Functional segregation in the default mode network:The left and right TPJ in attention, semantic and social processing

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The advent of functional neuroimaging techniques has enabled the serendipity discovery of the default-mode network (DMN) 15 years ago. Despite considerable research progress in healthy and disease, this network, a major source of energy consumption in the human brain, has almost exclusively been studied as a cohesive unit. In particular, it has seldom been appreciated that the right and left temporo-parietal junction (TPJ) within the DMN are diverging from cognitive, anatomical, and clinical perspectives.The three proposed work packages focus on the causal role of the left and right TPJ across diverse cognitive processes. Experimental tasks will prompt attentional reorienting, semantic processing and theory of mind as archetypical processes that exemplify the broader cognitive domains attention, language comprehension and social cognition. Established psychological paradigms will trigger lateralized neural responses within this major brain network by tasks related to attention (known for right-lateralized TPJ activation), semantic processing (known for left-lateralized TPJ activation) and social cognition (known for bilateral TPJ activation). These neural responses in the healthy brain will be causally perturbed by applying repetitive transcranial magnetic stimulation (rTMS) to the either left or right TPJ during task processing. rTMS wil also be combined with functional neuroimaging to probe adaptive short-term reorganization and plasticity in the healthy system. The induced dysfunction in the TPJ node is expected to entail functional activity and connectivity alterations of DMN dynamics that are specific to the ongoing task.The approach exploits prior knowledge by quantitative target region definition using coordinate-based activation likelihood meta-analysis of previously published studies in each cognitive domain. The approach is multimodal by combining neuroimaging of attentional, semantic and social cognitive performance with meta-analytically constrained rTMS perturbations. Finally, the approach is multivariate by analyzing the neuroimaging results using advanced statistical-learning methods. These methods will model i) between-node interaction patterns of the DMN (i.e., group-sparse graph lasso and dynamic causal modeling), ii) local differences in activation patterns of individual DMN nodes (searchlight analysis), and iii) complex brain-behavior relationships (Lasso/Ridge/ElasticNet regression with task performance). In this way, data-driven bottom-up and experimental top-down methods will be closely intertwined to elucidate functional segregation in the DMN. New insight is expected to emerge from the perturbation of the key regions during distinct cognitive operations that are analyzed by multivariate statistical analyses. Induced plasticity effects on the system level will contribute to a better understanding of general properties of brain mechanisms that allow for rapid functional compensation in response to focal dysfunctions.