{"title":"通过脑电-功能磁共振同时融合推断宏观脑动力学。","authors":"Marios G Philiastides, Tao Tu, Paul Sajda","doi":"10.1146/annurev-neuro-100220-093239","DOIUrl":null,"url":null,"abstract":"<p><p>Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.</p>","PeriodicalId":8008,"journal":{"name":"Annual review of neuroscience","volume":null,"pages":null},"PeriodicalIF":12.1000,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI.\",\"authors\":\"Marios G Philiastides, Tao Tu, Paul Sajda\",\"doi\":\"10.1146/annurev-neuro-100220-093239\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.</p>\",\"PeriodicalId\":8008,\"journal\":{\"name\":\"Annual review of neuroscience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.1000,\"publicationDate\":\"2021-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual review of neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-neuro-100220-093239\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/3/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual review of neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1146/annurev-neuro-100220-093239","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/3/24 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI.
Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.
期刊介绍:
The Annual Review of Neuroscience is a well-established and comprehensive journal in the field of neuroscience, with a rich history and a commitment to open access and scholarly communication. The journal has been in publication since 1978, providing a long-standing source of authoritative reviews in neuroscience.
The Annual Review of Neuroscience encompasses a wide range of topics within neuroscience, including but not limited to: Molecular and cellular neuroscience, Neurogenetics, Developmental neuroscience, Neural plasticity and repair, Systems neuroscience, Cognitive neuroscience, Behavioral neuroscience, Neurobiology of disease. Occasionally, the journal also features reviews on the history of neuroscience and ethical considerations within the field.