Evidence map›Paper›PMID 34303848›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2022

Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response.

Zening Fu, Jing Sui, Randall Espinoza, Katherine Narr, Shile Qi, Mohammad S E Sendi, Christopher C Abbott, Vince D Calhoun

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.0field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Zening FuTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.
Jing SuiTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Randall EspinozaDepartments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California.
Katherine NarrDepartments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California.
Shile QiTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.
Mohammad S E SendiTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.
Christopher C AbbottDepartment of Psychiatry, University of New Mexico, Albuquerque, New Mexico. Electronic address: CAbbott@salud.unm.edu.
Vince D CalhounTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia; Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia; Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, Connecticut.
University of California, Los Angeles · USCenter for Translational Research in Neuroimaging and Data Science · USGeorgia Institute of Technology · US

Funding

Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood dR01MH118695 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2019 to 2023
$3.8M
Unified multivariate data-driven solutions for static and dynamic brain connectivityR01EB020407 · NIBIB · THE MIND RESEARCH NETWORK · PI ADALI, TULAY, CALHOUN, VINCE D · 2015 to 2018
$2.7M
Data-driven approaches to identify biomarkers from multimodal imaging big dataR01MH117107 · NIMH · GEORGIA STATE UNIVERSITY · PI CALHOUN, VINCE D · 2019 to 2022
$1.6M
ECT current amplitude and medial temporal lobe engagementU01MH111826 · NIMH · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI ABBOTT, CHRISTOPHER C · 2017 to 2019
$1.5M
Electroconvulsive therapy amplitude titration for improved clinical outcomes in late-life depressionR61MH125126 · NIMH · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI ABBOTT, CHRISTOPHER C · 2021 to 2022
$1.4M
ECT current amplitude and medial temporal lobe engagementR01MH111826 · NIMH · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI ABBOTT, CHRISTOPHER C · 2016 to 2016
$522k
NIBIB NIH HHS R01 EB020407NIMH NIH HHS R01 MH111826NIMH NIH HHS R01 MH117107NIMH NIH HHS R01 MH118695NIMH NIH HHS R61 MH125126NIMH NIH HHS U01 MH111826
6 · The paper itself

Abstract

backgroundDepressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy.

methodsIn this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis.

resultsFive recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT.

conclusionsThese converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.

Indexed as

Bipolar DisorderElectroconvulsive TherapyBrainCognitionHumansMagnetic Resonance ImagingClustering analysisDepressive episodesDynamic functional connectivityElectroconvulsive therapyMetastate dynamismNeuroplasticity

Identifiers

PMID34303848
PMCPMC8783932
OpenAlexW3184362138

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.