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International Journal of Neuropsychopharmacology logoLink to International Journal of Neuropsychopharmacology
. 2020 Jul 30;23(12):791–798. doi: 10.1093/ijnp/pyaa056

Functional Dysconnectivity of Frontal Cortex to Striatum Predicts Ketamine Infusion Response in Treatment-Resistant Depression

Mu-Hong Chen 1,3,4, Wan-Chen Chang 1,2,5, Wei-Chen Lin 1,3,4, Pei-Chi Tu 1,2,3,6, Cheng-Ta Li 1,3,4, Ya-Mei Bai 1,3,4, Shih-Jen Tsai 1,3,4, Wen-Sheng Huang 7, Tung-Ping Su 1,3,4,8,
PMCID: PMC7770518  PMID: 32726408

Abstract

Background

Frontostriatal disconnectivity plays a crucial role in the pathophysiology of major depressive disorder. However, whether the baseline functional connectivity of the frontostriatal network could predict the treatment outcome of low-dose ketamine infusion remains unknown.

Methods

In total, 48 patients with treatment-resistant depression were randomly divided into 3 treatment groups (a single-dose 40-minute i.v. infusion) as follows: 0.5 mg/kg ketamine, 0.2 mg/kg ketamine, and saline placebo infusion. Patients were subsequently followed-up for 2 weeks. Resting-state functional magnetic resonance imaging was performed for each patient before infusion administration. In addition, the baseline frontostriatal functional connectivity of patients with treatment-resistant depression was also compared with that of healthy controls.

Results

Compared with the healthy controls, patients with treatment-resistant depression had a decreased functional connectivity in the frontostriatal circuits, especially between the right superior frontal cortex and executive region of the striatum and between the right paracingulate cortex and rostral-motor region of the striatum. The baseline hypoconnectivity of the bilateral superior frontal cortex to the executive region of the striatum was associated with a greater reduction of depression symptoms after a single 0.2-mg/kg ketamine infusion.

Conclusion

Reduced connectivity of the superior frontal cortex to the striatum predicted the response to ketamine infusion among patients with treatment-resistant depression.

Keywords: frontostriatal network, treatment-resistant depression, low-dose ketamine infusion, treatment response


Significance Statement.

A single low-dose ketamine infusion was effective for treatment-resistant depression (TRD). In the current study, approximately one-half (46.9%) of patients with TRD reached a ≥50% reduction in depressive symptoms at postinfusion. However, the brain biomarker to predict the treatment response to low-dose ketamine remains unknown. In our study, we compared the baseline frontostriatal functional connectivity (FC) between patients with TRD and healthy controls and further investigated whether frontostriatal FC may predict the treatment response to ketamine infusion. We found that patients with TRD had a decreased FC in the frontostriatal circuits compared with the controls and also that reduced connectivity of the superior frontal cortex to the striatum predicted the response to ketamine infusion among patients with TRD.

Introduction

Major depressive disorder, which has an estimated lifetime prevalence of 10%–25% among women and 5%–12% among men, is a chronic debilitating mental disorder (Malhi and Mann 2018). It has been the leading contributor to disease burden worldwide since 2015 (Kupfer et al., 2012; Otte et al., 2016). The sequenced treatment alternatives to relieve depression (STAR*D) study revealed that up to 40% of patients with major depressive disorder did not experience symptomatic remission despite at least 2 trials of conventional antidepressants, which was classified as treatment-resistant depression (TRD) (Sinyor et al., 2010; McIntyre et al., 2014; Johnston et al., 2019). TRD led to worse clinical outcomes, such as high relapse rates, suicidal thoughts, and diminished quality of life and psychosocial functioning (Sinyor et al., 2010; McIntyre et al., 2014; Johnston et al., 2019).

