Electroconvulsive therapy, or ECT for short, can be an effective treatment for severe depression. Many patients who do not respond to medication find that their symptoms improve after ECT. During an ECT session, the patient is placed under general anesthesia and two electrodes are attached to the scalp to produce an electric field that generates currents within the brain. These currents activate neurons and make them fire, causing a seizure, but it remains unclear how this reduces symptoms of depression.

For many years, researchers thought that the induced seizure must be key to the beneficial effects of ECT, but recent studies have cast doubt on this idea. They show that increasing the strength of the electric field alters the clinical effects of ECT, without affecting the seizure. This suggests that the benefits of ECT depend on the electric field itself.

Argyelan et al. now show that electric fields affect the brain by making a part of the brain known as the gray matter expand. In a large multinational study, 151 patients with severe depression underwent brain scans before and after a course of ECT. The scans revealed that the gray matter of the patients’ brains expanded during the treatment. The patients who experienced the strongest electric fields showed the largest increase in brain volume, and individual brain areas expanded if the electric field within them exceeded a certain threshold. This effect was particularly striking in two areas, the hippocampus and the amygdala. Both of these areas are critical for mood and memory.

Further studies are needed to determine why the brain expands after ECT, and how long the effect lasts. Another puzzle is why the improvements in depression that the patients reported after their treatment did not correlate with changes in brain volume. Disentangling the relationships between ECT, brain volume and depression will ultimately help develop more robust treatments for this disabling condition.

Electroconvulsive therapy (ECT) remains the most effective approach for treatment resistant depressive episodes, as well as the most established neuromodulation technique (UK ECT Review Group, 2003; Fink and Taylor, 2007). Despite intensive research, however, the mechanism of action for ECT remains unknown, but does involve at least two potentially therapeutic components: electric perturbation and/or seizure activity. One common element across various neuromodulation techniques is the application of different intensities of electric field (EF) on the human brain. Understanding how ECT-induced EF interacts with the cortex and subcortical structures can both advance our mechanistic understanding of ECT and enrich our understanding of other neuromodulation approaches such as magnetic seizure therapy (MST), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS).

A recent longitudinal ECT-imaging study from the Global ECT-MRI Collaboration (GEMRIC) (Oltedal et al., 2018) assessed hippocampal volume changes in a large cohort of subjects (N = 268) receiving right unilateral (RUL) or bilateral (BL) electrode placements. The results demonstrated that the volume of the hippocampus increased over the course of ECT treatment and correlated with the number of ECT sessions administered during the ECT series. In addition, the subjects receiving RUL electrode placement had a significantly larger volume change ipsilateral to the side of stimulation, consistent with previous ECT-neuroimaging observations (Abbott et al., 2014; Dukart et al., 2014; Pirnia et al., 2016; Bouckaert et al., 2016; Sartorius et al., 2016; Cano et al., 2018). Our most recent study of 331 subjects with longitudinal MRI scanning pre- and post-ECT showed brain volume increases across several subcortical and cortical regions with strong lateralization of the effects if the electrode placement was RUL (Ousdal et al., 2019). Contrary to a priori expectations (Joshi et al., 2016; Cano et al., 2017), increased volume in these key areas did not translate to better clinical outcome. While the association between the number of ECT sessions and volume change and the laterality of the volume changes all implied a dose–response causative relationship, the role of ECT-mediated neuroplasticity and the underlying mechanism for antidepressant response remains elusive. Furthermore, given the naturalistic design of the studies included for mega-analysis (e.g., non-responders had a longer ECT course and were frequently switched to bilateral treatment at varying intervals), both the number of ECT sessions and electrode placement varied depending on the clinical response, further confounding the dose-response association and its interpretation.

Recent research has challenged the notion that a primary purpose of electric stimulation in treating depression is to generate widespread seizure activity (Sackeim, 2015; Regenold et al., 2015). Alternatively, electric stimulation may be a therapeutic component of ECT and similar to other non-convulsive neuromodulation treatments. Finite-element simulation was developed to estimate the spatial distribution of the electric field on a voxel-wise basis (Lee et al., 2012; Bikson et al., 2012). The technique was recently validated in humans (Huang et al., 2017). Preliminary computational analyses based on three realistic head models suggested that the ECT electric field distribution had a direct association with clinical and cognitive outcomes, explaining the rationale behind different electrode placement strategies in ECT treatment (Bai et al., 2017). This finding is in agreement with our previous observation where RUL treatment induced higher volumetric changes in the right hippocampus compared to the left (Oltedal et al., 2018), implying that more lateralized electric stimulation rather than a global generalized seizure, may be responsible at least for part of the antidepressant effects of ECT. However, to date, no study has demonstrated the relationship between ECT electric field distribution and treatment response. In this study, we used the large Global ECT-MRI Research Collaboration (GEMRIC) ECT-imaging data set to explicitly determine the relationships between regional 1) electric field strength and volume changes, 2) volume changes and antidepressant response, and 3) electric field and antidepressant response. For the purpose of our primary research question and in contrast to previous GEMRIC investigations, we limited the analyses to subjects that only received right unilateral electrode placement.

This study investigated the relationship between electric field, volume change and clinical response to ECT. We used a large sample of subjects with depression receiving ECT with right unilateral electrode placement from the GEMRIC database. The key findings included a lateralization (right >left) of the electric field and changes in regional brain volume in association with ECT. The use of right unilateral electrode placement, which elicits greater right hemisphere electric fields, can thus be dissociated from generalized seizure activity such that their contributions to antidepressant mechanisms may be at least partially distinct. Further, regional volume increase and electric field distributions were strongly related, especially in the left hippocampus and left amygdala. Here, the observed relationships between electric field and volume change suggest that a minimum electric field of 30–40 V/m is necessary for subsequent changes in brain structure, and that EF may have a ‘ceiling effect’ above approximately 100 V/m as illustrated for right hippocampal volume (see Figure 3). However, volume change and electric field were not statistically related to clinical response after controlling for age, number of ECT sessions and site. Below, we discuss potential mechanisms for electric field and volume change that may be considered both independent and synergistic with seizure activity. We also discuss potential future directions to elucidate the role of electric field distributions with clinical response.

