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Neuroradiology
Erschienen in: Neuroradiology 5/2013

01.05.2013 | Diagnostic Neuroradiology

Towards automated detection of depression from brain structural magnetic resonance images

verfasst von: Kuryati Kipli, Abbas Z. Kouzani, Lana J. Williams

Erschienen in: Neuroradiology | Ausgabe 5/2013

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Abstract

Introduction

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).

Methods

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.

Results

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.

Conclusion

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.
Literatur
1.
Sayers J (2001) The world health report 2001—mental health: new understanding, new hope. Bull World Health Organ 79:1085
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.
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., Melbourne
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):90CrossRefPubMed
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):145–154CrossRefPubMed
6.
Aguirre AB (2008) Depression. Greenwood, Westport
7.
Gelenberg AJ (2010) Depression symptomatology and neurobiology. J Clin Psychiat 71(1)
8.
Gopal AA, Ropper AE, Tramontozzi LA (2008) Deja review: psychiatry. McGraw-Hill Medical, New York
9.
Helén I (2011) The depression paradigm and beyond. Sci Stud 24(1):81–112
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, Portugal
11.
Marti J, Hine A (1998) The alternative health & medicine encyclopedia, 2nd edn. Gale Research, Detroit
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):433–440CrossRef
13.
Satin JR, Linden W, Phillips MJ (2009) Depression as a predictor of disease progression and mortality in cancer patients. Cancer 115(22):5349–5361CrossRefPubMed
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):1145–1151CrossRefPubMed
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):6CrossRefPubMed
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):410–417CrossRefPubMed
17.
Pan ALMSQ et al (2011) Increased mortality risk in women with depression and diabetes mellitus. Arch Gen Psychiatry 68(1):42–50CrossRefPubMed
18.
Feinstein A (2011) Multiple sclerosis and depression. Multiple Sclerosis Journal 17(11):1276–1281CrossRefPubMed
19.
Barlow DH, Durand VM (2009) Abnormal psychology: an integrative approach, 5th edn. Wadsworth Cengage Learning, Belmont
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):229–245CrossRefPubMed
21.
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):293–298CrossRef
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):15–25CrossRefPubMed
24.
Wright SL, Persad C (2007) Distinguishing between depression and dementia in older persons: neuropsychological and neuropathological correlates. J Geriatr Psych Neur 20(4):189–198CrossRef
25.
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):381–389CrossRefPubMed
27.
Zigmond A, Snaith R (1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67(6):361–370CrossRefPubMed
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):189–198CrossRefPubMed
29.
Montgomery SA, Asberg M (1979) A new depression scale designed to be sensitive to change. Br J Psychiatry 134(4):382–389CrossRefPubMed
30.
Rush AJ, Gullion CM, Basco MR, Jarrett RB, Trivedi MH (1996) The Inventory of Depressive Symptomatology (IDS): psychometric properties. Psychol Med 26(03):477–486CrossRefPubMed
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):65–87CrossRef
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):573–583CrossRefPubMed
33.
Sen S, Sanacora G (2008) Major depression: emerging therapeutics. Mt Sinai J Med: J Transl Personalizes Med 75(3):204–225CrossRef
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 1081–1095
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 Services
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):407–414CrossRefPubMed
37.
Symms M, Jäger HR, Schmierer K, Yousry TA (2004) A review of structural magnetic resonance neuroimaging. J Neurol Neurosurg Psychiatry 75(9):1235–1244CrossRefPubMed
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):377–387CrossRefPubMed
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 22:1–16
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):606–612
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):825–831CrossRefPubMed
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):1–37CrossRefPubMed
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):347–356CrossRefPubMed
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):127–137CrossRef
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):837–846CrossRef
46.
Videbech P, Ravnkilde B (2004) Hippocampal volume and depression: a meta-analysis of MRI studies. Am J Psychiatry 161(11):1957–1966CrossRefPubMed
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):1–17CrossRefPubMed
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):67–77CrossRefPubMed
49.
Petrella JR, Coleman RE, Doraiswamy PM (2003) Neuroimaging and early diagnosis of Alzheimer disease: a look to the future. Radiology 226(2):315–336CrossRefPubMed
50.
Steffens DC, Krishnan KRR (1998) Structural neuroimaging and mood disorders: recent findings, implications for classification, and future directions. Biol Psychiatry 43(10):705–712CrossRefPubMed
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):1498–1507CrossRefPubMed
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):1358–1361CrossRefPubMed
53.
Sheline YI (2003) Neuroimaging studies of mood disorder effects on the brain. Biol Psychiatry 54(3):338–352CrossRefPubMed