In this decade, growing evidence has supported the rapid antidepressant effect of low-dose ketamine infusion on TRD (McGirr et al., 2015; Su et al., 2017). Unfortunately, approximately 30%–40% of Caucasian patients and at least one-half of Taiwanese patients with TRD poorly responded to a single low dose of ketamine infusion (McGirr et al., 2015; Su et al., 2017). Clinical studies have suggested that a higher body mass index, no prior history of suicide attempt, slower cognitive processing speed, and Val allele of BDNF rs6265 polymorphism were associated with a better treatment response to ketamine infusion (Niciu et al., 2014; Rong et al., 2018). However, few studies have investigated the association between pretreatment neurofunctioning and the rapid antidepressant effect of ketamine infusion (Niciu et al., 2014; Rong et al., 2018). A magnetoencephalographic study with a small sample size of 11 patients with TRD who received a single dose of ketamine infusion revealed that the rostral anterior cingulate cortex (ACC) activation at baseline was a biomarker identifying a subgroup of patients who responded favorably to ketamine’s antidepressant effects (Salvadore et al., 2009). Our previous positron emission tomography study indicated that the increased activity of the prefrontal cortex after ketamine infusion predicted the responses of antidepressants to ketamine infusion at 240 minutes after treatment (Li et al., 2016).

Furthermore, a recent study of 10 patients with TRD who were i.v. administered a single ketamine dose of 0.5 mg/kg and underwent a game-like reward task during functional magnetic resonance imaging (fMRI) demonstrated that the improvement of depression scores and the enhanced sensitivity for rewarded items were accompanied by an increased activity of the reward-related regions in the brain, such as the orbitofrontal cortex and ventral striatum (Sterpenich et al., 2019). Murrough et al. included 20 patients with TRD who underwent fMRI at baseline and after 24 hours following a single i.v. dose of ketamine and revealed that a greater connectivity of the right caudate during positive emotion perception was related to depression reduction following ketamine infusion (Murrough et al., 2015). Ye et al. (Ye et al., 2018) further reported that the differential restructuring of the corticostriatal and limbic circuits, including the delta high-frequency oscillations cross-frequency coupling in the dorsal striatum, may contribute to ketamine’s antidepressant benefits.

Previous studies have demonstrated that the integrity of frontostriatal connectivity played a major role in the pathophysiology of major depression and TRD (Furman et al., 2011; Segarra et al., 2016; Avissar et al., 2017; Walsh et al., 2017). Investigating the brain responses to unexpected rewards in 24 patients with depression and 21 controls using fMRI, Segarra et al. (Segarra et al., 2016) discovered that the hypofunction in the ventral striatal and orbitofrontal regions was related to depression psychopathology. Furman et al. (Furman et al., 2011) demonstrated that patients with major depression exhibited attenuated functional connectivity (FC) between the ventral striatum and both the ventromedial prefrontal cortex and subgenual ACC compared with the controls. A meta-analysis of the neural biomarkers of clinical response to antidepressant drugs in depression indicated that the increased activation in the amygdala, striatum, and insula enhanced the likelihood of poor response to the drugs and suggested that the dysfunction of the frontostriatal–limbic network may predict the response to pharmacological treatment in depression (Fu et al., 2013). Conflicting evidence indicated that a greater attenuation of the connectivity between the putamen and orbitofrontal cortex was related to the treatment response to psychotherapy, but a higher FC between the dorsolateral prefrontal cortex and striatum predicted a better treatment response to repetitive transcranial magnetic stimulation (Avissar et al., 2017; Walsh et al., 2017). However, the role of FC in the frontostriatal network in the treatment response to low-dose ketamine infusion for TRD remains unknown.

In the present study, 48 patients with TRD were randomly administered a single dose of ketamine (0.5 or 0.2 mg/kg) or a normal saline placebo infusion and were subsequently followed-up for 2 weeks. The baseline resting-state functional connectivity-MRI of frontostriatal connectivity was analyzed for the treatment response to ketamine infusion. We attempted to investigate whether disconnectivity of the frontostriatal network may predict the treatment response to low-dose ketamine infusion.

