Decoding mental states from brain activity in humans

被引:0
|
作者
John-Dylan Haynes
Geraint Rees
机构
[1] Max Planck Institute for Cognitive and Brain Sciences,Wellcome Department of Imaging Neuroscience
[2] Stephanstrasse 1a,undefined
[3] Institute of Neurology,undefined
[4] University College London,undefined
[5] Institute of Cognitive Neuroscience,undefined
[6] University College London,undefined
[7] Alexandra House,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Understanding whether cognitive and perceptual states can be decoded from brain activity alone is a fundamental question in cognitive neuroscience. It is not only relevant for scientific theories of how information is encoded in the brain, but also has important practical and ethical implications.Non-invasive techniques such as functional MRI (fMRI) can be used to record signals related to brain activity in humans from many locations in the brain simultaneously. However, many conventional approaches to analysing these data rely on considering signal changes at each location independently of all the other locations in the brainThese conventional approaches have proven successful in elucidating many aspects of the relationship between cognitive and mental states and brain activity. However, recent advances in data analysis procedures raise the possibility of deciphering additional and complementary information from neuroimaging data.Recently, a powerful approach has emerged that applies pattern-recognition techniques to neuroimaging data. The new strategy is to decode a person's current mental state by learning to recognize characteristic spatial patterns of brain activity associated with different mental states. This takes into account not just activity at single locations but the full spatial pattern of activity. Such pattern-based decoding reveals that substantially more information is encoded in fMRI signals than was previously recognised.These new approaches have a particular use in addressing the question of how information regarding perceptual and cognitive states is encoded in the human brain. Pattern-based decoding has now been successfully used to reveal the principles underlying the representation of objects in the ventral visual pathway. It can also reveal conscious and unconscious sensory representations of individual features, and can be used to track dynamic changes in the contents of consciousness over time.Decoding approaches therefore provide a particularly sensitive way to determine what types of information are represented in the spatially distributed pattern of brain responses recorded with current neuroimaging techniques. However, for more general applications, important technical and methodological barriers remain to be overcome, including the ability of such approaches to generalize across individuals and different cognitive and perceptual states.As these techniques have the possibility to reveal covert or unconscious mental states, they raise important ethical and privacy concerns. These can be addressed within existing ethical frameworks, but nevertheless necessitate careful and considered engagement by the neuroimaging community.
引用
收藏
页码:523 / 534
页数:11
相关论文
共 50 条
  • [31] Aging-Related Changes in Decoding Negative Complex Mental States from Faces
    Franklin, Robert G., Jr.
    Zebrowitz, Leslie A.
    EXPERIMENTAL AGING RESEARCH, 2016, 42 (05) : 471 - 478
  • [32] Mental imaging of motor activity in humans
    Jeannerod, M
    Frak, V
    CURRENT OPINION IN NEUROBIOLOGY, 1999, 9 (06) : 735 - 739
  • [33] Can humans perceive their brain states?
    Kotchoubey, B
    Kübler, A
    Strehl, U
    Flor, H
    Birbaumer, N
    CONSCIOUSNESS AND COGNITION, 2002, 11 (01) : 98 - 113
  • [34] Mental states as macrostates emerging from brain electrical dynamics
    Allefeld, Carsten
    Atmanspacher, Harald
    Wackermann, Jiri
    CHAOS, 2009, 19 (01)
  • [35] Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding
    Poldrack, Russell A.
    NEURON, 2011, 72 (05) : 692 - 697
  • [36] Decoding human brain activity with deep learning
    Zheng, Xiao
    Chen, Wanzhong
    Li, Mingyang
    Zhang, Tao
    You, Yang
    Jiang, Yun
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2020, 56
  • [37] Decoding brain activity with smooth sparse regression
    de Brecht, Matthew
    Yamagishi, Noriko
    NEUROSCIENCE RESEARCH, 2011, 71 : E201 - E202
  • [38] Decoding fMRI brain states in real-time
    LaConte, Stephen M.
    NEUROIMAGE, 2011, 56 (02) : 440 - 454
  • [39] Unsupervised Joint Domain Adaptation for Decoding Brain Cognitive States From tfMRI Images
    Zhang, Yameng
    Gao, Yufei
    Xu, Jing
    Zhao, Guohua
    Shi, Lei
    Kong, Lingfei
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2024, 28 (03) : 1494 - 1503
  • [40] Decoding Humor Experiences from Brain Activity of People Viewing Comedy Movies
    Sawahata, Yasuhito
    Komine, Kazuteru
    Morita, Toshiya
    Hiruma, Nobuyuki
    PLOS ONE, 2013, 8 (12):