Genuine high-order interactions in brain networks and neurodegeneration

被引:41
|
作者
Herzog, Ruben [1 ,2 ]
Rosas, Fernando E. [2 ,3 ,4 ,5 ,6 ]
Whelan, Robert [7 ]
Fittipaldi, Sol [1 ,7 ,9 ,10 ]
Santamaria-Garcia, Hernando [1 ]
Cruzat, Josephine [1 ,2 ]
Birba, Agustina [9 ,10 ]
Moguilner, Sebastian [1 ]
Tagliazucchi, Enzo [1 ,8 ]
Prado, Pavel [1 ]
Ibanez, Agustin [1 ,7 ,9 ,10 ,11 ]
机构
[1] Univ Adolfo Ibanez, Latin Amer Brain Hlth BrainLat, Santiago, Chile
[2] Fdn Estudio Conciencia Humana EcoH, Santiago, Chile
[3] Imperial Coll London, Ctr Psychedel Res, Dept Brain Sci, London, England
[4] Imperial Coll London, Data Sci Inst, London, England
[5] Imperial Coll London, Ctr Complex Sci, London, England
[6] Univ Sussex, Dept Informat, Brighton, England
[7] Trinity Coll Dublin, Global Brain Hlth Inst GBHI, Dublin, Ireland
[8] Univ Buenos Aires, Buenos Aires Phys Inst, Buenos Aires, Argentina
[9] Univ San Andres, Cognit Neurosci Ctr CNC, Buenos Aires, Argentina
[10] Consejo Nacl Invest Cient & Tecn, Buenos Aires, Argentina
[11] Univ Calif San Francisco UCSF, Global Brain Hlth Inst GBHI, San Francisco, CA USA
基金
美国国家卫生研究院;
关键词
Neurodegeneration; Neuroimaging; Neural networks; High-order interactions; Machine learning; Biomarkers; ALZHEIMERS-DISEASE; FRONTOTEMPORAL DEMENTIA; BEHAVIORAL VARIANT; CONNECTIVITY; EEG; INFORMATION; DEGENERATION; HYPOTHESIS; BIOMARKERS; DIAGNOSIS;
D O I
10.1016/j.nbd.2022.105918
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Brain functional networks have been traditionally studied considering only interactions between pairs of regions, neglecting the richer information encoded in higher orders of interactions. In consequence, most of the con-nectivity studies in neurodegeneration and dementia use standard pairwise metrics. Here, we developed a genuine high-order functional connectivity (HOFC) approach that captures interactions between 3 or more re-gions across spatiotemporal scales, delivering a more biologically plausible characterization of the pathophysi-ology of neurodegeneration. We applied HOFC to multimodal (electroencephalography [EEG], and functional magnetic resonance imaging [fMRI]) data from patients diagnosed with behavioral variant of frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and healthy controls. HOFC revealed large effect sizes, which, in comparison to standard pairwise metrics, provided a more accurate and parsimonious characterization of neu-rodegeneration. The multimodal characterization of neurodegeneration revealed hypo and hyperconnectivity on medium to large-scale brain networks, with a larger contribution of the former. Regions as the amygdala, the insula, and frontal gyrus were associated with both effects, suggesting potential compensatory processes in hub regions. fMRI revealed hypoconnectivity in AD between regions of the default mode, salience, visual, and auditory networks, while in bvFTD between regions of the default mode, salience, and somatomotor networks. EEG revealed hypoconnectivity in the gamma band between frontal, limbic, and sensory regions in AD, and in the delta band between frontal, temporal, parietal and posterior areas in bvFTD, suggesting additional pathophysiological processes that fMRI alone can not capture. Classification accuracy was comparable with standard biomarkers and robust against confounders such as sample size, age, education, and motor artifacts (from fMRI and EEG). We conclude that high-order interactions provide a detailed, EEG-and fMRI compatible, biologically plausible, and psychopathological-specific characterization of different neurodegenerative conditions.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] High-order networks that learn to satisfy logic constraints
    Pinkas, Gadi
    Cohen, Shimon
    Journal of Applied Logics, 2019, 6 (04): : 653 - 693
  • [42] Extreme vulnerability of high-order organization in complex networks
    Xia, Denghui
    Li, Qi
    Lei, Yi
    Shen, Xinyu
    Qian, Ming
    Zhang, Chengjun
    Physics Letters, Section A: General, Atomic and Solid State Physics, 2022, 424
  • [43] GENERALIZED LEARNING RULE FOR HIGH-ORDER NEURAL NETWORKS
    MATUS, IJ
    PEREZ, P
    PHYSICAL REVIEW A, 1991, 43 (10): : 5683 - 5688
  • [44] Phase transition in information propagation on high-order networks
    Nian, Fuzhong
    Yu, X.
    Cao, J.
    Luo, L.
    INTERNATIONAL JOURNAL OF MODERN PHYSICS B, 2020, 34 (21):
  • [45] Modeling concrete strength with high-order neural networks
    Tsai, Hsing-Chih
    NEURAL COMPUTING & APPLICATIONS, 2016, 27 (08): : 2465 - 2473
  • [46] Extreme vulnerability of high-order organization in complex networks
    Xia, Denghui
    Li, Qi
    Lei, Yi
    Shen, Xinyu
    Qian, Ming
    Zhang, Chengjun
    PHYSICS LETTERS A, 2022, 424
  • [47] Dynamical analysis on the multistability of high-order neural networks
    Wang, Lili
    NEUROCOMPUTING, 2013, 110 : 137 - 144
  • [48] Complete Stability Control on the High-order Neural Networks
    Wang Lili
    Chen Tianping
    PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 1595 - 1598
  • [49] LEARNING, INVARIANCE, AND GENERALIZATION IN HIGH-ORDER NEURAL NETWORKS
    GILES, CL
    MAXWELL, T
    APPLIED OPTICS, 1987, 26 (23): : 4972 - 4978
  • [50] HIGH-ORDER NETWORKS THAT LEARN TO SATISFY LOGIC CONSTRAINTS
    Pinkas, Gadi
    Cohen, Shimon
    JOURNAL OF APPLIED LOGICS-IFCOLOG JOURNAL OF LOGICS AND THEIR APPLICATIONS, 2019, 6 (04): : 653 - 693