Methods

Inclusion Criteria of Patients and the Study Procedure

Details of the clinical trial protocol of an adjunctive ketamine study of Taiwanese patients with treatment-resistant depression was comprehensively reported in our previous studies (Li et al., 2016; Su et al., 2017). TRD was defined as the failure of treatment response for at least 2 different antidepressants with adequate dosage and treatment duration (Su et al., 2017). The exclusion criteria included any major medical or neurological illness (i.e., stroke or seizure) or a history of alcohol or substance abuse. Following at least 2 weeks of concomitant stable antidepressant treatment, 48 patients with TRD received an add-on i.v. R,S-ketamine infusion using a randomized, double-blind, placebo-controlled design. Each patient received a single dose of ketamine infusion with 0.5 or 0.2 mg/kg, or normal saline (placebo), which was i.v. administered over 40 minutes. Baseline resting-state functional MRI was performed before a single dose of ketamine or placebo infusion. Patients were assessed using the 17-item Hamilton Depression Rating Scale (HAMD) prior to initiation of test infusions and at 40, 80, 120, and 240 minutes postinfusion. Telephone or face-to-face ratings were subsequently conducted on days 2, 3, 4, 5, 6, 7, and 14 after ketamine infusion. All clinical assessments were performed by the first author, Dr Mu-Hong Chen. Responder status was identified by response (≥50% reduction of mood ratings) at any 2 daily HAMD measures during the period of 24 to 96 hours (days 2–5) after infusion (Su et al., 2017). In addition, 48 age-/sex-matched healthy controls were included for the baseline FC analysis. This study was performed in accordance with the Declaration of Helsinki and was approved by the Taipei Veterans General Hospital Institutional Review Board. Written informed consent was provided by all of the participants (clinical trials registration: UMIN Clinical Trials Registry: registration no.: UMIN000016985 (https://www.umin.ac.jp/ctr/).

MRI Acquisition and Preprocessing

Image Acquisition

MRI images were acquired using a 3.0 T Discovery MR750 (GE Healthcare) MRI scanner with an 8-channel head coil at the Department of Radiology, Taipei Veterans General Hospital. Head stabilization was achieved using cushioning, and all participants wore earplugs (29 dB rating) to attenuate noise. During the functional scans, participants were instructed to remain awake with their eyes open and look at a fixation cross. The resting-state functional images were obtained using a gradient echo T2*-weighted sequence (repetition time/ repetition time/Flip = 2500 ms/30 ms /90°). Forty-three contiguous horizontal slices parallel to the intercommissural plane (voxel size: 3.5 mm × 3.5 mm × 3.5 mm) were acquired interleaved. These slices covered the cerebellum of each participant. During the functional scans, the participants were instructed to remain awake with their eyes open (each scan lasted 8 minutes and 24 seconds across 200 time points). In addition, a high-resolution structural image was acquired in the axial plane using FSPGR sequence (BRAVO) on GE equipment with parameters (repetition time = 12.23 ms, echo time = 5.18 ms, inversion time [TI] = 450 ms, and flip angle = 12°) and an isotropic 1-mm voxel (field-of-view 256 × 256).

Analysis of Functional Connectivity in the Resting State

Functional Connectivity Preprocessing

Resting-state fMRI data preprocessing was performed using the Data Processing & Analysis for (Resting-State) Brain Imaging, Data Processing & Analysis for (Resting-State) Brain Imaging (DPABI) (http://rfmri.org/dpabi), which is based on the statistical parametric mapping software package (SPM 12) toolbox on the platform of Matlab R2016b. Preprocessing of functional scans included slice-timing correction and motion correction, and the scans were registered to the Montreal Neurological Institute (MNI152) atlas. Additional preprocessing steps were used to prepare the data for FC analysis. These were as follows: spatial smoothing using a Gaussian kernel (6-mm full width at half-maximum), linear detrending, nuisance covariate regression (removal of spurious or nonspecific sources of variance by regression of the following variables: Friston 24 head motion parameters model, the mean whole-brain signal, and the mean signal within a deep white matter (WM) region and cerebrospinal fluid (CSF) signal, and temporal filtering (0.009 Hz < f < 0.08 Hz). The first temporal derivatives of these regressors were included in the linear model to account for the time-shifted versions of spurious variance. The regression of each of these signals was computed simultaneously, and the residual time course was retained for the correlation analysis.

