Posit Science, Inc., United States of America.
Department of Psychiatry, University of Minnesota, United States of America.
Schizophr Res. 2019 Jun;208:250-257. doi: 10.1016/j.schres.2019.01.047. Epub 2019 Feb 4.
Cognitive impairment in schizophrenia is often severe, enduring, and contributes significantly to chronic disability. A standardized platform for identifying cognitive impairments and measuring treatment effects in cognition is a critical aspect of comprehensive evaluation and treatment for individuals with schizophrenia. In this project, we developed and tested a suite of ten web-based, neuroscience-informed cognitive assessments that are designed to enable the interpretation of specific deficits that could signal that an individual is experiencing cognitive difficulties. The assessment suite assays speed of processing, sustained attention, executive functioning, learning and socio-affective processing in the auditory and visual modalities. We have obtained data from 283 healthy individuals who were recruited online and 104 individuals with schizophrenia who also completed formal neuropsychological testing. Our data show that the assessments 1) are acceptable and tolerable to users, with successful completion in an average of under 40 min; 2) reliably measure the distinct theoretical cognitive constructs they were designed to assess; 3) can discriminate schizophrenia patients from healthy controls with a fair degree of accuracy (AUROC > 0.70); and 4) have promising construct, convergent, and external validity. Further optimization and validation work is in progress to finalize the evaluation process prior to promoting the dissemination of these assessments in real-world settings.
精神分裂症患者的认知障碍通常较为严重、持久，并导致其慢性残疾。为了全面评估和治疗精神分裂症患者，为其提供一个标准化的平台，以识别认知障碍并衡量认知治疗效果是至关重要的。在本项目中，我们开发并测试了一套由 10 个基于网络的、受神经科学启发的认知评估工具组成的套件，旨在识别特定的认知缺陷，这些缺陷可能表明个体正在经历认知困难。该评估套件可评估个体在听觉和视觉模式下的处理速度、持续注意力、执行功能、学习和社会情感处理能力。我们从 283 名在线招募的健康个体和 104 名接受过正式神经心理测试的精神分裂症患者中获得了数据。我们的数据表明，这些评估工具：1）用户接受度和耐受性良好，平均完成时间不到 40 分钟；2）可靠地测量了其设计评估的不同理论认知结构；3）可以以相当高的准确性（AUROC > 0.70）区分精神分裂症患者和健康对照组；4）具有有前景的结构、收敛和外部有效性。进一步的优化和验证工作正在进行中，以在真实环境中推广这些评估工具之前，最终确定评估过程。
