Functional disconnectivity of the hippocampal network and neural correlates of memory impairment in treatment-resistant depression

Journal of Affective Disorders 253 (2019) 248–256

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Research paper

Functional disconnectivity of the hippocampal network and neural correlates of memory impairment in treatment-resistant depression

T

Ruiyang Gea, Ivan Torresb, Jennifer J. Browna, Elizabeth Gregorya, Emily McLellana, Jonathan H. Downarc,d, Daniel M. Blumbergerc,e, Zafiris J. Daskalakisc,e, Raymond W. Lamb, ⁎ Fidel Vila-Rodrigueza, a Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC V6T 2A1, Canada b Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC V6T 2A1, Canada c Department of Psychiatry, University of Toronto, Toronto, ON, Canada d MRI-Guided rTMS Clinic, Toronto Western Hospital, University Health Network, Toronto, ON, Canada e Temerty Centre for Therapeutic Brain Intervention, Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Depression Hippocampus Resting state FMRI Subregion Functional connectivity RAVLT

Background: Major depressive disorder (MDD) is a disabling neuropsychiatric condition associated with cognitive impairment. Neuroimaging studies have consistently linked memory deficits with hippocampal atrophy in MDD patients. However, there has been a paucity of research examining how the hippocampus functionally contributes to memory impairments in MDD. The present study examined whether hippocampal networks distinguish treatment-resistant depression (TRD) patients from healthy controls (HCs), and whether these networks underlie declarative memory deficits in TRD. We hypothesized that functional connectivity (FC) of the posterior hippocampus would correlate preferentially with memory in patients, whereas FC pattern of the anterior and intermediate hippocampus would correlate with emotion-mediated regions and show a significant correlation with memory. Methods: Resting-state functional magnetic resonance imaging (fMRI) scans were acquired in 56 patients and 42 age- and sex-matched HCs. We parcellated the hippocampus into three subregions based on a sparse representation-based method recently developed by our group. FC networks of hippocampal subregions were compared between patients and HCs and correlated with clinical measures and cognitive performance. Results: Decreased connectivity of the right intermediate hippocampus (RIH) with the limbic regions was a distinguishing feature between TRD and HCs. These functional abnormalities were present in the absence of structural volumetric differences. Furthermore, lower right amygdalar connectivity to the RIH related to a longer current depressive episode. Declarative memory deficits in TRD were significantly associated with left posterior and right intermediate hippocampal FC patterns. Limitations: Our patient samples were treatment-resistant, the conclusions from this study cannot be generalized to all MDD patients directly. Task-based imaging studies are needed to demonstrate hippocampal engagement in the memory deficits of patients. Finally, our findings are strongly in need of replication in independent validation samples. Conclusions: These findings demonstrate a transitional property of the intermediate hippocampal subregion between its anterior and posterior counterparts in TRD patients, and provide new insights into the neural network-level dysfunction of the hippocampus in TRD.

1. Introduction Major depressive disorder (MDD) is a debilitating neuropsychiatric disorder recently declared as the leading cause of disability worldwide

⁎

by the World Health Organization (World Health Organization, 2017). The core psychopathological and diagnostic feature is depressed mood and its associated constellation of related symptoms (e.g. anhedonia or suicidal ideation). While concentration difficulties are a diagnostic

Corresponding author. E-mail address: fi[email protected] (F. Vila-Rodriguez).

https://doi.org/10.1016/j.jad.2019.04.096 Received 17 December 2018; Received in revised form 29 January 2019; Accepted 27 April 2019 Available online 29 April 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

Journal of Affective Disorders 253 (2019) 248–256

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i.e., the intermediate subregion may be involved in memory and cognitive processes, as well as in emotional functions. In the present study, we aimed to investigate whether there are differences in the hippocampal networks between patients with TRD and healthy controls (HCs), and whether these networks underlie the declarative memory deficits in TRD. Specifically, the following hypotheses were tested: (1) Abnormal FC patterns between the anterior and intermediate hippocampus and emotion-mediated regions (e.g., anterior hippocampus, amygdala and orbitofrontal regions) will be present; (2) abnormal FC patterns of the intermediate hippocampus will be associated with both clinical variables as well as with declarative memory deficits; (3) abnormal FC patterns of the posterior hippocampus will be associated to declarative memory deficits only.