The biological underpinnings of ECT-mediated volume change (Oltedal et al., 2018; Ousdal et al., 2019) could be related to seizure activity, cerebral blood flow, electric field strength, or synergy between the generalized seizure and electric field (e.g. the electric field determines site and focality of seizure initiation, which can subsequently affect seizure propagation and termination). Several neuroplastic mechanisms including neurogenesis, angiogenesis, synaptogenesis, gliogenesis may be specific to the rapidly changing electric field (Bouckaert et al., 2014; Tang et al., 2017). Although heavily debated (Sorrells et al., 2018; Boldrini et al., 2018; Andreae, 2018), the support for adult neurogenesis is based on pre-clinical studies demonstrating neuronal division and differentiation related to suprathreshold electric stimulation (Scott et al., 2000; Madsen et al., 2000; Perera et al., 2007; Segi-Nishida, 2011). However, neurogenesis as the sole mechanism of neuroplasticity may be incompatible with the time frame and expansive volume change. Specifically, the ECT series is less than one month in duration, but pre-translational investigations have established that adult neurogenesis may take up to six months (Kohler et al., 2011). Furthermore, adult neurogenesis is limited to the hippocampus and olfactory bulb and does not support the volume change in 82 out of 85 regions demonstrated in our investigation (Kornack and Rakic, 1999). Alternatively, volume change may be related to fluid shifts due to vascularization (Hellsten et al., 2004), blood flow changes (Milo et al., 2001; Leaver et al., 2019) and inflammation (Wennström et al., 2004; Jansson et al., 2009; Fluitman et al., 2011; van Buel et al., 2015; Yrondi et al., 2018). Vasogenic edema secondary to the hypertensive surge commonly associated with electroconvulsive stimulation and possible breach of the blood brain barrier could be a potentially iatrogenic mechanism of volumetric increase, but the available pre-clinical and ECT-imaging studies (focused on T2 relaxtion time) so far have produced mixed results (Andrade and Bolwig, 2014; Kunigiri et al., 2007; Bolwig et al., 1977; Nordanskog et al., 2010; Takamiya et al., 2018). The generalized seizure and global changes in blood flow would not explain the laterality of volumetric changes (right >left) ipsilateral to the hemisphere of stimulation as seen in our current and previous investigations (Abbott et al., 2014; Dukart et al., 2014; Pirnia et al., 2016; Bouckaert et al., 2016; Sartorius et al., 2016; Cano et al., 2018). The laterality with electric field and volume change suggest a mechanistic role of the electric field that may be independent or synergistic with seizure generation. Pre-translational investigations have demonstrated that increased stimulus charge increased dendritic arborization in a dose-related fashion (Smitha et al., 2014). Furthermore, the behavioral improvement after electroconvulsive stimulation are related to increased dendritic complexity, synaptic remodeling, and neuronal survival (Jonckheere et al., 2018). However, additional pre-clinical studies are clearly needed to resolve the mechanistic link between electric field and neuroplasticity.

Our original hypothesis was that a) local electric field had a causal role in clinical outcome and that b) the corresponding volume change was mediating this relationship. In order to support this model, data analysis should have indicated 1) a significant correlation between volume change and electric field, 2) a significant correlation between clinical outcome and volume change, and 3) that only the effect of volume change is significant in a multilinear regression model when both electric field and volume change is added as covariates (outcome ~volume change + electric field). However, since volume change showed no correlation with clinical change, neither in this dataset, nor in the recently published broader dataset with more heterogeneous ECT electrode placement (Ousdal et al., 2019), only the first half of this model, namely that electric field strength was associated with volume change, was supported by our data.

The null relationship between electric field, volume change and clinical outcome may be attributed to demographic (age) and other treatment related factors (number of sessions, rate of response). For example, age-related structural brain changes may mediate these relationships and thus were an important consideration in our analysis. Our results are consistent with previous ECT investigations demonstrating that older patients often have higher response rates (O'Connor et al., 2001; Nordenskjöld et al., 2012; Brus et al., 2017). Previous electric field modeling investigations have demonstrated that age-related structural brain changes modulate the spatial distribution of the calculated electric field (Deng et al., 2009). However, when including age in the assessment with electric field, volume change and clinical outcome, our results suggest more complex or alternative mechanisms underlie differential age-related response to ECT.

Additionally, it was necessary to control in our regression models for the number of ECT treatments. In our earlier paper (Oltedal et al., 2018) we found a mild effect between hippocampus volume change and clinical response, but, counterintuitively, increased volume change was associated with worse outcomes. However, this relationship was completely absent when we controlled for the number of ECTs. We have previously demonstrated a dose-response relationship between hippocampal volume change and the number of ECT sessions (Oltedal et al., 2018). Also, due to the naturalistic design, clinical outcome correlated with the number of ECT sessions: patients with the worse or slower response received more ECT treatments. Mediation analysis supported a very similar situation in our sample with p=0.035 and p=0.034 in L and R Hippocampus reflectively (Sobel test).

It was, therefore, necessary to control for the number of ECT sessions to avoid detecting spurious correlations between clinical response and volume change. Without an earlier, fixed mid-point assessment, we are unable to assess differences in rate of change, which could be relevant to specific depression subtypes (Drysdale et al., 2017) and eventual clinical response. Notably, the overall volume changes measured in this study do not permit us to make conclusions about more structure-function relationships that might be better assessed with shape or hippocampal subfield analysis (Roddy et al., 2019; Takamiya et al., 2019).

Finally, the volume change required for response may be non-linear. A minimum electric field of 30–40 V/m may be necessary to induce neuroplasticity. Increasing the electric field between 30–40 V/m and 100 V/m is related to a monotonic increase in hippocampal volume. Electric field above 100 V/m is still associated with hippocampal neuroplasticity but the dose-response relationship may be less robust and represent a ceiling effect of electric-field induced neuroplasticity as illustrated in Figure 3. Surpassing the neurpolasticity threshold (100 V/m) appears to be unrelated to further volumetric increases and antidepressant response. Thus, the relationship between e-field and volumetric changes may be conceptualized as a ‘neuroplasticity threshold’ between 30–40 V/m and 100 V/m. This thresholding effect also preserves the laterality of electric field and neuroplasticity. Our sample was limited to right unilateral electrode placement. In the left hippocampus, the maximum electric field is ~80 V/m (Figure 3) and below the 100 V/m ‘ceiling effect’ noted in the right hemisphere. Consequently, in our right unilateral sample, hippocampal electric field and related changes in neuroplasticity will demonstrate laterality effects.

