Abstract
A key challenge in understanding mental (dys)functions is their etiological and functional
heterogeneity, and several multi-dimensional assessments have been proposed for their
comprehensive characterization. However, such assessments require lengthy testing,
which may hinder reliable and efficient characterization of individual differences
due to increased fatigue and distraction especially in clinical populations. Computational
modeling may address the challenge as it often provides more reliable measures of
latent neurocognitive processes underlying observed behaviors and captures individual
differences better than traditional assessments. However, even with a state-of-art
hierarchical modeling approach, reliable estimation of model parameters still requires
a large number of trials. Recent works suggest that Bayesian adaptive design optimization
(ADO) is a promising way to address the challenges. With ADO, experimental design
is optimized adaptively from trial to trial to extract the maximum amount of information
about an individual’s characteristics. In this review, we first describe the ADO methodology
and then summarize recent works demonstrating that ADO increases reliability and efficiency
of latent neurocognitive measures. We conclude by discussing the challenges and future
directions of ADO and propose we develop ADO-based computational fingerprints to reliably
and efficiently characterize the heterogeneous profiles of psychiatric disorders.
Keywords
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Article info
Publication history
Accepted:
December 11,
2022
Received in revised form:
November 21,
2022
Received:
August 31,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
The authors report no biomedical financial interests or potential conflicts of interest.
Identification
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© 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
