Neural plasticity is modified over the human menstrual cycle: Combined insight from sensory evoked potential LTP and repetition suppression.

In healthy women, fluctuations in hormones including progesterone and oestradiol lead to functional changes in the brain over the course of each menstrual cycle. Though considerable attention has been directed towards understanding changes in human cognition over the menstrual cycle, changes in underlying processes such as neural plasticity have largely only been studied in animals. In this study we explored predictive coding and repetition suppression via the roving mismatch negativity paradigm as a model of short-term plasticity (Garrido, Kilner, Kiebel, et al., 2009), and Hebbian learning via visual sensory long-term potentiation (LTP) as a model of long-term plasticity (Teyler et al., 2005). Electroencephalography (EEG) was recorded in 20 females during their early follicular and mid-luteal phases. Event-related potential (ERP) analyses were complemented with dynamic causal modelling (DCM) to characterise changes in the underlying neural architecture. More sustained variability in the ERP response to a change in tone during the luteal phase are interpreted as a delayed habituation of the P3a component in the luteal relative to the follicular phase. The additional increased forward connection strength over tone repetitions compared to the follicular phase suggests that, in this phase, females may be less efficient when processing deviations from predicted sensory input (error). In contrast, there appears to be no reliable change in sensory LTP. This suggests that predictive coding, but not Hebbian plasticity is modified in the mid-luteal compared to the follicular phase, at least at the days of the menstrual cycle tested. This finding implicates the human menstrual cycle in complex changes in neural plasticity and provides further evidence for the importance of considering the menstrual cycle when including females in electrophysiological research.

Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian learning mechanisms in the cerebral cortex.

The Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aim of the current study was to compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain. Forty participants were presented with both paradigms during EEG recording. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. This therefore indicates that both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations.

An fMRI investigation of the relationship between future imagination and cognitive flexibility.

While future imagination is largely considered to be a cognitive process grounded in default mode network activity, studies have shown that future imagination recruits regions in both default mode and frontoparietal control networks. In addition, it has recently been shown that the ability to imagine the future is associated with cognitive flexibility, and that tasks requiring cognitive flexibility result in increased coupling of the default mode network with frontoparietal control and salience networks. In the current study, we investigated the neural correlates underlying the association between cognitive flexibility and future imagination in two ways. First, we experimentally varied the degree of cognitive flexibility required during future imagination by manipulating the disparateness of episodic details contributing to imagined events. To this end, participants generated episodic details (persons, locations, objects) within three social spheres; during fMRI scanning they were presented with sets of three episodic details all taken from the same social sphere (Congruent condition) or different social spheres (Incongruent condition) and required to imagine a future event involving the three details. We predicted that, relative to the Congruent condition, future simulation in the Incongruent condition would be associated with increased activity in regions of the default mode, frontoparietal and salience networks. Second, we hypothesized that individual differences in cognitive flexibility, as measured by performance on the Alternate Uses Task, would correspond to individual differences in the brain regions recruited during future imagination. A task partial least squares (PLS) analysis showed that the Incongruent condition resulted in an increase in activity in regions in salience networks (e.g. the insula) but, contrary to our prediction, reduced activity in many regions of the default mode network (including the hippocampus). A subsequent functional connectivity (within-subject seed PLS) analysis showed that the insula exhibited increased coupling with default mode regions during the Incongruent condition. Finally, a behavioral PLS analysis showed that individual differences in cognitive flexibility were associated with differences in activity in a number of regions from frontoparietal, salience and default-mode networks during both future imagination conditions, further highlighting that the cognitive flexibility underlying future imagination is grounded in the complex interaction of regions in these networks.