Cortical Networks Relating to Arousal Are Differentially Coupled to Neural Activity and Hemodynamics

被引:1
|
作者
Meyer-Baese, Lisa [1 ,2 ]
Morrissette, Arthur E. [1 ]
Wang, Yunmiao [1 ]
Le Chatelier, Brune [1 ]
Borden, Peter Y. [2 ]
Keilholz, Shella D. [2 ]
Stanley, Garrett B. [2 ]
Jaeger, Dieter [1 ]
机构
[1] Emory Univ, Dept Biol, Atlanta, GA 30322 USA
[2] Emory & Georgia Tech, Dept Biomed Engn, Atlanta, GA 30322 USA
来源
JOURNAL OF NEUROSCIENCE | 2024年 / 44卷 / 25期
基金
美国国家卫生研究院;
关键词
cortex; fMRI; mouse; pupil diameter; voltage imaging; wide- fi eld optical imaging; GLOBAL SIGNAL REGRESSION; RESTING-STATE; FUNCTIONAL CONNECTIVITY; BRAIN; CORTEX; DYNAMICS; LOCOMOTION; TIME; FLUCTUATIONS; ACTIVATION;
D O I
10.1523/JNEUROSCI.0298-23.2024
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Even in the absence of speci fi c sensory input or a behavioral task, the brain produces structured patterns of activity. This organized activity is modulated by changes in arousal. Here, we use wide - fi eld voltage imaging to establish how arousal relates to cortical network voltage and hemodynamic activity in spontaneously behaving head - fi xed male and female mice expressing the voltage -sensitive fl uorescent FRET sensor Butter fl y 1.2. We fi nd that global voltage and hemodynamic signals are both positively correlated with changes in arousal with a maximum correlation of 0.5 and 0.25, respectively, at a time lag of 0 s. We next show that arousal in fl uences distinct cortical regions for both voltage and hemodynamic signals. These include a broad positive correlation across most sensorymotor cortices extending posteriorly to the primary visual cortex observed in both signals. In contrast, activity in the prefrontal cortex is positively correlated to changes in arousal for the voltage signal while it is a slight net negative correlation observed in the hemodynamic signal. Additionally, we show that coherence between voltage and hemodynamic signals relative to arousal is strongest for slow frequencies below 0.15 Hz and is near zero for frequencies >1 Hz. We fi nally show that coupling patterns are dependent on the behavioral state of the animal with correlations being driven by periods of increased orofacial movement. Our results indicate that while hemodynamic signals show strong relations to behavior and arousal, these relations are distinct from those observed by voltage activity.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Mutual control neural networks for sleep arousal detection
    Assimakopoulos, T
    Dingli, K
    Douglas, NJ
    ARTIFICIAL NEURAL NETWORKS IN MEDICINE AND BIOLOGY, 2000, : 119 - 124
  • [22] Coupled Ensembles of Neural Networks
    Dutt, Anuvabh
    Pellerin, Denis
    Quenot, Georges
    2018 16TH INTERNATIONAL CONFERENCE ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI), 2018,
  • [23] Coupled ensembles of neural networks
    Dutt, Anuvabh
    Pellerin, Denis
    Quenot, Georges
    NEUROCOMPUTING, 2020, 396 : 346 - 357
  • [24] Prediction in cultured cortical neural networks
    Lamberti, Martina
    Tripathi, Shiven
    van Putten, Michel J. A. M.
    Marzen, Sarah
    le Feber, Joost
    PNAS NEXUS, 2023, 2 (06):
  • [25] Differentially Private Mixture of Generative Neural Networks
    Acs, Gergely
    Melis, Luca
    Castelluccia, Claude
    De Cristofaro, Emiliano
    2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2017, : 715 - 720
  • [26] Interscale interactions in cortical neural networks
    Liljenström, H
    BEHAVIORAL AND BRAIN SCIENCES, 2000, 23 (03) : 408 - +
  • [27] Multiscale Modeling of Cortical Neural Networks
    Torben-Nielsen, Benjamin
    Stiefel, Klaus M.
    MULTISCALE PHENOMENA IN BIOLOGY, 2009, 1167 : 15 - 25
  • [28] Neural synchrony and the development of cortical networks
    Uhlhaas, Peter J.
    Roux, Frederic
    Rodriguez, Eugenio
    Rotarska-Jagiela, Anna
    Singer, Wolf
    TRENDS IN COGNITIVE SCIENCES, 2010, 14 (02) : 72 - 80
  • [29] Differentially Private Mixture of Generative Neural Networks
    Acs, Gergely
    Melis, Luca
    Castelluccia, Claude
    De Cristofaro, Emiliano
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2019, 31 (06) : 1109 - 1121
  • [30] One-dimensional convolutional neural networks for low/high arousal classification from electrodermal activity
    Sanchez-Reolid, Roberto
    Lopez de la Rosa, Francisco
    Lopez, Maria T.
    Fernandez-Caballero, Antonio
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 71