Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task

Previous studies have established the involvement of prefrontal cortex (PFC) neurons in decision processes in many task contexts. Single neurons and populations of neurons have been found to represent stimuli, actions, and internal deliberations. However, it is much less clear which underlying computations are affected during errors. Neural activity during errors can help to disambiguate confounds and clarify which computations are essential during a specific task. Here, we used a hidden Markov model (HMM) to perform a trial-by-trial analysis of ensembles of simultaneously recorded neurons from the dorsolateral prefrontal (PFdl) cortex of two rhesus monkeys performing a distance discrimination task. The HMM segments the neural activity into sequences of metastable states, allowing to link neural ensemble dynamics with task and behavioral features in the absence of external triggers. We report a precise relationship between the modulation of the metastable dynamics and task features. Specifically, we found that errors were made more often when the metastable dynamics slowed down, while trial difficulty influenced the latency of state transitions at a pivotal point during the trial. Both these phenomena occurred during the decision interval and not following the action, with errors occurring in both easy and difficult trials. Thus, modulations of metastable dynamics reflected a state of internal deliberation rather than actions taken or, in the case of error trials, objective trial difficulty. Our results show that temporal modulations of PFdl activity are key determinants of internal deliberations, providing further support for the emerging role of metastable cortical dynamics in mediating complex cognitive functions and behavior.

ANALYSE THE RISK FACTORS OF CORONARY HEART DISEASE IN YOUNG AND MIDDLE – AGED MALE PATIENTS USING MULTIVARIATE STATISTICAL

Objective: To study the correlation between various risk factors and coronary CTA calcification score ( CACS ) in young and middle-aged male patients with coronary heart disease by multiple linear regression, and to predict plaque properties by Mahalanobis distance discrimination method. This study provides evidence for early clinical evaluation of the extent of coronary artery calcification and the property of plaque in patients having suffered coronary heart disease. Methods: choose 98 male patients under 55 years old with coronary heart disease randomly and collecte relevant medical history data and test results while in hospital.Utilizing the theory of multiple linear regression and Mahalanobis distance discriminant to analyse these dates. Results: Factors including hypertension classification×years, number of cigarettes smoked(packs / week )×years,and time of diabetes(years) have a positive correlation between CACS respectively. Comparing calcified plaque and vulnerable plaque, mixed plaque and vulnerable plaque,there is a significant differences ( p < 0.01) while hs-CRP and IL-6 are as the indicator. Mahalanobis distance discrimination method has a discrimination accuracy of 91.83 % for the group wrih susceptible factors only. Conclusion: The model of predicting the extent of coronary artery calcification by multiple linear regression has high reliability. Vulnerable plaques can be distinguished from plaques of other properties efficiently using Mahalanobis distance discrimination method.

Long-term runoff prediction for reservoir based on Mahalanobis distance discrimination

An accurate and timely forecast of medium and long-term runoff forecast is of great significance to reservoir safety and water resources scheduling. In order to improve the long-term runoff forecast accuracy of the reservoir, a long-term runoff forecasting model was constructed based on the principle of Mahalanobis distance discrimination analysis. The data sequence from 1952 to 2008 of Danjiangkou reservoir was selected, the correlation coefficient method and AIC criterion were used to sift out the highly correlated and independent factors, a long-term runoff forecasting model was constructed based on the principle of Mahalanobis distance discrimination analysis. The result showed that under the permutation error of 10%，the pass rate during the simulation period was 93.9%, and the pass rate during the inspection period was 87.5%. The research results serve as a reference for the operation of Danjiangkou reservoir.