Introduction

Major depressive disorder (MDD) affects more than 163 million people, approximately 2% of the world’s population in 2017 [1]. MDD is characterized by persistently depressed mood, anhedonia, impaired cognitive function, and suicidal thoughts [2]. Electroconvulsive therapy (ECT) is one of the most effective treatments along with accelerated transcranial magnetic stimulation, repetitive transcranial magnetic stimulation, Ketamine, and deep brain stimulation for treatment-resistant depressive episodes, which passes a controlled electric current through the brain under general anesthesia, producing substantial improvement in 60 to 80 percent of patients [3]. Despite its effectiveness, ECT may cause cognitive side-effects, including impairment in attention, memory, and executive functioning [4,5,6]. ECT’s mechanisms of cognitive side-effects and antidepressant response are poorly understood. This gap in knowledge limits parameter development to optimize antidepressant benefits and reduce cognitive risk.

ECT promotes changes in how brain cells communicate to normalize aberrant depression-related brain functioning, which is commonly known as neuroplasticity [7]. A wide range of ECT-induced functional connectivity (the strength with which activity in brain regions correlates over time) changes have been implicated in both antidepressant and cognitive outcomes [8, 9]. ECT can normalize dysregulated brain networks in MDD [10], such as the default-mode network, which is involved in self-referential processing including emotion perception [11, 12]. A longitudinal study has demonstrated that ECT modulates the function of the default-mode network, accompanied by improved mood and impaired cognitive function, where the connectivity changes and impaired cognitive function recovered one month after the completion of ECT [17, 18] and functionally [19]. The cerebellum is considered in the pathological model of MDD and the alterations of cerebro-cerebellar connectivity implied neural deficits in depression [20]. Neuroimaging studies have also identified significant cerebellar changes following ECT [21, 22], indicating a potential association between ECT and cerebellar neuroplasticity. Cerebro-cerebellar connectivity changes may be associated with cognitive performance, which implies a potential neural pathway for the mitigation of ECT-induced side-effects [23]. Although increasing evidence has linked the functional connectivity changes to ECT, the mechanisms underlying the relationships between functional neuroplasticity and ECT response, especially the short-term cognition changes, are still unknown.

Research on electric field (E-field) modeling and ECT has tried to link the E-field strength to brain neuroplasticity, with robust associations identified between E-field and structural neuroplasticity [24]. Another study reported that E-field in the temporal lobes is correlated with less optimal ECT outcome [25]. In the context of E-field modeling, the electrode placement determines the geometric shape of the E-field, and the amplitude determines the E-field magnitude [26]. Note that the whole-brain E-field and stimulus amplitude is related (r = 0.7129, p = 6.31 × 10−9). However, with a fixed extracranial amplitude, the ECT “doses” as represented by the intracranial E-field is highly variable due to the anatomic difference in skin, skull, fluid, and brain tissue [27]. The anatomic variability is prominent in older patients with depressive episodes, which can compromise both antidepressant efficacy and safety. E-field is a more accurate depiction of the electric field dose relative to pulse amplitude. It requires pre-ECT anatomic images to achieve the goal of individualized amplitude and reducing the variability of ECT dose, equipment, and expertize. We believe that the investigation of E-field variability will create a more standardized and consistent ECT dosing strategy for treatment with ECT, thus improving the ECT-induced outcomes. We have previously identified a trade-off between amplitude strength on the antidepressant (higher is better) and cognitive outcomes (lower is better) [28]. We also demonstrated that hippocampal neuroplasticity significantly mediated the relationship between E-field strength and antidepressant outcomes, E-field strength was directly related to cognitive side-effects [29]. Nevertheless, previous ECT E-field investigations limited analyses to the structural changes induced by ECT. Functional neuroplasticity is also a key element in ECT investigations, as it may reflect the brain’s capability to restructure itself by forming new neural connections. To date, no study has examined the relationship between E-field strength, functional neuroplasticity, and clinical outcomes.

In this work, we shift the focus from structural neuroplasticity to functional neuroplasticity, and from localized analysis to whole-brain analysis. Via the fully automated independent component analysis (ICA) framework [E-Field modeling

The Simulation of Non-Invasive Brain Stimulation (SimNIBS) toolbox was used for E-field modeling to generate a subject-specific anatomically realistic volume conductor model [35]. Via a combination of the FSL toolbox and the SPM 12 toolbox, T1- and T2-weighted images were segmented into skin, bone, eyes, cerebral spinal fluid, ventricles, and gray and white matter. The segmented tissue compartments were meshed into a head model using Gmsh, and ECT electrodes were added to the head mesh in either RUL or received BT configuration, stimulated with 600, 700, or 800 mA as per arm assignment. The voltages and electric fields that correspond to the stimulation configuration were calculated throughout the head mesh.

Based on the electrode placement (BT or RUL) and the amplitude (600, 700, or 800 mA) from the last treatment of the ECT series, we calculated the whole-brain E-field strength (Ebrain). Ebrain was measured as the 90th percentile of E-field magnitude across the whole brain. Ebrain at 90th percentile is standard based on previous E-field investigations [29, 36]. Ebrain at 90th percentile is strongly correlated with those calculated at other percentiles: 50th (r = 0.95), 75th (r = 0.99), 85th (r = 1.0), and 95th (r = 1.0).

Neuromark framework and functional network connectivity

The QC resting-state fMRI data were analyzed via the Neuromark framework which provides a robust estimation of functional networks across subjects [

Code availability

The code of the Neuromark framework and the Neuromark template have been released and integrated into the group ICA Toolbox (GIFT, https://trendscenter.org/software/gift/), which can be downloaded and used directly by users worldwide. Other MATLAB codes of this study can be obtained from the corresponding author.

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Funding

This work was supported by National Institutes of Health (R01MH118695, R01EB020407, R01MH117107, U01MH111826, and R61MH125126), the National Science Foundation (2112455), the National Institute of Mental Health Intramural Research Program (ZIAMH002955), and the China Natural Science Foundation (82022035).

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Authors and Affiliations

  1. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA

    Zening Fu, Mohammad S. E. Sendi, **g Sui & Vince D. Calhoun

  2. Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA

    Christopher C. Abbott & Jeremy Miller

  3. Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

    Zhi-De Deng

  4. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA

    Zhi-De Deng

  5. Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA

    Shawn M. McClintock

  6. Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

    Mohammad S. E. Sendi

  7. State Key Laboratory of Cognitive Neuroscience and Learning, Bei**g Normal University, Bei**g, China

    **g Sui

  8. Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Vince D. Calhoun

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Contributions

ZF, CCA, SMMcC, and VDC designed the study; ZF and CCA performed the data analysis; ZF, JS, JM, Z-DD, and SMMcC wrote the paper. All authors contributed to the results interpretation and discussion.

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Correspondence to Zening Fu, Christopher C. Abbott or **g Sui.

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Fu, Z., Abbott, C.C., Miller, J. et al. Cerebro-cerebellar functional neuroplasticity mediates the effect of electric field on electroconvulsive therapy outcomes. Transl Psychiatry 13, 43 (2023). https://doi.org/10.1038/s41398-023-02312-w

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