Functional Connectivity Analysis

The regions of interest (ROIs) were adopted from previous studies describing frontostriatal connectivity regions that were based on the structural connectivity between functionally distinct frontal cortical regions and striatum (supplementary Table 1) (Alexander et al., 1986; Tziortzi et al., 2013). The striatal ROIs are publicly available as part of the Oxford-GSK-Imanova Striatal Connectivity Atlas (Tziortzi et al., 2013) (supplementary Table 1). To avoid the artifacts produced by movement or preprocessing, the adequate procedures based on previous studies (Fox et al., 2009; Satterthwaite et al., 2013; Power et al., 2017; Makowski et al., 2019) were adopted, which effectively removed noise related with motion or physiological signals to minimize the influence of nuisance variable as much as possible and also increase the spatial specificity in seed-based functional connectivity analysis. The FC maps of the striatum for each participant were identified based on correlations of low-frequency fMRI fluctuations with the ROIs, and Fisher’s r-to-z transformation was used to convert the correlation maps into z maps. The z-transformed maps were compared by ANCOVA with age, sex, and education as the covariates of no interest. We used an uncorrected threshold of P < .001 for the initial voxel-wise comparisons. To correct for multiple comparisons, a Monte Carlo simulation with 10 000 times was performed by 3dclustsim function of AFNI (http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dClustSim.html) to determine statistical thresholds for voxel cluster size (Cox, 1996). Only the clusters with a significance threshold of P < .05 at the cluster level (a minimum cluster size of 36 in this study) were reported. WM and CSF masks were generated in 2 ways: (1) setting a probability threshold (i.e., 0.99) on one’s own tissue segmentation maps based on his/her structural image; or (2) using SPM’s a priori tissue probability maps (empirical thresholds: 90% for WM mask and 70% for CSF mask) (Yan et al., 2016). The signals from WM and CSF were regressed out to reduce respiratory and cardiac effects (Yan et al., 2016). Furthermore, we limited our analysis to the frontal and basal ganglia ROIs for regression analysis because of their greater relevance in the pathophysiology of depression (Li et al., 2016; Gärtner et al., 2019). The spatial mean connectivity values (Z) of each cluster were then extracted for each participant to perform a logistic regression on ketamine response and baseline FC at each frontal cluster selected previously. Furthermore, we use general linear model to access the effect of ketamine dose, response to ketamine, and their interaction as well as strength of functional connectivity on average percentage of HAMD score reduction between day 2 and day 5. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Results

In total, 15 (46.9%) of the 32 patients with TRD who were administered a single ketamine dose of 0.5 or 0.2 mg/kg achieved the treatment response in contrast to 3 (18.8%) among the 16 patients who received the placebo (Table 1). Age at onset (P = .538), duration of illness (P = .853), baseline medications (antidepressants: P = 1.000; mood stabilizers: P = .067; atypical antipsychotics: P = .543), and psychiatric comorbidities (panic disorder: P = .226; generalized anxiety disorder: P > .999) did not differ between the responders and nonresponders (Table 1). Responders had lower HADM scores from day 2 postinfusion to day 5 postinfusion (all P < .001) than nonresponders (Table 1).

Table 1.

