BAIAP2 is related to emotional modulation of human memory strength

Memory performance is the result of many distinct mental processes, such as memory encoding, forgetting, and modulation of memory strength by emotional arousal. These processes, which are subserved by partly distinct molecular profiles, are not always amenable to direct observation. Therefore, computational models can be used to make inferences about specific mental processes and to study their genetic underpinnings. Here we combined a computational model-based analysis of memory-related processes with high density genetic information derived from a genome-wide study in healthy young adults. After identifying the best-fitting model for a verbal memory task and estimating the best-fitting individual cognitive parameters, we found a common variant in the gene encoding the brain-specific angiogenesis inhibitor 1-associated protein 2 (BAIAP2) that was related to the model parameter reflecting modulation of verbal memory strength by negative valence. We also observed an association between the same genetic variant and a similar emotional modulation phenotype in a different population performing a picture memory task. Furthermore, using functional neuroimaging we found robust genotype-dependent differences in activity of the parahippocampal cortex that were specifically related to successful memory encoding of negative versus neutral information. Finally, we analyzed cortical gene expression data of 193 deceased subjects and detected significant BAIAP2 genotype-dependent differences in BAIAP2 mRNA levels. Our findings suggest that model-based dissociation of specific cognitive parameters can improve the understanding of genetic underpinnings of human learning and memory

<i>BAIAP2</i> rs8067235 genotype-dependent differences in brain activity specifically related to negative modulation of memory strength.

<p>(<b>A</b>) Displayed are gene dose-dependent (with increasing number of <i>A</i> alleles) activity increases in left parahippocampal cortex (peak MNI coordinates [−22 −41 −12], Z(max) = 3.50, <i>P</i>_nominal = 2.3 ⋅ 10⁻⁴, <i>P</i>_{small-volume-FWE-corrected} = 0.033). Activations are overlaid on coronal (upper left), sagital (upper right), and axial sections of the study specific group template, displayed at an uncorrected threshold of P = 0.001 and using color-coded P values (number of voxels in the cluster: k = 10). L, left side of the brain; P, posterior; S, superior. (<b>B</b>) Genotype-dependent dissociation of negative and neutral Dm effects in left parahippocampal cortex (at the peak activation [−22 −41 −12]): progression from AA to GG genotype leads to shift in the parahippocampal sensitivity from negative to neutral Dm.</p

Parameter estimation results for the selected model.

<p>(<b>A</b>) The hill-climbing results of estimating three fixed parameters (Gaussian noise σ, sigmoidal steepness <i>s</i>, and forgetting rate γ) are shown, with bigger circles and lighter colors indicating better goodness-of-fit; ten best hill-climbing points (biggest light yellow circles) were selected for evaluating averages of all their possible combinations, shown in <b>B</b>. (<b>B</b>) Ten combinations with the best goodness-of-fit (χ²) are displayed. Overall, 267 out of 1023 combinations had better χ² than the best hill-climbing point (χ² = 1.522), which suggests that averaging parameters helps overcome step size gaps and leads to refined parameter values. (<b>C</b>) Histograms of the best-fitting individual parameters show distributions with the following means: ε_neg = 1.12, ε_pos = 1.09, α = 1.93, β = 1.27, <i>c</i> = 1.95.</p

Association between <i>BAIAP2</i> rs8067235 genotype and performance measures related to negative modulation of picture memory.

<p>Significance is calculated based on the additive genetic model. ρ: Spearman's rho. Values denote mean±s.e.m.</p

Genotype-independent subsequent memory (Dm) analysis for negative and neutral pictures.

<p>Genotype-independent subsequent memory (Dm) analysis for negative and neutral pictures.</p

Performance measures and their principal components.

<p>(<b>A</b>) Description of the performance measures (PM₁₋₈) in the verbal memory task and their population statistics. (<b>B</b>) Results of principal component analysis: the first five principal components (PC₁₋₅) explain 80% of variance in the data; their loadings suggest that the first component (PC₁) is related to general learning ability, PC₂ to delayed memory recall (as opposed to immediate recall performance), PC₃ to mistakes, PC₄ and PC₅ to the recall of negative and positive minus neutral words, respectively. Parameters of the best-fitting model that correlate the most with each PC are displayed on the right.</p

Association between <i>BAIAP2</i> rs8067235 genotype and model parameter ε_neg in the verbal memory task.

<p>Significance is calculated based on the additive genetic model. ρ: Spearman's rho</p