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Posts
Blog Post number 4
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Blog Post number 3
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Blog Post number 1
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publications
A Computational Model of Hopelessness and Active-Escape Bias in Suicidality
Computational Psychiatry, 2022
Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidal thoughts and behaviors (STB) to provide timely and personalized interventions. Developing computational models of STB that integrate across behavioral, cognitive and neural levels of analysis could help better understand STB vulnerabilities and guide personalized interventions. To that end, we present a computational model based on the active inference framework. With this model, we show that several STB risk markers – hopelessness, Pavlovian bias and active-escape bias – are interrelated via the drive to maximize one’s model evidence. We propose four ways in which these effects can arise: (1) increased learning from aversive outcomes, (2) reduced belief decay in response to unexpected outcomes, (3) increased stress sensitivity and (4) reduced sense of stressor controllability. These proposals stem from considering the neurocircuits implicated in STB: how the locus coeruleus – norepinephrine (LC-NE) system together with the amygdala (Amy), the dorsal prefrontal cortex (dPFC) and the anterior cingulate cortex (ACC) mediate learning in response to acute stress and volatility as well as how the dorsal raphe nucleus – serotonin (DRN-5-HT) system together with the ventromedial prefrontal cortex (vmPFC) mediate stress reactivity based on perceived stressor controllability. We validate the model by simulating performance in an Avoid/Escape Go/No-Go task replicating recent behavioral findings. This serves as a proof of concept and provides a computational hypothesis space that can be tested empirically and be used to distinguish planful versus impulsive STB subtypes. We discuss the relevance of the proposed model for treatment response prediction, including pharmacotherapy and psychotherapy, as well as sex differences as it relates to stress reactivity and suicide risk.
Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review
Network Neuroscience, 2022
Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.
Individual differences in computational psychiatry: A review of current challenges
Neuroscience & Biobehavioral Reviews, 2023
Cognitive sciences are grappling with the reliability paradox: measures that robustly produce within-group effects tend to have low test-retest reliability, rendering them unsuitable for studying individual differences. Despite the growing awareness of this paradox, its full extent remains underappreciated. Specifically, most research focuses exclusively on how reliability affects correlational analyses of individual differences, while largely ignoring its effects on studying group differences. Moreover, some studies explicitly and erroneously suggest that poor reliability does not pose problems for studying group differences, possibly due to conflating within- and between-group effects. In this short report, we aim to clarify this misunderstanding. Using both data simulations and mathematical derivations, we show how observed group differences get attenuated by measurement reliability. We consider multiple scenarios, including when groups are created based on thresholding a continuous measure (e.g., patients vs. controls or median split), when groups are defined exogenously (e.g., treatment vs. control groups, or male vs. female), and how the observed effect sizes are further affected by differences in measurement reliability and between-subject variance between the groups. Overall, we show that just as for correlational strength, observed standardized group differences are attenuated as a function of $\sqrt{reliability}$ for each measure. This has important implications for biomarker discovery, clinical translation, and other areas of group differences research that inform policy and real-world applications.
Clarifying the reliability paradox: poor test-retest reliability attenuates group differences (preprint)
PsyArXiv, 2025
Cognitive sciences are grappling with the reliability paradox: measures that robustly produce within-group effects tend to have low test-retest reliability, rendering them unsuitable for studying individual differences. Despite the growing awareness of this paradox, its full extent remains under-appreciated. Specifically, most research focuses exclusively on how reliability affects correlational analyses of individual differences, while largely ignoring its effects on studying group differences. Moreover, by conflating within- and between-group effects, some studies erroneously suggest that poor reliability does not pose problems for studying group differences. This brief report aims to clarify this misunderstanding through simple data simulations. To make the argument more intuitive, we consider two illustrative cases: comparing patients versus controls and comparing two groups formed by a median split. We demonstrate that reliability attenuates observed group differences just as much as it attenuates individual differences. Given that dichotomizing/grouping continuous data - which is implicit in many group differences analyses - leads to a loss of statistical power, low reliability proves to be even more problematic for studying group differences. We hope this work will bring more awareness to the relevance of the reliability paradox to studies investigating group differences. While here we focused on cognitive sciences and psychiatry, our findings are quite general and could inform many other areas of research, including education, sex, gender, age, race, ethnicity, etc.
software
E2P Simulator
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Interactive web tool to translate effect sizes into predictive utility.
talks
Talk 1 on Relevant Topic in Your Field
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Tutorial 1 on Relevant Topic in Your Field
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Talk 2 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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