Our findings indicate widespread and robust volume changes in both cortical and subcortical regions. The GEMRIC group recently published a comprehensive paper on a larger dataset with similar volumetric findings. The processing pipeline that was used has been validated against many commonly used tools for estimating longitudinal volume change (Holland et al., 2012; Holland et al., 2011). Specifically, it was previously compared head-to-head with FreeSurfer 5.3, and we have already repeated this comparison for data from one of the GEMRIC sites (Oltedal et al., 2017). Our comparisons of power estimations based on results from the FreeSurfer longitudinal pipeline and Quarc (Table 3 in Oltedal et al., 2017) were in line with those of the earlier publications. In agreement with previous research, the effect sizes show regional differences indicating that previous studies with smaller sample sizes were underpowered to detect cortical changes, and that can explain why they only found subcortical volume increase. Furthermore, using the same methodology, we did not find any significant volume change in the 95 healthy controls (received no ECT) who were imaged at two time points (Ousdal et al., 2019).

We acknowledge several limitations that influence result interpretation. First, our approach was agnostic to seizure duration, which may contribute to the effects of EF on regional volumes and clinical response. This investigation also does not preclude the possible role of seizure in both volume changes and clinical outcomes. However, the selection of right unilateral electrode placement subjects does attempt to disentangle the impact of the generalized seizure from the lateralized electric field. Second, the electric field models a single current pulse and ignores the temporal dynamics of stimulus (pulse-width, and frequency and duration of the pulse train) (Swartz, 2006; Swartz et al., 2012). Differences in pulse width, for example, may affect volume change and clinical outcomes. Furthermore, differences in maximal charge, unrelated to current amplitude, are different between the US and Europe (Europe permits twice the US maximal charge). The analysis included patients treated with one of two different ECT devices. We controlled for the differences in current related to the two devices with the electric field modeling (800mA for the spECTrum, 900mA for the Thymatron), but we are unable to control for other differences in stimulus delivery related to the device. Third, the study sites in this mega-analysis likely include heterogeneity in patient selection and other treatment related factors that were not controlled. Despite these site differences, the large sample size and additional inclusion criteria permitted whole brain analyses with electric field, and within-subject volume change and clinical outcomes. Finally, we did not assess cognitive correlates with electric field or volume change. General clinical experience and previous results from studies investigating electrode placement strategies indicate that ECT-mediated neurocognitive side effects are influenced by electrode placement (d’Elia, 1970; Sackeim et al., 2000; Kolshus et al., 2017). Previous electric field studies on simulated head models have already shown that cognitive side effects might be attributed to the electric field spatial distributions associated with different electrode placements (Bai et al., 2017; Deng et al., 2011). These considerations would indicate that these volumetric changes might be associated with cognitive side-effects, but further studies are needed to confirm this relationship.

Source data files (.csv) including processed data have been provided for Figures 1–4, Figure 3—figure supplement 1 and Figure 4—figure supplement 1. Raw data cannot be made available publicly because we do not have consent or ethical approval for this and the data cannot be anonymised. The data are stored on a secure centralized server at the University of Bergen, Norway. Participating GEMRIC sites have access to the raw data according to specific data policy and safety rules of the consortium, and in accord with the approval from the ethical committee. The GEMRIC consortium welcomes new members who are interested in the neuroimaging research of ECT. We hold board meetings twice a year when new members can apply to join and gain access to the database available on the GEMRIC servers. For more about the application process please visit https://mmiv.no/how-to-join-gemric/ or write to Leif Oltedal (leif.oltedal@uib.no). General information about the consortium can be found on the following website: https://mmiv.no/gemric/. For transparency and reproducibility, the entire analytical approach is uploaded to the https://github.com/argyelan/Publications/tree/master/ECTEFvsVOLUME (R scripts in org mode), and also was uploaded to eLife.

1. 1. Abbott CC
   2. Jones T
   3. Lemke NT
   4. Gallegos P
   5. McClintock SM
   6. Mayer AR
   7. Bustillo J
   8. Calhoun VD

   (2014) Hippocampal structural and functional changes associated with electroconvulsive therapy response

   Translational Psychiatry 4:e483.

   https://doi.org/10.1038/tp.2014.124

   • PubMed
   • Google Scholar
2. 1. Andrade C
   2. Bolwig TG

   (2014) Electroconvulsive therapy, hypertensive surge, blood-brain barrier breach, and amnesia: exploring the evidence for a connection

   The Journal of ECT 30:160–164.

   https://doi.org/10.1097/YCT.0000000000000133

   • PubMed
   • Google Scholar
3. 1. Andreae LC

   (2018) Adult neurogenesis in humans: dogma overturned, again and again?

   Science Translational Medicine 10:eaat3893.

   https://doi.org/10.1126/scitranslmed.aat3893

   • Google Scholar
4. 1. Bai S
   2. Gálvez V
   3. Dokos S
   4. Martin D
   5. Bikson M
   6. Loo C

   (2017) Computational models of bitemporal, bifrontal and right unilateral ECT predict differential stimulation of brain regions associated with efficacy and cognitive side effects

   European Psychiatry 41:21–29.

   https://doi.org/10.1016/j.eurpsy.2016.09.005

   • PubMed
   • Google Scholar
5. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society: Series B 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
6. 1. Bikson M
   2. Rahman A
   3. Datta A
   4. Fregni F
   5. Merabet L

   (2012) High-Resolution modeling assisted design of customized and individualized transcranial direct current stimulation protocols

   Neuromodulation: Technology at the Neural Interface 15:306–315.

   https://doi.org/10.1111/j.1525-1403.2012.00481.x

   • Google Scholar
7. 1. Boldrini M
   2. Fulmore CA
   3. Tartt AN
   4. Simeon LR
   5. Pavlova I
   6. Poposka V
   7. Rosoklija GB
   8. Stankov A
   9. Arango V
   10. Dwork AJ
   11. Hen R
   12. Mann JJ

   (2018) Human hippocampal neurogenesis persists throughout aging

   Cell Stem Cell 22:589–599.

   https://doi.org/10.1016/j.stem.2018.03.015

   • PubMed
   • Google Scholar
8. 1. Bolwig TG
   2. Hertz MM
   3. Westergaard E

   (1977)

   Blood-brain barrier permeability to protein during epileptic seizures in the rat

   Acta Neurologica Scandinavica 64:226–227.

