4.7 Article Book Chapter

Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future

期刊

YEAR IN COGNITIVE NEUROSCIENCE
卷 1296, 期 -, 页码 11-30

出版社

BLACKWELL SCIENCE PUBL
DOI: 10.1111/nyas.12110

关键词

state-dependence; effective connectivity; transcranial magnetic stimulation; causal inference; EEG; fMRI; MRS; computational neurostimulation

资金

  1. BBSRC [BB/F02424X/1] Funding Source: UKRI
  2. Biotechnology and Biological Sciences Research Council [BB/F02424X/1] Funding Source: researchfish
  3. European Research Council (ERC) [260424] Funding Source: European Research Council (ERC)
  4. Biotechnology and Biological Sciences Research Council [BB/F02424X/1] Funding Source: Medline
  5. European Research Council [260424] Funding Source: Medline

向作者/读者索取更多资源

Modern neurostimulation approaches in humans provide controlled inputs into the operations of cortical regions, with highly specific behavioral consequences. This enables causal structure-function inferences, and in combination with neuroimaging, has provided novel insights into the basic mechanisms of action of neurostimulation on distributed networks. For example, more recent work has established the capacity of transcranial magnetic stimulation (TMS) to probe causal interregional influences, and their interaction with cognitive state changes. Combinations of neurostimulation and neuroimaging now face the challenge of integrating the known physiological effects of neurostimulation with theoretical and biological models of cognition, for example, when theoretical stalemates between opposing cognitive theories need to be resolved. This will be driven by novel developments, including biologically informed computational network analyses for predicting the impact of neurostimulation on brain networks, as well as novel neuroimaging and neurostimulation techniques. Such future developments may offer an expanded set of tools with which to investigate structure-function relationships, and to formulate and reconceptualize testable hypotheses about complex neural network interactions and their causal roles in cognition.

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