Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

  1. Zhu, Yinghan
  2. Maikusa, Norihide ORCID logo
  3. Radua, Joaquim
  4. Sämann, Philipp ORCID logo
  5. Fusar-Poli, Paolo ORCID logo
  6. Agartz, Ingrid
  7. Andreassen, Ole A. ORCID logo
  8. Bachman, Peter
  9. Baeza, Inmaculada
  10. Wang, Qianjin ORCID logo
  11. Choi, Sunah
  12. Corcoran, Cheryl ORCID logo
  13. Ebdrup, Bjørn H. ORCID logo
  14. Fortea, Adriana ORCID logo
  15. Garani, Ranjini RG.
  16. Glenthøj, Birte Yding
  17. Glenthøj, Louise Birkedal
  18. Haas, Shalaila ORCID logo
  19. Hamilton, Holly K.
  20. Hayes, Rebecca A.
  21. He, Ying
  22. Heekeren, Karsten
  23. Kasai, Kiyoto ORCID logo
  24. Katagiri, Naoyuki
  25. kim, minah ORCID logo
  26. Kristensen, Tina Dam ORCID logo
  27. Kwon, Jun Soo ORCID logo
  28. Lawrie, Stephen ORCID logo
  29. Lebedeva, Irina
  30. Lee, Jimmy ORCID logo
  31. Loewy, Rachel L.
  32. Mathalon, Daniel ORCID logo
  33. McGuire, Philip ORCID logo
  34. Mizrahi, Romina ORCID logo
  35. Mizuno, Masafumi
  36. Møller, Paul ORCID logo
  37. Nemoto, Takahiro ORCID logo
  38. Nordholm, Dorte
  39. Omelchenko, Maria A.
  40. Mitta Raghava, jayachandra ORCID logo
  41. Røssberg, Jan I.
  42. Rössler, Wulf
  43. Salisbury, Dean F.
  44. Sasabayashi, Daiki ORCID logo
  45. Smigielski, Lukasz ORCID logo
  46. Sugranyes, Gisela ORCID logo
  47. Takahashi, Tsutomu ORCID logo
  48. Tamnes, Christian Krog ORCID logo
  49. Tang, Jinsong ORCID logo
  50. Theodoridou, Anastasia ORCID logo
  51. Tomyshev, Alexander S.
  52. Uhlhaas, Peter ORCID logo
  53. Værnes, Tor G.
  54. van Amelsvoort, Therese A. M. J.