criterion, broader cognitive impairment is a frequent and disabling feature in depression (Lee et al., 2012; Porter et al., 2003; Reppermund et al., 2009), and it appears to be independent of depressed mood (Rock et al., 2014). Specifically, impairments in cognitive domains such as learning and memory, attention, and executive functioning are often present in MDD and have been shown to contribute to poor functional outcomes (Lee et al., 2012; Porter et al., 2003). Dysfunction in learning and memory is of particular concern as it has been reported to occur even in the absence of other neurocognitive deficits (Austin et al., 2001; Sweeney et al., 2000), tends to persist despite remission of mood and affective symptoms (Maeshima et al., 2016; Neu et al., 2005; Reppermund et al., 2009), and may be associated to the increase risk of dementia later in life (Potter et al., 2013). Furthermore, there are some data pointing to premorbid memory impairment as a predictive feature for subsequently developing MDD (Mannie et al., 2009). Treatmentresistant depression (TRD) represents MDD patients who do not respond to adequate trials of antidepressant medications (Sackeim, 2001). The investigation of cognitive impairments in TRD has received little attention, and a better understanding of its underlying neurobiology in TRD may provide important clues about the mechanism of action of therapeutic interventions that modify the hippocampus (e.g. electroconvulsive therapy). Neuroimaging studies investigating the neural substrates of learning and memory deficits in MDD have focused mostly on the structural abnormalities of the hippocampus. Specifically, research employing neuroimaging techniques has shown that MDD patients’ memory dysfunction is associated with hippocampal atrophy (Hickie et al., 2005; Jayaweera et al., 2016). However, there has been a relative paucity of research examining how hippocampus functionally contributes to learning and memory impairments in MDD. The human hippocampal formation is a fundamental part of the limbic system and is considered to play an important role in MDD due to its connection with other limbic and paralimbic structures (Catani et al., 2013; Lewis and Shute, 1967; Morgane et al., 2005). The hippocampus is also involved in the default mode network (DMN), which is dysfunctional in MDD (Whitfield-Gabrieli and Ford, 2012). Several functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies have demonstrated dysfunction and treatment modulation of the hippocampus in MDD (Goldapple et al., 2004; Gong et al., 2018; Kennedy et al., 2001; Milne et al., 2012; Zhang et al., 2011). Although most of these studies have regarded the hippocampus as a singular and unitary region, the hippocampus is a functionally heterogeneous region along its anterior-posterior axis (Strange et al., 2014). In rodent models, gene expression patterns define at least three longitudinal subregions: ventral, middle, and dorsal subregions, which are homologous to the human anterior, intermediate, and posterior subregions (Dong et al., 2009; Strange et al., 2014). The ventral/anterior subregion is involved in processing emotional responses, and the dorsal/posterior subregion mediates memory and cognitive functions, while the specific function of the middle/intermediate subregion is less understood (Fanselow and Dong, 2010; Strange et al., 2014). The investigation of these fine-grained subregions may improve our understanding about the role of the hippocampus in the pathophysiology of MDD. Specifically, prominent deficits in memory, particularly in hippocampal-mediated declarative memory, support the disruption of hippocampal function in MDD (Jayaweera et al., 2016; Lee et al., 2012). At a system level, therefore, a correlation between memory performance and hippocampal functional connectivity (FC) patterns would be predicted in MDD patients, particularly in the posterior hippocampus (Fanselow and Dong, 2010; Strange et al., 2014). However, evidence points to a model whereby the patterns of connectivity between the hippocampus and cortical/subcortical regions along the longitudinal axis transit in a gradual fashion rather than being organized in a discrete way (Amaral and Witter, 1989; Strange et al., 2014). Therefore, it is plausible that the intermediate subregion exhibits transitional properties between the anterior and posterior subregions,

2. Materials and methods 2.1. Participants Sixty-two TRD patients and 42 HCs were recruited for clinical trials using repetitive transcranial magnetic stimulation (rTMS) (Blumberger et al., 2018; Gennatas et al., 2017). All MRI scans used in this study were baseline scans acquired before any rTMS administration; however, the inclusion and exclusion criteria reflect the requirements of the rTMS trials. The inclusion and exclusion criteria of patients and HCs are outlined in supplementary material Table S1 and Table S2. Specifically, patients were recruited if they had a moderate to severe depressive episode, had not responded to at least one adequate trial of antidepressant medication (but less than four trials) or were unable to tolerate at least two separate trials of antidepressants of inadequate dose and duration. One patient was absent from the resting-state fMRI scanning. Participants who had excessive head movement during the scan (head motion > 3 mm or 3° (Dai et al., 2014; Tamm et al., 2006; Yao et al., 2013), n = 4), or had severe artifacts in the functional data induced by orthodontic hardware (n = 1) were excluded. In order to rule out the potential confounding effect of head motion on restingstate connectivity, the mean frame-wise displacement (FD) (Power et al., 2012) value was used as a nuisance regressor in the following statistical analyses. The final sample included in the analyses consisted of 56 patients and 42 HCs (Table 1). Note that all of the participants were native English speakers, except three in the control group. The protocol was approved by the Clinical Research Ethics Board of the University of British Columbia, and the Vancouver Coastal Health Authority, and written informed consent was obtained from all participants. The studies were registered at clinicaltrials.gov (NCT01887782 and NCT02800226). Table 1 Sample demographics, clinical and cognitive characteristics for the two groups.

