Merritt Sean H, Krouse Michael, Alogaily Rana S, Zak Paul J
Center for Neuroeconomics Studies, Claremont Graduate University, Claremont, CA 91711, USA.
Brain Sci. 2022 Sep 14;12(9):1240. doi: 10.3390/brainsci12091240.
The elderly have an elevated risk of clinical depression because of isolation from family and friends and a reticence to report their emotional states. The present study explored whether data from a commercial neuroscience platform could predict low mood and low energy in members of a retirement community. Neurophysiologic data were collected continuously for three weeks at 1Hz and averaged into hourly and daily measures, while mood and energy were captured with self-reports. Two neurophysiologic measures averaged over a day predicted low mood and low energy with 68% and 75% accuracy. Principal components analysis showed that neurologic variables were statistically associated with mood and energy two days in advance. Applying machine learning to hourly data classified low mood and low energy with 99% and 98% accuracy. Two-day lagged hourly neurophysiologic data predicted low mood and low energy with 98% and 96% accuracy. This study demonstrates that continuous measurement of neurophysiologic variables may be an effective way to reduce the incidence of mood disorders in vulnerable people by identifying when interventions are needed.
由于与家人和朋友隔离以及不愿报告自己的情绪状态，老年人患临床抑郁症的风险较高。本研究探讨了来自商业神经科学平台的数据是否能够预测退休社区成员的情绪低落和精力不足。以1赫兹的频率连续三周收集神经生理数据，并将其平均为每小时和每天的测量值，同时通过自我报告获取情绪和精力数据。一天内平均的两项神经生理测量指标预测情绪低落和精力不足的准确率分别为68%和75%。主成分分析表明，神经学变量在两天前与情绪和精力在统计学上相关。将机器学习应用于每小时的数据，对情绪低落和精力不足进行分类的准确率分别为99%和98%。滞后两天的每小时神经生理数据预测情绪低落和精力不足的准确率分别为98%和96%。这项研究表明，持续测量神经生理变量可能是一种有效的方法，通过确定何时需要干预来降低弱势群体中情绪障碍的发生率。
