Kafadar Eren, Mittal Vijay A, Strauss Gregory P, Chapman Hannah C, Ellman Lauren M, Bansal Sonia, Gold James M, Alderson-Day Ben, Evans Samuel, Moffatt Jamie, Silverstein Steven M, Walker Elaine F, Woods Scott W, Corlett Philip R, Powers Albert R
Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, United States of America.
Northwestern University, Evanston, IL, United States of America.
Schizophr Res. 2020 Dec;226:167-175. doi: 10.1016/j.schres.2020.04.017. Epub 2020 Jun 24.
Early intervention in psychotic spectrum disorders is critical for maximizing key clinical outcomes. While there is some evidence for the utility of intervention during the prodromal phase of the illness, efficacy of interventions is difficult to assess without appropriate risk stratification. This will require biomarkers that robustly help to identify risk level and are also relatively easy to obtain. Recent work highlights the utility of computer-based behavioral tasks for understanding the pathophysiology of psychotic symptoms. Computational modeling of performance on such tasks may be particularly useful because they explicitly and formally link performance and symptom expression. Several recent studies have successfully applied principles of Bayesian inference to understanding the computational underpinnings of hallucinations. Within this framework, hallucinations are seen as arising from an over-weighting of prior beliefs relative to sensory evidence. This view is supported by recently-published data from two tasks: the Conditioned Hallucinations (CH) task, which determines the degree to which participants use expectations in detecting a target tone; and a Sine-Vocoded Speech (SVS) task, in which participants can use prior exposure to speech samples to inform their understanding of degraded speech stimuli. We administered both of these tasks to two samples of participants at clinical high risk for psychosis (CHR; N = 19) and healthy controls (HC; N = 17). CHR participants reported both more conditioned hallucinations and more pre-training SVS detection. In addition, relationships were found between participants' performance on both tasks. On computational modeling of behavior on the CH task, CHR participants demonstrate significantly poorer recognition of task volatility as well as a trend toward higher weighting of priors. A relationship was found between this latter effect and performance on both tasks. Taken together, these results support the assertion that these two tasks may be driven by similar latent factors in perceptual inference, and highlight the potential utility of computationally-based tasks in identifying risk.
对精神病性谱系障碍进行早期干预对于最大化关键临床结局至关重要。虽然有一些证据表明在疾病的前驱期进行干预是有用的，但如果没有适当的风险分层，干预的效果很难评估。这将需要能够有力地帮助识别风险水平且相对容易获得的生物标志物。最近的研究强调了基于计算机的行为任务在理解精神病性症状病理生理学方面的作用。对此类任务表现的计算建模可能特别有用，因为它们明确且正式地将表现与症状表达联系起来。最近的几项研究已成功将贝叶斯推理原理应用于理解幻觉的计算基础。在此框架内，幻觉被视为源于先验信念相对于感官证据的过度加权。这一观点得到了最近发表的来自两项任务的数据的支持：条件性幻觉（CH）任务，该任务确定参与者在检测目标音调时使用预期的程度；以及正弦编码语音（SVS）任务，在该任务中参与者可以利用先前接触语音样本的经验来帮助理解退化的语音刺激。我们对两组参与者进行了这两项任务，一组是临床高危精神病患者（CHR；N = 19），另一组是健康对照组（HC；N = 17）。CHR参与者报告的条件性幻觉和预训练SVS检测都更多。此外，还发现了参与者在这两项任务上的表现之间的关系。在对CH任务行为的计算建模中，CHR参与者对任务波动性的识别明显较差，并且存在先验加权更高的趋势。发现后一种效应与两项任务的表现之间存在关系。综上所述，这些结果支持了这样一种观点，即这两项任务可能由感知推理中的相似潜在因素驱动，并突出了基于计算的任务在识别风险方面的潜在效用。