Gender (M/F) Age (SD) Educational level (SD) Handedness (L/R/A) AED [mg (SD)] HDRS (SD) NAART (SD) RAVLT1-5 (SD) RAVLT-7 (SD)

Patients (n = 56)

Healthy volunteers (n = 42)

p

24/32 43.02 (11.87) 15.04 (2.31) 8/47/1 19.48 (18.48) 22.09 (3.92) 114.61 (5.49) 48.59 (9.82) 10.07 (3.18)

17/25 40.36 (13.20) 16.00 (2.14) 3/37/2 – – 111.27 (9.66) 54.40 (7.29) 11.21 (2.20)

0.81 0.30 0.04 –

a b b

– 0.05 b 0.002 b 0.04b

Abbreviations: A, ambidextrous; AED, equivalent escitalopram dose; F, female; HAMD, 17-item Hamilton Depression Rating Scale; L, left; M, male; NAART: National Adult Reading Test; R, right; RAVLT, Rey Auditory Verbal Learning Test; SD, standard deviation. a Chi-square test. b Two-sample t test for patients and healthy volunteer groups. 249

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2.2. Clinical and cognitive outcome measures

2.4. fMRI data analysis

Symptom severity was assessed with the 17-item Hamilton Depression Rating Scale (HDRS) (Hamilton, 1960; Williams, 1988) at baseline. Neuropsychological assessments were conducted by research personnel, who were trained and supervised by a registered clinical neuropsychologist (I.T.). In the present study, the hippocampal-mediated cognitive outcome measures included declarative learning and memory, assessed by the Rey Auditory Verbal Learning Test (RAVLT) immediate recall (summed score over five trials, RAVLT-1-5) and delayed recall (trial 7, RAVLT-7) (Strauss et al., 2006). The premorbid IQ was estimated using the North-American Adult Reading Test (NAART) (Blair and Spreen, 1989).

2.4.1. Functional parcellation of the hippocampus The bilateral hippocampi were extracted from the Harvard-Oxford subcortical structural atlas (Caviness et al., 1996; Kennedy et al., 1998; Smith et al., 2004) (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases) with a threshold of 25% minimum probability. The Harvard-Oxford atlas provides probabilities that each voxel falls into a particular anatomical region. Each voxel was assigned to the most likely region, as long as it had a likelihood of 25% or greater (Craddock et al., 2012; Salomons et al., 2016; Schiller et al., 2013; Smoski et al., 2013). We divided the hippocampus, along its longitudinal axis, into three sections (Chen and Etkin, 2013; Strange et al., 2014). Specifically, hippocampi were parcellated into three sub-divisions on each hemisphere, (i.e., left/right anterior hippocampus (LAH/RAH), left/right intermediate hippocampus (LIH/RIH), and left/right posterior hippocampus (LPH/RPH) using a sparse representation-based method (Ge et al., 2017). Specifically, we used a robust parcellation approach, which constructs a sparse similarity graph based on the sparse representation coefficients between hippocampal voxels and then uses a spectral clustering algorithm to identify distinct subregions (see supplementary material for details of this parcellation approach).

2.3. Imaging data acquisition and preprocessing Imaging was performed at the University of British Columbia's MRI Research Centre on a Philips Achieva 3.0T scanner. During the scanning, all participants were asked to keep still with their eyes open and to try not to think of anything systematically. A total of 300 vol of echo planar images were obtained with the following parameters: TR = 2000 ms, TE = 30 ms, flip angle (FA) = 90°, field of view (FOV) = 220 mm × 220 mm × 155 mm, acquisition matrix = 64 × 64, in-plane resolution = 2.75 mm × 2.75 mm and slice thickness = 5 mm with 1 mm slice gap. Twenty-six axial slices parallel to the AC-PC line were obtained in an interleaved bottom-up order to effectively cover the whole brain. High-resolution T1-weighted images were also acquired with the following parameters: 165 axial slices, TR = 8.1 ms, TE = 3.5 ms, FA = 8°, FOV = 256 mm × 256 mm × 165 mm, acquisition matrix = 256 × 250, in-plane resolution = 1 mm × 1 mm and slice thickness = 1 mm. Functional images were preprocessed using SPM12 software (http://www.fil.ion.ucl.ac.uk/spm) and DPABI (Yan et al., 2016). Briefly, the first ten functional volumes were discarded to allow for stabilization of the initial signal and adaptation of the participants to the circumstances. The remaining fMRI images were then slice-timing corrected and realigned. Head displacement across the resting scan did not differ significantly between the two groups (expressed as the mean FD values, p = 0.370). The individual T1-weighted structural image was co-registered to the mean functional image after motion correction using a linear transformation. The transformed structural images were then segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) by using a unified segmentation algorithm (Ashburner and Friston, 2005). The motion corrected (realigned) functional images were spatially normalized to the Montreal Neurological Institute (MNI) space using the normalization parameters estimated during unified segmentation. The functional data were then resampled to 3-mm isotropic voxels and smoothed with a 6-mm full width at half maximum (FWHM) Gaussian kernel. Linear trends and nuisance signals (Friston-24 head motion parameters, CSF, and WM signals with the SPM's a priori tissue probability maps) (Friston et al., 1996) were then removed, and temporal band-pass filtering (0.01–0.08 Hz) was performed. The residuals were used for the following analyses. Note that the global signal regression (GSR) step in fMRI preprocessing is controversial: the implementation of GSR could introduce spurious negative correlations in functional connectivity studies (Murphy et al., 2009; Murphy and Fox, 2017), and there is growing evidence that the global signal contains diagnostic information in clinical populations (Hahamy et al., 2014; Yang et al., 2014). Thus we opted for not using this step or its alternative (i.e., CompCor) (Behzadi et al., 2007; Muschelli et al., 2014), because while CompCor does not explicitly include GSR, the practical results of its application are quite similar (Ciric et al., 2017).