   • Google Scholar
9. 1. Bouckaert F
   2. Sienaert P
   3. Obbels J
   4. Dols A
   5. Vandenbulcke M
   6. Stek M
   7. Bolwig T

   (2014) ECT: its brain enabling effects: a review of electroconvulsive therapy-induced structural brain plasticity

   The Journal of ECT 30:143–151.

   https://doi.org/10.1097/YCT.0000000000000129

   • PubMed
   • Google Scholar
10. 1. Bouckaert F
    2. De Winter FL
    3. Emsell L
    4. Dols A
    5. Rhebergen D
    6. Wampers M
    7. Sunaert S
    8. Stek M
    9. Sienaert P
    10. Vandenbulcke M

    (2016) Grey matter volume increase following electroconvulsive therapy in patients with late life depression: a longitudinal MRI study

    Journal of Psychiatry & Neuroscience 41:105–114.

    https://doi.org/10.1503/jpn.140322

    • PubMed
    • Google Scholar
11. 1. Brus O
    2. Cao Y
    3. Gustafsson E
    4. Hultén M
    5. Landen M
    6. Lundberg J
    7. Nordanskog P
    8. Nordenskjöld A

    (2017) Self-assessed remission rates after electroconvulsive therapy of depressive disorders

    European Psychiatry 45:154–160.

    https://doi.org/10.1016/j.eurpsy.2017.06.015

    • PubMed
    • Google Scholar
12. 1. Cano M
    2. Martínez-Zalacaín I
    3. Bernabéu-Sanz Á
    4. Contreras-Rodríguez O
    5. Hernández-Ribas R
    6. Via E
    7. de Arriba-Arnau A
    8. Gálvez V
    9. Urretavizcaya M
    10. Pujol J
    11. Menchón JM
    12. Cardoner N
    13. Soriano-Mas C

    (2017) Brain volumetric and metabolic correlates of electroconvulsive therapy for treatment-resistant depression: a longitudinal neuroimaging study

    Translational Psychiatry 7:e1023.

    https://doi.org/10.1038/tp.2016.267

    • PubMed
    • Google Scholar
13. 1. Cano M
    2. Lee E
    3. Cardoner N
    4. Martínez-Zalacaín I
    5. Pujol J
    6. Makris N

    (2018) Brain volumetric correlates of right unilateral versus bitemporal electroconvulsive therapy for Treatment-Resistant depression

    Journal of Neuropsychiatry and Clinical Neurosciences.

    https://doi.org/10.1176/appi.neuropsych.18080177

    • Google Scholar
14. Conference

    1. Deng ZD
    2. Lisanby SH
    3. Peterchev AV

    (2009)

    Effect of anatomical variability on neural stimulation strength and focality in electroconvulsive therapy (ECT) and magnetic seizure therapy (MST). 2009 annual international conference of the IEEE engineering in medicine and biology society

    Presented at the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

    • Google Scholar
15. 1. Deng Z-D
    2. Lisanby SH
    3. Peterchev AV

    (2011) Electric field strength and focality in electroconvulsive therapy and magnetic seizure therapy: a finite element simulation study

    Journal of Neural Engineering 8:016007.

    https://doi.org/10.1088/1741-2560/8/1/016007

    • Google Scholar
16. 1. Deng ZD
    2. Lisanby SH
    3. Peterchev AV

    (2015) Effect of anatomical variability on electric field characteristics of electroconvulsive therapy and magnetic seizure therapy: a parametric modeling study

    IEEE Transactions on Neural Systems and Rehabilitation Engineering 23:22–31.

    https://doi.org/10.1109/TNSRE.2014.2339014

    • PubMed
    • Google Scholar
17. 1. Desikan RS
    2. Ségonne F
    3. Fischl B
    4. Quinn BT
    5. Dickerson BC
    6. Blacker D
    7. Buckner RL
    8. Dale AM
    9. Maguire RP
    10. Hyman BT
    11. Albert MS
    12. Killiany RJ

    (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

    NeuroImage 31:968–980.

    https://doi.org/10.1016/j.neuroimage.2006.01.021

    • PubMed
    • Google Scholar
18. 1. Drysdale AT
    2. Grosenick L
    3. Downar J
    4. Dunlop K
    5. Mansouri F
    6. Meng Y
    7. Fetcho RN
    8. Zebley B
    9. Oathes DJ
    10. Etkin A
    11. Schatzberg AF
    12. Sudheimer K
    13. Keller J
    14. Mayberg HS
    15. Gunning FM
    16. Alexopoulos GS
    17. Fox MD
    18. Pascual-Leone A
    19. Voss HU
    20. Casey BJ
    21. Dubin MJ
    22. Liston C

    (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression

    Nature Medicine 23:28–38.

    https://doi.org/10.1038/nm.4246

    • PubMed
    • Google Scholar
19. 1. Dukart J
    2. Regen F
    3. Kherif F
    4. Colla M
    5. Bajbouj M
    6. Heuser I
    7. Frackowiak RS
    8. Draganski B

    (2014) Electroconvulsive therapy-induced brain plasticity determines therapeutic outcome in mood disorders

    PNAS 111:1156–1161.

    https://doi.org/10.1073/pnas.1321399111

    • PubMed
    • Google Scholar
20. 1. d’Elia G

    (1970)

    Unilateral electroconvulsive therapy

    Acta Psychiatrica Scandinavica Supplement 215:1–98.

    • Google Scholar
21. 1. Fink M
    2. Taylor MA

    (2007) Electroconvulsive therapy: evidence and challenges

    Jama 298:330–332.

    https://doi.org/10.1001/jama.298.3.330

    • PubMed
    • Google Scholar
22. 1. Fischl B
    2. Salat DH
    3. Busa E
    4. Albert M
    5. Dieterich M
    6. Haselgrove C
    7. van der Kouwe A
    8. Killiany R
    9. Kennedy D
    10. Klaveness S
    11. Montillo A
    12. Makris N
    13. Rosen B
    14. Dale AM

    (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain

    Neuron 33:341–355.

    https://doi.org/10.1016/s0896-6273(02)00569-x

    • PubMed
    • Google Scholar
23. 1. Fluitman SB
    2. Heijnen CJ
    3. Denys DA
    4. Nolen WA
    5. Balk FJ
    6. Westenberg HG

    (2011) Electroconvulsive therapy has acute immunological and neuroendocrine effects in patients with major depressive disorder

    Journal of Affective Disorders 131:388–392.

    https://doi.org/10.1016/j.jad.2010.11.035

    • PubMed
    • Google Scholar
24. 1. Hamilton M

    (1960) A rating scale for depression

    Journal of Neurology, Neurosurgery & Psychiatry 23:56–62.

    https://doi.org/10.1136/jnnp.23.1.56

    • Google Scholar
25. 1. Haq AU
    2. Sitzmann AF
    3. Goldman ML
    4. Maixner DF
    5. Mickey BJ

    (2015) Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors

    The Journal of Clinical Psychiatry 76:1374–1384.

    https://doi.org/10.4088/JCP.14r09528

    • PubMed
    • Google Scholar
26. 1. Hellsten J
    2. Wennström M
    3. Bengzon J
    4. Mohapel P
    5. Tingström A

    (2004) Electroconvulsive seizures induce endothelial cell proliferation in adult rat hippocampus

