JR, Krishnan KRR, Payne ME, Steffens DC, Taylor WD (2006) Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 60(12):1356–1363CrossRefPubMedGoogle Scholar
  162. 162.
    Steffens DC, McQuoid DR, Welsh-Bohmer KA, Krishnan KRR (2003) Left orbital frontal cortex volume and performance on the Benton visual retention test in older depressives and controls. Neuropsychopharmacology 28(12):2179–2183PubMedGoogle Scholar
  163. 163.
    Steffens DC, Trost WT, Payne ME, Hybels CF, MacFall JR (2003) Apolipoprotein E genotype and subcortical vascular lesions in older depressed patients and control subjects. Biol Psychiatry 54(7):674–681CrossRefPubMedGoogle Scholar
  164. 164.
    Lee S-H, Payne ME, Steffens DC, McQuoid DR, Lai T-J, Provenzale JM, Krishnan KRR (2003) Subcortical lesion severity and orbitofrontal cortex volume in geriatric depression. Biol Psychiatry 54(5):529–533CrossRefPubMedGoogle Scholar
  165. 165.
    Lai T-J, Payne ME, Byrum CE, Steffens DC, Krishnan KRR (2000) Reduction of orbital frontal cortex volume in geriatric depression. Biol Psychiatry 48(10):971–975CrossRefPubMedGoogle Scholar
  166. 166.
    Payne ME, Fetzer DL, MacFall JR, Provenzale JM, Byrum CE, Krishnan KRR (2002) Development of a semi-automated method for quantification of MRI gray and white matter lesions in geriatric subjects. Psychiatr Res: Neuroimag 115(1–2):63–77CrossRefGoogle Scholar
  167. 167.
    Steffens DC, Tupler LA, Krishnan KRR (1998) Magnetic resonance imaging signal hypointensity and iron content of putamen nuclei in elderly depressed patients. Psychiatr Res - Neuroimag 83(2):95–103CrossRefGoogle Scholar
  168. 168.
    Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Reiss AL, Schatzberg AF (2007) Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry 62(5):429–437CrossRefPubMedGoogle Scholar
  169. 169.
    Bell-McGinty S, Butters MA, Meltzer CC, Greer PJ, Reynolds CF III, Becker JT (2002) Brain morphometric abnormalities in geriatric depression: long-term neurobiological effects of illness duration. Am J Psychiatry 159(8):1424–1427CrossRefPubMedGoogle Scholar
  170. 170.
    Weber K, Giannakopoulos P, Delaloye C, de Bilbao F, Moy G, Moussa A, Rubio MM, Ebbing K, Meuli R, Lazeyras F, Meiler-Mititelu C, Herrmann FR, Gold G, Canuto A (2010) Volumetric MRI changes, cognition and personality traits in old age depression. J Affect Disord 124(3):275–282CrossRefPubMedGoogle Scholar
  171. 171.
    Jaeuk H, In Kyoon L, Dager SR, Friedman SD, Jung Su O, Jun Young L, Seogju K, Dunner DL, Renshaw PF (2006) Basal ganglia shape alterations in bipolar disorder. Am J Psychiatry 163(2):276–285CrossRefGoogle Scholar
  172. 172.
    Styner M, Gerig G, Lieberman J, Jones D, Weinberger D (2003) Statistical shape analysis of neuroanatomical structures based on medial models. Med Image Anal 7(3):207–220CrossRefPubMedGoogle Scholar
  173. 173.
    Styner M, Oguz I, Xu S, Brechbuehler C, Pantazis D, Levitt J, Shenton M, Gerig G (2006) Framework for the statistical shape analysis of brain structures using SPHARM-PDM. Insight J (1071):242–250Google Scholar
  174. 174.
    Priebe CE, Youngser P, Miller MI, Mohan NR, Botteron KN (2007) Hippocampus shape–space analysis of clinically depressed, high risk, and control populations. In: Frontiers in the Convergence of Bioscience and Information Technologies 2007 (FBIT 2007), 11–13 Oct, pp 465–469Google Scholar
  175. 175.