Demographic Data of Patients With TRD and Controls

Patients with TRD (n = 48) Healthy controls (n = 48) P
Responders (n = 18) Nonresponders (n = 30)
Age (years, SD) 43.00 ± 10.26 48.03 ± 10.68 42.44±7.38 .116
Sex (n, M/F) 4/14 9/21 26/22 .557
Education level (years, SD) 13.89 ± 3.12 11.57 ± 3.27 14.71±1.73 .019
Age at onset (years, SD) 34.83 ± 13.98 37.27 ± 12.65 .538
Duration of illness (years, SD) 10.67 ± 8.46 11.13 ± 8.37 .853
Psychiatric comorbidities (n, %)
 Panic disorder 5 (27.8) 15 (50.0) .226
 Generalized anxiety disorder 11 (61.1) 19 (63.3) >.999
History of attempted suicide (n, %) 9 (50.0) 12 (40.0) .558
Baseline medications (n, %)
 Antidepressants 18 (100.0) 30 (100.0) 1.000
 Mood stabilizers 1 (5.6) 9 (30.0) .067
 Atypical antipsychotic 10 (55.6) 20 (66.7) .543
Treatment group (n, %) .155
 0.5 mg/kg ketamine 7 (39.9) 9 (30.0)
 0.2 mg/kg ketamine 8 (44.4) 8 (26.7)
 Placebo 3 (16.7) 13 (43.3)
HAMD-17 Total score (SD)
 Baseline 21.06 ± 5.35 22.43 ± 4.21 .327
 Day 2 7.50 ± 3.11 17.83 ± 5.94 .000
 Day 3 8.06 ± 3.70 17.45 ± 6.20 .000
 Day 4 7.11 ± 2.87 18.24 ± 6.07 .000
 Day 5 7.44 ± 3.22 19.07 ± 5.73 .000
 Average from D2 to D5 7.53 ± 2.65 18.15 ± 5.65 .000

Abbreviations: HAMD-17, Hamilton Depression 17 items Scale; TRD, treatment-resistant depression.

Patients with TRD exhibited frontostriatal hypoconnectivity mainly in the executive (i.e., bilateral superior frontal cortex) and rostral-motor (i.e., paracingulate cortex and precentral cortex) divisions compared with the healthy controls (Table 2; Figure 1). However, FC between the right frontal pole and corresponding executive and rostral-motor regions of the striatum was increased in patients with TRD compared with the controls (Table 2; Figure 1).

Table 2.

Difference of Functional Connectivity of Frontostriatal Circuits at Baseline Between Patients With TRD and Controls

Harvard-Oxford Cortical
p(FWE-corr) qFDRcorr kE x y z Structural Atlas
Executive
TRD > HC 0.787 0.765 49 24 68 2 R. Frontal pole
HC > TRD 0.215 0.156 130 28 2 8 R. Putamen
0.042 0.276 84 −26 −2 6 L. Putamen
0.476 0.054 227 24 22 32 R. Superior frontal cortex
0.888 0.701 37 −22 28 34 L. Superior frontal cortex
Rostral-motor
TRD > HC 0.915 0.578 33 24 70 4 R. Frontal pole
0.377 0.333 97 2 44 −24 R. Frontal medial cortex
0.909 0.578 34 32 58 −10 R. Frontal pole
HC > TRD 0.029 0.038 246 −22 8 4 L. Putamen
0.005 0.010 358 24 18 2 R. Putamen
0.049 0.047 215 34 −2 −26 R. Amygdala
0.334 0.230 104 −20 26 32 L. Superior frontal cortex
0.002 0.008 430 12 34 38 R. Paracingulate cortex
0.922 0.688 32 −58 12 34 L. Precentral cortex

Abbreviations: HC, healthy control; L, left; R, right; TRD, treatment-resistant depression.

Figure 1.

Figure 1.

Functional connectivity of the executive (1a) and rostro-motor (1b) frontostriatal projections in patients with TRD vs healthy controls. Abbreviations: L, left; R, right.

General linear model analyses reported that FC of the right superior frontal cortex to the striatum was independently associated with the reduction of depression symptoms (P = .019) (Table 3). Furthermore, the interactions between the FCs of the bilateral superior frontal cortex (right: P = .007; left: P = .044) to the corresponding striatum regions and ketamine infusion groups were related to the average percentage of HAMD score reduction between day 2 and day 5 (Table 3). Among patients with TRD who received ketamine infusion, the baseline functional hypoconnectivity of the bilateral superior frontal cortex to the striatum predicted the treatment response, especially among patients who received a 0.2-mg/kg dose of ketamine infusion (Figure 2).

Table 3.