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):57–69CrossRef
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):112–117CrossRef
56.
Gotlib IH, Hamilton JP (2008) Neuroimaging and depression. Curr Dir Psychol Sci 17(2):159–163CrossRef
57.
Cannon DM (2010) Neuroimaging and the pathophysiology and treatment of depression: recent advances and future needs. Karger, Basel
58.
Schmidt HD, Shelton RC, Duman RS (2011) Functional biomarkers of depression: diagnosis, treatment, and pathophysiology. Neuropsychopharmacology 36:2375–2394
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 346–350
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 Intelligent
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 439–442
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):361–368CrossRefPubMed
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):656–662CrossRefPubMed
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):50–64CrossRef
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):574–586CrossRef
66.
Baltes C, Mueggler T, Rudin M (2010) Magnetic resonance imaging. In: Stolerman IP (ed) Encyclopedia of psychopharmacology, vol 2. Springer, Berlin, p 739
67.
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):1140–1152CrossRefPubMed
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)
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):1508–1521CrossRefPubMed
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 95:703–717
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):89–95CrossRef
73.
Davidson RJ, Pizzagalli D, Nitschke JB, Putnam K (2002) Depression: perspectives from affective neuroscience. Annu Rev Psychol 53(1):545–574CrossRefPubMed
74.
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):141–174CrossRef
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):675–690CrossRefPubMed
77.
Gotlib IH, Joormann J (2010) Cognition and depression: current status and future directions. Annu Rev Clin Psychol 6(1):285–312CrossRefPubMed
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 33:2123–2128
79.
Haynes J-D, Rees G (2006) Decoding mental states from brain activity in humans. Nat Rev Neurosci 7(7):523–534CrossRefPubMed
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):29–37CrossRefPubMed
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):569–581CrossRefPubMed
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):64–70CrossRef
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):15–22CrossRef
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):237–244CrossRef
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):318–325CrossRefPubMed
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):445–456CrossRefPubMed
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 34:295–303
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):1193–1198CrossRef
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):21–26CrossRefPubMed
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):e1837CrossRefPubMed
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):111–118CrossRefPubMed
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):1112–1117CrossRefPubMed
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):127–135
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):105–113CrossRef
95.
Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. NeuroImage 11(6):805–821CrossRefPubMed
96.
97.
Friston KJ, Ashburner J, Kiebel SJ, Nichols TE, Penny WD (2007) Statistical parametric mapping: the analysis of functional brain images. Academic, Boston
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):749–761CrossRefPubMed
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):855–866CrossRefPubMed
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):408–412CrossRefPubMed
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 17:412–420
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 York
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):539–543CrossRefPubMed
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):598–607CrossRefPubMed
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):3719–3735CrossRefPubMed
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):36–51CrossRefPubMed
107.
Costafreda SG, Chu C, Ashburner J, Fu CHY (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 4(7):e6353CrossRefPubMed
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):809–813CrossRefPubMed
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):1497–1503CrossRefPubMed
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):64–71CrossRefPubMed
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 Neuroscience
112.
Arimura H, Magome T, Yamashita Y, Yamamoto D (2009) Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms 2(3):925–952CrossRef
113.
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):619–629CrossRefPubMed
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):301–309CrossRefPubMed
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):125–133
117.
118.
119.
120.
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):347–354CrossRefPubMed
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):43–52CrossRefPubMed
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):812–818CrossRefPubMed
124.
Chen MC, Hamilton JP, Gotlib IH (2010) Decreased hippocampal volume in healthy girls at risk of depression. Arch Gen Psychiatry 67(3):270–276CrossRefPubMed
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):1154–1162CrossRefPubMed
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):338–344CrossRefPubMed
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, Japan
128.