    Biological Psychiatry 55:420–427.

    https://doi.org/10.1016/j.biopsych.2003.08.013

    • Google Scholar
27. 1. Heo M
    2. Murphy CF
    3. Meyers BS

    (2007) Relationship between the hamilton depression rating scale and the Montgomery-Asberg depression rating scale in depressed elderly: a meta-analysis

    The American Journal of Geriatric Psychiatry 15:899–905.

    https://doi.org/10.1097/JGP.0b013e318098614e

    • PubMed
    • Google Scholar
28. 1. Holland D
    2. Dale AM
    3. Alzheimer's Disease Neuroimaging Initiative

    (2011) Nonlinear registration of longitudinal images and measurement of change in regions of interest

    Medical Image Analysis 15:489–497.

    https://doi.org/10.1016/j.media.2011.02.005

    • PubMed
    • Google Scholar
29. 1. Holland D
    2. McEvoy LK
    3. Dale AM
    4. Alzheimer's Disease Neuroimaging Initiative

    (2012) Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI

    Human Brain Mapping 33:2586–2602.

    https://doi.org/10.1002/hbm.21386

    • PubMed
    • Google Scholar
30. 1. Huang Y
    2. Liu AA
    3. Lafon B
    4. Friedman D
    5. Dayan M
    6. Wang X
    7. Bikson M
    8. Doyle WK
    9. Devinsky O
    10. Parra LC

    (2017) Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

    eLife 6:e18834.

    https://doi.org/10.7554/eLife.18834

    • Google Scholar
31. 1. Jansson L
    2. Wennström M
    3. Johanson A
    4. Tingström A

    (2009) Glial cell activation in response to electroconvulsive seizures

    Progress in Neuro-Psychopharmacology and Biological Psychiatry 33:1119–1128.

    https://doi.org/10.1016/j.pnpbp.2009.06.007

    • PubMed
    • Google Scholar
32. 1. Jonckheere J
    2. Deloulme JC
    3. Dall'Igna G
    4. Chauliac N
    5. Pelluet A
    6. Nguon AS
    7. Lentini C
    8. Brocard J
    9. Denarier E
    10. Brugière S
    11. Couté Y
    12. Heinrich C
    13. Porcher C
    14. Holtzmann J
    15. Andrieux A
    16. Suaud-Chagny MF
    17. Gory-Fauré S

    (2018) Short- and long-term efficacy of electroconvulsive stimulation in animal models of depression: the essential role of neuronal survival

    Brain Stimulation 11:1336–1347.

    https://doi.org/10.1016/j.brs.2018.08.001

    • PubMed
    • Google Scholar
33. 1. Joshi SH
    2. Espinoza RT
    3. Pirnia T
    4. Shi J
    5. Wang Y
    6. Ayers B
    7. Leaver A
    8. Woods RP
    9. Narr KL

    (2016) Structural plasticity of the Hippocampus and amygdala induced by electroconvulsive therapy in major depression

    Biological Psychiatry 79:282–292.

    https://doi.org/10.1016/j.biopsych.2015.02.029

    • PubMed
    • Google Scholar
34. 1. Jovicich J
    2. Czanner S
    3. Greve D
    4. Haley E
    5. van der Kouwe A
    6. Gollub R
    7. Kennedy D
    8. Schmitt F
    9. Brown G
    10. Macfall J
    11. Fischl B
    12. Dale A

    (2006) Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data

    NeuroImage 30:436–443.

    https://doi.org/10.1016/j.neuroimage.2005.09.046

    • PubMed
    • Google Scholar
35. 1. Kohler SJ
    2. Williams NI
    3. Stanton GB
    4. Cameron JL
    5. Greenough WT

    (2011) Maturation time of new granule cells in the dentate gyrus of adult macaque monkeys exceeds six months

    PNAS 108:10326–10331.

    https://doi.org/10.1073/pnas.1017099108

    • PubMed
    • Google Scholar
36. 1. Kolshus E
    2. Jelovac A
    3. McLoughlin DM

    (2017) Bitemporal v. high-dose right unilateral electroconvulsive therapy for depression: a systematic review and meta-analysis of randomized controlled trials

    Psychological Medicine 47:518–530.

    https://doi.org/10.1017/S0033291716002737

    • PubMed
    • Google Scholar
37. 1. Kornack DR
    2. Rakic P

    (1999) Continuation of neurogenesis in the Hippocampus of the adult macaque monkey

    PNAS 96:5768–5773.

    https://doi.org/10.1073/pnas.96.10.5768

    • PubMed
    • Google Scholar
38. 1. Kunigiri G
    2. Jayakumar PN
    3. Janakiramaiah N
    4. Gangadhar BN

    (2007) MRI T(2) relaxometry of brain regions and cognitive dysfunction following electroconvulsive therapy

    Indian Journal of Psychiatry 49:195–199.

    https://doi.org/10.4103/0019-5545.37321

    • PubMed
    • Google Scholar
39. 1. Leaver AM
    2. Vasavada M
    3. Joshi SH
    4. Wade B
    5. Woods RP
    6. Espinoza R
    7. Narr KL

    (2019) Mechanisms of antidepressant response to electroconvulsive therapy studied with perfusion magnetic resonance imaging

    Biological Psychiatry 85:466–476.

    https://doi.org/10.1016/j.biopsych.2018.09.021

    • PubMed
    • Google Scholar
40. 1. Lee WH
    2. Deng Z-D
    3. Kim T-S
    4. Laine AF
    5. Lisanby SH
    6. Peterchev AV

    (2012) Regional electric field induced by electroconvulsive therapy in a realistic finite element head model: influence of white matter anisotropic conductivity

    NeuroImage 59:2110–2123.

    https://doi.org/10.1016/j.neuroimage.2011.10.029

    • Google Scholar
41. 1. Madsen TM
    2. Treschow A
    3. Bengzon J
    4. Bolwig TG
    5. Lindvall O
    6. Tingström A

    (2000) Increased neurogenesis in a model of electroconvulsive therapy

    Biological Psychiatry 47:1043–1049.

    https://doi.org/10.1016/S0006-3223(00)00228-6

    • PubMed
    • Google Scholar
42. Website

    1. Merkel D

    (2014) Docker: lightweight linux containers for consistent development and deployment

    Linux J. Accessed June 20, 2019.

    http://dl.acm.org/citation.cfm?id=2600239.2600241
43. 1. Milo TJ
    2. Kaufman GE
    3. Barnes WE
    4. Konopka LM
    5. Crayton JW
    6. Ringelstein JG
    7. Shirazi PH