2.4.2. Comparison of functional organizations of hippocampal subregions between groups One aim of the present study was to assess the potential differences in the functional organizations of hippocampal subregions between TRD patients and HCs. In the present study, functional organization was measured as the parcel size (number of voxels) in each hippocampal subregion (Nebel et al., 2014; Yamada et al., 2016). Therefore, we examined between-group differences of the volumes of specific subregions by performing permutation tests (5000 times permutations using an in-house in-house MATLAB17a program which is available upon request), as previously described (Ge et al., 2017). Specifically, we randomly assigned participants to one of the two groups by permuting the labels of all the subjects and applied our parcellation method (Ge et al., 2017) to each of the permuted groups and repeated this procedure 5000 times. Parcel size differences between corresponding subregions from each permuted group were calculated and null distribution of between-group parcel size differences was generated. We deemed between-group parcel size difference statistically significant if it falls above the 95th percentile of the relevant empirical null distribution. 2.4.3. Functional connectivity of the hippocampal subregions Regions of interest (ROIs, i.e., LAH, LIH, LPH, RAH, RIH and RPH seeds) were constructed from group-level parcellation results, and the whole-brain correlation maps were produced by extracting the mean time series from each hippocampal subregion and computing the Pearson's correlation coefficient between that time series and time series from all other brain voxels. Correlation values were then converted into z-scores using a Fisher transformation. The functional connectivity analysis was confined to the SPM a priori probabilistic GM mask, with a minimum threshold of 0.25. For the group comparisons, two-sample t-tests were performed for each ROI-related correlation map, respectively. The significance level for each comparison was set to α< 0.05/6 = 0.008 (Bonferroni correction was applied here to correct for multiple comparisons of the 6 subregions), with a cluster level family-wise error (FWE) correction and height threshold of p< 0.005. To investigate the correlation between the clinical outcome, cognitive outcomes and the FC of the hippocampal subregions, regression analyses for the HDRS, length of current episode, length of lifetime episode, RAVLT1-5, RAVLT-7, and NAART scores were conducted. We first confined our regression analysis using small-volume correction to the regions that survived the between-group comparisons, we then analyzed the whole brain. The significance level for each regression 250

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map was set to α< 0.05/72 = 0.0007 (Bonferroni correction was applied to correct for multiple comparisons of the 6 subregions × 6 regressors × 2 [one small-volume correction, and one whole brain correction]) with cluster level FWE correction and height threshold p< 0.001. To summarize the extent to which the significant clusters correlated to the neurocognitive profiles, the averaged z-score values across voxels within the significant clusters obtained from the regression analyses were extracted for patients and HCs, respectively, and correlated with the neurocognitive outcomes. All aforementioned statistical tests were conducted with age, sex, educational level, handedness, mean FD values, and mean GM volume over the subregion as nuisance regressors. 2.5. Supplementary analyses Patients with MDD have previously been reported to have smaller hippocampus (Bremner et al., 2000; McKinnon et al., 2009) (but see (Vythilingam et al., 2004)), here we examined whether changes in gray matter volume within the hippocampus, as measured by voxel-based morphometry (VBM), may contribute to the functional connectivity alterations observed in our TRD patients. VBM (Ashburner and Friston, 2000) was performed using the VBM8 toolbox. The individual T1-weighted images from all subjects were first segmented into GM, WM, and CSF, using a unified segmentation routine. GM population templates were generated from the entire image dataset using the Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) technique (Kurth et al., 2015). After an initial affine registration of the GM DARTEL templates to the tissue probability maps in MNI space, non-linear warping of GM images was performed to the DARTEL GM template in MNI space. Images were then modulated and smoothed with a 6-mm FWHM Gaussian kernel. The volumes of interest corresponding to the modulated (Chen and Etkin, 2013; Gennatas et al., 2017) images of the bilateral hippocampal subregions were extracted by using the “spm_read_vols” function in SPM12 for the between-group comparisons by two-sample t-tests. Specifically, we extract the GMV (gray matter volume) values of voxels within each hippocampal subregion, and the average GMV value of these voxels was used for the comparisons between groups. As a supplementary analysis, we re-analyzed our data without any nuisance variables added to the statistical models to mitigate the risk that the present results were driven by these nuisance variables (Gennatas et al., 2017).

