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

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Název: Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk
Autoři: Zhu, Yinghan, Maikusa, Norihide, Radua, Joaquim, Sämann, Philipp, Fusar-Poli, Paolo, Agartz, Ingrid, Andreassen, Ole A., Bachman, Peter, Baeza, Inmaculada, Wang, Qianjin, Choi, Sunah, Corcoran, Cheryl, Ebdrup, Bjørn H., Fortea, Adriana, Garani, Ranjini RG., Glenthøj, Birte Yding, Glenthøj, Louise Birkedal, Haas, Shalaila, Hamilton, Holly K., Hayes, Rebecca A., He, Ying, Heekeren, Karsten, Kasai, Kiyoto, Katagiri, Naoyuki, kim, minah, Kristensen, Tina Dam, Kwon, Jun Soo, Lawrie, Stephen, Lebedeva, Irina, Lee, Jimmy, Loewy, Rachel L., Mathalon, Daniel, McGuire, Philip, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria A., Mitta Raghava, jayachandra, Røssberg, Jan I., Rössler, Wulf, Salisbury, Dean F., Sasabayashi, Daiki, Smigielski, Lukasz, Sugranyes, Gisela, Takahashi, Tsutomu, Tamnes, Christian Krog, Tang, Jinsong, Theodoridou, Anastasia, Tomyshev, Alexander S., Uhlhaas, Peter, Værnes, Tor G., van Amelsvoort, Therese A. M. J., Waltz, James, Westlye, Lars T., Zhou, Juan, Thompson, Paul M., Hernaus, Dennis, Jalbrzikowski, Maria, Koike, Shinsuke, the ENIGMA Clinical High Risk for Psychosis Working Group, Allen, Paul, Baldwin, Helen, Catalano, Sabrina, Chee, Michael W. L., Cho, Kang Ik K., de Haan, Lieuwe, Horton, Leslie E., Klaunig, Mallory J., Bin Kwak, Yoo, Ma, Xiaoqian, Nordentoft, Merete, Ouyang, Lijun, Pariente, Jose C., Resch, Franz, Schiffman, Jason, Sørensen, Mikkel E., Suzuki, Michio, Vinogradov, Sophia, Wenneberg, Christina, Yamasue, Hidenori, Yuan, Liu
Přispěvatelé: Zhu, Yinghan, Maikusa, Norihide, Radua, Joaquim, Sämann, Philipp G., Fusar-Poli, Paolo, Agartz, Ingrid, Andreassen, Ole A., Bachman, Peter, Baeza, Inmaculada, Chen, Xiaogang, Choi, Sunah, Corcoran, Cheryl M., Ebdrup, Bjørn H., Fortea, Adriana, Garani, Ranjini RG., Glenthøj, Birte Yding, Glenthøj, Louise Birkedal, Haas, Shalaila S., Hamilton, Holly K., Hayes, Rebecca A., Ying, He, Heekeren, Karsten, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kristensen, Tina D., Kwon, Jun Soo, Lawrie, Stephen M., Lebedeva, Irina, Lee, Jimmy, Loewy, Rachel L., Mathalon, Daniel H., Mcguire, Philip, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria A., Raghava, Jayachandra M., Røssberg, Jan I., Rössler, Wulf, Salisbury, Dean F., Sasabayashi, Daiki, Smigielski, Lukasz, Sugranyes, Gisela, Takahashi, Tsutomu, Tamnes, Christian K., Tang, Jinsong, Theodoridou, Anastasia, Tomyshev, Alexander S., Uhlhaas, Peter J., Værnes, Tor G., van Amelsvoort, Therese A. M. J., Waltz, James A., Westlye, Lars T., Zhou, Juan H., Thompson, Paul M., Hernaus, Denni, Jalbrzikowski, Maria, Koike, Shinsuke, Allen, Paul, Baldwin, Helen, Catalano, Sabrina, Chee, Michael W. L., Cho, Kang Ik K., de Haan, Lieuwe, Horton, Leslie E., Klaunig, Mallory J., Bin Kwak, Yoo, Xiaoqian, Ma, Nordentoft, Merete, Ouyang, Lijun, Pariente, Jose C., Resch, Franz, Schiffman, Jason, Sørensen, Mikkel E., Suzuki, Michio, Vinogradov, Sophia, Wenneberg, Christina, Yamasue, Hidenori, Yuan, Liu
Zdroj: Mol Psychiatry
Zhu, Y, Maikusa, N, Radua, J, Sämann, P G, Fusar-Poli, P, Agartz, I, Andreassen, O A, Bachman, P, Baeza, I, Chen, X, Choi, S, Corcoran, C M, Ebdrup, B H, Fortea, A, Garani, R RG, Glenthøj, B Y, Glenthøj, L B, Haas, S S, Hamilton, H K, Hayes, R A, He, Y, Heekeren, K, Kasai, K, Katagiri, N, Kim, M, Kristensen, T D, Kwon, J S, Lawrie, S M, Lebedeva, I, Lee, J, Loewy, R L, Mathalon, D H, McGuire, P, Mizrahi, R, Mizuno, M, Møller, P, Nemoto, T, Nordholm, D, Omelchenko, M A, Raghava, J M, Røssberg, J I, Rössler, W, Salisbury, D F, Sasabayashi, D, Smigielski, L, Sugranyes, G, Takahashi, T, Tamnes, C K, Tang, J, Theodoridou, A, Tomyshev, A S, Uhlhaas, P J, Værnes, T G, van Amelsvoort, T A M J, Waltz, J A, Westlye, L T, Zhou, J H, Thompson, P M, Hernaus, D, Jalbrzikowski, M, Koike, S & ENIGMA Clinical High Risk for Psychosis Working Group 2024, ' Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk ', Molecular Psychiatry, vol. 29, no. 5, pp. 1465-1477 . https://doi.org/10.1038/s41380-024-02426-7
