Genuine high-order interactions in brain networks and neurodegeneration

被引:43
|
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
  • [1] Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke
    Pirovano, I.
    Antonacci, Y.
    Mastropietro, A.
    Bara, C.
    Sparacino, L.
    Guanziroli, E.
    Molteni, F.
    Tettamanti, M.
    Faes, L.
    Rizzo, G.
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2023, 31 : 4549 - 4560
  • [2] The effect of high-order interactions on the functional brain networks of boys with ADHD
    Xi, Xiaojian
    Li, Jianhui
    Wang, Zhen
    Tian, Huaigu
    Yang, Rui
    EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2024, 233 (04): : 817 - 829
  • [3] Estimating high-order brain functional networks by correlation-preserving embedding
    Su, Hui
    Zhang, Limei
    Qiao, Lishan
    Liu, Mingxia
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2022, 60 (10) : 2813 - 2823
  • [4] Hybrid High-order Brain Functional Networks for Schizophrenia-Aided Diagnosis
    Xin, Junchang
    Zhou, Keqi
    Wang, Zhongyang
    Wang, Zhiqiong
    Chen, Jinyi
    Wang, Xinlei
    Chen, Qi
    COGNITIVE COMPUTATION, 2022, 14 (04) : 1303 - 1315
  • [5] High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia
    Plis, Sergey M.
    Sui, Jing
    Lane, Terran
    Roy, Sushmita
    Clark, Vincent P.
    Potluru, Vamsi K.
    Huster, Rene J.
    Michael, Andrew
    Sponheim, Scott R.
    Weisend, Michael P.
    Calhoun, Vince D.
    NEUROIMAGE, 2014, 102 : 35 - 48
  • [6] High-Order Interdependencies in the Aging Brain
    Gatica, Marilyn
    Cofre, Rodrigo
    Mediano, Pedro A. M.
    Rosas, Fernando E.
    Orio, Patricio
    Diez, Ibai
    Swinnen, Stephan P.
    Cortes, Jesus M.
    BRAIN CONNECTIVITY, 2021, 11 (09) : 734 - 744
  • [7] Turing patterns in systems with high-order interactions
    Muolo, Riccardo
    Gallo, Luca
    Latora, Vito
    Frasca, Mattia
    Carletti, Timoteo
    CHAOS SOLITONS & FRACTALS, 2023, 166
  • [8] High-order social interactions in groups of mice
    Shemesh, Yair
    Sztainberg, Yehezkel
    Forkosh, Oren
    Shlapobersky, Tamar
    Chen, Alon
    Schneidman, Elad
    ELIFE, 2013, 2
  • [9] HIGH-ORDER HOPFIELD AND TANK OPTIMIZATION NETWORKS
    SAMAD, T
    HARPER, P
    PARALLEL COMPUTING, 1990, 16 (2-3) : 287 - 292
  • [10] CHAOTIC DYNAMICS OF HIGH-ORDER NEURAL NETWORKS
    LEMKE, N
    ARENZON, JJ
    TAMARIT, FA
    JOURNAL OF STATISTICAL PHYSICS, 1995, 79 (1-2) : 415 - 427