# Hippocampal microstructural changes following electroconvulsive therapy in severe depression

Alice Le Berre, David Attali, Ivy Uszynski, Clement Debacker, Maliesse Lui, Sylvain Charron, Mylène Moyal, François Ramon, Adèle Henensal, Joseph Benzakoun, et al.

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## To cite this version:

Alice Le Berre, David Attali, Ivy Uszynski, Clement Debacker, Maliesse Lui, et al. Hippocampal microstructural changes following electroconvulsive therapy in severe depression. Molecular Psychiatry, inPress, 10.21203/rs.3.rs-4258655/v1. hal-05021396

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# Title Page

## Title: Hippocampal microstructural changes following electroconvulsive therapy in severe depression

Running title: Hippocampal changes after ECT in severe depression

A. Le Berre, MD, MSc1,2†, D. Attali, MD, PhD2,3†, I. Uszynski, PhD4, C. Debacker, PhD1,2, M. Lui, MSc1,2, S. Charron, PhD1,2, M. Moyal, MD1,2, F. Ramon, MSc1, A. Henensal, MSc1,2, J. Benzakoun, MD, PhD1,2, L. Mekaoui, MD2, P. Gorwood, MD, PhD1,2, C. Poupon, PhD4, A. Cachia, PhD5,1, C. Oppenheim, MD, PhD1,2†, M. Plaze, MD, PhD1,2†

1. Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Ima-Brain team, 75014 Paris, France.

2. GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France.

3. Physics for Medicine Paris, Inserm U1273, CNRS UMR 8063, ESPCI Paris, PSL University, Paris, France

4. BAOBAB, UMR 9027, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France

5. Université Paris Cité, Laboratory for the Psychology of Child Development and Education, CNRS UMR 8240, Sorbonne, Paris, France

A Le Berre†, D Attali† These authors contributed equally to this work and share first authorship

C Oppenheim†, M Plaze† These authors contributed equally to this work and share last authorship

### Corresponding author:

Alice Le Berre

GHU PARIS Hopital Sainte-Anne

1 rue Cabanis

75014 PARIS

alice.le_berre@ghu-paris.fr

0145656890
Abstract

Electroconvulsive therapy (ECT) induces hippocampal volume increases in depressed patients, potentially reflecting neuroplasticity. We hypothesized that Neurite Orientation Dispersion and Density Imaging (NODDI) could provide in vivo evidence of hippocampal neuroplasticity following ECT. This longitudinal study evaluated 43 depressed patients undergoing ECT and 24 controls. MRI and clinical assessments were performed at baseline (V1), after 5 sessions (V2), and post-treatment (V3). Evaluations included a 3T MR-scan with 3DT1-weighted and multi-shell diffusion (b=200/1500/2500s/mm², 30/45/60directions) sequences. Q-ball, Diffusion Tensor, and NODDI models provided: axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), fractional anisotropy (FA), generalized FA (GFA), neurite density index (NDI), isotropic fraction (Fiso), and orientation dispersion index (ODI). FreeSurfer extracted whole hippocampal and subfield volumes from T1-weighted images. Longitudinal changes were assessed with linear mixed-effect models. 107 MRIs from patients and 24 MRIs from controls were analyzed. ECT induced significant bilateral hippocampal volume increases (p < 0.001). Group comparisons showed consistently higher FA, lower GFA and ODI in patients compared to controls at all time-points. Following ECT, significant diffusion changes included decreased hippocampal GFA, FA, AD, MD and Fiso, along with increased ODI and NDI. NDI and Fiso changes were localized to the dentate gyrus but not the hippocampal tail. ECT responders showed a significant right hippocampal volume increase at V2 compared to non-responders. After ECT, hippocampal volume increases are accompanied by bilateral changes in NODDI parameters, particularly in the dentate gyrus, consistent with hippocampal neuroplasticity.
# 1. INTRODUCTION

Depression is a common illness, with an estimated 3.4% of the adult population affected, and a leading cause of disability worldwide (1). A third of cases remain unresponsive to usual treatments (2). Electroconvulsive therapy (ECT) is a fast and effective treatment for severe and drug-resistant depression (3,4) and has proven its safety (5). However, its mechanism of action still remains elusive. The main hypothesis relates to ECT-induced hippocampal neurogenesis. Animal experiments have shown that electroconvulsive stimulation (ECS), the animal analog of ECT, induces hippocampal neurogenesis (6,7). In humans, cross-sectional studies using brain MRI found smaller hippocampi in patients with major depressive disorder compared to healthy subjects (8,9), and importantly, bilateral hippocampal volume increase induced by ECT in longitudinal studies (10). Although meta-analyses found no correlation between clinical outcome and volumetric changes in the entire hippocampus (10,11), a 7T MRI study of the hippocampal subfields found a specific association between the volume of the dentate gyrus (DG), containing neural stem cells able to differentiate into new neurons, and clinical improvements (12). A post-mortem study found an increase in neuroplasticity markers within the human hippocampus of depressed donors treated with ECT compared to depressed donors not treated by ECT (13). In addition, a novel in vivo molecular approach based on the extraction of central nervous system-derived extracellular vesicles showed a reduced level of doublecortin, a neurogenesis marker, in patients with treatment-resistant depression and a significant increase after ECT (14). Taken together, these findings support the hypothesis that hippocampal neuroplasticity, including neurogenesis, may contribute to the increased hippocampus volume and clinical improvements observed after ECT. To further examine this hypothesis, imaging biomarkers of neuroplasticity would be useful to monitor ECT-induced hippocampal and clinical changes over time.