Fig. 1. (a) Illustration of the hippocampal subregions using sparse representation-based parcellation method. (b) Brain regions whose functional connectivity (FC) with the RIH showed significant difference between patients and healthy controls. LAH/RAH, left/right anterior hippocampus; LIH/RIH, left/ right intermediate hippocampus; LPH/RPH, left/right posterior hippocampus.

occupied 38% and 38% of the entire left and right hippocampus; and the left and right posterior subregions occupied 27% and 23% of the entire left and right hippocampus, respectively. The correspondence between the subregions and anatomical subfields of the hippocampus can be found in the supplement (Supplement Fig. S2). Supplement Fig. S3 summarizes the results of the permutation tests on the functional organization of the hippocampal subregions. The results revealed no significant differences in the organizational arrangement of each subregion between patients and HCs. In the supplementary material, we presented the FC patterns of the subregions (supplement Fig. S5). 3.3. Differences of the hippocampal FC patterns between patients and HCs The clusters show lower FC of the RIH seed in patients relative to HCs with the bilateral anterior hippocampus, the parahippocampus, the amygdala, and the orbitofrontal gyrus (Fig. 1b and Table 2). 3.4. Correlation between hippocampal FC with clinical and cognitive outcomes Within regions that exhibited significant between-group difference, small-volume correction revealed that lower right amygdalar connectivity to the RIH correlated with increased duration of current episode (p< 0.001, Fig. 2 and Table 2). No significant correlations were found between the hippocampal FC and other clinical (including the HDRS scores) and neurocognitive measurements within regions that exhibited significant between-group differences. The regression analysis in the whole brain level showed that better delayed recall (RAVLT-7) scores were significantly associated with lower FC patterns, using the LPH and RIH as the seeds (Table 2). For the FC pattern of LPH, the significant clusters located at the bilateral dorsal medial prefrontal cortex (DMPFC, primarily in the right hemisphere) (r= −0.44, p = 1.03 × 10−5), the right dorsolateral prefrontal cortex (DLPFC) (r= −0.44, p = 1.03 × 10−5) and the right posterior parietal cortex (rPPC) (r= −0.43, p = 1.98 × 10−5; Fig. 3a) were significantly negatively-correlated with RAVLT-7 scores. No FC patterns of the other subregions showed significant association with the delayed recall or immediate recall scores. However, if we lowered the multiple comparisons correction threshold to cluster level FWE correction p < 0.05 and height threshold p < 0.001, uncorrected for the 72 multiple comparisons, the RIH FC pattern showed negative correlation with the delayed recall scores. Specifically, the clusters located at the bilateral DMPFC (primarily in the right hemisphere, r= −0.43, p = 1.74 × 10−5) and right DLPFC (r= −0.40, p = 7.56 × 10−5;

3. Results 3.1. Demographic and cognitive outcomes Patients and HCs did not differ significantly in age, sex (Table 1), and head motion (expressed as FD, mean (SD), patients vs. HCs: 0.16 (0.09) vs. 0.14 (0.05), p = 0.34). There was a significant difference between groups for highest level of education (Table 1). On average, depressive symptom severity was in the moderate to severe range (mean HDRS = 22.09, SD = 3.92, range 14–32) for depressed subjects. There were no significant differences between the two groups with regards to the predicted IQ using NAART. Patients scored lower on the immediate (RAVLT1-5) and delayed recall (RAVLT-7) compared to HCs (Table 1). 3.2. Functional parcellation of hippocampus The unilateral hippocampus was parcellated, along its longitudinal axis, into three sections (Fig. 1a), i.e., the left/right anterior hippocampus (LAH/RAH), left/right intermediate hippocampus (LIH/RIH), and left/right posterior hippocampus (LPH/RPH) hereafter. The left and right anterior subregions occupied 35% and 39% of the entire left and right hippocampus; the left and right intermediate subregions 251

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Table 2 Significant clusters revealed in the two-sample t-tests and regression analysis. Contrast

PTs < HCs Seed: RIH (Fig. 1)

Cluster

MNI coordinates x y

z

1

Left inferior frontal cortex Left amygdala Right hippocampus Right amygdala Right amygdala

−18 −24 27 21 21

18 −6 −9 −6 −3

1 2 3 1 2

Bilateral DMPFC Right DLPFC Right PPC Bilateral DMPFC Right DLPFC

3 39 51 3 39

45 15 −54 54 18

1 2

Association with length of current episode Seed: RIH (Fig. 2) Association with RAVLT7 Seed: LPH (Fig. 3) Association with RAVLT7 Seed: RIH (Fig. 3)

Location

Cluster Size K

Peak t

−24 −21 −18 −18 −18

100

25

4.53 3.52 5.39 3.79 4.11

45 42 42 36 42

190 183 215 57 55

4.77 4.84 5.09 4.06 4.03

120

Note. DMPFC, dorsomedial prefrontal cortex; DLPFC, dorsolateral prefrontal cortex; PPC, posterior parietal cortex.

3.5. Supplementary analyses We examined the GMV in each hippocampal subregion, and found no significant difference between groups (see supplement Fig. S4). Additionally, the GMV in these subregions did not account for group differences in the RIH connectivity, suggesting that the GM volume did not likely account for the group differences in hippocampal FC patterns observed in the present study. When the analyses were repeated without inclusion of the nuisance variables, we found that the observed group differences of the RIH FC pattern between patients and HCs remained significant (Fig. S7), and the negative correlation between the RIH FC pattern and the duration of current depressive episode remained significant (Fig. S8). The correlations between the LPH FC pattern and RIH FC pattern with the RAVLT-7 scores remained (Fig. S9). These results suggested that the present results were not mainly driven by the nuisance variables. Furthermore, we have looked at the effects of antidepressant medication on the present results. We calculated antidepressant equivalent doses (AED) using previously defined ratios from the literature (Ge et al., 2019; Hayasaka et al., 2015; Inada and Inagaki, 2015), and added this normalized dose into the statistical analyses. The results showed that including AED as a nuisance variable did not significantly change the present results, suggesting that the present results were unlikely a result of different antidepressant medication levels.

