Pushing the boundaries of behavioral analysis could aid psychiatric drug discovery

Sweis, Brian M.; Nestler, Eric J.

doi:10.1371/journal.pbio.3001904

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…Behavior analysis/valuation is a concept derived from psychology and medicine, which is used to predict and treat diseases [ 17 ]. Behavior analysis includes many applications and methods, some of which are carried out from the medical view [ 18 ], and some of which use data mining methods, including wavelet neural network [ 19 ] and recurrent neural network [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Research on User Behavior Based on Higher-Order Dependency Network

Qian

Dou

Gong

et al. 2023

Entropy

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In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.

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Section: Related Workmentioning

confidence: 99%

Research on User Behavior Based on Higher-Order Dependency Network

Qian

Dou

Gong

et al. 2023

Entropy

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“…[20][21][22][23][24][25] Recent discoveries in the field of neuroeconomics have begun to push the boundaries on what can be investigated in animal research, offering novel approaches using clever tasks to extract hidden features of behavior in order to study interactions between mood and decision-making in a manner that is biologically tractable and readily translatable across species. [26][27][28][29][30][31][32][33] The sunk cost bias has been extensively studied in the human psychology literature but has been repeatedly ascertained that this phenomenon is unique to humans. 34,35 Using a neuroeconomic approach, it was recently discovered for the first time evidence that rodents are capable of demonstrating sensitivity to sunk costs in a manner that is directly matched to human behavior using a novel set of neuroeconomic decision-making tasks recently developed for use across species.…”

Section: Introductionmentioning

confidence: 99%

“…[37][38][39] Furthermore, the biological and psychological mechanisms through which antidepressant treatments, including newer rapid-acting drugs like ketamine, exert their effects are not well understood. 33,[40][41][42] Here, we tested the effects of stress using a well-established animal model of depression (the chronic social defeat stress paradigm) on mice tested in a novel neuroeconomic decision-making paradigm (the Restaurant Row task) in which mice forage for food rewards of varying costs while on a limited budget. 36,43 We found that exposure to a psychosocial stressor produced a robust increase in sensitivity to sunk costs without affecting gross changes in locomotion, motivation, body weight, or feeding behaviors.…”

Section: Introductionmentioning

confidence: 99%

Ketamine reverses stress-induced hypersensitivity to sunk costs

Cuttoli,

Sweis

2024

Preprint

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How mood interacts with information processing in the brain is thought to mediate the maladaptive behaviors observed in depressed individuals. However, the neural mechanisms underlying impairments in emotion-cognition interactions are poorly understood. This includes influencing the balance between how past-sensitive vs. future-looking one is during decision-making. Recent insights from the field of neuroeconomics offer novel approaches to study changes in such valuation processes in a manner that is biologically tractable and readily translatable across species. We recently discovered that rodents are sensitive to sunk costs - a feature of higher cognition previously thought to be unique to humans. The sunk costs bias describes the phenomenon in which an individual overvalues and escalates commitment to continuing an ongoing endeavor, even if suboptimal, as a function of irrecoverable past (sunk) losses - information that, according to classic economic theory, should be ignored. In the present study, mice were exposed to chronic social defeat stress paradigm, a well-established animal model used for the study of depression. Mice were then tested on our longitudinal neuroeconomic foraging task, Restaurant Row. We found mice exposed to this severe stressor displayed an increased sensitivity to sunk costs, without altering overall willingness to wait. Mice were then randomly assigned to receive a single intraperitoneal injection of either saline or ketamine (20 mg/kg). We discovered that stress-induced hypersensitivity to sunk costs was renormalized following a single dose of ketamine. Interestingly, in non-defeated mice, ketamine treatment completely abolished sunk cost sensitivity, causing mice to no longer value irrecoverable losses during re-evaluation decisions who instead based choices solely on the future investment required to obtain a goal. These findings suggest that the antidepressant effects of ketamine may be mediated in part through changes in the processing of past-sensitive information during on-going decision-making, reducing its weight as a potential source of cognitive dissonance that could modulate behavior and instead promoting more future-thinking behavior.

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“…information processing capable of resolving behavior into discretely measurable computational units in a manner that is biologically tractable and readily translatable across species [23][24][25] .…”

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confidence: 99%

“…Such approaches would be critically informative if applied toward translational studies of diabetes. Recent insights from neuroeconomic principles offer novel approaches to investigate decision-making information processing capable of resolving behavior into discretely measurable computational units in a manner that is biologically tractable and readily translatable across species [23][24][25] .…”

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confidence: 99%

Diabetes alters neuroeconomically dissociable forms of mental accounting

Nwakama,

Durand-de Cuttoli,

Oketokoun

et al. 2024

Preprint

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Those with diabetes mellitus are at high-risk of developing psychiatric disorders, yet the link between hyperglycemia and alterations in motivated behavior has not been explored in detail. We characterized value-based decision-making behavior of a streptozocin-induced diabetic mouse model on a naturalistic neuroeconomic foraging paradigm called Restaurant Row. Mice made self-paced choices while on a limited time-budget accepting or rejecting reward offers as a function of cost (delays cued by tone-pitch) and subjective value (flavors), tested daily in a closed-economy system across months. We found streptozocin-treated mice disproportionately undervalued less-preferred flavors and inverted their meal-consumption patterns shifted toward a more costly strategy that overprioritized high-value rewards. We discovered these foraging behaviors were driven by impairments in multiple decision-making systems, including the ability to deliberate when engaged in conflict and cache the value of the passage of time in the form of sunk costs. Surprisingly, diabetes-induced changes in behavior depended not only on the type of choice being made but also the salience of reward-scarcity in the environment. These findings suggest complex relationships between glycemic regulation and dissociable valuation algorithms underlying unique cognitive heuristics and sensitivity to opportunity costs can disrupt fundamentally distinct computational processes and could give rise to psychiatric vulnerabilities.

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Pushing the boundaries of behavioral analysis could aid psychiatric drug discovery

Abstract: Drug discovery for psychiatric conditions is stagnating. Behavioral changes could be used as a primary phenotypic screen for new drug candidates, if enough useful data can be generated from behavioral models. Could machine learning be the answer to extracting the data we need?

Cited by 6 publications

References 12 publications

Research on User Behavior Based on Higher-Order Dependency Network

Research on User Behavior Based on Higher-Order Dependency Network

Ketamine reverses stress-induced hypersensitivity to sunk costs

Diabetes alters neuroeconomically dissociable forms of mental accounting

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