Electroconvulsive therapy treatment responsive multimodal brain networks

Qi, Shile; Abbott, Christopher; Narr, Katherine L.; Jiang, Rongtao; Upston, Joel; McClintock, Shawn M.; Espinoza, Randall; Jones, Tom; Zhi, Dongmei; Sun, Hai-Lun; Yang, Xiao; Sui, Jing; Calhoun, Vince D.

doi:10.1002/hbm.24910

Cited by 20 publications

(18 citation statements)

References 51 publications

(71 reference statements)

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“…The clinical characteristics of the 18 articles that were excluded from meta-analysis were partially similar to those of articles included in the meta-analysis. Among these studies ( Supplementary Table 4 ), all had a sample size of <30 subjects except for 3 studies with sample sizes of 45 (Bai et al, 2018a ), 118 (Qi et al, 2020 ), and 122 (Sun et al, 2020 ), respectively. Ten of them recruited patients with unipolar depression only, and six of them solely focused on treatment-resistant depression.…”

Section: Resultsmentioning

confidence: 99%

“…An almost equal number of studies used bifrontal (BF), bitemporal (BT), and right unilateral (RUL) as electrode placements during ECT. Some had provision to switch to BT (Leaver et al, 2020 ; Qi et al, 2020 ; Sun et al, 2020 ) or BF (Leaver et al, 2016b ) if RUL did not provide significant improvement. Except for Leaver et al ( 2020 ) (ultra-brief pulse-wave ECT), all studies used brief pulse-wave ECT.…”

Section: Resultsmentioning

confidence: 99%

“…In network-based and ROI (with rsFC) to ROI analysis, ACC, PCC, different regions of DMN (PCC, precuneus, medial PFC, intraparietal sulcus), and left cerebellum more often had prominent changes in rsFC with ECT. In the study that jointly analyzed structural MRI and rsfMRI images using HAM-D scores as a reference, FC in PFC, HC, insula, and left caudate were found to be reduced after ECT (Qi et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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Resting State Functional Connectivity of Brain With Electroconvulsive Therapy in Depression: Meta-Analysis to Understand Its Mechanisms

Sinha

Joshi

Ithal

2021

Front. Hum. Neurosci.

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Introduction: Electroconvulsive therapy (ECT) is a commonly used brain stimulation treatment for treatment-resistant or severe depression. This study was planned to find the effects of ECT on brain connectivity by conducting a systematic review and coordinate-based meta-analysis of the studies performing resting state fMRI (rsfMRI) in patients with depression receiving ECT.Methods: We systematically searched the databases published up to July 31, 2020, for studies in patients having depression that compared resting-state functional connectivity (rsFC) before and after a course of pulse wave ECT. Meta-analysis was performed using the activation likelihood estimation method after extracting details about coordinates, voxel size, and method for correction of multiple comparisons corresponding to the significant clusters and the respective rsFC analysis measure with its method of extraction.Results: Among 41 articles selected for full-text review, 31 articles were included in the systematic review. Among them, 13 articles were included in the meta-analysis, and a total of 73 foci of 21 experiments were examined using activation likelihood estimation in 10 sets. Using the cluster-level interference method, one voxel-wise analysis with the measure of amplitude of low frequency fluctuations and one seed-voxel analysis with the right hippocampus showed a significant reduction (p < 0.0001) in the left cingulate gyrus (dorsal anterior cingulate cortex) and a significant increase (p < 0.0001) in the right hippocampus with the right parahippocampal gyrus, respectively. Another analysis with the studies implementing network-wise (posterior default mode network: dorsomedial prefrontal cortex) resting state functional connectivity showed a significant increase (p < 0.001) in bilateral posterior cingulate cortex. There was considerable variability as well as a few key deficits in the preprocessing and analysis of the neuroimages and the reporting of results in the included studies. Due to lesser studies, we could not do further analysis to address the neuroimaging variability and subject-related differences.Conclusion: The brain regions noted in this meta-analysis are reasonably specific and distinguished, and they had significant changes in resting state functional connectivity after a course of ECT for depression. More studies with better neuroimaging standards should be conducted in the future to confirm these results in different subgroups of depression and with varied aspects of ECT.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Resting State Functional Connectivity of Brain With Electroconvulsive Therapy in Depression: Meta-Analysis to Understand Its Mechanisms

Sinha

Joshi

Ithal

2021

Front. Hum. Neurosci.

