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Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using whole-brain tract-based spatial statistics and machine learning approaches

  • Bernis Sutcubasi
  • Sinem Zeynep Metin
  • Turker Tekin Erguzel
  • Baris MetinEmail author
  • Cumhur Tas
  • Mehmet Kemal Arikan
  • Nevzat Tarhan
Original Article
  • 38 Downloads

Abstract

Schizophrenia and bipolar disorder have similar clinical features. Their differential diagnosis is crucial because each has different prognostic and therapeutic characteristics. Earlier studies have used numerous methods, including magnetic resonance investigation, in an effort to differentiate these two disorders. Research has consistently shown that there is reduced white matter density in the fronto-temporal and fronto-thalamic pathways in both patients with bipolar disorder and schizophrenia; however, the sensitivity of the methods used is limited. Tract-based spatial statistics is a method of whole-brain analysis that relies on voxel-based comparison, and uses nonlinear image transformation and permutation tests with correction for multiple comparisons. The primary aim of the present study was to investigate anatomical connectivity changes in patients with bipolar disorder and schizophrenia using tract-based spatial statistics, to classify the patients according to white matter integrity patterns using machine learning, and to identify features that represent the key differences between the disorders. Whole-brain images of 41 bipolar disorder patients, 39 schizophrenia patients, and 23 controls were acquired using a 1.5 T magnetic resonance investigation scanner. As compared to the controls, the schizophrenia and bipolar disorder patients had reduced fractional anisotropy in similar white matter tracts. In addition, the imaging method employed differentiated the schizophrenia and bipolar disorder patients with 81.25% accuracy. Although the bipolar disorder and schizophrenia patients exhibited similar anatomical connectivity changes, as compared to the controls, the connectivity reductions in the right hemisphere in the bipolar disorder patients differentiated them from the schizophrenia patients. The present findings improve our understanding of the etiology and pathogenesis of bipolar disorder and schizophrenia, and can potentially be used as a biomarker for the diagnosis and treatment of both disorders.