    Qiu A, Taylor WD, Zhao Z, MacFall JR, Miller MI, Key CR, Payne ME, Steffens DC, Krishnan KRR (2009) APOE related hippocampal shape alteration in geriatric depression. NeuroImage 44(3):620–626CrossRefPubMedGoogle Scholar
  176. 176.
    Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1–2):155–176CrossRefGoogle Scholar
  177. 177.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182Google Scholar
  178. 178.
    De Martino F, Valente G, Staeren N, Ashburner J, Goebel R, Formisano E (2008) Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage 43(1):44–58CrossRefPubMedGoogle Scholar
  179. 179.
    Kessler D, Bennewith O, Lewis G, Sharp D (2002) Detection of depression and anxiety in primary care: follow up study. BMJ 325(7371):1016–1017CrossRefPubMedGoogle Scholar
  180. 180.
    Shen L, Firpi HA, Saykin AJ, West JD (2009) Parametric surface modeling and registration for comparison of manual and automated segmentation of the hippocampus. Hippocampus 19(6):588–595CrossRefPubMedGoogle Scholar
  181. 181.
    Bishop CA, Jenkinson M, Andersson J, Declerck J, Merhof D (2011) Novel Fast Marching for Automated Segmentation of the Hippocampus (FMASH): method and validation on clinical data. NeuroImage 55(3):1009–1019CrossRefPubMedGoogle Scholar
  182. 182.
    Khan AR, Wang L, Beg MF (2008) FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping. NeuroImage 41(3):735–746CrossRefPubMedGoogle Scholar
  183. 183.
    Coupé P, Manjón JV, Fonov V, Pruessner J, Robles M, Collins DL (2011) Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2):940–954CrossRefPubMedGoogle Scholar
  184. 184.
    Mortazavi D, Kouzani A, Soltanian-Zadeh H (2012) Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology 54(4):299–320CrossRefPubMedGoogle Scholar
  185. 185.
    Herrmann LL, Le Masurier M, Ebmeier KP (2008) White matter hyperintensities in late life depression: a systematic review. J Neurol Neurosurg Psychiatry 79(6):619–624CrossRefPubMedGoogle Scholar
  186. 186.
    Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62(6):1609–1618CrossRefPubMedGoogle Scholar
  187. 187.
    El-Dahshan E-SA, Hosny T, Salem A-BM (2010) Hybrid intelligent techniques for MRI brain images classification. Digit Signal Process 20(2):433–441CrossRefGoogle Scholar
  188. 188.
    Savio A, Grańa M, Villanúa J (2011) Deformation based features for Alzheimer’s disease detection with linear SVM. Proceeding of the 6th International Conference on Hybrid Artificial Intelligent Systems (HAIS ’11)—Volume Part II, pp 336–343Google Scholar
  189. 189.
    Marquand AF, Mourão-Miranda J, Brammer MJ, Cleare AJ, Fu CHY (2008) Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. NeuroReport 19(15):1507–1511CrossRefPubMedGoogle Scholar
  190. 190.
    Chyzhyk D, Graña M, Savio A, Maiora J (2012) Hybrid dendritic computing with kernel-LICA applied to Alzheimer’s disease detection in MRI. Neurocomputing 75(1):72–77CrossRefGoogle Scholar
  191. 191.
    Craddock RC, Holtzheimer PE, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619–1628CrossRefPubMedGoogle Scholar
  192. 192.
    Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR, Ashburner J, Frackowiak RSJ (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3):681–689CrossRefPubMedGoogle Scholar
  193. 193.
    Uddin LQ, Menon V, Young CB, Ryali S, Chen T, Khouzam A, Minshew NJ, Hardan AY (2011) Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biol Psychiatry 70(9):833–841CrossRefPubMedGoogle Scholar
  194. 194.
    Shen H, Wang L, Liu Y, Hu D (2010) Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage 49(4):3110–3121CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kuryati Kipli
    • 1
    • 2
  • Abbas Z. Kouzani
    • 1
  • Lana J. Williams
    • 3
  1. 1.School of EngineeringDeakin UniversityWaurn PondsAustralia
  2. 2.Department of Electronic, Faculty of EngineeringUniversiti Malaysia SarawakKota SamarahanMalaysia
  3. 3.School of MedicineDeakin UniversityWaurn PondsAustralia

Personalised recommendations