The Diffusion Tensor Imaging (DTI) model is the most commonly employed diffusion MRI technique for the in-vivo examination of brain microstructure. While it is well-suited and widely used for white matter analysis, its application to grey matter encounters constraints due to its relatively low spatial resolution compared to cortical thickness. This incongruity exposes the model to partial volume effects and contamination by free cerebrospinal fluid, thereby introducing artificial elevations in diffusivity (15). Additionally, the intricate composition of grey matter, marked by dispersed axons, dendrites, and neuronal cell bodies that collectively reduce anisotropy, proves challenging for modeling with a simple diffusion tensor, even with high spatial resolution (16). Indeed, a limited number of studies have employed DTI to evaluate the microstructure of grey matter in individuals with major psychiatric disorders (17,18). Higher order multi-compartmental biophysical models, like the recent neurite orientation dispersion and density imaging (NODDI) (19) have been proposed
for studying the grey matter microstructure in psychiatric disorders (20). NODDI extracts sub-voxel information from complex neural tissue, providing three metrics: neurite density index (NDI), isotropic fraction (Fiso), and orientation and dispersion index (ODI). NDI reflects the dendritic and axonal density. ODI quantifies the variability of the neurite orientation and is higher when the dendritic complexity increases in the grey matter and when the axonal complexity increases in the white matter. For instance, ODI increases in the sharp turn regions where white matter tracts connect to the cortical surface, and in crossing fiber areas in transparent mouse brain studies, notably in the hippocampus (21). Lastly, Fiso reflects the quantity of free water in the extra-neurite space within a voxel. Interestingly, NODDI has been shown to detect deviations from normal in the hippocampus (22) and its subfields (23) in cases of temporal lobe epilepsy. These deviations may reflect microstructural alterations, as supported by a recent study combining NODDI with histological data (24). In depression, NODDI revealed reductions in NDI in various white matter regions, which are thought to reflect lower axonal density (25). Notably, as of now, this approach has not been employed to investigate the microstructure of the hippocampus in depressed patients, nor has it been used to explore the effects of ECT on brain microstructure.

In this context, the aim of the current study was to test the hypothesis that hippocampal neuroplastic changes, probed with NODDI metrics, may explain the observed increased hippocampus volume and clinical improvements following ECT. Specifically, we anticipated an increase in neurite density and orientation dispersion indices after ECT. We conducted a prospective longitudinal study in severely depressed patients to monitor macro- and microstructure changes induced by ECT in the hippocampus and its subfields at three time points: baseline (V1), after 5 ECT sessions (V2), and 14 days after completing the ECT course (V3). We aimed to evaluate the relationship between these changes and clinical response. A control group of age- and sex-matched healthy subjects, free of any mental illness, was also included as a reference.

# 2. METHODS

## 2.1. Patients' selection and study design

Patients above 18 years old were recruited at the GHU-Paris University Hospital (France) between June 2018 and April 2022. The inclusion criteria were as follows: (1) Fulfillment of DSM-5 criteria for uni- or bipolar major depressive disorder, and (2) Requirement for ECT treatment. The exclusion criteria were as follows: co-morbid psychiatric disorders, including schizophrenia, pervasive developmental disorder, substance abuse other than tobacco, co-morbidity of dementia or any neurological illness, ECT in the previous 6 months, pregnancy,
and MR contraindications. Patients were assessed at three time points: baseline (V1), after 5 ECT sessions (V2), and 14 days after completing the ECT course (V3). In addition, we recruited a group of age- and sex-matched healthy controls to serve as a reference for the neuroimaging analyses. The control group was free from any psychiatric or neurological conditions. Our sample size was based on prior cross-sectional and longitudinal studies using NODDI to detect microstructural changes, which demonstrated significant effects with smaller groups (22,26,27).

## 2.2. Ethics approval and consent to participate

The study protocol received approval from the Comité de Protection des Personnes (CPP) Sud Méditerranée (CPP17-046), and all participants provided written informed consent before participating in the study. All methods were performed in accordance with the relevant guidelines and regulations.

## 2.3. ECT procedure

ECT (5000M MECTA spECTrum, Tualatin, OR, USA) was administered twice a week, using propofol or etomidate for anesthesia and succinylcholine for paralysis. Electrode placement was bitemporal in all patients. An age-based method was used to determine individual initial charge of ECT. Dose (in mC) and EEG seizure length were collected after each ECT session. The treatment was continued until remission (Montgomery-Asberg depression rating scale [MADRS] score < 11 for two consecutive weeks) or discontinued at the discretion of the clinical psychiatric team if the patient showed no further improvement. All participants remained under the care of their treating psychiatrists throughout the trial, with decisions about concomitant medications made by the clinical team. Individual pharmacological treatment data at each time point are provided in Supplementary Table 9.

