Reflections of idiographic long-term memory characteristics in resting-state neuroimaging data

Abstract

Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individual's biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting-state EEG data. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels low (13-15 Hz) and upper (15-17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and robust correlations methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings..

Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

“…Specifically, the individual rate of forgetting correlates with the connectivity in networks associated with different facets of memory function, like the sensory and associative cortices (involved in representation) and the DMN (involved in memory encoding and retrieval). Additionally, our results show remarkable similarity to the EEG findings of Zhou et al (2021). In their study, a significant positive correlation between power in the beta band and rate of forgetting α was observed distributing at bilateral prefrontal (channel AF3, AF4) and right parietal (channel P8) regions, in locations that are consistent with the ventromedial prefrontal DMN nodes and the dorsal visual attention nodes in Figure 6, respectively.…”

“…The resulting values ranged from 0.253 to 0.385, with a mean of 0.305 and standard deviation of 0.029. Both the mean and the range of values are in line with previously published studies (Sense et al, 2016;Zhou et al, 2021).…”

“…Specifically: Sense et al, 2018Sense et al, , 2015Florian Sense et al, 2016van der Velde et al, 2020), thus meeting the psychometric criteria of reliability and validity (Shrout & Lane, 2012). Finally, at least one previous study has established that rates of forgetting, thus measured, reliably correlate with individual differences in neural activity (Zhou et al, 2021).…”

Qualitative Invariant Effects Arise from Neural Constraints: Common Architecture and Sources of Individual Differences

“…Due to bilateral removal of the hippocampus, HM was unable to form new memories. His post-surgical behavior cannot be interpreted on a quantitative scale of, for instance extreme memory decay (Zhou et al 2021); instead, it is consistent with what would be expected if we take any cognitive architecture and selectively remove one specific module.…”

When Fear Shrinks the Brain: A Computational Model of the Effects of Posttraumatic Stress on Hippocampal Volume

“…Each successive encoding, re-encoding, or retrieval of a memory m results leaves a new trace of the memory. The odds of retrieving any trace decay exponentially over time with a decay rate d, which represents an individual-specific rate of forgetting (Sense, Behrens, Meijer, & van Rijn, 2016;Zhou et al, 2021). Thus, the odds of retrieving m correspond to the sum of the odds of retrieving any of its individual decaying traces, and the base-level activation B(m) can be expressed as the log of this sum:…”