Relationship Between Groups, Dose of Ketamine, and Strength of Functional Connectivity Among Patients Receiving Low-Dose Ketamine Infusion

FC of frontostriatal circuits at baseline Main effect of ROIs FC Main effect of ketamine dose Interaction: FC ×  ketamine dose Corrected model
B Sig. B Sig. B Sig. Sig.
R. Putamen −0.51 0.838 −0.12 0.889 0.34 0.826 0.983
L. Putamen 0.35 0.857 0.17 0.814 −0.21 0.886 0.985
R. Superior frontal cortex 5.40 0.019 0.06 0.705 −3.75 0.007 0.033
L. Superior frontal cortex 2.77 0.122 0.04 0.821 −2.13 0.044 0.096
L. Putamen −0.73 0.701 −0.16 0.808 0.41 0.729 0.966
R. Putamen 0.18 0.939 0.12 0.893 −0.10 0.949 0.990
R. Amygdala −3.20 0.247 0.03 0.872 1.60 0.322 0.638
L. Superior frontal cortex 1.76 0.416 −0.04 0.825 −1.53 0.275 0.608
R. Paracingulate cortex 3.09 0.127 0.01 0.936 −2.41 0.070 0.273
L. Precentral cortex 1.49 0.397 0.10 0.604 −0.53 0.632 0.584

Abbreviations: FC, functional connectivity; HAMD, Hamilton Depression 17 items Scale; L, left; R, right; ROI, region of interest.

General linear model in groups of HAMD scores (responder/nonresponder) and ketamine infusion dose.

Figure 2.

Figure 2.

Baseline functional connectivity of left superior frontal cortex and right superior frontal cortex to the executive region of the striatum predicts the depression reduction after low-dose ketamine infusion. Abbreviations: HAMD: Hamilton Depression 17 items Scale; L: left; R: right.

Discussion

Our study findings supported the study hypothesis that patients with TRD exhibit a decreased FC in the frontostriatal circuits, especially between the right superior frontal cortex and executive region of the striatum and between the right paracingulate cortex and rostral-motor region of the striatum compared with the healthy controls. Furthermore, the baseline hypoconnectivity of the bilateral superior frontal cortex to the striatum was associated with a greater reduction of depression symptoms after a single low-dose ketamine infusion.

The frontostriatal circuits played crucial roles in the executive and psychomotor functions, reward processing, and pathophysiology of major depression (Porter et al., 2007; Eshel and Roiser, 2010, Furman et al., 2011; Segarra et al., 2016). As aforementioned, Segarra et al. (Segarra et al., 2016) studied the brain responses to unexpected rewards between patients with depression and healthy controls and observed hypofunction in the ventral striatal and orbitofrontal regions in patients with depression during unexpected reward receipt. Furman et al. (Furman et al., 2011) demonstrated that patients with depression exhibited hypoconnectivity between the ventral striatum and ventromedial prefrontal and subgenual ACCs but presented a stronger connectivity between the dorsal caudate and dorsal prefrontal cortex compared with controls. Resting-state fMRI studies have indicated pervasive deficits in dorsolateral, ACC, medial frontal, and basal ganglion structures in depression (Rogers et al., 1998). Naismith et al. (Naismith et al., 2006) further revealed that frontostriatal dysconnectivity may be related to impaired executive functions (i.e., visual motor speed and mental flexibility), longer duration of depressive episodes, severity of acute stress, and past suicide attempts. In our study, the majority of hypoconnectivity was noted in the frontostriatal networks, including bilateral superior frontal cortex, paracingulate cortex, putamen, and amygdala, in patients with TRD compared with healthy controls.