    (2001) Changes in regional cerebral blood flow after electroconvulsive therapy for depression

    The Journal of ECT 17:15–21.

    https://doi.org/10.1097/00124509-200103000-00004

    • PubMed
    • Google Scholar
44. 1. Montgomery SA
    2. Asberg M

    (1979) A new depression scale designed to be sensitive to change

    British Journal of Psychiatry 134:382–389.

    https://doi.org/10.1192/bjp.134.4.382

    • PubMed
    • Google Scholar
45. 1. Nordanskog P
    2. Dahlstrand U
    3. Larsson MR
    4. Larsson EM
    5. Knutsson L
    6. Johanson A

    (2010) Increase in hippocampal volume after electroconvulsive therapy in patients with depression: a volumetric magnetic resonance imaging study

    The Journal of ECT 26:62–67.

    https://doi.org/10.1097/YCT.0b013e3181a95da8

    • PubMed
    • Google Scholar
46. 1. Nordenskjöld A
    2. von Knorring L
    3. Engström I

    (2012) Predictors of the short-term responder rate of electroconvulsive therapy in depressive disorders--a population based study

    BMC Psychiatry 12:115.

    https://doi.org/10.1186/1471-244X-12-115

    • PubMed
    • Google Scholar
47. 1. O'Connor MK
    2. Knapp R
    3. Husain M
    4. Rummans TA
    5. Petrides G
    6. Smith G
    7. Mueller M
    8. Snyder K
    9. Bernstein H
    10. Rush AJ
    11. Fink M
    12. Kellner C

    (2001) The influence of age on the response of major depression to electroconvulsive therapy: a C.O.R.E. report

    The American Journal of Geriatric Psychiatry 9:382–390.

    https://doi.org/10.1097/00019442-200111000-00006

    • PubMed
    • Google Scholar
48. 1. Oltedal L
    2. Bartsch H
    3. Sørhaug OJ
    4. Kessler U
    5. Abbott C
    6. Dols A
    7. Stek ML
    8. Ersland L
    9. Emsell L
    10. van Eijndhoven P
    11. Argyelan M
    12. Tendolkar I
    13. Nordanskog P
    14. Hamilton P
    15. Jorgensen MB
    16. Sommer IE
    17. Heringa SM
    18. Draganski B
    19. Redlich R
    20. Dannlowski U
    21. Kugel H
    22. Bouckaert F
    23. Sienaert P
    24. Anand A
    25. Espinoza R
    26. Narr KL
    27. Holland D
    28. Dale AM
    29. Oedegaard KJ

    (2017) The global ECT-MRI research collaboration (GEMRIC): Establishing a multi-site investigation of the neural mechanisms underlying response to electroconvulsive therapy

    NeuroImage: Clinical 14:422–432.

    https://doi.org/10.1016/j.nicl.2017.02.009

    • PubMed
    • Google Scholar
49. 1. Oltedal L
    2. Narr KL
    3. Abbott C
    4. Anand A
    5. Argyelan M
    6. Bartsch H
    7. Dannlowski U
    8. Dols A
    9. van Eijndhoven P
    10. Emsell L
    11. Erchinger VJ
    12. Espinoza R
    13. Hahn T
    14. Hanson LG
    15. Hellemann G
    16. Jorgensen MB
    17. Kessler U
    18. Oudega ML
    19. Paulson OB
    20. Redlich R
    21. Sienaert P
    22. Stek ML
    23. Tendolkar I
    24. Vandenbulcke M
    25. Oedegaard KJ
    26. Dale AM

    (2018) Volume of the human Hippocampus and clinical response following electroconvulsive therapy

    Biological Psychiatry 84:574–581.

    https://doi.org/10.1016/j.biopsych.2018.05.017

    • PubMed
    • Google Scholar
50. 1. Ousdal OT
    2. Argyelan M
    3. Narr KL
    4. Abbott C
    5. Wade B
    6. Vandenbulcke M
    7. Urretavizcaya M
    8. Tendolkar I
    9. Takamiya A
    10. Stek ML
    11. Soriano-Mas C
    12. Redlich R
    13. Paulson OB
    14. Oudega ML
    15. Opel N
    16. Nordanskog P
    17. Kishimoto T
    18. Kampe R
    19. Jorgensen A
    20. Hanson LG
    21. Hamilton JP
    22. Espinoza R
    23. Emsell L
    24. van Eijndhoven P
    25. Dols A
    26. Dannlowski U
    27. Cardoner N
    28. Bouckaert F
    29. Anand A
    30. Bartsch H
    31. Kessler U
    32. Oedegaard KJ
    33. Dale AM
    34. Oltedal L
    35. GEMRIC

    (2019) Brain changes induced by electroconvulsive therapy are broadly distributed

    Biological Psychiatry.

    https://doi.org/10.1016/j.biopsych.2019.07.010

    • PubMed
    • Google Scholar
51. 1. Perera TD
    2. Coplan JD
    3. Lisanby SH
    4. Lipira CM
    5. Arif M
    6. Carpio C
    7. Spitzer G
    8. Santarelli L
    9. Scharf B
    10. Hen R
    11. Rosoklija G
    12. Sackeim HA
    13. Dwork AJ

    (2007) Antidepressant-induced neurogenesis in the Hippocampus of adult nonhuman primates

    Journal of Neuroscience 27:4894–4901.

    https://doi.org/10.1523/JNEUROSCI.0237-07.2007

    • PubMed
    • Google Scholar
52. 1. Pirnia T
    2. Joshi SH
    3. Leaver AM
    4. Vasavada M
    5. Njau S
    6. Woods RP
    7. Espinoza R
    8. Narr KL

    (2016) Electroconvulsive therapy and structural neuroplasticity in neocortical, limbic and paralimbic cortex

    Translational Psychiatry 6:e832.

    https://doi.org/10.1038/tp.2016.102

    • Google Scholar
53. Software

    1. R Development Core Team

    (2013) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
54. 1. Regenold WT
    2. Noorani RJ
    3. Piez D
    4. Patel P

    (2015) Nonconvulsive Electrotherapy for Treatment Resistant Unipolar and Bipolar Major Depressive Disorder: A Proof-of-concept Trial

    Brain Stimulation 8:855–861.

    https://doi.org/10.1016/j.brs.2015.06.011

    • Google Scholar
55. 1. Roddy DW
    2. Farrell C
    3. Doolin K
    4. Roman E
    5. Tozzi L
    6. Frodl T
    7. O’Keane V
    8. O’Hanlon E