Zhu, Y, Maikusa, N, Radua, J, G. Sämann, P & Fusar-Poli, P 2024, ' Using Brain Structural Neuroimaging Measures to Predict Psychosis Onset for Individuals at Clinical High-Risk ', Molecular Psychiatry, pp. 1465–1477 . https://doi.org/10.1038/s41380-024-02426-7
Zhu, Y, Maikusa, N, Radua, J, Sämann, P G, Fusar-Poli, P, Agartz, I, Andreassen, O A, Bachman, P, Baeza, I, Chen, X, Choi, S, Corcoran, C M, Ebdrup, B H, Fortea, A, Garani, R R, Glenthøj, B Y, Glenthøj, L B, Haas, S S, Hamilton, H K, Hayes, R A, He, Y, Heekeren, K, Kasai, K, Katagiri, N, Kim, M, Kristensen, T D, Kwon, J S, Lawrie, S M, Lebedeva, I, Lee, J, Loewy, R L, Mathalon, D H, McGuire, P, Mizrahi, R, Møller, P, Mizuno, M, Nemoto, T, Nordholm, D, Omelchenko, M A, Raghava, J M, Røssberg, J I, Rössler, W, Salisbury, D F, Sasabayashi, D, Smigielski, L, Sugranyes, G, Takahashi, T, Tamnes, C K, Tang, J, Theodoridou, A, Tomyshev, A S, Uhlhaas, P J, Værnes, T G, van Amelsvoort, T A M J, Waltz, J A, Westlye, L T, Zhou, J H, Thompson, P M, Hernaus, D, Jalbrzikowski, M, Koike, S 2024, ' Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk ', Molecular Psychiatry . https://doi.org/10.1038/s41380-024-02426-7
Molecular Psychiatry, vol 29, iss 5
Informace o vydavateli: Springer Science and Business Media LLC, 2024.
Rok vydání: 2024
Témata: Male, Adult, Adolescent [MeSH], Female [MeSH], Brain/pathology [MeSH], Brain/diagnostic imaging [MeSH], Psychotic Disorders/diagnostic imaging [MeSH], 59/57, Adult [MeSH], Humans [MeSH], 692/53/2423, Prodromal Symptoms [MeSH], 692/699/476/1799, Neuroimaging/methods [MeSH], Article, Male [MeSH], Young Adult [MeSH], Machine Learning [MeSH], Magnetic Resonance Imaging/methods [MeSH], Psychotic Disorders/pathology [MeSH], article, Adolescent, Brain, Prodromal Symptoms, Neuroimaging, Magnetic Resonance Imaging, 3. Good health, Machine Learning, Young Adult, Psychotic Disorders, Humans, Female
Popis: 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+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 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+ (n = 120) and HC (n = 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.
Druh dokumentu: Article
Other literature type
Popis souboru: application/pdf
Jazyk: English
ISSN: 1476-5578
1359-4184
DOI: 10.1038/s41380-024-02426-7
Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38332374
https://ora.ox.ac.uk/objects/uuid:8a31efa5-5123-4749-9de8-e8fe03030ce2
https://doi.org/10.1038/s41380-024-02426-7
https://curis.ku.dk/ws/files/395869749/s41380_024_02426_7.pdf
https://kclpure.kcl.ac.uk/ws/files/334161129/s41380-024-02426-7.pdf
https://hdl.handle.net/20.500.11820/8f096621-8016-4a22-a58e-d692074b2b03
https://www.pure.ed.ac.uk/ws/files/416128876/s41380-024-02426-7.pdf
https://repository.publisso.de/resource/frl:6508785
https://escholarship.org/uc/item/8z05d5gz
https://escholarship.org/content/qt8z05d5gz/qt8z05d5gz.pdf
Rights: CC BY
Přístupové číslo: edsair.doi.dedup.....0f70947b18e1697beb0ebd2de67b2be8
Databáze: OpenAIRE
Popis
Abstrakt: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+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 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+ (n = 120) and HC (n = 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.
ISSN:14765578
13594184
DOI:10.1038/s41380-024-02426-7