Cognitive Analysis based on Brain Connectivity for Learning Task fMRI Data using CONN Software Package

The educational psychology is dependent upon the cognition and learning. The productive research in this domain yields the results for improvements in novel pedagogical methods at various educational level. The learning and memory are closely affiliated with brain structure and functioning i.e., neurobiology which has been the topic of research in philosophy, psychology and social sciences. Hence, to study the learning mechanism for improvements in educational methods, its essential to understand theoretical background of cognitive analysis through brain sciences. The human brain consists of billions of neurons with trillions of synaptic connections structurally and functionally organized in multiple scales of space and time, respectively. This complex structure is responsible for various complex and simple mental/physical tasks such as learning, memory, listening, sequencing, reasoning, motion, thinking, controlling, etc. The brain activity is analyzed through either source analysis or connectivity analysis. The connectivity analysis is carried out by either anatomical way, which is termed as anatomical connectivity, or through the functional way, which is termed as functional connectivity. However, the third explores the cause and effect relationship for a set of neurons and is termed as effective connectivity. This research work produces a generalized review for educational learning and then its relationship with connectivity analysis, including its introduction, graph theory implication, discussion for its types, and parameter discussion. Finally, functional connectivity was implemented on brain data using the CONN software package to compare various degrees, Eigenvalue, and betweenness centrality using fMRI data. The results show that the Eigenvalue centrality measure performs the best result for functional connectivity compared with other centrality measures.