Mäki-Marttunen, T. | bioRxiv Science

bioRxiv2020

Experience-dependent modulation of the visual evoked potential: testing effect sizes, retention over time, and associations with age in 415 healthy individuals

Experience-dependent modulation of the visual evoked potential (VEP) is a promising proxy measure of synaptic plasticity in the cerebral cortex. However, existing studies are limited by small to moderate sample sizes as well as by considerable variability in how VEP modulation is quantified. In the present study, we used a large sample (n = 415) of healthy volunteers to compare different quantifications of VEP modulation with regards to effect sizes and retention of the modulation effect over time. We observed significant modulation for VEP components C1 (Cohens d = 0.53), P1 (d = 0.66), N1 (d = -0.27), N1b (d = -0.66), but not P2 (p = 0.1), and in one time-frequency cluster (~30 Hz and ~70 ms post-stimulus; d = -0.48), 2-4 minutes after 2 Hz prolonged visual stimulation. For components N1 (d = -0.21) and N1b (d = -0.38), as well for the time-frequency cluster (d = -0.33), this effect was retained after 54-56 minutes. Moderate to high correlations ({rho} = [0.39, 0.69]) between modulation at different postintervention blocks revealed a relatively high temporal stability in the modulation effect for each VEP component. However, different VEP components also showed markedly different temporal retention patterns. Finally, P1 modulation correlated positively with age (t = 5.26), and was larger for female participants (t = 3.91), with no effects of either age or sex on N1 and N1b potentiation. These results provide strong support for VEP modulation, and especially N1b modulation, as a robust measure of synaptic plasticity, but underscore the need to differentiate between components, and to control for demographic confounders.

neuroscience↗

bioRxiv2020

A unified computational model for cortical post-synaptic plasticity

Cortical synapses possess a machinery of signalling pathways that leads to various modes of post-synaptic plasticity. Such pathways have been examined to a great detail separately in many types of experimental studies. However, a unified picture on how multiple biochemical pathways collectively shape the observed synaptic plasticity in the neocortex is missing. Here, we built a biochemically detailed model of post-synaptic plasticity that includes the major signalling cascades, namely, CaMKII, PKA, and PKC pathways which, upon activation by Ca2+, lead to synaptic potentiation or depression. We adjusted model components from existing models of intracellular signalling into a single-compartment simulation framework. Furthermore, we propose a statistical model for the prevalence of different types of membrane-bound AMPA-receptor tetramers consisting of GluR1 and GluR2 subunits in proportions suggested by the biochemical signalling model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance. We show that our model can reproduce neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex. Moreover, we demonstrate that our model can be fit to data from many cortical areas and that the resulting model parameters reflect the involvement of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity. Significance statementNeocortical synaptic plasticity has been studied experimentally in a number of cortical areas, showing how interactions between neuromodulators and post-synaptic proteins shape the outcome of the plasticity. On the other hand, non-detailed computational models of long-term plasticity, such as Hebbian rules of synaptic potentiation and depression, have been widely used in modelling of neocortical circuits. In this work, we bridge the gap between these two branches of neuroscience by building a detailed model of post-synaptic plasticity that can reproduce observations on cortical plasticity and provide biochemical meaning to the simple rules of plasticity. Our model can be used for predicting the effects of chemical or genetic manipulations of various intracellular signalling proteins on induction of plasticity in health and disease.

neuroscience↗