Feasibility Analysis of Phenotype Quantification from Unstructured Clinical Interactions [0.03%]
非结构化临床交互中表型量化可行性分析
Daniel S Barron,Stephen Heisig,Carla Agurto et al.
Daniel S Barron et al.
We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source softw...
Reliability of Decision-Making and Reinforcement Learning Computational Parameters [0.03%]
决策制定和强化学习计算参数的可靠性
Anahit Mkrtchian,Vincent Valton,Jonathan P Roiser
Anahit Mkrtchian
Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative tha...
Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling [0.03%]
运用动态因果模型推理精神病障碍的病程发展路径
Jingwen Jin,Peter Zeidman,Karl J Friston et al.
Jingwen Jin et al.
Introduction: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal ...
Alexandra C Pike,Ágatha Alves Anet,Nina Peleg et al.
Alexandra C Pike et al.
Background: Catastrophizing, when an individual overestimates the probability of a severe negative outcome, is related to various aspects of mental ill-health. Here, we further characterize catastrophizing by investigatin...
Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data [0.03%]
自闭症的功能连接与神经兴奋性交互作用：计算建模的新框架及在生物数据中的应用
Yuta Takahashi,Shingo Murata,Masao Ueki et al.
Yuta Takahashi et al.
Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (fM...
Electrophysiological Markers of Aberrant Cue-Specific Exploration in Hazardous Drinkers [0.03%]
危险饮酒者寻求线索的异常探索的电生理标志物研究
Ethan M Campbell,Garima Singh,Eric D Claus et al.
Ethan M Campbell et al.
Background: Hazardous drinking is associated with maladaptive alcohol-related decision-making. Existing studies have often focused on how participants learn to exploit familiar cues based on prior reinforcement, but littl...
Using Drift Diffusion and RL Models to Disentangle Effects of Depression On Decision-Making vs. Learning in the Probabilistic Reward Task [0.03%]
使用漂移扩散和RL模型解缠抑郁对概率奖励任务中决策和学习的影响
Daniel G Dillon,Emily L Belleau,Julianne Origlio et al.
Daniel G Dillon et al.
The Probabilistic Reward Task (PRT) is widely used to investigate the impact of Major Depressive Disorder (MDD) on reinforcement learning (RL), and recent studies have used it to provide insight into decision-making mechanisms affected by M...
Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis [0.03%]
首发精神病患者在社会学习过程中对环境波动性的感知改变
Daniel J Hauke,Michelle Wobmann,Christina Andreou et al.
Daniel J Hauke et al.
Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that overly ...
Decomposition of Reinforcement Learning Deficits in Disordered Gambling via Drift Diffusion Modeling and Functional Magnetic Resonance Imaging [0.03%]
基于漂移扩散模型和功能磁共振成像的赌博障碍增强学习缺陷分解研究
Antonius Wiehler,Jan Peters
Antonius Wiehler
Gambling disorder is associated with deficits in reward-based learning, but the underlying computational mechanisms are still poorly understood. Here, we examined this issue using a stationary reinforcement learning task in combination with...
Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making [0.03%]
奖赏敏感性和噪音对消极情绪偏向的影响：决策过程中的学习信号检测理论方法
Isabel K Lütkenherm,Shannon M Locke,Oliver J Robinson
Isabel K Lütkenherm
In patients with mood disorders, negative affective biases - systematically prioritising and interpreting information negatively - are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced...