A neural implementation of cognitive reserve: Insights from a longitudinal fMRI study of set-switching in aging.

Aging is often accompanied by changes in brain structure and executive functions, particularly in tasks involving cognitive flexibility, such as task-switching. However, substantial individual differences in the degree of cognitive impairment indicate that some individuals can cope with brain changes more effectively than others, suggesting higher cognitive reserve (CR). This study identified a neural basis for CR by examining the longitudinal relationship between task-related brain activation, structural brain changes, and changes in cognitive performance during an executive task-switching paradigm including single and dual conditions.

A Nonlinear Effective Connectivity Measure Based on Granger Causality and Volterra Series.

Estimating effective connectivity, especially in brain networks, is an important topic to find out the brain functions. Various effective connectivity measures are presented, but they have drawbacks, including bivariate structure, the problem in detecting nonlinear interactions, and high computational cost. In this paper, we have proposed a novel multivariate effective connectivity measure based on a hierarchical realization of the Volterra series model and Granger causality concept, namely hierarchical Volterra Granger causality (HVGC).

Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal.

Objective: Analysis of functional and structural brain networks has suggested that major depressive disorder (MDD) is associated with a disruption in brain networks. This paper aims to investigate the abnormalities of brain networks in MDD.

Approach: To this aim, we constructed weighted directed functional networks based on electroencephalography (EEG) signals of 26 MDD patients and 23 normal (N) subjects.

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal.

Background: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we proposed a method based on machine learning to classify responders (R) and non- responders (NR) to rTMS treatment for major depression disorder (MDD) patients.

Methods: 19 electrodes resting state EEG was recorded from 46 MDD patients before treatment.