Non-Invasive Human Brain Stimulation in Cognitive Neuroscience: A Primer

The use of non-invasive brain stimulation is widespread in studies of human cognitive neuroscience. This has led to some genuine advances in understanding perception and cognition, and has raised some hopes of applying the knowledge in clinical contexts. There are now several forms of stimulation, the ability to combine these with other methods, and ethical questions that are special to brain stimulation. In this Primer, we aim to give the users of these methods a starting point and perspective from which to view the key questions and usefulness of the different forms of non-invasive brain stimulation. We have done so by taking a critical view of recent highlights in the literature, selected case studies to illustrate the elements necessary and sufficient for good experiments, and pointed to questions and findings that can only be addressed using interference methods

Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.