A network engineering perspective on probing and perturbing cognition with neurofeedback

被引:43
作者
Bassett, Danielle S. [1 ,2 ]
Khambhati, Ankit N. [1 ]
机构
[1] Univ Penn, Dept Bioengn, Philadelphia, PA 19104 USA
[2] Univ Penn, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA
基金
美国国家科学基金会;
关键词
graph theory; network neuroscience; neurofeedback; cognition; control theory; ANTERIOR CINGULATE CORTEX; HUMAN BRAIN NETWORKS; TIME FMRI NEUROFEEDBACK; FUNCTIONAL CONNECTIVITY; DYNAMIC RECONFIGURATION; MACHINE INTERFACES; EPILEPTIC SEIZURES; LEARNED REGULATION; LASTING CHANGES; SELF-REGULATION;
D O I
10.1111/nyas.13338
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition.
引用
收藏
页码:126 / 143
页数:18
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