Wu M (2010) Registration and segmentation of brain MR images from elderly individuals. Dissertation, Universiy of Pittsburgh
129.
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):655–662CrossRefPubMed
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):393–405CrossRef
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):382–389CrossRefPubMed
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):139–152CrossRefPubMed
134.
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):1500–1507CrossRefPubMed
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):415–422CrossRefPubMed
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):98–103CrossRef
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):231–238CrossRefPubMed
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):744–750CrossRefPubMed
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):302–310CrossRefPubMed
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):112–119CrossRefPubMed
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):55–60CrossRef
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):1003–1007CrossRef
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):423–430
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):316–323
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):1112CrossRefPubMed
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):629–638CrossRefPubMed
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):83–89CrossRefPubMed
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):708–714CrossRefPubMed
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):76–83CrossRef
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):411–420CrossRefPubMed
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):177–183CrossRefPubMed
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):539–544CrossRef
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):1763–1773CrossRefPubMed
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):144–149CrossRefPubMed
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):537–544CrossRefPubMed
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):263–271CrossRef
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):847–855CrossRef
159.
Greenberg DL, Payne ME, MacFall JR, Steffens DC, Krishnan RR (2008) Hippocampal volumes and depression subtypes. Psychiatr Res: Neuroimag 163(2):126–132CrossRef
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):2135–2142CrossRefPubMed
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):1356–1363CrossRefPubMed
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):2179–2183PubMed
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):674–681CrossRefPubMed
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):529–533CrossRefPubMed
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):971–975CrossRefPubMed
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):63–77CrossRef
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):95–103CrossRef
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):429–437CrossRefPubMed
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):1424–1427CrossRefPubMed
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):275–282CrossRefPubMed
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):276–285CrossRef
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):207–220CrossRefPubMed
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):242–250
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 465–469
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):620–626CrossRefPubMed
176.
Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1–2):155–176CrossRef
177.
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
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):44–58CrossRefPubMed
179.
Kessler D, Bennewith O, Lewis G, Sharp D (2002) Detection of depression and anxiety in primary care: follow up study. BMJ 325(7371):1016–1017CrossRefPubMed
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):588–595CrossRefPubMed
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):1009–1019CrossRefPubMed
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):735–746CrossRefPubMed
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):940–954CrossRefPubMed
184.
Mortazavi D, Kouzani A, Soltanian-Zadeh H (2012) Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology 54(4):299–320CrossRefPubMed
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):619–624CrossRefPubMed
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):1609–1618CrossRefPubMed
187.
El-Dahshan E-SA, Hosny T, Salem A-BM (2010) Hybrid intelligent techniques for MRI brain images classification. Digit Signal Process 20(2):433–441CrossRef
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 336–343
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):1507–1511CrossRefPubMed
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):72–77CrossRef
191.
Craddock RC, Holtzheimer PE, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619–1628CrossRefPubMed
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):681–689CrossRefPubMed
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):833–841CrossRefPubMed
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):3110–3121CrossRefPubMed
Metadaten
Titel
Towards automated detection of depression from brain structural magnetic resonance images
verfasst von
Kuryati Kipli
Abbas Z. Kouzani
Lana J. Williams
Publikationsdatum
01.05.2013
Verlag
Springer-Verlag
Erschienen in
Neuroradiology / Ausgabe 5/2013
Print ISSN: 0028-3940
Elektronische ISSN: 1432-1920
DOI
https://doi.org/10.1007/s00234-013-1139-8

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