Abstract
Diagnoses of psychiatric disorders only based on clinical presentation are less reliable. In clinical practice, it is difficult to distinguish bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ) as they have many overlapping symptoms. Therefore, there is an urgent need to develop new methods to help increase diagnostic reliability or even explore biotypes for the psychiatric disorders by using neuroimaging measures such as brain functional connectivity (FC). Partial label learning can extract valid information from subjects with incompletely accurate labels, however it has not been well studied in the neuroscience field. Here, we propose a new partial label learning method to explore transdiagnostic biotypes using FC estimated from functional magnetic resonance imaging (fMRI) data. Our method iteratively mines reliable information from available subjects and then propagates the gained knowledge in a typical \(K+N\) graph structure. Based on fMRI data from 113 BPP patients, 113 SAD patients, 113 SZ patients, and 113 healthy controls (HC), meaningful biotypes are obtained using our method, showing significant differences in FC. In conclusion, the proposed method is promising in extracting biotypes of psychiatric disorders.
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References
Bzdok, D., Meyer-Lindenberg, A.: Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatry: Cogn. Neurosci. Neuroimaging 3(3), 223–230 (2018)
Clementz, B.A., Sweeney, J.A., et al.: Identification of distinct psychosis biotypes using brain-based biomarkers. Am. J. Psychiatry 173(4), 373–384 (2016)
Ge, R., Sassi, R., et al.: Neuroimaging profiling identifies distinct brain maturational subtypes of youth with mood and anxiety disorders. Mol. Psychiatry 28(3), 1072–1078 (2023)
Honnorat, N., Dong, A., et al.: Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods. Schizophr. Res. 214, 43–50 (2019)
Chand, G.B., Dwyer, D.B., et al.: Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 143(3), 1027–1038 (2020)
Xu, N., Qiao, C., et al.: Instance-dependent partial label learning. Adv. Neural. Inf. Process. Syst. 34, 27119–27130 (2021)
Tamminga, C.A., Ivleva, E.I., et al.: Clinical phenotypes of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). Am. J. Psychiatry 170(11), 1263–1274 (2013)
Du, Y., Pearlson, G.D., et al.: Identifying dynamic functional connectivity biomarkers using GIG-ICA: application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder. Hum. Brain Mapp. 38(5), 2683–2708 (2017)
Acknowledgment
This work was supported by National Natural Science Foundation of China (Grant No. 62076157 and 61703253, to Yuhui Du). We acknowledge the contribution of the participants in the Bipolar-Schizophrenia Network for Intermediate Phenotypes-1 (BSNIP-1).
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Du, Y., Li, B., Niu, J., Calhoun, V.D. (2024). A Nearest Neighbor Propagation-Based Partial Label Learning Method for Identifying Biotypes of Psychiatric Disorders. In: Wang, G., Yao, D., Gu, Z., Peng, Y., Tong, S., Liu, C. (eds) 12th Asian-Pacific Conference on Medical and Biological Engineering. APCMBE 2023. IFMBE Proceedings, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-031-51485-2_32
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DOI: https://doi.org/10.1007/978-3-031-51485-2_32
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