## 2.4. Clinical evaluation

MADRS and the 21-item Hamilton depression rating scale (HDRS-21) were used to assess clinical symptoms and treatment responses at V1, V2 and V3. Individual patients were evaluated by the same psychiatrist at the three time points. Treatment response was defined as a >50% decrease in MADRS score (ECT-responders) and remission as MADRS score <11 (ECT-remitters).

## 2.5. MRI acquisition
All patients were scanned on a 3T MR scanner with a 16-channels head coil (MR750; GE Healthcare, Milwaukee, WI). Imaging sequences were as follows: a structural 3D T1-weighted (T1-w) Spoiled Gradient Recall with Inversion Recovery sequence with 1 mm isotropic resolution (TE/TR/TI = 3.2/8.2/400 ms, flip angle = 11°) and an echo-planar diffusion-weighted multiple-shell sequence with three shells of the diffusion q-space optimized for NODDI, with 2 mm isotropic resolution (TE/TR = 80/440 ms, b=200/1500/2500 s/mm², 30/45/60 diffusion directions) as well as three b=0 s/mm² T2-weighted volumes for reference. A reverse phase-encoding direction sequence (b=2500 s/mm², 6 directions) was acquired to correct for susceptibility-induced distortions.

## 2.6. MRI analysis

Anatomical and diffusion MRI images were processed using the Ginkgo Toolbox, CEA (available at: https://framagit.org/cpoupon/gkg). **Figure 1** summarizes the image analysis steps.

Each shell was first corrected for susceptibility-induced distortions using a previously described method (28) implemented in FSL (29). The pairs of diffusion images with reversed phase-encode blips were combined into a single corrected one. Preprocessed shells were then concatenated into a single three-shell diffusion volume on which outlier, eddy-current, and motion correction steps were performed. For each subject, a brain mask was computed based on the anatomical 3D T1-w, which was beforehand matched with the average b=0 s/mm² T2 image using a 3D rigid registration method. Signal intensity correction was not applied to avoid modification of the raw diffusion images intensity. NDI, Fiso and ODI maps were computed from the multi-compartmental NODDI model based on the three-shell corrected diffusion-weighted volume, with intrinsic free diffusivity and isotropic diffusivity respectively fixed to 1.7×10⁻³ mm²/s and 3×10⁻³ mm²/s (19). A DTI model was also used to extract standard diffusion maps such as axial (AD), radial (RD) and mean diffusivities (MD) as well as the fractional anisotropy (FA). A higher-order analytical Q-ball field (30), more robust to fiber crossings configurations (31), was computed to estimate generalized FA (GFA) maps, using a spherical harmonics order 8 and a regularization factor λ=0.006. Each individual 3D T1-w and corrected-diffusion MR images along with inter-modality registrations were reviewed for quality check.

FreeSurfer 7.0.0 (32) was then used to segment the T1-w images, extract the left and right hippocampi, and perform subfield segmentation (33). To account for imperfect boundary definition of the dentate gyrus, a notably thin region, and considering the DW image resolution (2 mm), we opted, like others (33), for the 'CA' version of subfield segmentation, in which internal labels (granular cells, molecular layer, and dentate gyrus) are aggregated to
the cornu ammonis 4. This amalgamated region will be termed the Dentate Gyrus in what follows. Of note, in a previous post-mortem study, hippocampal neurogenesis histological markers were found not only in the granule cell layer and subgranular zone of the dentate gyrus but also in the cornu ammonis 4 (13). The hippocampal tail was also segmented as a control region to test the specific neurogenesis-related changes in the dentate gyrus. The quality of segmentation was reviewed for errors by a neuroradiologist (ALB).

Macroscopic volumes and averaged diffusion-derived metrics were estimated for the bilateral hippocampi, dentate gyri and hippocampal tails. Outliers, defined as values beyond 3 standard deviations (SD), were iteratively removed for each group separately.

## 2.7. Statistical analyses

To assess the longitudinal effects of ECT on hippocampal macrostructure (volume) and microstructure (DTI and NODDI metrics), we fitted univariate linear mixed-effect models. These models included sex, age, and the number of ECT sessions received at the time of MR scanning as between-subject factors, and time (V1 vs. V2 vs. V3) as a within-subject factor, with participants modeled as a random factor. ANOVA was performed to assess main effects and interactions, and statistical significance was tested using Wald χ² tests.

We also analyzed the relationships between hippocampal changes and clinical changes from baseline (V1) to subsequent time points (V2 and V3). Changes were controlled for baseline values and defined as (V3 – V1) / V1 and (V2 – V1) / V1 to account for baseline levels. The relationships between the ratios of hippocampal and clinical changes (continuous variables) or clinical outcomes (categorical variables) were analyzed using linear models. In these models, hippocampal change was treated as the dependent variable and clinical data as the independent variables. As noted previously by others (34), patients unresponsive to ECT tend to undergo more ECT sessions than early responders. Considering the dose-response effect of ECT on hippocampal volume, the number of ECT sessions was included as a confounding variable, along with age and sex. These analyses were conducted for both the hippocampus and dentate gyrus.

Group comparisons between depressed patients and age- and sex-matched healthy controls were performed at each time point using t-tests for all metrics, except for GFA and volumes, where Mann-Whitney tests were applied due to the non-normal distribution of the data.

