Electroconvulsive therapy (ECT) remains the gold-standard treatment for severe, treatment-resistant patients with depressive episodes. During a typical 4-week ECT series, most depressive episodes remit, and formerly suicidal or psychotically depressed patients will resume their premorbid levels of functioning. ECT is one of psychiatry's most invasive treatments and remains limited to academic medical centers or larger, metropolitan hospitals. Despite ECT's 80-year history, the lack of understanding regarding the neurobiology and the mechanism of action of ECT response limit the development of safer, more accessible treatments. The overall aim of this investigation is to identify the biomarkers of ECT response. A case-control longitudinal design and resting state functional magnetic resonance imaging (fMRI) will simultaneously assess between (Aim 1) and within network changes (Aim 2) associated with ECT response. Our preliminary data supports our hypothesis that ECT response increases between network relationships amid frontal and default mode networks and reduces within network low frequency power (0.01 to 0.1 hertz (Hz)) in the subcallosal cingulate gyrus. The ECT series is associated with increased seizure threshold and reduced seizure duration. These anticonvulsant properties of ECT may be related to the reduction in spectral power within functional networks.
In Aim 3, proton spectroscopy (H-MRS) will measure changes in concentrations of gamma-aminobutyric acid (GABA), the main inhibitory neurotransmitter, associated with ECT response. The multi-modal aspect of this investigation will link changes in GABA concentrations with changes in the fMRI biomarkers of ECT response (Aims 1 and 2). Specifically, we hypothesize that increased GABA concentrations in the posterior cingulate will correlate with reduced low frequency spectral power (0.01 and 0.1 Hz) within limbic and para-limbic networks. This research will impact the field with a better understanding of the functional neural correlates of ECT response, which will help to optimize ECT treatment parameters (e.g., pulse width, stimulus delivery method, number of treatments) and extend to other therapeutic interventions for treatment-resistant depression.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
5P20GM103472-10
Application #
9276023
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
2019-04-30
Budget Start
2017-05-01
Budget End
2018-04-30
Support Year
10
Fiscal Year
2017
Total Cost
Indirect Cost
Name
The Mind Research Network
Department
Type
DUNS #
098640696
City
Albuquerque
State
NM
Country
United States
Zip Code
87106
Bridwell, David A; Rachakonda, Srinivas; Silva, Rogers F et al. (2018) Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data. Brain Topogr 31:47-61
Liu, Jingyu; Chen, Jiayu; Perrone-Bizzozero, Nora et al. (2018) A Perspective of the Cross-Tissue Interplay of Genetics, Epigenetics, and Transcriptomics, and Their Relation to Brain Based Phenotypes in Schizophrenia. Front Genet 9:343
Mennigen, Eva; Miller, Robyn L; Rashid, Barnaly et al. (2018) Reduced higher-dimensional resting state fMRI dynamism in clinical high-risk individuals for schizophrenia identified by meta-state analysis. Schizophr Res 201:217-223
Fu, Zening; Tu, Yiheng; Di, Xin et al. (2018) Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism. Neuroimage :
Hjelm, R Devon; Damaraju, Eswar; Cho, Kyunghyun et al. (2018) Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks. Front Neurosci 12:600
Fang, Jian; Xu, Chao; Zille, Pascal et al. (2018) Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation. IEEE Trans Med Imaging 37:860-870
Quinn, Davin K; Mayer, Andrew R; Master, Christina L et al. (2018) Prolonged Postconcussive Symptoms. Am J Psychiatry 175:103-111
Steele, Vaughn R; Maurer, J Michael; Arbabshirani, Mohammad R et al. (2018) Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion. Biol Psychiatry Cogn Neurosci Neuroimaging 3:141-149
Kong, Xiang-Zhen; Mathias, Samuel R; Guadalupe, Tulio et al. (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A 115:E5154-E5163
Du, Yuhui; Fryer, Susanna L; Lin, Dongdong et al. (2018) Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study. Neuroimage Clin 17:335-346

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