  55. Waltz, James ORCID logo
  56. Westlye, Lars T. ORCID logo
  57. Zhou, Juan ORCID logo
  58. Thompson, Paul M.
  59. Hernaus, Dennis
  60. Jalbrzikowski, Maria ORCID logo
  61. Koike, Shinsuke ORCID logo
  62. the ENIGMA Clinical High Risk for Psychosis Working Group
  63. Allen, Paul
  64. Baldwin, Helen
  65. Catalano, Sabrina
  66. Chee, Michael W. L.
  67. Cho, Kang Ik K.
  68. de Haan, Lieuwe
  69. Horton, Leslie E.
  70. Klaunig, Mallory J.
  71. Bin Kwak, Yoo
  72. Ma, Xiaoqian
  73. Nordentoft, Merete
  74. Ouyang, Lijun
  75. Pariente, Jose C.
  76. Resch, Franz
  77. Schiffman, Jason
  78. Sørensen, Mikkel E.
  79. Suzuki, Michio
  80. Vinogradov, Sophia
  81. Wenneberg, Christina
  82. Yamasue, Hidenori
  83. Yuan, Liu

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1000 Titel
  • Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk
1000 Autor/in
  1. Zhu, Yinghan |
  2. Maikusa, Norihide |
  3. Radua, Joaquim |
  4. Sämann, Philipp |
  5. Fusar-Poli, Paolo |
  6. Agartz, Ingrid |
  7. Andreassen, Ole A. |
  8. Bachman, Peter |
  9. Baeza, Inmaculada |
  10. Wang, Qianjin |
  11. Choi, Sunah |
  12. Corcoran, Cheryl |
  13. Ebdrup, Bjørn H. |
  14. Fortea, Adriana |
  15. Garani, Ranjini RG. |
  16. Glenthøj, Birte Yding |
  17. Glenthøj, Louise Birkedal |
  18. Haas, Shalaila |
  19. Hamilton, Holly K. |
  20. Hayes, Rebecca A. |
  21. He, Ying |
  22. Heekeren, Karsten |
  23. Kasai, Kiyoto |
  24. Katagiri, Naoyuki |
  25. kim, minah |
  26. Kristensen, Tina Dam |
  27. Kwon, Jun Soo |
  28. Lawrie, Stephen |
  29. Lebedeva, Irina |
  30. Lee, Jimmy |
  31. Loewy, Rachel L. |
  32. Mathalon, Daniel |
  33. McGuire, Philip |
  34. Mizrahi, Romina |
  35. Mizuno, Masafumi |
  36. Møller, Paul |
  37. Nemoto, Takahiro |
  38. Nordholm, Dorte |
  39. Omelchenko, Maria A. |
  40. Mitta Raghava, jayachandra |
  41. Røssberg, Jan I. |
  42. Rössler, Wulf |
  43. Salisbury, Dean F. |
  44. Sasabayashi, Daiki |
  45. Smigielski, Lukasz |
  46. Sugranyes, Gisela |
  47. Takahashi, Tsutomu |
  48. Tamnes, Christian Krog |
  49. Tang, Jinsong |
  50. Theodoridou, Anastasia |
  51. Tomyshev, Alexander S. |
  52. Uhlhaas, Peter |
  53. Værnes, Tor G. |
  54. van Amelsvoort, Therese A. M. J. |
  55. Waltz, James |
  56. Westlye, Lars T. |
  57. Zhou, Juan |
  58. Thompson, Paul M. |
  59. Hernaus, Dennis |
  60. Jalbrzikowski, Maria |
  61. Koike, Shinsuke |
  62. the ENIGMA Clinical High Risk for Psychosis Working Group |
  63. Allen, Paul |
  64. Baldwin, Helen |
  65. Catalano, Sabrina |
  66. Chee, Michael W. L. |
  67. Cho, Kang Ik K. |
  68. de Haan, Lieuwe |
  69. Horton, Leslie E. |
  70. Klaunig, Mallory J. |
  71. Bin Kwak, Yoo |
  72. Ma, Xiaoqian |
  73. Nordentoft, Merete |
  74. Ouyang, Lijun |
  75. Pariente, Jose C. |
  76. Resch, Franz |
  77. Schiffman, Jason |
  78. Sørensen, Mikkel E. |
  79. Suzuki, Michio |
  80. Vinogradov, Sophia |
  81. Wenneberg, Christina |
  82. Yamasue, Hidenori |
  83. Yuan, Liu |
1000 Verlag Nature Publishing Group UK
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-02-09
1000 Erschienen in
1000 Quellenangabe
  • 29(5):1465-1477
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1038/s41380-024-02426-7 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11189817/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:p>Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, <jats:italic>n</jats:italic> = 144; CHR-PS-, <jats:italic>n</jats:italic> = 793; and CHR-UNK, <jats:italic>n</jats:italic> = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (<jats:italic>n</jats:italic> = 120) and HC (<jats:italic>n</jats:italic> = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.</jats:p>
1000 Sacherschließung
lokal Adolescent [MeSH]
lokal Female [MeSH]
lokal Brain/pathology [MeSH]
lokal Brain/diagnostic imaging [MeSH]
lokal Psychotic Disorders/diagnostic imaging [MeSH]
lokal /59/57
lokal Adult [MeSH]
lokal Humans [MeSH]
lokal /692/53/2423
lokal Prodromal Symptoms [MeSH]
lokal /692/699/476/1799
lokal Neuroimaging/methods [MeSH]
lokal Article
lokal Male [MeSH]
lokal Young Adult [MeSH]
lokal Machine Learning [MeSH]
lokal Magnetic Resonance Imaging/methods [MeSH]
lokal Psychotic Disorders/pathology [MeSH]
lokal article
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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