Contributions in Merlin

Measurements of motor evoked potentials (MEPs) have shown that anodal and cathodal transcranial direct current stimulations (tDCS) have facilitatory or inhibitory effects on corticospinal excitability in the stimulated area of the primary motor cortex (M1). Here, we investigated the online effects of short periods of anodal and cathodal tDCS on human brain activity of healthy subjects and associated hemodynamics by concurrent blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) at 3T. Using a block design, 20s periods of tDCS at 1 mA intensity over the left M1 altered with 20s periods without tDCS. In different fMRI runs, the effect of anodal or cathodal tDCS was assessed at rest or during finger tapping. A control experiment was also performed, in which the electrodes were placed over the left and right occipito-temporo-parietal junction. Neither anodal nor cathodal tDCS over the M1 for 20s stimulation duration induced a detectable BOLD signal change. However, in comparison to a voluntary finger tapping task without stimulation, anodal tDCS during finger tapping resulted in a decrease in the BOLD response in the supplementary motor area (SMA). Cathodal stimulation did not result in significant change in BOLD response in the SMA, however, a tendency toward decreased activity could be seen. In the control experiment neither cathodal nor anodal stimulation resulted in a significant change of BOLD signal during finger tapping in any brain area including SMA, PM, and M1. These findings demonstrate that the well-known polarity-dependent shifts in corticospinal excitability that have previously been demonstrated using measurements of MEPs after M1 stimulation are not paralleled by analogous changes in regional BOLD signal. This difference implies that the BOLD signal and measurements of MEPs probe diverse physiological mechanisms. The MEP amplitude reflects changes in transsynaptic excitability of large pyramidal neurons while the BOLD signal is a measure of net synaptic activity of all cortical neurons.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that alters cortical excitability and activity in a polarity-dependent way. Stimulation for a few minutes has been shown to induce plastic alterations of cortical excitability and to improve cognitive performance. These effects might be related to stimulation-induced alterations of functional cortical network connectivity. We aimed to investigate the impact of tDCS on cortical network function by functional connectivity and graph theoretical analysis of the BOLD fMRI spontaneous activity. fMRI resting-state datasets were acquired immediately before and after 10-min bipolar tDCS during rest, with the anode placed over the left primary motor cortex (M1) and the cathode over the contralateral frontopolar cortex. For each dataset, grey matter voxel-based synchronization matrices were calculated and thresholded to construct undirected graphs. Nodal connectivity degree and minimum path length maps were calculated and compared before and after tDCS. Nodal minimum path lengths significantly increased in the left somatomotor (SM1) cortex after anodal tDCS, which means that the number of direct functional connections from the left SM1 to topologically distant grey matter voxels significantly decreased. In contrast, functional coupling between premotor and superior parietal areas with the left SM1 significantly increased. Additionally, the nodal connectivity degree in the left posterior cingulate cortex (PCC) area as well as in the right dorsolateral prefrontal cortex (right DLPFC) significantly increased. In summary, we provide initial support that tDCS-induced neuroplastic alterations might be related to functional connectivity changes in the human brain. Additionally, we propose our approach as a powerful method to track for neuroplastic changes in the human brain.

We study the effect of stochastically delayed communication on common knowledge acquisition (common learning). If messages do not report dispatch times, communication prevents common learning under general conditions even if common knowledge is acquired without communication. If messages report dispatch times, communication can destroy common learning under more restrictive conditions. The failure of common learning in the two cases is based on different infection arguments. Communication can destroy common learning even if it ends in finite time, or if agents communicate all of their information. We also identify conditions under which common learning is preserved in the presence of communication.

In a companion paper [1], we have presented a generic approach for inferring how subjects make optimal decisions under uncertainty. From a Bayesian decision theoretic perspective, uncertain representations correspond to “posterior” beliefs, which result from integrating (sensory) information with subjective “prior” beliefs. Preferences and goals are encoded through a “loss” (or “utility”) function, which measures the cost incurred by making any admissible decision for any given (hidden or unknown) state of the world. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. In this paper, we describe a concrete implementation of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions) and demonstrate its utility by applying it to both simulated and empirical reaction time data from an associative learning task. Here, inter-trial variability in reaction times is modelled as reflecting the dynamics of the subjects' internal recognition process, i.e. the updating of representations (posterior densities) of hidden states over trials while subjects learn probabilistic audio-visual associations. We use this paradigm to demonstrate that our meta-Bayesian framework allows for (i) probabilistic inference on the dynamics of the subject's representation of environmental states, and for (ii) model selection to disambiguate between alternative preferences (loss functions) human subjects could employ when dealing with trade-offs, such as between speed and accuracy. Finally, we illustrate how our approach can be used to quantify subjective beliefs and preferences that underlie inter-individual differences in behaviour.

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