To account for multiple comparisons, we applied the False Discovery Rate (FDR) method; for clarity, only the adjusted p-values are presented. A two-tailed p-value < 0.05 was considered significant.
All statistical analyses were performed using R version 3.4 (http://www.r-project.org/), with the 'car' and 'lmerTest' packages for the mixed-effect linear models.

# 3. RESULTS

## 3.1. Patients

Details of the demographic and clinical characteristics of the patients and the healthy controls are provided in **Table 1**. The mean interval between V1 and V3 time-points was 70 ± 25 days. Note that the second MRI, scheduled between the 5th and 6th ECT sessions, was delayed for 8 of 35 participants due to logistical or patient constraints: after the 4th session (n=6), the 6th session (n=1), or the 7th session (n=1).

## 3.2. Macroscopic changes

### 3.2.1. Whole hippocampus volume

A total of 107 MR examinations were analyzed for macroscopic changes in depressed patients, along with 24 MR examinations from healthy controls. The mixed-effect analyses revealed a significant ECT-related bilateral change in the whole hippocampus volume between V1 and V2 (Left: +3.9%, p < 0.001; Right: +4.5%, p < 0.001), and between V1 and V3 for the right hippocampus (+5.6%, p < 0.001). The volumes in depressed patients did not show a statistically significant difference compared to controls at any time point. See **Figure 2**. Please refer to the supplementary information section for mean values and standard deviations at each time point, as well as mean changes and standard deviations at V2 and V3 relative to baseline.

### 3.2.2. Dentate Gyrus and Hippocampus Tail volumes

The volume of the dentate gyrus increased in bilaterally between V1 and V2 (left: +5.7%, p=0.004; right: +5.3%, p<0.001) and between V1 and V3 (left: +6%, p<0.001; right: +7.5%, p<0.001). Additionally, the volume of the hippocampal tail increased on the left hippocampus between V1 and V2 (+4.9%, p=0.015) and on the right hippocampus between V1 and V3 (+1.8%, p=0.021). There was no significant difference in DG and hippocampal tail volumes between controls and patients at any time point. Please refer to the supplementary information section for mean values and standard deviations at each time point, as well as mean changes and standard deviations at V2 and V3 relative to baseline.
## 3.3. Microstructure changes

### 3.3.1. Whole hippocampus

Diffusion-weighted images from 4 MR examinations displayed major artifacts, making them unusable, leaving 103 out of the 107 scans available for analysis in the patient group and all 24 scans in the control group. Outlier removal resulted in the exclusion of 18 observations out of 2032 observations (i.e., 2 hemispheres x 8 metrics x 127 MR examinations).

Group comparisons (depressed patients versus controls) and longitudinal analyses of DTI and QBI-derived metrics (Figure 3) revealed consistently lower GFA values in depressed patients at all time points bilaterally compared to healthy controls, and a significant decrease between V1 and V2 following ECT in both the left and right hippocampi. In contrast, FA values were significantly higher in depressed patients at all time points and showed a trend toward normalization following ECT, with a significant decrease between V1 and V2 in the left hippocampus, although they did not reach the levels observed in healthy controls. Regarding MD, no significant group differences were measured at baseline. After ECT, there were no significant longitudinal changes in the right hemisphere; however, MD values at V2 and V3 were significantly lower in depressed patients compared to healthy controls. In the left hippocampus, MD values decreased significantly between V1 and V3 without deviating from normal control values. For AD, depressed patients had significantly higher values in the left hemisphere at V1 and V2 compared to healthy controls. Longitudinally, AD values decreased significantly in the left and right hippocampi between V1 and V3, tending to normalize, with no significant difference remaining at V3 between patients and controls. A significant decrease was also observed in the right hippocampus between V1 and V2. RD did not show significant group differences at baseline. After ECT, no significant longitudinal changes were detected; however, RD values were significantly lower in the right hippocampus at V2 and V3, and in the left hippocampus at V3 in depressed patients compared to healthy controls.

Regarding the NODDI metrics (Figure 4), NDI values were significantly lower in the left hemisphere at baseline in depressed patients compared to healthy controls. Longitudinal analysis indicated that NDI values increased in both hippocampi between V1 and V3, and in the right hippocampus between V1 and V2. For ODI, depressed patients demonstrated lower values compared to healthy controls bilaterally at all time points. Furthermore, ODI values increased longitudinally in both hippocampi between V1 and V2, and again between V1 and V3 during ECT treatment, though they did not reach control levels. Finally, no significant group differences were observed for Fiso at baseline. Longitudinal analysis, however, revealed a steady decrease in Fiso values in both the left and right hippocampi between V1
and V2 and between V1 and V3 during ECT treatment, with depressed patients having lower Fiso values in the left hippocampus at V3 than controls.

Average values for the DTI, QBI and NODDI parameters, along with mean changes and standard deviations at V2 and V3 relative to baseline and a detailed breakdown of the statistical analysis, are provided in the supplementary information section.

