Computational dissection of human episodic memory reveals mental process-specific genetic profiles

Abstract

Episodic memory performance is the result of distinct mental processes, such as learning, memory maintenance, and emotional modulation of memory strength. Such processes can be effectively dissociated using computational models. Here we performed gene set enrichment analyses of model parameters estimated from the episodic memory performance of 1,765 healthy young adults. We report robust and replicated associations of the amine compound SLC (solute-carrier) transporters gene set with the learning rate, of the collagen formation and transmembrane receptor protein tyrosine kinase activity gene sets with the modulation of memory strength by negative emotional arousal, and of the L1 cell adhesion molecule (L1CAM) interactions gene set with the repetition-based memory improvement. Furthermore, in a large functional MRI sample of 795 subjects we found that the association between L1CAM interactions and memory maintenance revealed large clusters of differences in brain activity in frontal cortical areas. Our findings provide converging evidence that distinct genetic profiles underlie specific mental processes of human episodic memory. They also provide empirical support to previous theoretical and neurobiological studies linking specific neuromodulators to the learning rate and linking neural cell adhesion molecules to memory maintenance. Furthermore, our study suggests additional memory-related genetic pathways, which may contribute to a better understanding of the neurobiology of human memory.learning and memory | computational model | gene set enrichment analysis | functional neuroimaging | frontal lobe H uman memory is a highly heritable and complex trait that is the outcome of numerous neurocognitive processes (1, 2). Although many genetic variations associated with human memory performance have been identified (3-9), most were based on general memory scores, such as the number of recalled items. Such scores may serve as good correlates of overall memory ability, but they offer little insight into the genetic underpinnings of specific memory-related mental processes, such as encoding, memory maintenance, modulation by emotional arousal, or guessing strategies. Importantly, neurobiological and molecular profiles of such processes are known to be partly distinct (10-12). Because some of the mental processes involved in memory are not readily amenable to direct observation, computational modeling can be used to make inferences about them (13,14). Modelbased analyses provided insights into neurocomputational mechanisms of reward-based learning and decision making (15-17), related model parameters such as the learning rate to genetic polymorphisms (18,19), and provided a computational explanation for the inverted-U-shaped relation between stress intensity and behavioral performance (20). As a relatively recent development, the model-based analysis approach has largely been missing from studies of human episodic memory and genome-wide association studies (GWAS).GWAS already had identified a number of associations betwee...

Distinct genetic signatures of cortical and subcortical regions associated with human memory

“…Presently, most human memory evidence are derived from popular non-invasive methods such as Genome-Wide Association Studies 131 (GWAS), which identifies links between gene variants and cognition 68 . GWAS was useful in discovery of a number of associations between genes and cognitive traits, such as L1CAM and repetition-based memory improvement 68,69,132,133 . However, GWAS ignores the spatially distributed gene expression in the brain by solely analyzing gene variants in relation to brain or behavioral measures 8,134 .…”

“…GSEA analyzes genes as potentially working towards a common biological function, instead of independent individual entities 63,64 . GSEA and LEA were effective in identifying genetic signatures of cognitive functions [65][66][67] , including episodic and working memory 68,69 . Third, we validate the link between these discovered genes and human memory by drawing from animal memory literature ( Fig.…”

Introducing COSMOS: a Web Platform for Multimodal Game-Based Psychological Assessment Geared Towards Open Science Practice

“…Currently, three different sceneries are available et al 2006;Behrens et al 2007;Frank et al 2007;Luksys et al 2009), they have also been applied to study working (Collins and Frank 2012;Collins et al 2014) and episodic (Luksys et al 2014(Luksys et al , 2015 memory as well as decisionmaking (Forstmann et al 2008), including strategic reasoning (Zhu et al 2012;Seo et al 2014). The main principle is that a computational model is fitted to experimental data (based on how well model-produced behaviors match experimentally observed ones), and then the best-fitting model parameters and/or variables are used as correlates for neurobiological data such as neuron recordings (Samejima et al 2005), fMRI activations (Tanaka et al 2016;Daw et al 2006, Behrens et al 2007), genetic differences (Frank et al 2007;Set et al 2014;Luksys et al 2014Luksys et al , 2015, levels of stress (Luksys et al 2009), and neuropsychiatric disorders (Collins et al 2014). The main advantage of model-based analysis is that it can test neurocomputational mechanisms of behavior, which different candidate models aim to represent, and reduce a variety of behavioral measures, which can strongly depend on the specific task, to fewer model parameters that are directly comparable between the tasks.…”

Linguistic Approaches in Teaching History of Science and Technology Courses through a Content Block on Cognitive Sciences

“…For instance, Harden (2014), through quantitative genetic studies (e.g., twin and family studies), argues for the existence of heritable variations in adolescent sexual behavior, meaning that genetic differences shape sexual preferences. Human memory also correlates with genetics (Gedeminas Luksys et al, 2015). In a broad future perspective, this allows science to understand and to foresee human behavior much better, sizing it up via behavioral genetics and other behaviorrelated genetic means.…”