Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data

Okada, Go; Yoshioka, Toshinori; Yamashita, A.; Itai, Eri; Yokoyama, Satoshi; Kamishikiryo, Toshiharu; Shinzato, Hotaka; Masuda, Yoshikazu; Mitsuyama, Yuki; Kan, Shigeyuki; Kurata, Akiko; Takamura, Masahiro; Yoshino, Atsuo; Mantani, Akio; Yamamoto, Osamu; Yokota, N; Tamura, Tatsuji; Jitsuiki, Hiroaki; Kawato, Mitsuo; Yamashita, Okito; Okamoto, Yasumasa

doi:10.1016/j.jad.2023.01.087

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Against the concerns on session variability of rsfMRI, we reduced the variability and improved generalisability to independent validation cohorts through spatial averaging of 100 classifiers, each analysing tens of FCs. Furthermore, a previous study 45 revealed that test–retest reliability was acceptable with the same methodology as demonstrated in this study.…”

Section: Discussionsupporting

confidence: 76%

“…This achievement was possible through bi-directional (prospective and retrospective) harmonisation, which enabled the extraction of the disease factor itself, and with optimised machine learning method. Against the concerns on session variability of rsfMRI, our previous study 35 revealed that test-retest reliability was acceptable with the same methodology as demonstrated in this study.…”

Section: Discussionsupporting

confidence: 71%

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Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

Kawashima,

Yamashita,

Yoshihara

et al. 2024

Preprint

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This study introduces a novel resting-state functional connectivity (rs-FC) magnetic resonance imaging (MRI) biomarker for diagnosing schizophrenia spectrum disorder (SSD), developed using customized machine learning on an anterogradely and retrogradely harmonized dataset from multiple sites, including 617 healthy controls and 116 patients with SSD. Unlike previous rs-FC MRI biomarkers, this new biomarker demonstrated a high accuracy rate of 77.3% in an independent validation cohort, including 404 healthy controls and 198 patients with SSD from seven different sites, while overcoming across-scan variability. It specifically identifies SSD, differentiating it from other psychiatric disorders. Our analysis identified 47 important FCs significant in SSD classification, many of which are involved in the pathophysiology of SSD. These FCs could be potential trait, state, and staging markers for effectively predicting delusional tendencies, specific symptoms, and disease stages. This research underscores the potential of rs-FC as a clinically applicable neural phenotype marker for SSD.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionsupporting

confidence: 71%

Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

Kawashima,

Yamashita,

Yoshihara

et al. 2024

Preprint

Self Cite

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show abstract

“…Against the concerns on session variability of rsfMRI, we reduced the variability and improved generalisability to independent validation cohorts through spatial averaging of 100 classi ers, each analysing tens of FCs. Furthermore, a previous study 45 revealed that test-retest reliability was acceptable with the same methodology as demonstrated in this study.…”

Section: Discussionsupporting

confidence: 75%

Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

Kawashima,

Yamashita,

Yoshihara

et al. 2024

Preprint

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Schizophrenia spectrum disorder (SSD) is one of the top causes of disease burden; similar to other psychiatric disorders, SSD lacks widely applicable and objective biomarkers. This study aimed to introduce a novel resting-state functional connectivity (rs-FC) magnetic resonance imaging (MRI) biomarker for diagnosing SSD. It was developed using customised machine learning on an anterogradely and retrogradely harmonised dataset from multiple sites, including 617 healthy controls and 116 patients with SSD. Unlike previous rs-FC MRI biomarkers, this new biomarker demonstrated a notable accuracy rate of 77.3% in an independent validation cohort, including 404 healthy controls and 198 patients with SSD from seven different sites, effectively mitigating across-scan variability. Importantly, our biomarker specifically identified SSD, differentiating it from other psychiatric disorders. Our analysis identified 47 important FCs significant in SSD classification, several of which are involved in SSD pathophysiology. Beyond their potential as trait markers, we explored the utility of these FCs as both state and staging markers. First, based on aggregated FCs, we built prediction models for clinical scales of trait and/or state. Thus, we successfully predicted delusional inventory scores (r = 0.331, P = 0.0177), but not the overall symptom severity (r = 0.128, P = 0.178). Second, through comprehensive analysis, we uncovered associations between individual FCs and symptom scale scores or disease stages, presenting promising candidate FCs for state or staging markers. This study underscores the potential of rs-FC as a clinically applicable neural phenotype marker for SSD and provides actionable targets to neuromodulation therapies.

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“…Although altered functional connections between patient groups and healthy controls have been identified [30][31][32][33] , the individual-level classifications can be only achieved with the help of the machine-learning algortihms. In our multicenter study, we successfully developed major depressive disorder (MDD), schizophrenia (SCZ), and autism spectrum disorder (ASD) biomarkers using ensemble sparse classifiers, which generalized well across data from various centers [18][19][20] and maintained consistent performance on new data (anterograde generalization) 34 . However, its discrimination ability evaluated with completely independent datasets, with areas under the curve of 0.74, 0.82, and 0.66-0.81 for MDD, SCZ, and ASD, respectively, may not yet meet high standards.…”

Section: Introductionmentioning

confidence: 99%

“…The copyright holder for this preprint this version posted April 2, 2024. ; https://doi.org/10.1101/2024.04.01.587535 doi: bioRxiv preprint schizophrenia (SCZ), and autism spectrum disorder (ASD) biomarkers using ensemble sparse classifiers, which generalized well across data from various centers [18][19][20] and maintained consistent performance on new data (anterograde generalization) 34 . However, its discrimination ability evaluated with completely independent datasets, with areas under the curve of 0.74, 0.82, and 0.66-0.81 for MDD, SCZ, and ASD, respectively, may not yet meet high standards.…”

Section: Introductionmentioning

confidence: 99%

Computational Mechanisms of Neuroimaging Biomarkers Uncovered by Multicenter Resting-State fMRI Connectivity Variation Profile

Yamashita,

Takahara

et al. 2024

Preprint

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Resting-state functional connectivity (rsFC) is increasingly used to develop biomarkers for psychiatric disorders. Despite progress, development of the reliable and practical FC biomarker remains an unmet goal, particularly one that is clinically predictive at the individual level with generalizability, robustness, and accuracy. In this study, we propose a new approach to profile each connectivity from diverse perspective, encompassing not only disorder-related differences but also disorder-unrelated variations attributed to individual difference, within-subject across-runs, imaging protocol, and scanner factors. By leveraging over 1500 runs of 10-minute resting-state data from 84 traveling-subjects across 29 sites and 900 participants of the case-control study with three psychiatric disorders, the disorder-related and disorder-unrelated FC variations were estimated for each individual FC. Using the FC profile information, we evaluated the effects of the disorder-related and disorder-unrelated variations on the output of the multi-connectivity biomarker trained with ensemble sparse classifiers and generalizable to the multicenter data. Our analysis revealed hierarchical variations in individual functional connectivity, ranging from within-subject across-run variations, individual differences, disease effects, inter-scanner discrepancies, and protocol differences, which were drastically inverted by the sparse machine-learning algorithm. We found this inversion mainly attributed to suppression of both individual difference and within-subject across-runs variations relative to the disorder-related difference by weighted-averaging of the selected FCs and ensemble computing. This comprehensive approach will provide an analytical tool to delineate future directions for developing reliable individual-level biomarkers.

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Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data

Cited by 4 publications

References 12 publications

Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

Computational Mechanisms of Neuroimaging Biomarkers Uncovered by Multicenter Resting-State fMRI Connectivity Variation Profile

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