### 3.3.2. Dentate Gyrus and Hippocampus Tail

The analysis of hippocampal subregions revealed significant decreases in generalized FA in the right dentate gyrus between V1 and V2 (p = 0.016), with a similar decrease in the left dentate gyrus between V1 and V2 (p = 0.019). FA also showed significant decreases in both hemispheres, with a decrease in the right hemisphere between V1 and V2 (p = 0.003) and in the left hemisphere between V1 and V2 (p = 0.006). Regarding AD, a significant decrease was noted in the right hemisphere between V1 and V2 (p = 0.043), while in the left hemisphere, the decrease occurred between V1 and V3 (p = 0.002). For Fiso and ODI, significant changes were observed in both hemispheres between V1 and V2, and between V1 and V3. In the right hemisphere, Fiso decreased between V1 and V2 (p = 0.008) and between V1 and V3 (p = 0.023), while in the left hemisphere, it decreased between V1 and V2 (p = 0.001) and between V1 and V3 (p = 0.005). In the left hemisphere, ODI increased significantly between V1 and V2 (p = 0.024) and between V1 and V3 (p = 0.019). In the right hemisphere, ODI also increased significantly between V1 and V2 (p = 0.001) and between V1 and V3 (p = 0.009).

Some microstructural changes in the hippocampal tail were observed between V1 and V2, including decreased FA and GFA on the left side (p = 0.022 and 0.021, respectively) and increased ODI on the right side (p = 0.021). These changes were limited to these metrics.

For group comparisons, FA, GFA, and ODI were significantly different between healthy controls and patients at all time points in both the dentate gyrus and tail, similar to observations for the entire hippocampus (p < 0.001).

### 3.4. Clinical correlations

The analysis of the relationships between hippocampal changes and clinical improvements revealed a significant volume increase in the right hippocampus between V1 and V2 for ECT-responders and ECT-remitters (+4.8% in responders versus -0.7% in non-responders, p < 0.001; +4.9% in remitters versus +1.1% in non-remitters, p = 0.017) (see **Figure 5**). None of the other correlations between clinical and imaging changes (volume, QBI, DTI and NODDI metrics, in whole hippocampus and DG) were significant (not detailed).
Discussion

In this longitudinal study, we present the first investigation of hippocampal microstructural
changes following ECT in severely depressed patients and healthy controls using neurite
imaging. Our aim was to test the hypothesis and identify in vivo biomarkers of ECT-induced
hippocampal neuroplasticity. Alongside a volumetric increase, we observed microstructural
changes, including increased neurite density and dispersion, coupled with a reduction in
isotropic fraction (i.e., free water), suggesting enhanced synaptic branching in the
hippocampus. Notably, changes in FA, NDI, and ODI tended toward normalization, though
FA and ODI did not fully reach normal values.

Our findings of hippocampus volume increase after ECT are in line with recent meta-
analyses (10,35–37) and mega-analyses from the Global ECT-MRI Research Collaboration
(GEMRIC) (11,38) reporting a significant increase in hippocampus volume within one or two
weeks after ECT.

After ECT, our DTI analyses revealed a decrease in left FA, left MD, and bilateral AD, consistent with previous DTI studies reporting decreased MD in the bilateral hippocampi following ECT (39–41) and a transient FA decrease post-ECT (42). Notably, we did not observe an association between decreases in hippocampal MD and FA and a favorable response to ECT, as reported in earlier studies (39–41).

This study is the first to use the NODDI model to analyze ECT-induced hippocampal
changes at the sub-voxel level. Our specific aim was to determine whether the changes
observed using DTI in our study and in previous studies could be attributed to hippocampal
neuroplasticity rather than cellular swelling, such as the intracellular edema seen in postictal
states or acute ischemic stroke (43). We observed an increase in NDI and ODI and a
decrease in Fiso in both hippocampi from baseline to post-ECT, with changes in Fiso and
ODI remaining steady across the three time points. In cases of cellular swelling, an NDI
increase and Fiso decrease would also be expected due to the shift of water from the
extracellular to the intracellular space; however, no change in dendritic orientation would be
anticipated. The observed ODI increase in both hippocampi makes cellular swelling an
unlikely explanation, instead supporting enhanced dendritic complexity and synaptogenesis,
similar to that seen in neonatal gray matter (44), thus aligning with our initial hypothesis.

By including a control group, this study provides insights to better understand the
hippocampal diffusion changes observed in patients post-ECT. Specifically, GFA, FA, and
ODI were significantly different between controls and patients at all time points. Hippocampal
GFA was lower in depressed patients compared to healthy subjects, while FA was higher in
the depressed patients. Indeed, in grey matter, GFA tends to be high because diffusion is
complex but not necessarily anisotropic, reflecting the intricate microstructure of dendrites
and synapses (45). The pattern observed in depressed patients may indicate reduced microstructural complexity (lower GFA) due to synaptic or dendritic loss, which is further supported by the significantly lower ODI, a marker of microstructural complexity, in depressed patients. The higher FA could suggest a shift toward more directional diffusion, possibly reflecting altered cellular organization or compensatory structural changes associated with depression. Notably, while FA, NDI, and ODI showed trends toward normalization after ECT, FA and ODI did not completely return to normal values.