Previous studies have assessed the predictive role of the frontostriatal structure and connectivity in the therapeutic response to depression treatments, including antidepressant medications, psychotherapy, and repetitive transcranial magnetic stimulation (Fu et al., 2013; Avissar et al., 2017; Drysdale et al., 2017; Walsh et al., 2017). A meta-analysis revealed that the area in the right putamen extending into the caudate nucleus presented an increased activation significantly associated with the reduced likelihood of the treatment response to antidepressant drugs (Fu et al., 2013). Fu et al. (Fu et al., 2013) demonstrated that reduced baseline activation in the right striatum was predictive of a better clinical response to medication treatment. Walsh et al. (Walsh et al., 2017) revealed that a greater attenuation of the connectivity between several frontostriatal seeds (i.e., striatum, dorsal ACC, and medial prefrontal cortex) and the paracingulate gyrus was associated with an improved response to behavioral activation treatment for depression. In our study, we discovered that the hypoconnectivity between the superior frontal cortex and executive region of the striatum may predict depression reduction after the administration of a low-dose (especially 0.2 mg/kg) ketamine infusion. This evidence suggests that an attenuated baseline activation in the brain regions including frontostriatal networks was a common predictor of the response to various depression treatments, such as conventional antidepressants, psychotherapy, and low-dose ketamine infusion.

However, in the current study, we observed that the reduced FC between the superior frontal cortex and striatum predicted the treatment response only to 0.2 mg/kg but not to 0.5 mg/kg of ketamine infusion in Taiwanese patients with TRD. The results may be indirectly consistent with our previous positron emission tomography study according to which a 0.2-mg/kg ketamine infusion more pervasively increased activation in the prefrontal cortex compared with a 0.5-mg/kg ketamine infusion (Li et al., 2016). Combining our current and previous findings, we suggest that a lower baseline connectivity of frontostriatal networks predicted the treatment response to ketamine, and hyperactivation of the prefrontal cortex at the postinfusion stage was related to the rapid antidepressant efficacy of low-dose ketamine infusion. However, whether there were different associations between baseline FC and treatment responses to 0.5-mg/kg and 0.2-mg/kg ketamine infusion and whether 0.5-mg/kg and 0.2-mg/kg ketamine infusion may differently modulate brain functioning would need further investigation.

Limitation

Several limitations of this study need to be addressed here. First, only 3 patients with TRD responded to the placebo infusion in our study, which may be owing to the reason that great treatment refractoriness may reduce the placebo response (Rutherford and Roose, 2013). We could not assess the role of frontostriatal connectivity in the placebo response because the sample size was small. Second, our clinical trial was an add-on ketamine study because the medications used by the patients with TRD were not discontinued during ketamine infusion treatment. Therefore, the observed responses to ketamine could have resulted from a combinative or a regulatory effect of ketamine and other medications already being used by the patients. Specifically, the baseline frontostriatal hypoconnectivity predicted the therapeutic response to a combination therapy of ketamine and antidepressant drugs. However, the add-on study design was ethically more appropriate in such patients with severe depression, and it could provide more naturalistic data. Third, we performed the procedure of global signal regression for image pre-processing because previous studies suggested that the procedure effectively removed noise related to motion or physiological signals (Power et al., 2017) and also increased the spatial specificity in seed-based functional connectivity analysis (Fox et al., 2009). However, the procedure may induce artificial anti-correlation between different brain regions (Murphy et al., 2009). In our previous study (Tu et al., 2019), we performed an analysis without the global signal regression method and indicated the absence of prefrontal cortex-related findings. The result was consistent with a recent analysis suggesting that some case-control difference emerged only after global signal regression were adopted (Parkes et al., 2018). Therefore, we should be more cautious in interpreting our results with global signal regression method. Fourth, despite that the total sample size of TRD patients was relatively large (n = 48) in current study, the sample size became quite small for a functional connectivity MRI study when dividing them into 3 equal groups. Further clinical functional connectivity MRI studies with a large sample size would be necessary to validate our results. Fifth, in our study, telephone or face-to-face ratings were subsequently conducted on days 2 through 7 and day 14 after infusion. The inconsistency regarding the interviewing modalities (either via telephone or in person) may skew the rating scores. Sixth, various information, such as smoking status, was not collected in our clinical trial. Without that information, we could not investigate the effects of these parameters.

