Signal complexity of human intracranial EEG tracks successful associative memory formation across individuals

Abstract

Memory performance is highly variable between individuals. Most studies examining human memory, however, have largely focused on the neural correlates of successful memory formation within individuals, rather than the differences between them. As such, what gives rise to this variability is poorly understood. Here, we examined intracranial EEG (iEEG) recordings captured from 43 participants (23 male) implanted with subdural electrodes for seizure monitoring as they performed a paired-associates verbal memory task. We identified three separate but related signatures of neural activity that tracked differences in successful memory formation across individuals. High performing individuals consistently exhibited less broadband power, flatter power spectral density (PSD) slopes, and greater complexity in their iEEG signals. Furthermore, within individuals across three separate time scales ranging from seconds to days, successful recall was positively associated with these same metrics. Our data therefore suggest that memory ability across individuals can be indexed by increased neural signal complexity.Significance StatementWe show that participants whose intracranial EEG exhibits less low frequency power, flatter power spectrums, and greater sample entropy overall are better able to memorize associations, and that the same metrics track fluctuations in memory performance across time within individuals. These metrics together signify greater neural signal complexity which may index the brain’s ability to flexibly engage with information and generate separable memory representations. Critically, the current set of results provide a unique window into the neural markers of individual differences in memory performance which have hitherto been underexplored.

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

“…Another way in which our study helps to extend prior work is by explicitly linking the entropy of brain signals to the 1/ƒ χ component of power spectra (see Sheehan, Sreekumar, Inati, &Zaghloul, 2018 andWaschke et al, 2017). The PSD slope of large-scale field potentials has been proposed to be a measure of neural noise that reflects population spiking statistics .…”

Complexity and 1/f slope jointly reflect brain states

“…Moreover, E/I balance is not a static property of the cortex. It changes depending on the behavioral state (Waschke et al, 2019), task demands (Pfeffer et al, 2018;Waschke et al, 2019), performance (Sheehan et al, 2018) and depending on circadian rhythms (Bridi et al, 2020), which suggests that this property is under fine dynamic control. It has been proposed that cortical states and neural complexity could be regulated by subcortical cholinergic and noradrenergic activity (D'Andola et al, 2018); (Nghiem et al, 2020).…”

“…A particularly successful way to quantify E/I balance is the slope of the power law decay of spectral power of brain field potentials. Specifically, models have been shown that the background 1/f slope of the power spectral density (PSD) emerges from the sum of stochastic excitatory and inhibitory currents (Destexhe et al, 2001;Sheehan et al, 2018;Gao et al, 2017). Moreover, empirical validation of these models has shown that the E/I balance can be properly inferred from background activity by parameterizing the 1/f shape of the PSD (Gao et al, 2017;Trakoshis et al, 2020).…”

Principled Approaches to Direct Brain Stimulation for Cognitive Enhancement

“…Neural oscillatory changes accompanying successful memory formation relative to unsuccessful encoding, i.e., the subsequent memory effect (SME; Paller and Wagner, 2002 ), have been found in different frequency bands, with most studies reporting a general decrease in low frequency power and increase in high frequency power during successful encoding (Sederberg et al, 2006 ; Guderian et al, 2009 ; Fell et al, 2011 ; Long et al, 2014 ; but see Hanslmayr and Staudigl, 2014 on the sensitivity of SMEs on encoding task and contextual overlap between study and test). Successful memory formation is also accompanied by flatter power spectral density (PSD) slopes and increases in sample entropy (Sheehan et al, 2017 ), both measures of signal complexity, suggesting that the ability to successfully encode information in indexed by neural signal complexity. Finally, improved functional interactions between brain regions such as the MTL and PFC may underlie successful memory formation (Fell et al, 2003 ).…”