Fig. 2. Right intermediate (RIH) hippocampal connectivity to the right amygdala was negatively correlated with length of current episode. Residual hippocampal connectivity and residual episode length (after correcting for nuisance variables) were used in the scatterplots. The r values represent partial correlation coefficient. Note that there was an outlier point (marked with a white arrow), however, the correlation remained significant after excluding the outlier (p< 0.001, upper right panel).

Fig. 3b) showed significantly negative correlation with RAVLT-7 scores. To determine the relationship between the hippocampal FC and RAVLT scores for each group separately, we calculated the partial correlation coefficients between the z-scores, averaged across the voxels within the clusters and the corresponding RAVLT scores for each group, respectively. Results revealed that the delayed recall scores were significantly anti-correlated with the RIH hippocampal FC pattern in both patients and HCs. Similarly, the delayed recall scores were negatively correlated with LPH FC pattern in patients significantly (p< 0.001 for both frontal clusters and parietal cluster), and the anti-correlation was marginally significant in HCs (p = 0.07 for the right DLPFC cluster and p = 0.08 for the parietal cluster). To further explore whether the relationship between hippocampal FC patterns differed by clinical status, post-hoc exploratory 2-way continuous covariate interaction analyses were conducted within the regions that showed significant correlation with the RAVLT-7 scores (see supplementary material for details). The results (supplement Fig. S6) showed that for the LPH FC pattern, there were two sub-clusters (within the right DLPFC and right PPC clusters) that showed significant difference of the correlation to the RAVLT-7 scores between TRD patients and HCs (supplement Fig. S6a). For the RIH FC pattern, there was a sub-cluster (within the right DLPFC cluster) that showed significant difference of the correlation to the RAVLT-7 scores between TRD patients and HCs (supplement Fig. S6b). There was no significant correlation/anti-correlation of the clinical measures and NAART scores with hippocampal FC in any regions in the entire brain. Sensitivity analyses excluding the three HC who were not native English speakers did not change results.

4. Discussion The purpose of this study was to examine the differences in hippocampal networks between TRD patients and HCs, and to investigate whether these networks underlie declarative memory and clinical profiles observed in TRD. We provide evidence for functional organizations of the human hippocampal subregions in both TRD and HCs by using a novel and robust brain parcellation method. As predicted, we demonstrated a distinctive pattern of RIH connectivity within the anterior hippocampus, parahippocampus, the amygdala, the putamen and the orbitofrontal gyrus between the two groups, in the absence of structural and functional organization changes of the hippocampal subregions. Further, we found that lower RIH-amygdala functional connectivity was related to the duration of current depressive episode. Significant anti-correlations were found between FC pattern of LPH, RIH and neurocognitive memory performance, suggesting a role for the posterior and intermediate hippocampus in memory deficits of TRD patients. 4.1. Hippocampal volume and functional organization are similar in TRD and HCs Although decreased hippocampal volume has been shown in TRD patients (McKinnon et al., 2009), this finding has not been universally 252

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Fig. 3. Hippocampal functional connectivity (FC) correlates with cognitive outcome measures. (a) Left posterior (LPH) hippocampal connectivity to the dorsal medial prefrontal cortex (DMPFC), the right dorsolateral prefrontal cortex (DLPFC) and the right posterior parietal cortex (rPPC) was negatively correlated with the delayed recall (RAVLT-7) scores. (b) Right intermediate (RIH) hippocampal connectivity to the DMPFC and right DLPFC was negatively correlated with the delayed recall (RAVLT-7) scores. Residual hippocampal connectivity and residual recall performance (after correcting for nuisance variables) were used in the scatterplots to show linear relationship between hippocampal connectivity and recall performance in each group. The r values represent partial correlation coefficient controlling for nuisance variables.

located at the CA and subiculum subfields, rather than the dentate gyrus. The dentate gyrus is believed to be the primary site of newly developing neurons (Eriksson et al., 1998; Ho et al., 2013), and it has been correlated with memory performance in healthy older adults (Engvig et al., 2012). More importantly, the dentate gyrus has been associated with the impairments in memory functioning in MDD patients (Bremner et al., 2004; MacQueen and Frodl, 2011). Considering the key role of dentate gyrus in depression, the negative findings of the anterior hippocampal regions in the present study might due to this subregion did not overlap with the dentate gyrus. The functional role of the hippocampus remains a topic of much debate (Poppenk et al., 2013; Strange et al., 2014). Given the evidence of gene expression, behavioral domains and anatomical connectivity (Amaral and Witter, 1989; Chase et al., 2015; Dong et al., 2009), a provisional model of anterior, intermediate and posterior thirds was proposed (Strange et al., 2014). This model suggests that the anterior portion of hippocampus is more engaged in limbic processes (i.e., “hot” processes), and the posterior portion is preferentially enrolled in cognitive functions such as spatial navigation and declarative memory (i.e., “cold” processes) (Fanselow and Dong, 2010; Robinson et al., 2016), while the function of the intermediate portion remains poorly characterized. Given the role of the anterior hippocampus in affective processing, one might expect to discover dysfunction of the anterior hippocampal FC patterns. However, we found no significant differences in LAH and RAH-related FC patterns. Instead, our results provide evidence that the intermediate portion may play a role in the emotional deficits of TRD, indicated by a hypoconnectivity with the limbic regions (i.e., the hippocampal formation and the amygdala) and the orbitofrontal gyrus that are intensively implied in emotional processing (Fanselow and Dong, 2010; Phan et al., 2002; Strange et al., 2014) and may be impaired in MDD (Ballmaier et al., 2004; Drysdale et al., 2017; Hamani et al., 2011; Lui et al., 2011; Matthews et al., 2008). The network of decreased RIH-amygdala-frontal connectivity emerging from the present study corresponds well to the limbic

