Analysing the Effect of Working Memory Training on Brain Networks Using MEG and Neuroimaging

Abstract: Introduction: The brain can change its structure and functionality as a result ofexternal factors. The working memory (WM) of the brain is where informationcan be held and manipulated during a short period of time, with the purpose ofachieving higher cognitive functions such as reasoning and learning. The WMimproves in capacity during the development from childhood into adulthood,and variation of improvement is possible as an effect of situational factors andstimuli.Goal: The main goal of this project was to examine the effects of a WMtraining program on power distribution, connectivity and synchronicity withinbrain networks, using an intra-individual analysis approach.Method: A series of magnetoencephalography (MEG) measurements wasacquired for four subjects while they were performing WM and control tasks,during a WM training program, along with an MRI image of the brain for eachof the participants. The data was preprocessed for noise and artifact removaland a source reconstruction was performed. Time-frequency representationsof the data were created and the frequencies were categories into alpha,beta and gamma bands. The power difference between the WM and controltask was calculated as a function of cognitive load of each frequency band,and its variation over load was calculated as a constructed metric called’area under power difference curve’ (AUPDC), and visualised using colourscale representation upon the brain MRI of each subject. Brain parcels thatsignificantly deviated from a random distribution of AUPDC values wereidentified using a Gaussian distribution fit.Results and discussion: All subjects showed a clear improvement inperformance accuracy of the tasks, but as the effect on the power distributionsvaried considerably for each subject and frequency band, other aspects besidepower need to be investigated in order to understand the mechanisms behindthe improvement. However, the overall results indicate that many significantAUPDC values seem to have decreased during the WM training, both forthe positive and negative significant AUPDC values, suggesting a strongerdecreasing trend in power difference over cognitive load and a weaker increasingtrend. This could suggest an improved brain activation efficiency as an effectof the WM training.