Through subfield analysis, we aimed to further elucidate the nature of hippocampal neuroplastic changes after ECT by comparing the dentate gyrus, where neurogenesis occurs, to the hippocampal tail. The dentate gyrus volumes for both the control and patient groups, as defined in our study, fell within the range reported in two studies that employed similar segmentation techniques (46,47), supporting the reliability of our method. Dentate gyrus volumes increased steadily across all time points, remaining within normal ranges, in contrast to the hippocampal tail volume, which showed a more modest increase. We observed a significant increase in NDI and a decrease in Fiso specifically in the dentate gyrus, but not in the hippocampal tail. Since neurogenesis occurs in the dentate gyrus, these findings support the hypothesis that ECT enhances hippocampal neurogenesis (13). The unilateral decrease in FA and increase in ODI measured in the hippocampal tail were less pronounced and could potentially reflect synaptogenesis across all hippocampal subfields rather than true neurogenesis (48).

The time-course of these microstructural changes after ECT raises the question: are we truly observing active new cell formation? In our study, changes in right NDI and bilateral Fiso and ODI were observed at V2 (two weeks after the start of ECT). Spontaneous hippocampal neurogenesis takes place over a period of several months, during which neural stem cells mature into progenitor cells, immature granule cells that migrate in the dentate granule cell layer, to become mature and finally establish functional connections (49). The recent findings of a post-mortem study (13) contribute to the discussion. While they observed an increase in doublecortin, a dendritic growth marker (50) in subjects treated with ECT, they found no differences in Ki-67, a marker of cell proliferation. If the short duration of Ki-67 expression, compared to the delay between ECT and the time of death, could be an explanation, the authors also proposed that it may indicate that neuroplasticity, rather than active new cell formation, increased after ECT in their sample. The literature presents two potential theories that could explain the pathophysiology of such hippocampal neuroplasticity. Firstly, ECT might enable existing neurons in the dentate gyrus to undergo 'dematuration', i.e. mature cells could become activated and make new dendritic connections (51). An alternative hypothesis could be that ECT promotes the dendritic maturation of immature granule cells stored in the dentate granule cell layer, bypassing the previous
maturation steps and thereby accelerating the spontaneous neurogenesis process (52). Interestingly, animal studies (7,53–55) have shown that ECS induces several neuroplastic changes, including enhanced neurogenesis and neurotrophic signaling, increased brain-derived neurotrophic factor (BDNF) levels, both of which are crucial for the antidepressant effects. ECS also promotes synaptic plasticity by increasing synaptic density, modifying synapse morphology, and enhancing dendritic branching, while also increasing microglial activation, particularly in the cornu ammonis 1 (CA1) region. Moreover, it promotes angiogenesis, supporting neuroplasticity by increasing BDNF release from endothelial cells, and restores glutamatergic neurotransmission, contributing to long-term synaptic changes. Finally, a recent MRI and histological study identified an increase in excitatory synaptic density as the major contributor to the hippocampal volume increase observed following electroconvulsive stimulation (56).

Our imaging study reinforces the hypothesis that ECT induces several hippocampal neuroplastic changes. To deepen our understanding of these observed changes, additional imaging analyses could be conducted to assess structural (57) and functional (58) connectivity in the hippocampus during ECT. Such analyses could help determine whether the observed regional modifications are associated with connectivity changes, providing additional support for the hypothesis of hippocampal neuroplasticity.

Regarding the correlation between these changes and clinical improvement, we observed a significantly larger volume increase in the right hippocampus after 5 ECT sessions among ECT-responders compared to ECT-non responders, a finding not previously reported in the literature (38,59). These findings may enable the early prediction of treatment efficacy and expedite discontinuation of ECT in non-responsive patients. These findings were possibly facilitated by the fixed V2 time-point, scheduled after 5 ECT sessions, enabling a more reliable comparison between groups. However, unlike a study using a 7T MR scanner (60), volumetric variations of the dentate gyrus were not correlated with the clinical response in our study, possibly attributable to the thinness of this region and the lower spatial resolution of our 3T MR acquisitions. Limited spatial resolution of diffusion MRI might also account for the lack of correlation between microstructural metrics and clinical response, studied here for the first time in human depression. While achieving high-resolution brain diffusion MRI has historically been challenging due to prolonged acquisition times to avoid signal loss, recent advancements in diffusion MRI suggest that achieving submillimeter resolution may become clinically feasible in the near future. This potential advancement could overcome this limitation in future studies.

Our study has several limitations. First, only 27 out of 43 patients completed all three MRI evaluations, while 7 patients only had a baseline MRI. To address this issue, we used linear mixed-effects models, which are well adapted to handle missing data in longitudinal designs.
However, despite small sample and attrition, we were able to demonstrate significant
changes in hippocampal microstructure, supporting the robustness of the observed effects.
Second, concomitant pharmacological treatments were managed by the treating
psychiatrists, leading to inter- and intra-individual variability. This variability contributed to the
heterogeneity of the population, which we were unable to account for in the statistical
models. Third, time was modeled as a categorical variable with three time points; however,
an alternative approach could have been to utilize trajectory analysis techniques to account
for the variability in the intervals between baseline and the V3 time point (61,62). Additionally,
the spatial resolution of diffusion is limited (2 mm isotropic), so we combined several
subfields into a single label, 'Dentate Gyrus,' to ensure a region of adequate size for analysis
and to minimize partial volume effects on diffusion metrics. This approach may have included
adjacent changes, limiting definitive conclusions about the neurogenesis hypothesis within
the anatomical dentate gyrus. Finally, due to the study design, which aim to elucidate the
pathophysiology of the hippocampal changes observed in the weeks following ECT, our data
only capture changes occurring during the first three months after initiating ECT. Several
studies report a normalization of the hippocampus volume between 6 to 12 months after ECT
(39,63–65), suggesting a transient effect of ECT. Further studies are needed to investigate
the long-term persistence of the neuroplastic changes observed following ECT.

