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  Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study

Liang, S., Deng, W., Li, X., Wang, Q., Greenshaw, A. J., Guo, W., et al. (2020). Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study. Schizophrenia Research, 220, 187-193. doi:10.1016/j.schres.2020.03.022.

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© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/4.0/).
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 Urheber:
Liang, Sugai1, Autor
Deng, Wei1, Autor
Li, Xiaojing1, Autor
Wang, Qiang1, Autor
Greenshaw, Andrew J.2, Autor
Guo, Wanjun1, Autor
Kong, Xiangzhen3, Autor           
Li, Mingli1, Autor
Zhao, Liansheng1, Autor
Meng, Yajing1, Autor
Zhang, Chengcheng1, Autor
Yu, Hua 1, Autor
Li, Xin-min2, Autor
Ma, Xiaohong1, Autor
Li, Tao1, Autor
Affiliations:
1West China Hospital, Sichuan University, Sichuan, China, ou_persistent22              
2University of Alberta, Edmonton, Canada, ou_persistent22              
3Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society, ou_792549              

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 Zusammenfassung: Background

Posterior cingulate cortex (PCC) is a key aspect of the default mode network (DMN). Aberrant PCC functional connectivity (FC) is implicated in schizophrenia, but the potential for PCC related changes as biological classifier of schizophrenia has not yet been evaluated.
Methods

We conducted a data-driven approach using resting-state functional MRI data to explore differences in PCC-based region- and voxel-wise FC patterns, to distinguish between patients with first-episode schizophrenia (FES) and demographically matched healthy controls (HC). Discriminative PCC FCs were selected via false discovery rate estimation. A gradient boosting classifier was trained and validated based on 100 FES vs. 93 HC. Subsequently, classification models were tested in an independent dataset of 87 FES patients and 80 HC using resting-state data acquired on a different MRI scanner.
Results

Patients with FES had reduced connectivity between PCC and frontal areas, left parahippocampal regions, left anterior cingulate cortex, and right inferior parietal lobule, but hyperconnectivity with left lateral temporal regions. Predictive voxel-wise clusters were similar to region-wise selected brain areas functionally connected with PCC in relation to discriminating FES from HC subject categories. Region-wise analysis of FCs yielded a relatively high predictive level for schizophrenia, with an average accuracy of 72.28% in the independent samples, while selected voxel-wise connectivity yielded an accuracy of 68.72%.
Conclusion

FES exhibited a pattern of both increased and decreased PCC-based connectivity, but was related to predominant hypoconnectivity between PCC and brain areas associated with DMN, that may be a useful differential feature revealing underpinnings of neuropathophysiology for schizophrenia.

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Sprache(n): eng - English
 Datum: 2020-03-242020-07
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.schres.2020.03.022
 Art des Abschluß: -

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Titel: Schizophrenia Research
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Amsterdam : Elsevier
Seiten: - Band / Heft: 220 Artikelnummer: - Start- / Endseite: 187 - 193 Identifikator: ISSN: 0920-9964
CoNE: https://pure.mpg.de/cone/journals/resource/954925564675