    (2019) The Hippocampus in Depression: More Than the Sum of Its Parts? Advanced Hippocampal Substructure Segmentation in Depression

    Biological Psychiatry 85:487–497.

    https://doi.org/10.1016/j.biopsych.2018.08.021

    • Google Scholar
56. 1. Sackeim HA
    2. Prudic J
    3. Devanand DP
    4. Nobler MS
    5. Lisanby SH
    6. Peyser S
    7. Fitzsimons L
    8. Moody BJ
    9. Clark J

    (2000) A prospective, randomized, double-blind comparison of bilateral and right unilateral electroconvulsive therapy at different stimulus intensities

    Archives of General Psychiatry 57:425–434.

    https://doi.org/10.1001/archpsyc.57.5.425

    • PubMed
    • Google Scholar
57. 1. Sackeim HA

    (2015) Is the seizure an unnecessary component of electroconvulsive therapy? A startling possibility

    Brain Stimulation 8:851–854.

    https://doi.org/10.1016/j.brs.2015.07.026

    • PubMed
    • Google Scholar
58. 1. Sartorius A
    2. Demirakca T
    3. Böhringer A
    4. Clemm von Hohenberg C
    5. Aksay SS
    6. Bumb JM
    7. Kranaster L
    8. Ende G

    (2016) Electroconvulsive therapy increases temporal gray matter volume and cortical thickness

    European Neuropsychopharmacology 26:506–517.

    https://doi.org/10.1016/j.euroneuro.2015.12.036

    • PubMed
    • Google Scholar
59. 1. Schulte E
    2. Davison D
    3. Dye T
    4. Dominik C

    (2012) A Multi-Language computing environment for literate programming and reproducible research

    Journal of Statistical Software 46:1–24.

    https://doi.org/10.18637/jss.v046.i03

    • Google Scholar
60. 1. Scott BW
    2. Wojtowicz JM
    3. Burnham WM

    (2000) Neurogenesis in the dentate gyrus of the rat following electroconvulsive shock seizures

    Experimental Neurology 165:231–236.

    https://doi.org/10.1006/exnr.2000.7458

    • PubMed
    • Google Scholar
61. 1. Segi-Nishida E

    (2011) Exploration of new molecular mechanisms for antidepressant actions of electroconvulsive seizure

    Biological & Pharmaceutical Bulletin 34:939–944.

    https://doi.org/10.1248/bpb.34.939

    • PubMed
    • Google Scholar
62. 1. Smitha JS
    2. Roopa R
    3. Khaleel N
    4. Kutty BM
    5. Andrade C

    (2014) Images in electroconvulsive therapy: electroconvulsive shocks dose-dependently increase dendritic arborization in the CA1 region of the rat Hippocampus

    The Journal of ECT 30:191–192.

    https://doi.org/10.1097/YCT.0000000000000077

    • PubMed
    • Google Scholar
63. 1. Sorrells SF
    2. Paredes MF
    3. Cebrian-Silla A
    4. Sandoval K
    5. Qi D
    6. Kelley KW
    7. James D
    8. Mayer S
    9. Chang J
    10. Auguste KI
    11. Chang EF
    12. Gutierrez AJ
    13. Kriegstein AR
    14. Mathern GW
    15. Oldham MC
    16. Huang EJ
    17. Garcia-Verdugo JM
    18. Yang Z
    19. Alvarez-Buylla A

    (2018) Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults

    Nature 555:377–381.

    https://doi.org/10.1038/nature25975

    • PubMed
    • Google Scholar
64. 1. Swartz CM

    (2006) Electroconvulsive therapy stimulus dose expressed as volume of seizure foci

    The Journal of ECT 22:54–58.

    https://doi.org/10.1097/00124509-200603000-00012

    • Google Scholar
65. 1. Swartz CM
    2. Krohmer R
    3. Michael N

    (2012) ECT stimulus dose dependence on current separately from charge

    Psychiatry Research 198:164–165.

    https://doi.org/10.1016/j.psychres.2011.12.046

    • PubMed
    • Google Scholar
66. 1. Takamiya A
    2. Chung JK
    3. Liang KC
    4. Graff-Guerrero A
    5. Mimura M
    6. Kishimoto T

    (2018) Effect of electroconvulsive therapy on hippocampal and amygdala volumes: systematic review and meta-analysis

    The British Journal of Psychiatry 212:19–26.

    https://doi.org/10.1192/bjp.2017.11

    • PubMed
    • Google Scholar
67. 1. Takamiya A
    2. Plitman E
    3. Chung JK
    4. Chakravarty M
    5. Graff-Guerrero A
    6. Mimura M
    7. Kishimoto T

    (2019) Acute and long-term effects of electroconvulsive therapy on human dentate gyrus

    Neuropsychopharmacology 44:1805–1811.

    https://doi.org/10.1038/s41386-019-0312-0

    • PubMed
    • Google Scholar
68. 1. Tang A
    2. Thickbroom G
    3. Rodger J

    (2017) Repetitive transcranial magnetic stimulation of the brain: mechanisms from animal and experimental models

    The Neuroscientist : A Review Journal Bringing Neurobiology, Neurology and Psychiatry 23:82–94.

    https://doi.org/10.1177/1073858415618897

    • PubMed
    • Google Scholar
69. 1. UK ECT Review Group

    (2003) Efficacy and safety of electroconvulsive therapy in depressive disorders: a systematic review and meta-analysis

    The Lancet 361:799–808.

    https://doi.org/10.1016/S0140-6736(03)12705-5

    • PubMed
    • Google Scholar
70. 1. van Buel EM
    2. Patas K
    3. Peters M
    4. Bosker FJ
    5. Eisel UL
    6. Klein HC

    (2015) Immune and neurotrophin stimulation by electroconvulsive therapy: is some inflammation needed after all?