Conclusion

Using neurite imaging, this study identified abnormal microstructural deviations in the
hippocampus of depressed patients, with post-ECT changes indicating hippocampal
neuroplasticity. These findings offer potential insights into the mechanisms underlying the
therapeutic effects of ECT in depressed patients. Additionally, an increase in the volume of
the right hippocampus two weeks after the start of ECT correlated with a positive treatment
response, highlighting its potential as a promising prognostic biomarker for ECT efficacy.
## Acknowledgements

We thank the patients for their trust and commitment in making this clinical trial possible. We thank all the nursing and administrative staff who devoted time and effort to the success of this research project, in particular Bénédicte Launay-Saucet, the clinical trial nurse coordinator.

The clinical trial was sponsored by the Groupe Hospitalier Universitaire Paris Psychiatrie et Neurosciences and its Delegation for Clinical Research and Innovation; we thank Viviane Awassi, Kenza Sabi, Khaoussou Sylla and Marin Chapelle.

This work was supported and funded by the Groupe Hospitalier Universitaire Paris Psychiatrie et Neurosciences ('Appel à projets 2017') and the Fondation de France ('AO 2017 sur les maladies psychiatriques').

We thank Denis David and Jean-Philippe Guilloux for fruitful discussions. Finally, we thank the reviewers for their significant contributions, which helped to improve this work.

## Contributions

DA and MP contributed to the conception and design of the study. DA, MP, CD, SC, LM, PG, ML, CO contributed to clinical and MRI data acquisition. IU and CP provided methodological development. ALB, IU, CD, CP, AC, FR conducted MRI data preprocessing and statistical data analysis. ALB, DA, MP, AC, CO conducted the interpretation of the analyses with additional contributions from MM and AH. DA, MP, ML, CD, SC provided administrative, technical, and material support. ALB drafted the manuscript. CO, AC and MP supervised the study and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.

## Ethics declaration

All methods were performed in accordance with the relevant guidelines and regulations.

### Competing interests

The authors declare no competing interests.

## Data Availability Statement

The dataset analyzed during the current study are not publicly available but are available from the corresponding author upon motivated request.
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## Figure legends

**Figure 1:** Image processing steps of the 3D T1-w and diffusion-weighted data. NODDI = neurite orientation and density imaging, DTI = diffusion tensor imaging, QBI = Q-ball imaging, NDI = neurite density index, Fiso = isotropic fraction, ODI = orientation dispersion index, FA = fractional anisotropy, MD = mean diffusivity, AD = axial diffusivity, RD = radial diffusivity, GFA = generalized fractional anisotropy.

**Figure 2:** Volumes of the Whole Hippocampus, Dentate Gyrus, and Hippocampal Tail over time in depressed patients across three time points and in healthy controls.

Values are expressed in mm³. Time points: V1 = baseline, V2 = 2 weeks into ECT, V3 = after completing ECT. HC = Healthy Controls. Brackets indicate significant changes after correction (*: p < 0.05; **: p < 0.01; *: p < 0.001).

**Figure 3:** Changes in Whole Hippocampus DTI Metrics over time in depressed patients across three time points and in healthy controls. X axis shows the three time points of evaluation (V1 = baseline, V2 = at 2 weeks into ECT, V3 = after completing ECT) and the control group (HC = Healthy Controls) and Y axis shows the DTI metrics. Yellow brackets indicate group comparisons, and black brackets indicate longitudinal analysis using ANOVA on linear mixed effects models (lmer). Brackets show significant changes after correction (*: p < 0.05; **: p < 0.01; *: p < 0.001). On the left side of the figure, color-coded parametric maps registered with 3D T1-w images (coronal view). Note that CSF was excluded from the brain masks. GFA = generalized fractional anisotropy, FA = fractional anisotropy, MD = mean diffusivity, AD = axial diffusivity, RD = radial diffusivity. Values are expressed in m²/s for MD, AD and RD.

