Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging DOI Creative Commons
Roberto Limongi,

Adam J. Skelton,

Lydia Helen Tzianas

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1278 - 1278

Published: Dec. 19, 2024

After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, emergence computational has paved a new path not only psychopathology illness but also practical tools practice terms metrics, specifically phenotypes. these phenotypes still lack sufficient test–retest reliability. In this review, we describe recent works revealing that mind brain-related show structural (not random) variation over time, longitudinal changes. Furthermore, findings suggest causes changes will improve construct validity an ensuing increase We propose active inference framework offers general-purpose approach causally by incorporating as observations within partially observable Markov decision processes.

Language: Английский

Individuals with Methamphetamine Use Disorder Show Reduced Directed Exploration and Learning Rates Independent of an Aversive Interoceptive State Induction DOI Creative Commons
Carter M Goldman, Tōru Takahashi, Claire A. Lavalley

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 21, 2024

Abstract Methamphetamine Use Disorder (MUD) is associated with substantially reduced quality of life. Yet, decisions to use persist, due in part avoidance anticipated withdrawal states. However, the specific cognitive mechanisms underlying this decision process, and possible modulatory effects aversive states, remain unclear. Here, 56 individuals MUD 58 healthy comparisons (HCs) performed a task, both without an interoceptive state induction. Computational modeling measured tendency test beliefs about uncertain outcomes (directed exploration) ability update response (learning rates). Compared HCs, those exhibited less directed exploration slower learning rates, but these differences were not affected by Follow-up analyses further suggested that was best explained greater uncertainty on trait reflectiveness might account for task behavior. These results suggest novel, state-independent computational whereby may have difficulties testing tolerability abstinence adjusting behavior consequences continued use.

Language: Английский

Citations

7
Directed exploration is reduced by an aversive interoceptive state induction in healthy individuals but not in those with affective disorders DOI Creative Commons
Ning Li, Claire A. Lavalley,

Ko-Ping Chou

et al.

Molecular Psychiatry, Journal Year: 2025, Volume and Issue: unknown

Published: April 5, 2025

Language: Английский

Citations

0
Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging DOI Creative Commons
Roberto Limongi,

Adam J. Skelton,

Lydia Helen Tzianas

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1278 - 1278

Published: Dec. 19, 2024

After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, emergence computational has paved a new path not only psychopathology illness but also practical tools practice terms metrics, specifically phenotypes. these phenotypes still lack sufficient test–retest reliability. In this review, we describe recent works revealing that mind brain-related show structural (not random) variation over time, longitudinal changes. Furthermore, findings suggest causes changes will improve construct validity an ensuing increase We propose active inference framework offers general-purpose approach causally by incorporating as observations within partially observable Markov decision processes.

Language: Английский

Citations

0