Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression

被引:31
|
作者
Atluri, Sravya [1 ,2 ]
Wong, Willy [2 ,3 ]
Moreno, Sylvain [4 ]
Blumberger, Daniel M. [1 ,5 ,6 ]
Daskalakis, Zafiris J. [1 ,5 ,6 ]
Farzan, Faranak [1 ,5 ,6 ,7 ]
机构
[1] Ctr Addict & Mental Hlth, 1001 Queen St W, Toronto, ON M6J 1A8, Canada
[2] Univ Toronto, Inst Biomat & Biomed Engn, Rosebrugh Bldg,Room 407,164 Coll St, Toronto, ON M5S 3G9, Canada
[3] Univ Toronto, Edward S Rogers Sr Dept Elect & Comp Engn, 10 Kings Coll Rd, Toronto, ON M5S 3G4, Canada
[4] Simon Fraser Univ, Sch Interact Art & Technol, 250-13450 102 Ave, Surrey, BC V3T 0A3, Canada
[5] Univ Toronto, Dept Psychiat, 250 Coll St,8th Floor, Toronto, ON M5T 1R8, Canada
[6] Univ Toronto, Fac Med, Inst Med Sci, Med Sci Bldg,1 Kings Coll Circle, Toronto, ON M5S 1A8, Canada
[7] Simon Fraser Univ, Sch Mech Syst Engn, 250-13450 102 Ave,Room 4138, Surrey, BC V3T 0A3, Canada
关键词
Microstate analysis; Network dynamics; Treatment-resistant depression; Electroconvulsive therapy; Magnetic seizure therapy; Electroencephalography; STATE FUNCTIONAL CONNECTIVITY; ELECTROCONVULSIVE-THERAPY; MAJOR DEPRESSION; GLUCOSE-METABOLISM; NEUROLEPTIC-NAIVE; DEFAULT-MODE; MAP SERIES; EEG; METAANALYSIS; 1ST-EPISODE;
D O I
10.1016/j.nicl.2018.10.015
中图分类号
R445 [影像诊断学];
学科分类号
100207 ;
摘要
Background: Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive impairment. Given the neuroimaging evidence for disrupted resting-state network dynamics in depression, we investigated whether seizure therapy (ECT and MST) selectively modifies brain network dynamics for therapeutic efficacy. Methods: EEG microstate analysis was used to evaluate resting-state network dynamics in patients at baseline and following seizure therapy, and in healthy controls. Microstate analysis defined four classes of brain states (labelled A, B, C, D). Source localization identified the brain regions associated with these states. Results: An increase in duration and decrease in frequency of microstates was specific to responders of seizure therapy. Significant changes in the dynamics of States A, C and D were observed and predicted seizure therapy outcome (specifically ECT). Relative change in the duration of States C and D was shown to be a strong predictor of ECT response. Source localization partly associated C and D to the salience and frontoparietal networks, argued to be impaired in depression. An increase in duration and decrease in frequency of microstates was also observed following MST, however it was not specific to responders. Conclusion: This study presents the first evidence for the modulation of global brain network dynamics by seizure therapy. Successful seizure therapy was shown to selectively modulate network dynamics for therapeutic efficacy.
引用
收藏
页码:1176 / 1190
页数:15
相关论文
共 50 条
  • [31] Deep Brain Stimulation for Treatment-Resistant Depression
    Holtzheimer, Paul E., III
    Mayberg, Helen S.
    AMERICAN JOURNAL OF PSYCHIATRY, 2010, 167 (12) : 1437 - 1444
  • [32] Neuromodulation for treatment-resistant depression: Functional network targets contributing to antidepressive outcomes
    Idlett-Ali, Shaquia L.
    Salazar, Claudia A.
    Bell, Marcus S.
    Short, E. Baron
    Rowland, Nathan C.
    FRONTIERS IN HUMAN NEUROSCIENCE, 2023, 17
  • [33] Deep Brain Stimulation for Treatment-Resistant Depression
    Taghva, Alexander S.
    Malone, Donald A.
    Rezai, Ali R.
    WORLD NEUROSURGERY, 2013, 80 (3-4) : S27.e17 - S27.e24
  • [34] Distinct patterns of functional brain network integration between treatment-resistant depression and non treatment-resistant depression: A resting-state functional magnetic resonance imaging study
    Sun, Jifei
    Ma, Yue
    Guo, Chunlei
    Du, Zhongming
    Chen, Limei
    Wang, Zhi
    Li, Xiaojiao
    Xu, Ke
    Luo, Yi
    Hong, Yang
    Yu, Xue
    Xiao, Xue
    Fang, Jiliang
    Lu, Jie
    PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY, 2023, 120
  • [35] Treatment-resistant depression: An overview for psychiatric advanced practice nurses
    Kameg, Brayden N.
    Kameg, Kirstyn M.
    PERSPECTIVES IN PSYCHIATRIC CARE, 2021, 57 (02) : 689 - 694
  • [36] Electroconvulsive therapy vs. paroxetine in treatment-resistant depression - a randomized study
    Folkerts, HW
    Michael, M
    Tolle, R
    Schonauer, K
    Mucke, S
    SchulzeMonking, H
    ACTA PSYCHIATRICA SCANDINAVICA, 1997, 96 (05) : 334 - 342
  • [37] Management of Treatment-Resistant Depression
    Keitner, Gabor I.
    Mansfield, Abigail K.
    PSYCHIATRIC CLINICS OF NORTH AMERICA, 2012, 35 (01) : 249 - +
  • [38] Propofol for Treatment-Resistant Depression: A Pilot Study
    Mickey, Brian J.
    White, Andrea T.
    Arp, Anna M.
    Leonardi, Kolby
    Torres, Marina M.
    Larson, Adam L.
    Odell, David H.
    Whittingham, Sara A.
    Beck, Michael M.
    Jessop, Jacob E.
    Sakata, Derek J.
    Bushnell, Lowry A.
    Pierson, Matthew D.
    Solzbacher, Daniela
    Kendrick, E. Jeremy
    Weeks, Howard R., III
    Light, Alan R.
    Light, Kathleen C.
    Tadler, Scott C.
    INTERNATIONAL JOURNAL OF NEUROPSYCHOPHARMACOLOGY, 2018, 21 (12) : 1079 - 1089
  • [39] The Role of Psychotherapy in the Management of Treatment-Resistant Depression
    Rogan, Taylor
    Wilkinson, Samuel T.
    PSYCHIATRIC CLINICS OF NORTH AMERICA, 2023, 46 (02) : 349 - 358
  • [40] Management of Treatment-Resistant Depression: Challenges and Strategies
    Voineskos, Daphne
    Daskalakis, Zafiris J.
    Blumberger, Daniel M.
    NEUROPSYCHIATRIC DISEASE AND TREATMENT, 2020, 16 : 221 - 234