Furthermore, multiple regression analysis was performed with average cortical thickness extracted from each cluster with age, gender and ICV as independent variables and changes in cognitive function as the dependent variable. Diffusion-weighted images were acquired using the 3. Diffusion tensor matrices from the sets of diffusion-weighted images were generated using a general linear fitting algorithm. A threshold FA value of 0. For analysis, the images were also processed by applying the FA non-linear registration and projecting them onto the skeleton using identical projection methods to those inferred from the original FA data.
Then, voxel-wise statistics across subjects on the skeleton-space FA images were carried out. Furthermore, to identify the brain regions correlated with the changes in cognitive function, a multiple regression analysis was performed with the average FA values in different brain regions, with age and gender as independent variables and changes in cognitive function as the dependent variable. Randomization procedure was carried out to categorized participants into the training and control groups. A random digit was generated for each participant according to the table of random numbers.
If the random digit was an even number, the participant was assigned to the control group; if it was an odd number, the participant was assigned to the training group. Demographic variables age, gender and years of education and neuropsychological measures i. Two blind, trained clinical neuropsychologists conducted the neuropsychological assessments.
Post-training neuropsychological evaluations via EMS were conducted within three-month after finishing the training. Program participation was free of charge and there was no financial reward for participation. Among the participants, 49 were randomly selected to test for the changes in the brain structure.
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