Keywords

Bipolar disorder Schizophrenia Tract-based spatial statistics Machine learning 

Notes

Compliance with ethical standards

Conflict of interest

The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

References

  1. 1.
    American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Publications, WashingtonCrossRefGoogle Scholar
  2. 2.
    Walker J, Curtis V, Murray RM (2002) Schizophrenia and bipolar disorder: similarities in pathogenic mechanisms but differences in neurodevelopment. Int Clin Psychopharmacol 17:S11–S19Google Scholar
  3. 3.
    Hill SK, Reilly JL, Harris MS, Rosen C, Marvin RW, DeLeon O, Sweeney JAA (2009) A comparison of neuropsychological dysfunction in first-episode psychosis patients with unipolar depression, bipolar disorder, and schizophrenia. Schizophr Res 113(2):167–175CrossRefGoogle Scholar
  4. 4.
    Berrettini WH (2000) Are schizophrenic and bipolar disorders related? A review of family and molecular studies. Biol Psychiatry 48(6):531–538CrossRefGoogle Scholar
  5. 5.
    Abi-Dargham A, Rodenhiser J, Printz D, Zea-Ponce Y, Gil R, Kegeles LS et al (2000) Increased baseline occupancy of D2 receptors by dopamine in schizophrenia. Proc Natl Acad Sci 97(14):8104–8109CrossRefGoogle Scholar
  6. 6.
    Jacobs D, Silverstone T (1986) Dextroamphetamine-induced arousal in human subjects as a model for mania. Psychiatr Med 16(02):323–329Google Scholar
  7. 7.
    Koreen AR, Siris SG, Chakos M, Alvir J (1993) Depression in first-episode schizophrenia. Am J Psychiatry 150(11):1643CrossRefGoogle Scholar
  8. 8.
    Pearlson GD, Ford JM (2014) Distinguishing between schizophrenia and other psychotic disorders. Schizophr Bull 40:501–503CrossRefGoogle Scholar
  9. 9.
    Vederine FE, Wessa M, Leboyer M, Houenou J (2011) A meta-analysis of whole-brain diffusion tensor imaging studies in bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 35(8):1820–1826CrossRefGoogle Scholar
  10. 10.
    Lin F, Weng S, Xie B, Wu G, Lei H (2011) Abnormal frontal cortex white matter connections in bipolar disorder: a DTI tractography study. J Affect Disord 131(1):299–306CrossRefGoogle Scholar
  11. 11.
    Barnea-Goraly N, Chang KD, Karchemskiy A, Howe ME, Reiss AL (2009) Limbic and corpus callosum aberrations in adolescents with bipolar disorder: a tract-based spatial statistics analysis. Biol Psychiatry 66(3):238–244CrossRefGoogle Scholar
  12. 12.
    Versace A, Almeida JR, Hassel S, Walsh ND, Novelli M, Klein CR et al (2008) Elevated left and reduced right orbitomedial prefrontal fractional anisotropy in adults with bipolar disorder revealed by tract-based spatial statistics. Arch Gen Psychiatry 65(9):1041–1052CrossRefGoogle Scholar
  13. 13.
    Koch K, Wagner G, Dahnke R, Schachtzabel C, Schultz C, Roebel M et al (2010) Disrupted white matter integrity of corticopontine-cerebellar circuitry in schizophrenia. Eur Arch Psychiatry Clin Neurosci 260(5):419–426CrossRefGoogle Scholar
  14. 14.
    Lee SH, Kubicki M, Asami T, Seidman LJ, Goldstein JM, Mesholam-Gately RI et al (2013) Extensive white matter abnormalities in patients with first-episode schizophrenia: a diffusion tensor imaging (DTI) stud. Schizophr Res 143(2):231–238CrossRefGoogle Scholar
  15. 15.
    Liu X, Lai Y, Wang X, Hao C, Chen L, Zhou Z et al (2013) Reduced white matter integrity and cognitive deficit in never-medicated chronic schizophrenia: a diffusion tensor study using TBSS. Behav Brain Res 252:157–163CrossRefGoogle Scholar
  16. 16.
    Seal ML, Yücel M, Fornito A, Wood SJ, Harrison BJ, Walterfang M et al (2008) Abnormal white matter microstructure in schizophrenia: a voxelwise analysis of axial and radial diffusivity. Schizophr Res 101(1):106–110CrossRefGoogle Scholar
  17. 17.
    McIntosh AM, Maniega SM, Lymer GKS, McKirdy J, Hall J, Sussmann JE et al (2008) White matter tractography in bipolar disorder and schizophrenia. Biol Psychiatry 64(12):1088–1092CrossRefGoogle Scholar
  18. 18.
    Lu LH, Zhou XJ, Keedy SK, Reilly JL, Sweeney JA (2011) White matter microstructure in untreated first episode bipolar disorder with psychosis: comparison with schizophrenia. Bipolar Disord 13(7–8):604–613CrossRefGoogle Scholar
  19. 19.
    Jones DK, Symms MR, Cercignani M, Howard RJ (2005) The effect of filter size on VBM analyses of DT-MRI data. Neuroimage 26(2):546–554CrossRefGoogle Scholar
  20. 20.
    Simon TJ, Ding L, Bish JP, McDonald-McGinn DM, Zackai EH, Gee J (2005) Volumetric, connective, and morphologic changes in the brains of children with chromosome 22q11. 2 deletion syndrome: an integrative stud. Neuroimage 25(1):169–180CrossRefGoogle Scholar
  21. 21.
    Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4):1487–1505CrossRefGoogle Scholar
  22. 22.
    Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219CrossRefGoogle Scholar
  23. 23.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155CrossRefGoogle Scholar
  24. 24.
    Mori S, Wakana S, Nagae-Poetscher LM, Van Zijl PCM (2006) MRI atlas of human white matter. Am J Neuroradiol 27(6):1384Google Scholar
  25. 25.
    Philippi CL, Mehta S, Grabowski T, Adolphs R, Rudrauf D (2009) Damage to association fiber tracts impairs recognition of the facial expression of emotion. J Neurosci 29(48):15089–15099CrossRefGoogle Scholar
