Functional Characteristics of Neural Networks in Human Associative Learning

Learning and memory are highly complex and adaptive cognitive processes of the human brain. The present body of work examined functional characteristics of neural networks that supported associative learning in multiple dimensions (e.g. space, time, frequency) and related them to cognitive markers and behavioral performance. Research was conducted with magnetoencephalography (MEG). Given the multidimensional nature of MEG data, several methodological tools were developed concurrently to enable better identification of learning-induced changes in the brain. Study 1 systematically examined the impact of different artifacts on scalp signals. Combined with simulations, it was established that detection of deep/weak sources (e.g. medial temporal lobes, striatum etc.) was substantially improved by removing noise from artifacts. This work culminated in the development of an artifact correction tool that is freely available for reseach use. Study 2 used artifact-free data from Study 1 to examine relationships between time-dependent changes in functional network organization, cognitive skills and behavioral performance. Results showed that individual variations in learning were supported by differences in cognitive ability and time-sensitive connectivity in functional networks. This research has translational scope for customizing rehabilitative practices based on an individualâ s learner profile. Study 3 examined frequency-specific changes in functional network interactions and their implications for adaptive control of behavior during learning. First, an automated data-driven pipeline was developed to obtain distributed brain sources. Phase synchronization was used to measure changes in network interactions. Reorganization of functional networks was related to distinct behavior types â eye movements and error rate. Results highlighted a shift in theta frequencies from early to late periods of learning. Both behavioral measures were shaped by similar network configurations early in learning and dissociable functional networks late in learning. The direct comparison made between implicit and explicit behaviors in this study has practical implications for research in special populations (e.g. development, disease) whereby implicit measures may be the only option for evaluating integrity of brain function.Ph.D.2018-12-01 00:00:0