Introduction: Elucidating the underlying neural mechanisms of experiential and associative learning is a central theme of neuroscience research (Corlett et al., 2004; Daniel and Pollman 2014; Downar et al., 2011; Shin et al., 2016). Temporal expectancy, a subset of associative learning, involves the implicit learning of temporal contingencies between the associated stimuli. It is known to be present starting in early infancy (Colombo and Richman 2002) and preserved throughout various species (Dallerac et. al, 2017). Thus, temporal expectancy learning may incorporate some of the most fundamental neural circuitry responsible for learning. The spectral and phase dynamics of this network remain largely unknown, and no studies have performed comparative physiologic analysis between human and NHP species. We employed stereotactic electroencephelography to comparatively study neural network substrates underlying implicit associative learning in both humans and non-human primates (NHP) using the same temporal expectancy task.
Methods: Patients undergoing intracranial electrode implantation for seizure foci localization and two NHPs were included in this study. Subjects are shown a cue that changes color after a short delay with instruction to press a button following the color change. We used power spectral analysis to identify frequency bands related to task activity. We compared power in these bands between delay periods, reaction time, and early and late learning stages.
Results: We find that increased theta power in anterior hippocampus just before cue presentation in NHP is predictive of trial performance. A similar temporal coupling of low frequency power just prior to cue presentation is found in humans in amygdala, hippocampus, medial temporal lobe, and cingulate nodes. Further, we find that learning induces a decrease in this low frequency power during the anticipatory period.
Conclusions: These results help to validate the primate model for use in a temporal expectancy task, and may provide insight into the network dynamics driving implicit associative learning.
Patient Care: Greater understanding of rudimentary learning network activity is the first step toward developing novel therapies to improve cognitive performance and functional outcomes for patients with intellectual disabilities.
Learning Objectives: By the conclusion of this session, participants should be able to: 1) Identify power spectral changes related to encoding of temporal expectancy. 2) Understand the role of dynamic networks in learning and performance. 3) Identify changes in network interaction as learning occurs.
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Dallérac G, Graupner M, Knippenberg J, Martinez RC, Tavares TF, Tallot L, El Massioui N, Verschueren A, Höhn S, Bertolus JB, Reyes A, LeDoux JE, Schafe GE, Diaz-Mataix L, Doyère V. (2017). Updating temporal expectancy of an aversive event engages striatal plasticity under amygdalacontrol. Nature Communications; 8:13920. doi: 10.1038/ncomms13920.
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