Effect of antipsychotics on community structure in functional brain networks

被引:6
|
作者
Flanagan, Ryan [1 ]
Lacasa, Lucas [1 ]
Towlson, Emma K. [2 ,3 ,4 ]
Lee, Sang Hoon [5 ]
Porter, Mason A. [6 ]
机构
[1] Queen Mary Univ London, Sch Math Sci, London E1 4NS, England
[2] Northeastern Univ, Ctr Complex Network Res, Boston, MA 02115 USA
[3] Northeastern Univ, Dept Phys, Boston, MA 02115 USA
[4] MIT, Media Lab, Cambridge, MA 02139 USA
[5] Gyeongnam Natl Univ Sci & Technol, Dept Liberal Arts, Jinju 52725, South Korea
[6] Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90095 USA
基金
英国工程与自然科学研究理事会; 新加坡国家研究基金会;
关键词
structural analysis of networks; brain networks; mathematical analysis of networks; community structure; HEAD MOTION; SHORT-TERM; CONNECTIVITY; SCHIZOPHRENIA; 2ND-GENERATION; CONNECTOME;
D O I
10.1093/comnet/cnz013
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Schizophrenia, a mental disorder that is characterized by abnormal social behaviour and failure to distinguish one's own thoughts and ideas from reality, has been associated with structural abnormalities in the architecture of functional brain networks. In this article, we (1) investigate whether mesoscale network properties give relevant information to distinguish groups of patients from controls in different scenarios and (2) use this lens to examine network effects of different antipsychotic treatments. Using various methods of network analysis, we examine the effect of two classical therapeutic antipsychotics-Aripiprazole and Sulpiride-on the architecture of functional brain networks of both controls (i.e., a set of people who were deemed to be healthy) and patients (who were diagnosed with schizophrenia). We compare community structures of functional brain networks of different individuals using mesoscopic response functions, which measure how community structure changes across different scales of a network. Our approach does a reasonably good job of distinguishing patients from controls, and the distinction is sharper for patients and controls who have been treated with Aripiprazole. Unexpectedly, we find that this increased separation between patients and controls is associated with a change in the control group, as the functional brain networks of the patient group appear to be predominantly unaffected by this drug. This suggests that Aripiprazole has a significant and measurable effect on community structure in healthy individuals but not in individuals who are diagnosed with schizophrenia, something that conflicts with the naive assumption that the drug alters the mesoscale network properties of the patients (rather than the controls). By contrast, we are less successful at separating the networks of patients from those of controls when the subjects have been given the drug Sulpiride. Taken together, in our results, we observe differences in the effects of the drugs (and a placebo) on community structure in patients and controls and also that this effect differs across groups. From a network-science perspective, we thereby demonstrate that different types of antipsychotic drugs selectively affect mesoscale properties of brain networks, providing support that structures such as communities are meaningful functional units in the brain.
引用
收藏
页码:932 / 960
页数:29
相关论文
共 50 条
  • [21] Topological correlations of structural and functional networks in patients with traumatic brain injury
    Caeyenberghs, Karen
    Leemans, Alexander
    Leunissen, Inge
    Michiels, Karla
    Swinnen, Stephan P.
    FRONTIERS IN HUMAN NEUROSCIENCE, 2013, 7
  • [22] Fluctuations in Global Brain Activity Are Associated With Changes in Whole-Brain Connectivity of Functional Networks
    Scheinost, Dustin
    Tokoglu, Fuyuze
    Shen, Xilin
    Finn, Emily S.
    Noble, Stephanie
    Papademetris, Xenophon
    Constable, R. Todd
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2016, 63 (12) : 2540 - 2549
  • [23] Structure and dynamics of functional networks in child-onset schizophrenia
    de Arruda, Guilherme Ferraz
    Costa, Luciano da Fontoura
    Schubert, Dirk
    Rodrigues, Francisco A.
    CLINICAL NEUROPHYSIOLOGY, 2014, 125 (08) : 1589 - 1595
  • [24] Community structure of the creative brain at rest
    Kenett, Yoed N.
    Betzel, Richard F.
    Beaty, Roger E.
    NEUROIMAGE, 2020, 210
  • [25] DETECTING STATE CHANGES IN COMMUNITY STRUCTURE OF FUNCTIONAL BRAIN NETWORKS USING A MARKOV-SWITCHING STOCHASTIC BLOCK MODEL
    Samdin, S. Balqis
    Ting, Chee-Ming
    Ombao, Hernando
    2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), 2019, : 1483 - 1487
  • [26] Evidence for Hubs in Human Functional Brain Networks
    Power, Jonathan D.
    Schlaggar, Bradley L.
    Lessov-Schlaggar, Christina N.
    Petersen, Steven E.
    NEURON, 2013, 79 (04) : 798 - 813
  • [27] Scaling Properties of Human Brain Functional Networks
    Zucca, Riccardo
    Arsiwalla, Xerxes D.
    Le, Hoang
    Rubinov, Mikail
    Verschure, Paul F. M. J.
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT I, 2016, 9886 : 107 - 114
  • [28] Functional brain networks involved in lexical decision
    Wong, Samantha Tze Sum
    Goghari, Vina M.
    Sanford, Nicole
    Lim, Ryan
    Clark, Cameron
    Metzak, Paul D.
    Rossell, Susan L.
    Menon, Mahesh
    Woodward, Todd S.
    BRAIN AND COGNITION, 2020, 138
  • [29] Sex Differences in Functional Brain Networks for Language
    Xu, Min
    Liang, Xiuling
    Ou, Jian
    Li, Hong
    Luo, Yue-jia
    Tan, Li Hai
    CEREBRAL CORTEX, 2020, 30 (03) : 1528 - 1537
  • [30] Simple models of human brain functional networks
    Vertes, Petra E.
    Alexander-Bloch, Aaron F.
    Gogtay, Nitin
    Giedd, Jay N.
    Rapoport, Judith L.
    Bullmore, Edward T.
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2012, 109 (15) : 5868 - 5873