    Translational Psychiatry 5:e609.

    https://doi.org/10.1038/tp.2015.100

    • PubMed
    • Google Scholar
71. 1. Vickers AJ

    (2001) The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study

    BMC Medical Research Methodology 1:6.

    https://doi.org/10.1186/1471-2288-1-6

    • PubMed
    • Google Scholar
72. 1. Wennström M
    2. Hellsten J
    3. Tingström A

    (2004) Electroconvulsive seizures induce proliferation of NG2-expressing glial cells in adult rat amygdala

    Biological Psychiatry 55:464–471.

    https://doi.org/10.1016/j.biopsych.2003.11.011

    • PubMed
    • Google Scholar
73. 1. Yrondi A
    2. Sporer M
    3. Péran P
    4. Schmitt L
    5. Arbus C
    6. Sauvaget A

    (2018) Electroconvulsive therapy, depression, the immune system and inflammation: A systematic review

    Brain Stimulation 11:29–51.

    https://doi.org/10.1016/j.brs.2017.10.013

    • Google Scholar

Author details

1. Miklos Argyelan

   1. Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, United States
   2. Center for Neuroscience, Feinstein Institute for Medical Research, Manhasset, United States
   3. Department of Psychiatry, Zucker School of Medicine, Hempstead, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   argyelan@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7254-1776

2. Leif Oltedal

   1. Department of Clinical Medicine, University of Bergen, Bergen, Norway
   2. Department of Radiology, Haukeland University Hospital, Mohn Medical Imaging and Visualization Centre, Bergen, Norway

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

3. Zhi-De Deng

   Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, United States

   Contribution

   Supervision, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Benjamin Wade

   Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, United States

   Contribution

   Conceptualization, Software, Formal analysis, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

5. Marom Bikson

   Department of Biomedical Engineering, The City College of the City University of New York, New York, United States

   Contribution

   Conceptualization, Software, Methodology, Writing—review and editing

   Competing interests

   reports that The City University of New York (CUNY) has intellectual property (IP) on neuro-stimulation system and methods with MB as inventor; serves on the scientific advisory boards of Boston Scientific and GSK; has equity in Soterix Medical

6. Andrea Joanlanne

   Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, United States

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Sohag Sanghani

   Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, United States

   Contribution

   Conceptualization, Writing—review and editing

   Competing interests

   No competing interests declared

8. Hauke Bartsch

   1. Department of Radiology, Haukeland University Hospital, Mohn Medical Imaging and Visualization Centre, Bergen, Norway
   2. Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, United States

   Contribution

   Resources, Software, Formal analysis, Methodology, Project administration

   Competing interests

   No competing interests declared

9. Marta Cano

   1. Department of Psychiatry, Bellvitge University Hospital-IDIBELL, Barcelona, Spain
   2. CIBERSAM, Carlos III Health Institute, Barcelona, Spain

   Contribution

   Conceptualization, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0675-9483

10. Anders M Dale

    1. Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, United States
    2. Department of Radiology, University of California, San Diego, San Diego, United States
    3. Department of Neurosciences, University of California, San Diego, San Diego, United States

    Contribution

    Conceptualization, Resources, Data curation, Software, Methodology

    Competing interests

    reports that he is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc and receives funding through research agreements with General Electric Healthcare and Medtronic, Inc

11. Udo Dannlowski

    Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany

    Contribution

    Conceptualization, Data curation, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

12. Annemiek Dols

    Department of Psychiatry, Amsterdam UMC, location VUmc, GGZinGeest, Old Age Psychiatry, Amsterdam Neuroscience, Amsterdam, Netherlands

    Contribution

    Conceptualization, Data curation, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

13. Verena Enneking

    Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

14. Randall Espinoza

    1. Department of Neurology, University of California, Los Angeles, Los Angeles, United States
    2. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

15. Ute Kessler

    1. Department of Clinical Medicine, University of Bergen, Bergen, Norway
    2. Division of Psychiatry, Haukeland University Hospital, University of Bergen, Bergen, Norway

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

16. Katherine L Narr

    1. Department of Neurology, University of California, Los Angeles, Los Angeles, United States
    2. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Conceptualization, Data curation, Supervision, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

17. Ketil J Oedegaard

    1. Department of Clinical Medicine, University of Bergen, Bergen, Norway
    2. Division of Psychiatry, Haukeland University Hospital, University of Bergen, Bergen, Norway

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

18. Mardien L Oudega

    Department of Psychiatry, Amsterdam UMC, location VUmc, GGZinGeest, Old Age Psychiatry, Amsterdam Neuroscience, Amsterdam, Netherlands

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

19. Ronny Redlich

    Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

20. Max L Stek

    Department of Psychiatry, Amsterdam UMC, location VUmc, GGZinGeest, Old Age Psychiatry, Amsterdam Neuroscience, Amsterdam, Netherlands

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

21. Akihiro Takamiya

    1. Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
    2. Center for Psychiatry and Behavioral Science, Komagino Hospital, Tokyo, Japan

    Contribution

    Conceptualization, Writing—review and editing

    Competing interests

    No competing interests declared

22. Louise Emsell

    Department of Geriatric Psychiatry, University Psychiatric Center, KU Leuven, Leuven, Belgium

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

23. Filip Bouckaert

    1. Department of Geriatric Psychiatry, University Psychiatric Center, KU Leuven, Leuven, Belgium
    2. Academic center for ECT and Neurostimulation (AcCENT), University Psychiatric Center, KU Leuven, Kortenberg, Belgium

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

24. Pascal Sienaert

    Academic center for ECT and Neurostimulation (AcCENT), University Psychiatric Center, KU Leuven, Kortenberg, Belgium

    Contribution

    Conceptualization, Data curation, Writing—review and editing

    Competing interests

    No competing interests declared

25. Jesus Pujol

    1. CIBERSAM, Carlos III Health Institute, Barcelona, Spain
    2. MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain

    Contribution

    Conceptualization, Writing—review and editing

    Competing interests

    No competing interests declared

26. Indira Tendolkar

    1. Department of Psychiatry, Radboud University Medical Center, Nijmegen, Netherlands
    2. Donders Institute for Brain Cognition and Behavior, Centre for Cognitive Neuroimaging, Nijmegen, Netherlands
    3. Faculty of Medicine and LVR Clinic for Psychiatry and Psychotherapy, University of Duisburg-Essen, Essen, Germany

    Contribution

    Conceptualization, Supervision, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

27. Philip van Eijndhoven

    1. Department of Psychiatry, Radboud University Medical Center, Nijmegen, Netherlands
    2. Donders Institute for Brain Cognition and Behavior, Centre for Cognitive Neuroimaging, Nijmegen, Netherlands

    Contribution

    Conceptualization, Data curation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

28. Georgios Petrides

    1. Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, United States
    2. Center for Neuroscience, Feinstein Institute for Medical Research, Manhasset, United States
    3. Department of Psychiatry, Zucker School of Medicine, Hempstead, United States

    Contribution

    Conceptualization, Resources, Data curation, Methodology, Writing—original draft, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

29. Anil K Malhotra

    1. Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, United States
    2. Center for Neuroscience, Feinstein Institute for Medical Research, Manhasset, United States
    3. Department of Psychiatry, Zucker School of Medicine, Hempstead, United States

    Contribution

    Conceptualization, Resources, Data curation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

30. Christopher Abbott

    Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, United States

    Contribution

    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

• 2,600

  views

• 322

  downloads

• 55

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.