Phase separation of competing memories along the human hippocampal theta rhythm.

Competition between overlapping memories is considered one of the major causes of forgetting, and it is still unknown how the human brain resolves such mnemonic conflict. In the present magnetoencephalography (MEG) study, we empirically tested a computational model that leverages an oscillating inhibition algorithm to minimise overlap between memories. We used a proactive interference task, where a reminder word could be associated with either a single image (non-competitive condition) or two competing images, and participants were asked to always recall the most recently learned word-image association. Time-resolved pattern classifiers were trained to detect the reactivated content of target and competitor memories from MEG sensor patterns, and the timing of these neural reactivations was analysed relative to the phase of the dominant hippocampal 3 Hz theta oscillation. In line with our pre-registered hypotheses, target and competitor reactivations locked to different phases of the hippocampal theta rhythm after several repeated recalls. Participants who behaviourally experienced lower levels of interference also showed larger phase separation between the two overlapping memories. The findings provide evidence that the temporal segregation of memories, orchestrated by slow oscillations, plays a functional role in resolving mnemonic competition by separating and prioritising relevant memories under conditions of high interference.

The brain time toolbox, a software library to retune electrophysiology data to brain dynamics.

Human thought is highly flexible, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. We argue that this goal is impeded by the time format of our data-clock time. The brain is a system with its own dynamics and regime of time, with no intrinsic concern for the human-invented second. Here, we present the Brain Time Toolbox, a software library that retunes electrophysiology data in line with oscillations that orchestrate neural patterns of cognition. These oscillations continually slow down, speed up and undergo abrupt changes, introducing a disharmony between the brain's internal regime and clock time. The toolbox overcomes this disharmony by warping the data to the dynamics of coordinating oscillations, setting oscillatory cycles as the data's new time axis. This enables the study of neural patterns as they unfold in the brain, aiding neuroscientific enquiry into dynamic cognition. In support of this, we demonstrate that the toolbox can reveal results that are absent in a default clock time format.

Strategic retrieval prevents memory interference: The temporal dynamics of retrieval orientation.

Resolving interference between overlapping memories is crucial to remember the past. This study tests the novel prediction that orienting search focus benefits goal-relevant retrieval by reducing competition from unwanted memories. In a modified retrieval-practice paradigm, participants encoded word-pairs in one of two encoding tasks. Critically, to evaluate whether this retrieval orientation (RO) reduces memory interference, target and competitor memories were always related to different encoding tasks. At retrieval, instructions were provided for half of the blocks with the intention to bias remembering towards items encoded with one of the ROs. Behavioural data show that adopting an RO improved target accessibility, strengthened the testing effect, and reduced retrieval-induced forgetting (RIF) of competitors. Specifically, RIF - typically attributed to inhibitory control of memory interference - was prominent when no retrieval orientation (NRO) instruction was provided. Furthermore, a neural correlate of RO was calculated by training a linear discriminant analysis (LDA) to discriminate the electroencephalographic (EEG) spatial brain patterns correspondent to the two ROs over the time course of selective retrieval. RO was characterised by increases in the theta and decreases in the beta frequency band, evident both before and after category-cue onset. While the pre-cue RO reinstatement effect predicted both immediate retrieval-practice success and later target accessibility, the post-cue effect predicted disengagement of inhibitory control, such that participants showing a stronger RO reinstatement effect showed lower levels of RIF. These data suggest that strategically orienting search focus during retrieval both increases target memory accessibility and reduces memory interference, which consequently protects related memories from inhibition and later forgetting. Furthermore, they also highlight the roles of theta and beta oscillations in establishing and maintaining a task-relevant bias towards target memory representations during competitive memory retrieval.

Evidence that neural information flow is reversed between object perception and object reconstruction from memory.

Remembering is a reconstructive process, yet little is known about how the reconstruction of a memory unfolds in time in the human brain. Here, we used reaction times and EEG time-series decoding to test the hypothesis that the information flow is reversed when an event is reconstructed from memory, compared to when the same event is initially being perceived. Across three experiments, we found highly consistent evidence supporting such a reversed stream. When seeing an object, low-level perceptual features were discriminated faster behaviourally, and could be decoded from brain activity earlier, than high-level conceptual features. This pattern reversed during associative memory recall, with reaction times and brain activity patterns now indicating that conceptual information was reconstructed more rapidly than perceptual details. Our findings support a neurobiologically plausible model of human memory, suggesting that memory retrieval is a hierarchical, multi-layered process that prioritises semantically meaningful information over perceptual details.

An Optimal Oscillatory Phase for Pattern Reactivation during Memory Retrieval.

Computational models and in vivo studies in rodents suggest that the hippocampal system oscillates between states that are optimal for encoding and states that are optimal for retrieval. Here, we show that in humans, neural signatures of memory reactivation are modulated by the phase of a theta oscillation. Electroencephalography (EEG) was recorded while participants were cued to recall previously learned word-object associations, and time-resolved pattern classifiers were trained to detect neural reactivation of the target objects. Classifier fidelity rhythmically fluctuated at 7 or 8 Hz and was modulated by theta phase across the entire recall period. The phase of optimal classification was shifted approximately 180° between encoding and retrieval. Inspired by animal work, we then computed "classifier-locked averages" to analyze how ongoing theta oscillations behaved around the time points at which the classifier indicated memory retrieval. We found strong theta (7 or 8 Hz) phase consistency approximately 300 ms before the time points of maximal neural memory reactivation. Our findings provide important evidence that the neural signatures of memory retrieval fluctuate and are time locked to the phase of an ongoing theta oscillation.

Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA).

BACKGROUND: Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in neuroscience. They allow for the unique opportunity to record brain activity with high spatial and temporal resolution. A common pre-processing choice in stereotactic EEG (S-EEG) is to re-reference the data with a bipolar montage. In this, each channel is subtracted from its neighbor, to reduce commonalities between channels and isolate activity that is spatially confined. NEW METHOD: We challenge the assumption that bipolar reference effectively performs this task. To extract local activity, the distribution of the signal source of interest, interfering distant signals, and noise need to be considered. Referencing schemes with fixed coefficients can decrease the signal to noise ratio (SNR) of the data, they can lead to mislocalization of activity and consequently to misinterpretation of results. We propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. RESULTS: We describe and demonstrate this on human S-EEG recordings. In a simulation with real data, we quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when revealing local time series from the superposition of neighboring channels. COMPARISON WITH EXISTING METHOD(S): We argue that ICA already performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and will identify activity that is local. CONCLUSIONS: When investigating local sources in human S-EEG, ICA should be preferred over re-referencing the data with a bipolar montage.