replicated (Vythilingam et al., 2004). Our data are convergent with the latter, as we did not observe significant difference in the GM volume between TRD and HCs for all subregions. A plausible explanation of the discrepancies is that the studies revealing differences were mostly based on the measurement of hippocampal volume size (Kempton et al., 2011; McKinnon et al., 2009; Schmaal et al., 2016; Tae et al., 2008), whereas we compared the GM volume which is the “modulated” GM concentration (Gennatas et al., 2017) within an a priori-defined hippocampal mask. Specifically, GMV employed in the present study quantifies the amount of gray matter existing in a voxel, and it is a voxelbased measurement. In contrast, the volume of hippocampus used in other studies is a measurement of the hippocampal size, rather than the intensity of the hippocampal voxels. Compared to the large amount of studies which found smaller hippocampal size in MDD patients (Kempton et al., 2011; Schmaal et al., 2016), there is relatively a lack of evidence that demonstrates the lower GMV of hippocampus in MDD patients (Bora et al., 2012), and our results were convergent with the latter. We further demonstrated normal functional organizations of all hippocampal subregions in patients compared to HCs. 4.2. Decreased subregional hippocampal connectivity in TRD Contrary to one of our hypotheses, we did not observe dysfunctional connectivity of the bilateral anterior hippocampus subregions in patients relative to HCs. However, a major finding of our study was the hypoconnectivity of the FC profile of the RIH subregion with the anterior hippocampus, the parahippocampus, amygdala, and orbitofrontal gyrus in TRD patients, suggesting that hippocampal FC dysfunction is a distinguishing feature of TRD in the absence of detectable structural (Vythilingam et al., 2004) and functional (Ge et al., 2017) organization changes. Our findings of the RIH FC patterns echo the results that have demonstrated the key role of the right hippocampus in MDD (Campbell et al., 2004; Videbech and Ravnkilde, 2004). The bilateral anterior hippocampal subregions in the present study mainly 253

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compartment of the limbic-cortical model of MDD (Goldapple et al., 2004; Mayberg, 2003). In support of this model, our findings further demonstrate that the intermediate hippocampus showed disrupted FC with other limbic regions in our samples. We also found that a lower RIH-amygdala connectivity was related to a longer duration of current depressive episode in TRD. This phenomenon was in line with the functional hypoconnectivity between RIH and the amygdala in the patient group relative to the healthy controls, suggesting that RIHamygdala connectivity is reflective of the clinical profile of the current episode length. Note that though bilateral amygdala showed decreased FC with the RIH, the decreased FC pattern was primarily located at the right amygdala. Therefore, it is likely that the right lateralized amygdala-RIH correlation with the clinical data was due to the pronounced hypoconnectivity of the right amygdala. No significant correlation was found between hippocampal networks and other clinical variables except for the length of current episode, indicating that the abnormal FC pattern in amygdala may be state-dependent associated to the current depressive episode. In other words, the RIH-amygdalar connectivity was related to the length of the current depressive episode might be signaling that abnormal FC pattern might be a state marker which fluctuates with current episode and presents only during the acute stage of episode (Graham et al., 2013; Maalouf et al., 2011). The lack of correlation of FC patterns with other measures such as degree of treatment resistance, age of onset, or prior number of episodes would increase the plausibility of such interpretation. This hypothesis, however, would warrant further research to test this interpretation of current findings. Moreover, the missing link between abnormal FC to the cognitive impairment may suggest that cognitive impairment being an independent component from mood disability.

