Environment Enrichment Facilitates Long-Term Memory Consolidation through Behavioral Tagging.

The behavioral tagging (BT) hypothesis provides crucial insights into the mechanism of long-term memory (LTM) consolidation. Novelty exposure in BT is a decisive step in activating the molecular machinery of memory formation. Several studies have validated BT using different neurobehavioral tasks; however, the novelty given in all studies is open field (OF) exploration. Environment enrichment (EE) is another key experimental paradigm to explore the fundamentals of brain functioning. Recently, several studies have highlighted the importance of EE in enhancing cognition, LTM, and synaptic plasticity. Hence, in the present study, we investigated the effects of different types of novelty on LTM consolidation and plasticity-related protein (PRP) synthesis using the BT phenomenon. Novel object recognition (NOR) was used as the learning task for rodents (male Wistar rats), while OF and EE were two types of novel experiences provided to the rodents. Our results indicated that EE exposure efficiently leads to LTM consolidation through the BT phenomenon. In addition, EE exposure significantly enhances protein kinase Mζ (PKMζ) synthesis in the hippocampus region of the rat brain. However, the OF exposure did not lead to significant PKMζ expression. Further, our results did not find alterations in BDNF expression after EE and OF exposure in the hippocampus. Hence, it is concluded that different types of novelty mediate the BT phenomenon up to the same extent at the behavioral level. However, the implications of different novelties may differ at molecular levels.

Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG.

Time-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdFC) that incorporates higher order statistics to generate a multi-order connectivity pattern by analyzing neurophysiological data at multiple time scales. The technique builds a hierarchical graph between various temporal scales as opposed to traditional approaches that analyze each scale independently. We examined more than a million rdFC patterns obtained from morphologically diverse EEGs of 2378 subjects of varied age and neurological health. Spatiotemporal evaluation of these patterns revealed three dominant connectivity patterns that represent a universal underlying correlation structure seen across subjects and scalp locations. The three patterns are both mathematically equivalent and observed with equal prevalence in the data. The patterns were observed across a range of distances on the scalp indicating that they represent a spatially scale-invariant correlation structure. Moreover, the number of patterns representing the correlation structure has been shown to be linked with the number of nodes used to generate them. We also show evidence that temporal changes in the rdFC patterns are linked with seizure dynamics.

Automated Epilepsy Diagnosis Using EEG With Test Set Evaluation.

Several electroencephalogram (EEG)-based predictive models for automated epilepsy diagnosis have been proposed over more than a decade. However, to the best of our knowledge, none have been evaluated on a holdout/test set. A vast majority of these studies have reported accuracies above 95% on a benchmark EEG dataset, but the dataset has been shown here to have certain limitations when used for building classifiers for epilepsy diagnosis. We implemented two previously reported classifiers trained on the benchmark dataset whose accuracies were observed to drop sharply when evaluated on a test set. We propose a feature, engineered specifically, for epilepsy diagnosis that attempts to characterize the neuronal synchronization using scalp EEG by extending the concept of the impulse response of linear time-invariant systems to matrices. This feature was tested on the EEG of 50 epileptics and 50 healthy subjects and yielded an area under the curve (AUC) of 0.87. It outperforms the existing models implemented by us that gave the AUC of 0.80 when trained and tested on scalp EEG data, thereby, setting the new benchmark for automated epilepsy diagnosis on test set evaluation. The feature has also been shown to have statistical consistency across time and vigilance states with robustness against EEG artifacts.