Literature DB >> 28335039

Brain temporal complexity in explaining the therapeutic and cognitive effects of seizure therapy.

Faranak Farzan1,2,3, Sravya Atluri1, Ye Mei1, Sylvain Moreno4, Andrea J Levinson1,2, Daniel M Blumberger1,2, Zafiris J Daskalakis1,2.   

Abstract

Over 350 million people worldwide suffer from depression, a third of whom are medication-resistant. Seizure therapy remains the most effective treatment in depression, even when many treatments fail. The utility of seizure therapy is limited due to its cognitive side effects and stigma. The biological targets of seizure therapy remain unknown, hindering design of new treatments with comparable efficacy. Seizures impact the brains temporal dynamicity observed through electroencephalography. This dynamicity reflects richness of information processing across distributed brain networks subserving affective and cognitive processes. We investigated the hypothesis that seizure therapy impacts mood (depressive symptoms) and cognition by modulating brain temporal dynamicity. We obtained resting-state electroencephalography from 34 patients (age = 46.0 ± 14.0, 21 females) receiving two types of seizure treatments-electroconvulsive therapy or magnetic seizure therapy. We used multi-scale entropy to quantify the complexity of the brain's temporal dynamics before and after seizure therapy. We discovered that reduction of complexity in fine timescales underlined successful therapeutic response to both seizure treatments. Greater reduction in complexity of fine timescales in parieto-occipital and central brain regions was significantly linked with greater improvement in depressive symptoms. Greater increase in complexity of coarse timescales was associated with greater decline in cognition including the autobiographical memory. These findings were region and timescale specific. That is, change in complexity in occipital regions (e.g. O2 electrode or right occipital pole) at fine timescales was only associated with change in depressive symptoms, and not change in cognition, and change in complexity in parieto-central regions (e.g. Pz electrode or intra and transparietal sulcus) at coarser timescale was only associated with change in cognition, and not depressive symptoms. Finally, region and timescale specific changes in complexity classified both antidepressant and cognitive response to seizure therapy with good (80%) and excellent (95%) accuracy, respectively. In this study, we discovered a novel biological target of seizure therapy: complexity of the brain resting state dynamics. Region and timescale dependent changes in complexity of the brain resting state dynamics is a novel mechanistic marker of response to seizure therapy that explains both the antidepressant response and cognitive changes associated with this treatment. This marker has tremendous potential to guide design of the new generation of antidepressant treatments.
© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  complexity; depression; electroencephalography; seizure therapy

Mesh:

Substances:

Year:  2017        PMID: 28335039     DOI: 10.1093/brain/awx030

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


  17 in total

1.  Neurocognitive Effects of Combined Electroconvulsive Therapy (ECT) and Venlafaxine in Geriatric Depression: Phase 1 of the PRIDE Study.

Authors:  Sarah H Lisanby; Shawn M McClintock; George Alexopoulos; Samuel H Bailine; Elisabeth Bernhardt; Mimi C Briggs; C Munro Cullum; Zhi-De Deng; Mary Dooley; Emma T Geduldig; Robert M Greenberg; Mustafa M Husain; Styliani Kaliora; Rebecca G Knapp; Vassilios Latoussakis; Lauren S Liebman; William V McCall; Martina Mueller; Georgios Petrides; Joan Prudic; Peter B Rosenquist; Matthew V Rudorfer; Shirlene Sampson; Abeba A Teklehaimanot; Kristen G Tobias; Richard D Weiner; Robert C Young; Charles H Kellner
Journal:  Am J Geriatr Psychiatry       Date:  2019-10-12       Impact factor: 4.105

2.  Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression.

Authors:  Faranak Farzan; Sravya Atluri; Matthew Frehlich; Prabhjot Dhami; Killian Kleffner; Rae Price; Raymond W Lam; Benicio N Frey; Roumen Milev; Arun Ravindran; Mary Pat McAndrews; Willy Wong; Daniel Blumberger; Zafiris J Daskalakis; Fidel Vila-Rodriguez; Esther Alonso; Colleen A Brenner; Mario Liotti; Moyez Dharsee; Stephen R Arnott; Kenneth R Evans; Susan Rotzinger; Sidney H Kennedy
Journal:  Sci Rep       Date:  2017-08-07       Impact factor: 4.379

3.  Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression.

Authors:  Reza Shalbaf; Colleen Brenner; Christopher Pang; Daniel M Blumberger; Jonathan Downar; Zafiris J Daskalakis; Joseph Tham; Raymond W Lam; Faranak Farzan; Fidel Vila-Rodriguez
Journal:  Front Pharmacol       Date:  2018-10-30       Impact factor: 5.810

4.  Electroconvulsive therapy "corrects" the neural architecture of visuospatial memory: Implications for typical cognitive-affective functioning.

Authors:  Raluca Petrican; Hedvig Söderlund; Namita Kumar; Zafiris J Daskalakis; Alastair Flint; Brian Levine
Journal:  Neuroimage Clin       Date:  2019-04-05       Impact factor: 4.881

5.  A novel approach for assessing neuromodulation using phase-locked information measured with TMS-EEG.

Authors:  Eri Miyauchi; Masayuki Ide; Hirokazu Tachikawa; Kiyotaka Nemoto; Tetsuaki Arai; Masahiro Kawasaki
Journal:  Sci Rep       Date:  2019-01-23       Impact factor: 4.379

6.  Magnetic seizure therapy for treatment-resistant depression.

Authors:  Jiangling Jiang; Caidi Zhang; Chunbo Li; Zhimin Chen; Xinyi Cao; Hongyan Wang; Wei Li; Jijun Wang
Journal:  Cochrane Database Syst Rev       Date:  2021-06-16

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

Authors:  Sravya Atluri; Willy Wong; Sylvain Moreno; Daniel M Blumberger; Zafiris J Daskalakis; Faranak Farzan
Journal:  Neuroimage Clin       Date:  2018-10-17       Impact factor: 4.881

8.  Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

Authors:  Andrey Zhdanov; Sravya Atluri; Willy Wong; Yasaman Vaghei; Zafiris J Daskalakis; Daniel M Blumberger; Benicio N Frey; Peter Giacobbe; Raymond W Lam; Roumen Milev; Daniel J Mueller; Gustavo Turecki; Sagar V Parikh; Susan Rotzinger; Claudio N Soares; Colleen A Brenner; Fidel Vila-Rodriguez; Mary Pat McAndrews; Killian Kleffner; Esther Alonso-Prieto; Stephen R Arnott; Jane A Foster; Stephen C Strother; Rudolf Uher; Sidney H Kennedy; Faranak Farzan
Journal:  JAMA Netw Open       Date:  2020-01-03

9.  Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters.

Authors:  Joanna Dreszer; Marek Grochowski; Monika Lewandowska; Jan Nikadon; Joanna Gorgol; Bibianna Bałaj; Karolina Finc; Włodzisław Duch; Patrycja Kałamała; Adam Chuderski; Tomasz Piotrowski
Journal:  Hum Brain Mapp       Date:  2020-08-18       Impact factor: 5.038

10.  Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis.

Authors:  Aron T Hill; Itay Hadas; Reza Zomorrodi; Daphne Voineskos; Faranak Farzan; Paul B Fitzgerald; Daniel M Blumberger; Zafiris J Daskalakis
Journal:  Sci Rep       Date:  2020-10-13       Impact factor: 4.379

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