networks; these two networks usually show anti-correlation, and the disrupted interactions between them may contribute to the pathology of a variety of mental disorders (Menon, 2011), including MDD (Manoliu et al., 2014). Because the hippocampus is an important node of the DMN (Buckner et al., 2008), it is plausible that the higher functional connectivity between the hippocampus (representative of the DMN) and the DLPFC, the DMPFC and the posterior parietal cortex (representatives of the CEN) was detrimental to the cognitive processing, thereby producing the lower behavioral performance in RAVLT tests that was discovered in our samples. The functional connectivity patterns (to both LPH and RIH seeds) that related to the RAVLT-7 scores were located at a set of right-lateralized regions, including right DLPFC, PPC, and DMPFC. This right-lateralized pattern could be considered consistent with the explanation that the right central executive network (CEN) interacts with the default mode network (DMN, with hippocampus as a representative node) to support memory recall (Fornito et al., 2012). A potential implication is that enhancing the hippocampus-related memory functions of the TRD patients targeting the right lateralized CEN nodes may be an avenue to address hard-to-treat cognitive impairment in TRD. Non-invasive neurostimulation treatments such as repetitive transcranial magnetic stimulation (Wang et al., 2014) or magnetic seizure therapy (Wang et al., 2018) have been reported to accomplish such improvements and the mechanism may be associated with targeting this particular network. 4.4. Limitations Despite these encouraging results, limitations should be acknowledged. First, growing evidence suggest that TRD is a sub-category of MDD, and there are biological differences between TRD and treatmentresponsive patients (Wu et al., 2011). Because our patient samples were treatment-resistant, the conclusions from this study cannot be generalized to MDD patients directly. Future research is needed to compare TRD with treatment-responsive patients. Second, because our results were obtained with the assessment of intrinsic connectivity patterns, task-based imaging studies are needed to demonstrate hippocampal engagement in the memory deficits of patients. Third, our findings are strongly in need of replication in independent validation samples, ideally across multi-site studies.

4.3. Declarative memory deficits are related to intrinsic functional connectivity patterns Indeed, we demonstrated a declarative memory deficit in TRD patients. Specifically, we found impairment in immediate declarative memory, as well as in delayed memory function, as indexed by the RAVLT. Somewhat opposed to this, others have demonstrated preserved verbal memory (Porter et al., 2003). Thus, factors that might contribute to these disparate findings, such as depression subtype, sample characteristic, severity and neurocognitive tests methodologic issues, should be considered in future studies. In addition, our data showed that these declarative memory impairments were associated with patterns of functional connectivity between hippocampal subregions and areas of the brain not overlapping with between-group FC differences. Our finding of correlates between the posterior hippocampal FC pattern and declarative memory performance in the patients as well as in the healthy controls is in keeping with cognitive, genetic and clinical evidence relating learning and memory with this structure (Fanselow and Dong, 2010; Jayaweera et al., 2016; Strange et al., 2014). Importantly, we demonstrated that the FC pattern of the intermediate hippocampus was also related to memory performance. Taken together, the evidence from the intermediate hippocampus (vide supra) demonstrates a dual property of this region (Fanselow and Dong, 2010). The intermediate hippocampus, which has partially overlapping characteristics with its anterior and posterior neighbors, may serve as a transitional zone between the anterior and posterior portions, as demonstrated by showing hypoconnectivity with emotion-mediated regions and significant correlates with memory performance in TRD patients. For intermediate and posterior FC patterns, regions that showed correlates with memory performance were located within the right frontoparietal central executive network (CEN, included the DLPFC, the DMPFC and the posterior parietal cortex) (Hamilton et al., 2013; Mulders et al., 2015; Seeley et al., 2007). The CEN is a network of structures that increases in activation during the performance of attention-demanding tasks. The CEN and DMN are often seen as opposing

4.5. Concluding remarks In summary, based on a recently introduced brain parcellation method and functional connectivity technique with resting state fMRI, we demonstrated functional hypoconnectivity in the RIH of MDD patients in the absence of detectable structural and functional organization changes of the hippocampal subregions. Within these disconnections, the RIH-amygdala connectivity was correlated with the duration of patients’ current depressive episodes. Neural correlates of declarative memory deficits in MDD were found within the intermediate and posterior hippocampal FC patterns. Taken together, this evidence suggests a dual property of the intermediate hippocampus in processing cognitive memory and affective information in MDD, and provides us with new insights into the network-level neural correlates of memory deficits in MDD. 4.6. Declarations of interest The authors declare no financial interests relevant to this work. D.M.B receives research support from the Canadian Institutes of Health Research (CIHR), National Institutes of Health – US (NIH), Weston Brain Institute, Brain Canada and the Temerty Family through the CAMH Foundation and the Campbell Research Institute. He received research support and in-kind equipment support for an investigatorinitiated study from Brainsway Ltd. and he is the site principal investigator for three sponsor-initiated studies for Brainsway Ltd. He 254

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received in-kind equipment support from Magventure for this investigator-initiated study. He received medication supplies for an investigator-initiated trial from Indivior. He participated on advisory board for Janssen. F.V.R received in-kind equipment support from Magventure for an investigator-initiated study. He has participated on an advisory board for Janssen. Other authors report no biomedical financial interests or potential conflicts of interest. R.W.L. has received honoraria for ad hoc speaking or advising/consulting, or received research funds, from: Akili, Allergan, Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, Brain Canada, Canadian Institutes of Health Research, Canadian Depression Research and Intervention Network, Canadian Network for Mood and Anxiety Treatments, Canadian Psychiatric Association, CME Institute, Hansoh, Janssen, Lundbeck, Lundbeck Institute, Medscape, Mind Mental Health Technologies, Otsuka, Pfizer, St. Jude Medical, University Health Network Foundation, and VGH Foundation.

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