  26. 26.
    Catani M, De Schotten MT (2008) A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44(8):1105–1132CrossRefGoogle Scholar
  27. 27.
    Martino J, Brogna C, Robles SG, Vergani F, Duffau H (2010) Anatomic dissection of the inferior fronto-occipital fasciculus revisited in the lights of brain stimulation data. Cortex 46(5):691–699CrossRefGoogle Scholar
  28. 28.
    Bruno S, Cercignani M, Ron MA (2008) White matter abnormalities in bipolar disorder: a voxel-based diffusion tensor imaging study. Bipolar Disord 10(4):460–468CrossRefGoogle Scholar
  29. 29.
    Garibotto V, Scifo P, Gorini A, Alonso CR, Brambati S, Bellodi L, Perani D (2010) Disorganization of anatomical connectivity in obsessive compulsive disorder: a multi-parameter diffusion tensor imaging study in a subpopulation of patients. Neurobiol Disord 37(2):468–476CrossRefGoogle Scholar
  30. 30.
    Jacobson S, Kelleher I, Harley M, Murtagh A, Clarke M, Blanchard M et al (2010) Structural and functional brain correlates of subclinical psychotic symptoms in 11–13 year old schoolchildren. Neuroimage 49(2):1875–1885CrossRefGoogle Scholar
  31. 31.
    Adler CM, Holland SK, Schmithorst V, Wilke M, Weiss KL, Pan H, Strakowski SM (2004) Abnormal frontal white matter tracts in bipolar disorder: a diffusion tensor imaging study. Bipolar Disord 6(3):197–203CrossRefGoogle Scholar
  32. 32.
    Catani M, Jones DK, Donato R, Ffytche DH (2003) Occipito-temporal connections in the human brain. Brain 126(9):2093–2107CrossRefGoogle Scholar
  33. 33.
    Shinoura N, Suzuki Y, Tsukada M, Katsuki S, Yamada R, Tabei Y et al (2007) Impairment of inferior longitudinal fasciculus plays a role in visual memory disturbance. Neurocase 13(2):127–130CrossRefGoogle Scholar
  34. 34.
    Townsend J, Altshuler LL (2012) Emotion processing and regulation in bipolar disorder: a review. Bipolar Disord 14(4):326–339CrossRefGoogle Scholar
  35. 35.
    Carlson PJ, Singh JB, Zarate CA, Drevets WC, Manji HK (2006) Neural circuitry and neuroplasticity in mood disorders: insights for novel therapeutic targets. NeuroRx 3(1):22–41CrossRefGoogle Scholar
  36. 36.
    Frey BN, Andreazza AC, Nery FG, Martins MR, Quevedo J, Soares JC, Kapczinski F (2007) The role of hippocampus in the pathophysiology of bipolar disorder. Behav Pharmacol 18(5–6):419–430CrossRefGoogle Scholar
  37. 37.
    Strasser HC, Lilyestrom J, Ashby ER, Honeycutt NA, Schretlen DJ, Pulver A et al (2005) Hippocampal and ventricular volumes in psychotic and nonpsychotic bipolar patients compared with schizophrenia patients and community control subjects: a pilot study. Biol Psychiatry 57(6):633–639CrossRefGoogle Scholar
  38. 38.
    De Schotten MT, Dell’Acqua F, Forkel SJ, Simmons A, Vergani F, Murphy DG, Catani M (2011) A lateralized brain network for visuospatial attention. Nat Neurosci 14(10):1245–1246CrossRefGoogle Scholar
  39. 39.
    Magioncalda P, Martino M, Conio B, Piaggio N, Teodorescu R, Escelsior A et al (2016) Patterns of microstructural white matter abnormalities and their impact on cognitive dysfunction in the various phases of type I bipolar disorder. J Affect Disord 193:39–50CrossRefGoogle Scholar
  40. 40.
    Strakowski SM, DelBello MP, Adler CM (2005) The functional neuroanatomy of bipolar disorder: a review of neuroimaging findings. Mol Psychiatry 10(1):105CrossRefGoogle Scholar
  41. 41.
    Forstner AJ, Hecker J, Hofmann A, Maaser A, Reinbold CS, Mühleisen TW et al (2017) Identification of shared risk loci and pathways for bipolar disorder and schizophrenia. PLoS ONE 12(2):e0171595CrossRefGoogle Scholar
  42. 42.
    Oouchi H, Yamada K, Sakai K, Kizu O, Kubota T, Ito H, Nishimura T (2007) Diffusion anisotropy measurement of brain white matter is affected by voxel size: underestimation occurs in areas with crossing fibers. Am J Neuroradiol 28(6):1102–1106CrossRefGoogle Scholar
  43. 43.
    Zhang J, Chen RH, Wang MJ, Tian WX, Su GH, Qiu SZ (2017) Prediction of LBB leakage for various conditions by genetic neural network and genetic algorithms. Nucl Eng Des 325:33–43CrossRefGoogle Scholar
  44. 44.
    Sergeeva M, Delahaye D, Mancel C, Vidosavljevic A (2017) Dynamic airspace configuration by genetic algorithm. J Traffic Transp Eng (English edition) 4(3):300–314CrossRefGoogle Scholar
  45. 45.
    Chowdhury B, Garai G (2017) A review on multiple sequence alignment from the perspective of genetic algorithm. Genomics 109(5–6):419–431CrossRefGoogle Scholar
  46. 46.
    Jafari-Marandi R, Smith BK (2017) Fluid genetic algorithm (FGA). J Comput Des Eng 4(2):158–167Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Bernis Sutcubasi
    • 1
  • Sinem Zeynep Metin
    • 2
  • Turker Tekin Erguzel
    • 3
  • Baris Metin
    • 1
    Email author
  • Cumhur Tas
    • 1
  • Mehmet Kemal Arikan
    • 1
  • Nevzat Tarhan
    • 2
  1. 1.Department of Psychology, Faculty of Humanities and Social SciencesUskudar UniversityIstanbulTurkey
  2. 2.Psychiatry Unit, NPIstanbul HospitalUskudar UniversityIstanbulTurkey
  3. 3.Department of Computer Engineering, Faculty of Engineering and Natural SciencesUskudar UniversityIstanbulTurkey

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