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“…Multimodal brain imaging 21,22 data of SZ (n = 238), drinkers (n = 313), smokers (n = 104), MDD (n = 260), ASD (n = 421), and ADHD (n = 244) were collected across multiple studies [23][24][25][26] . We used specific symptom scores for each disorder as a reference to guide restingstate functional MRI (fractional amplitude of low frequency fluctuations, fALFF) and structural MRI (gray matter volume, GMV) fusion analysis to identify the multimodal brain networks that were associated with symptom scores in Center for Biomedical Research Excellence (COBRE) SZ, drinkers, smokers, MDD, ASD, and ADHD separately.…”

Section: Introductionmentioning

confidence: 99%

The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study

Bustillo

Turner

et al. 2020

Transl Psychiatry

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Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g., SZ vs. substance use, SZ vs. depression, SZ vs. developmental disorders) with SZ on specific symptoms and cognition is unclear. Symptom scores were used as a reference to guide fMRI-sMRI fusion for SZ (n = 94), substance use with drinking (n = 313), smoking (n = 104), major depressive disorder (MDD, n = 260), developmental disorders with autism spectrum disorder (ASD, n = 421) and attention-deficit/hyperactivity disorder (ADHD, n = 244) respectively. Common brain regions were determined by overlapping the symptom-related components between SZ and these other groups. Correlation between the identified common brain regions and cognition/symptoms in an independent SZ dataset (n = 144) was also performed. Results show that (1): substance use was related with cognitive deficits in schizophrenia through gray matter volume (GMV) in anterior cingulate cortex and thalamus; (2) depression was linked to PANSS negative dimensions and reasoning in SZ through a network involving caudate-thalamus-middle/inferior temporal gyrus in GMV; (3) developmental disorders pattern was correlated with poor attention, speed of processing and reasoning in SZ through inferior temporal gyrus in GMV. This study reveals symptom driven transdiagnostic shared networks between SZ and other mental disorders via multi-group data mining, indicating that some potential common underlying brain networks associated with schizophrenia differently with respect to symptoms and cognition. These results have heuristic value and advocate specific approaches to refine available treatment strategies for comorbid conditions in schizophrenia.

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“…In this study, multimodal [28] brain imaging data from the Autism Brain Imaging Data Exchange (ABIDE) [29,30] were used as the discovery (ABIDE II) and replication (ABIDE I) cohorts. Cumulating evidence has demonstrated that ASD reflects fundamental differences in both brain structure and function [31][32][33], while fractional amplitude of low-frequency fluctuations (fALFF) and GMV are two representative measures of (functional magnetic resonance imaging) fMRI and (structural MRI) sMRI.…”

mentioning

confidence: 99%

Common and unique multimodal covarying patterns in autism spectrum disorder subtypes

Morris

Turner

et al. 2020

Molecular Autism

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Background The heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment. Heterogeneity across biological etiologies, genetics, neural systems, neurocognitive attributes and clinical subtypes or phenotypes has been observed across individuals with ASD. Methods In this study, we aim to investigate the heterogeneity in ASD from a multimodal brain imaging perspective. The Autism Diagnostic Observation Schedule (ADOS) was used as a reference to guide functional and structural MRI fusion. DSM-IV-TR diagnosed Asperger’s disorder (n = 79), pervasive developmental disorder-not otherwise specified [PDD-NOS] (n = 58) and Autistic disorder (n = 92) from ABIDE II were used as discovery cohort, and ABIDE I (n = 400) was used for replication. Results Dorsolateral prefrontal cortex and superior/middle temporal cortex are the primary common functional–structural covarying cortical brain areas shared among Asperger’s, PDD-NOS and Autistic subgroups. Key differences among the three subtypes are negative functional features within subcortical brain areas, including negative putamen–parahippocampus fractional amplitude of low-frequency fluctuations (fALFF) unique to the Asperger’s subtype; negative fALFF in anterior cingulate cortex unique to PDD-NOS subtype; and negative thalamus–amygdala–caudate fALFF unique to the Autistic subtype. Furthermore, each subtype-specific brain pattern is correlated with different ADOS subdomains, with social interaction as the common subdomain. The identified subtype-specific patterns are only predictive for ASD symptoms manifested in the corresponding subtypes, but not the other subtypes. Conclusions Although ASD has a common neural basis with core deficits linked to social interaction, each ASD subtype is strongly linked to unique brain systems and subdomain symptoms, which may help to better understand the underlying mechanisms of ASD heterogeneity from a multimodal neuroimaging perspective. Limitations This study is male based, which cannot be generalized to the female or the general ASD population.

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Electroconvulsive therapy treatment responsive multimodal brain networks

Cited by 20 publications

References 51 publications

Resting State Functional Connectivity of Brain With Electroconvulsive Therapy in Depression: Meta-Analysis to Understand Its Mechanisms

Resting State Functional Connectivity of Brain With Electroconvulsive Therapy in Depression: Meta-Analysis to Understand Its Mechanisms

The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study

Common and unique multimodal covarying patterns in autism spectrum disorder subtypes

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