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

被引:79
作者
Bestmann, Sven [1 ]
Feredoes, Eva [2 ]
机构
[1] UCL, Sobell Dept Motor Neurosci & Movement Disorder, UCL Inst Neurol, London WC1N 3BG, England
[2] Univ Reading, Sch Psychol & Clin Language Sci, Reading, Berks, England
来源
YEAR IN COGNITIVE NEUROSCIENCE | 2013年 / 1296卷
基金
英国生物技术与生命科学研究理事会; 欧洲研究理事会;
关键词
state-dependence; effective connectivity; transcranial magnetic stimulation; causal inference; EEG; fMRI; MRS; computational neurostimulation; TRANSCRANIAL MAGNETIC STIMULATION; HUMAN VISUAL-CORTEX; DORSAL PREMOTOR CORTEX; ALTERNATING-CURRENT STIMULATION; CONTRALATERAL PRIMARY MOTOR; DYNAMIC CAUSAL-MODELS; CONCURRENT TMS-FMRI; BOLD MRI RESPONSES; PREFRONTAL CORTEX; CORTICAL EXCITABILITY;
D O I
10.1111/nyas.12110
中图分类号
Q189 [神经科学];
学科分类号
071006 [神经生物学];
摘要
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.
引用
收藏
页码:11 / 30
页数:20
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