Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders : A systematic review and meta-analysis
Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved..
Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:160 |
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| Enthalten in: |
Neuroscience and biobehavioral reviews - 160(2024) vom: 26. Apr., Seite 105640 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Meinke, Charlotte [VerfasserIn] |
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| Links: |
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| Themen: |
Depression |
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| Anmerkungen: |
Date Completed 15.04.2024 Date Revised 15.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.neubiorev.2024.105640 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370375297 |
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| 520 | |a Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed | ||
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| 650 | 4 | |a Feature importance | |
| 650 | 4 | |a Functional connectivity | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Post-traumatic stress disorder | |
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| 700 | 1 | |a Hilbert, Kevin |e verfasserin |4 aut | |
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