Rapid Sequential Implication of the Human Medial Temporal Lobe in Memory Encoding and Recognition

The medial temporal lobe (MTL) is crucial for memory encoding and recognition. The time course of these processes is unknown. The present study juxtaposed encoding and recognition in a single paradigm. Twenty healthy subjects performed a continuous recognition task as brain activity was monitored with a high-density electroencephalography. The task presented New pictures thought to evoke encoding. The stimuli were then repeated up to 4 consecutive times to produce over-familiarity. These repeated stimuli served as “baseline” for comparison with the other stimuli. Stimuli later reappeared after 9–15 intervening items, presumably associated with new encoding and recognition. Encoding-related differences in evoked response potential amplitudes and in spatiotemporal analysis were observed at 145–300 ms, whereby source estimation indicated MTL and orbitofrontal activity from 145 to 205 ms. Recognition-related activity evoked by late repetitions occurred at 405–470 ms, implicating the MTL and neocortical structures. These findings indicate that encoding of information is initiated before it is recognized. The result helps to explain modifications of memories over time, including false memories, confabulation, and consolidation.

Propofol-Induced Unresponsiveness is Associated with Impaired Feedforward Connectivity in the Cortical Hierarchy

BackgroundImpaired consciousness has been associated with impaired cortical signal propagation following transcranial magnetic stimulation (TMS). Herein we hypothesized that the reduced current propagation under propofol-induced unresponsiveness is associated with changes in both feedforward and feedback connectivity across the cortical hierarchy.MethodsEight subjects underwent left occipital TMS coupled with high-density electroencephalograph (EEG) recordings during wakefulness and propofol-induced unconsciousness. Spectral analysis was applied to responses recorded from sensors overlying six hierarchical cortical sources involved in visual processing. Dynamic causal modelling (DCM) of evoked and induced source-space responses was used to investigate propofol’s effects on connectivity between regions.ResultsPropofol produced a wideband reduction in evoked power following TMS in five out of six electrodes. Bayesian Model Selection supported a DCM with hierarchical feedforward and feedback connections to best fit the data. DCM of induced responses revealed that the primary effect of propofol was impaired feedforward responses in cross frequency theta/alpha-gamma coupling and within frequency theta coupling (F contrast, Family Wise Error corrected p<0.05). An exploratory analysis (thresholded at uncorrected p<0.001) also suggested that propofol impaired feedforward and feedback beta band coupling. Posthoc analyses showed impairments in all feedforward connections and one feedback connection from parietal to occipital cortex. DCM of the evoked response potential showed impaired feedforward connectivity between left sided occipital and parietal cortex (T contrast p=0.004, Bonferroni corrected).ConclusionsOur data suggest that propofol-induced loss of consciousness is associated with reduced evoked power and impaired hierarchical feedforward connectivity following occipital TMS.

Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals

The scaling behavior of human electroencephalogram (EEG) signals is well exploited by appropriate extraction of time – frequency domain and entropy based features. Such measurable inherently helps understanding the neurophysiological phenomenon of brain as well as its associated cortical activities. Being a non-linear time series, EEG's are assumed to be fragment of fluctuations. Several attempts have been made to study the EEG signals for clinical applications such as epileptic seizure detection, evoked response potential recognition, tumor detection, identification of alcoholics and so on. In all such applications appropriate selection of feature parameter plays an important role in discriminating normal EEG from abnormal. In the recent past one can find the importance of wavelet and wavelet packet towards EEG analysis. This proposed research work investigates the effect of wavelet packet log energy entropy on EEG signals. Entropy being the measure of relative information, the proposed study attempts to discriminate the normal EEGs from abnormal EEG's by employing the log energy entropy features. For better brevity, this study restricts to the analysis of epileptic seizure from normal EEGs. Different decomposition levels from 2 to 5 were considered for wavelet packets with application of Haar, rbio3.1, sym7, dmey wavelets. A one second windowing was introduced for the data segmentation and Shannon's log energy entropy was estimated. Then the statistical non-parametric Wilcoxon model was employed. The result shows that the application of wavelet packet log energy entropy found to be a potential indicator for discriminating epileptic seizure from normal.

Not so primitive: context-sensitive meta-learning about unattended sound sequences

Mismatch negativity (MMN), an evoked response potential elicited when a “deviant” sound violates a regularity in the auditory environment, is integral to auditory scene processing and has been used to demonstrate “primitive intelligence” in auditory short-term memory. Using a new multiple-context and -timescale protocol we show that MMN magnitude displays a context-sensitive modulation depending on changes in the probability of a deviant at multiple temporal scales. We demonstrate a primacy bias causing asymmetric evidence-based modulation of predictions about the environment, and we demonstrate that learning how to learn about deviant probability (meta-learning) induces context-sensitive variation in the accessibility of predictive long-term memory representations that underpin the MMN. The existence of the bias and meta-learning are consistent with automatic attributions of behavioral salience governing relevance-filtering processes operating outside of awareness.