**Figure 4:** Changes in Whole Hippocampus NODDI metrics over time in depressed patients across three time points and in healthy controls. X axis shows the three time points of evaluation and the control group (V1 = baseline, V2 = at 2 weeks into ECT, V3 = after completing ECT, HC = Healthy Controls) and Y axis the NODDI metrics. Yellow brackets indicate group comparisons, and black brackets indicate longitudinal analysis using ANOVA on linear mixed effects models (lmer). Brackets show significant changes after correction (*: p < 0.05; **: p < 0.01; *: p < 0.001). On the left side of the figure, color-coded parametric maps registered with T1-w images (coronal view). Note that CSF was excluded from the brain masks. NDI = neurite density index, Fiso = isotropic fraction, ODI = orientation dispersion index.
**Figure 5:** Change in whole hippocampal volume at V2 (2 weeks into ECT) in ECT responders versus non-responders, defined at V3 (after completing ECT).
<table><caption>Table</caption><thead><tr><th></th><th>Baseline (V1)</th><th>At 2 weeks into ECT (V2)</th><th>After completing ECT (V3)</th><th>Healthy controls</th><th>p-value <sup>b</sup></th></tr></thead><tbody><tr><td>Number of patients (n)</td><td>43</td><td>35</td><td>30</td><td>24</td><td></td></tr><tr><td>Sex (M / F)</td><td>17 / 26</td><td>14 / 21</td><td>12 / 18</td><td>10 / 14</td><td>1</td></tr><tr><td>Age</td><td>54 ± 18</td><td>53 ± 19</td><td>52 ± 19</td><td>49 ± 21</td><td>0.38</td></tr><tr><td>Unipolar / Bipolar disorder</td><td>31 / 12</td><td>27 / 8</td><td>22 / 8</td><td>NA</td><td></td></tr><tr><td>Current episode duration (months)</td><td>23.0 ± 19.1</td><td>22.7 ± 18.1</td><td>21.5 ± 17.4</td><td></td><td></td></tr><tr><td>MADRS</td><td>35.7 ± 8.1</td><td>20.4 ± 11.2</td><td>11.2 ± 8.7</td><td>NA</td><td></td></tr><tr><td>HDRS-21</td><td>28.0 ± 7.5</td><td>15.1 ± 8.2</td><td>9.5 ± 6.5</td><td>NA</td><td></td></tr><tr><td>Response rate (n, %)</td><td>NA</td><td>12 (34%)</td><td>23 (77%)</td><td>NA</td><td></td></tr><tr><td>Remission rate (n, %)</td><td>NA</td><td>9 (26%)</td><td>20 (67%)</td><td>NA</td><td></td></tr><tr><td>Number of ECT sessions (mean ± SD, [range])</td><td>NA</td><td>4.9 ± 0.6 [4 - 7]</td><td>16.1 ± 6.5 [6 - 27]</td><td>NA</td><td></td></tr><tr><td>ECT dose (in mC; mean ± SD, [range]) <sup>a</sup></td><td>NA</td><td>286.5 ± 122.4 [78.2 - 541.5]</td><td>541.5 ± 234.5 [215.0 - 962.4]</td><td>NA</td><td></td></tr><tr><td>EEG seizure duration (in s; mean ± SD, [range]) <sup>a</sup></td><td>NA</td><td>56.2 ± 21.3 [22.8 - 123.5]</td><td>50.1 ± 14.1 [23.1 - 90.3]</td><td>NA</td><td></td></tr></tbody></table>

**Table 1:** Characteristics of the patient sample at three time points and the control group. Values are expressed as mean ± standard deviation unless otherwise stated. a. ECT dose and EEG seizure length were computed for each participant as the mean at V2 and V3. These data were not available for 39 ECT sessions out of a total of 513 ECT sessions. b. Age comparison was performed using the Wilcoxon Rank Sum Test, and gender ratio comparison was performed using Pearson's Chi-Square Test. ECT: Electroconvulsive Therapy; HDRS-21: 21-item Hamilton Depression Rating Scale; MADRS: Montgomery-Asberg Depression Rating Scale; EEG: electroencephalogram; M: male; F: female.
3D T1-w

<figure><img src="image_3.png" /></figure>

Freesurfer parcellation:

- Whole Hippocampus

- Dentate Gyrus

- Hippocampal Tail

DWI raw data

<figure><img src="image_4.png" /></figure>

Susceptibility
correction

Outlier
correction

Eddy currents &
motion correction

<figure><img src="image_5.png" /></figure>

NODDI

NDI, Fiso,
ODI

DTI

FA, MD,
AD, RD

QBI

GFA
Left
hippocampus

Whole Hippocampus
Volume in mm³

<figure><img src="image_6.png" /></figure>

Right
hippocampus

<figure><img src="image_7.png" /></figure>

Dentate Gyrus
Volume in mm³

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

Hippocampal Tail
Volume in mm³

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>
GFA

<figure><img src="image_12.png" /></figure>

Mean GFA

Left hippocampus

<figure><img src="image_13.png" /></figure>

Right hippocampus

<figure><img src="image_14.png" /></figure>

FA

<figure><img src="image_15.png" /></figure>

Mean FA

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

MD

<figure><img src="image_18.png" /></figure>

Mean MD (m²/s)

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

AD

<figure><img src="image_21.png" /></figure>

Mean AD (m²/s)

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

RD

<figure><img src="image_24.png" /></figure>

Mean RD (m²/s)

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>
NDI

<figure><img src="image_27.png" /></figure>

Mean NDI

Left hippocampus

<figure><img src="image_28.png" /></figure>

Right hippocampus

<figure><img src="image_29.png" /></figure>

Fiso

<figure><img src="image_30.png" /></figure>

Mean Fiso

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

ODI

<figure><img src="image_33.png" /></figure>

Mean ODI

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>
Change in Whole
Hippocampal Volume:
(V2 - V1) / V1

Left

<figure><img src="image_36.png" /></figure>

Right

<figure><img src="image_37.png" /></figure>