In conclusion, patients with TRD exhibited frontostriatal hypoconnectivity, especially between the right superior frontal cortex and executive region of the striatum and between the right paracingulate cortex and rostral-motor region of the striatum, compared with the healthy controls. Furthermore, a reduced baseline FC between the bilateral superior frontal cortex and executive region of the striatum was associated with the better treatment response to add-on low-dose (0.2 mg/kg) ketamine infusion. Further studies may be required to elucidate the role of the frontostriatal network in ketamine infusion monotherapy.

Supplementary Material

pyaa056_suppl_Supplementary_Table_1

Acknowledgments

We thank all research assistants, physicians, pharmacists, and nursing staff at D020 Unit of Taipei Veterans General Hospital for their assistance during the study process, without whom this work could not have been possible. We thank Mr I-Fan Hu for his support and friendship.

The study was supported by grants from Taipei Veterans General Hospital (V103E10-001, V104E10-002, V105E10-001-MY2-1, V105A-049, V106B-020, V107B-010, V107C-181, V108B-012), Yen Tjing Ling Medical Foundation (CI-109–21, CI-109–22), Kun-Po Soo Medical Foundation, and Ministry of Science and Technology, Taiwan (101-2314-B-010-060, 102-2314-B-010-060, 107-2314-B-075-063-MY3, 108-2314-B-075 -037). The funding source had no role in any process of our study.

Statement of Interest

None of the authors in this study had any conflict of interest to declare.

References

  1. Alexander GE, DeLong MR, Strick PL (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 9:357–381. [DOI] [PubMed] [Google Scholar]
  2. Avissar M, Powell F, Ilieva I, Respino M, Gunning FM, Liston C, Dubin MJ (2017) Functional connectivity of the left DLPFC to striatum predicts treatment response of depression to TMS. Brain Stimul 10:919–925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cox RW. (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173. [DOI] [PubMed] [Google Scholar]
  4. Drysdale AT, et al. (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23:28–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Eshel N, Roiser JP (2010) Reward and punishment processing in depression. Biol Psychiatry 68:118–124. [DOI] [PubMed] [Google Scholar]
  6. Fox MD, Zhang D, Snyder AZ, Raichle ME (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101:3270–3283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Fu CH, Steiner H, Costafreda SG (2013) Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies. Neurobiol Dis 52:75–83. [DOI] [PubMed] [Google Scholar]
  8. Furman DJ, Hamilton JP, Gotlib IH (2011) Frontostriatal functional connectivity in major depressive disorder. Biol Mood Anxiety Disord 1:11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Gärtner M, Aust S, Bajbouj M, Fan Y, Wingenfeld K, Otte C, Heuser-Collier I, Böker H, Hättenschwiler J, Seifritz E, Grimm S, Scheidegger M (2019) Functional connectivity between prefrontal cortex and subgenual cingulate predicts antidepressant effects of ketamine. Eur Neuropsychopharmacol 29:501–508. [DOI] [PubMed] [Google Scholar]
  10. Johnston KM, Powell LC, Anderson IM, Szabo S, Cline S (2019) The burden of treatment-resistant depression: a systematic review of the economic and quality of life literature. J Affect Disord 242:195–210. [DOI] [PubMed] [Google Scholar]
  11. Kupfer DJ, Frank E, Phillips ML (2012) Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet 379:1045–1055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Li CT, Chen MH, Lin WC, Hong CJ, Yang BH, Liu RS, Tu PC, Su TP (2016) The effects of low-dose ketamine on the prefrontal cortex and amygdala in treatment-resistant depression: a randomized controlled study. Hum Brain Mapp 37:1080–1090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Makowski C, Lepage M, Evans AC (2019) Head motion: the dirty little secret of neuroimaging in psychiatry. J Psychiatry Neurosci 44:62–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Malhi GS, Mann JJ (2018) Depression. Lancet 392:2299–2312. [DOI] [PubMed] [Google Scholar]
  15. McGirr A, Berlim MT, Bond DJ, Fleck MP, Yatham LN, Lam RW (2015) A systematic review and meta-analysis of randomized, double-blind, placebo-controlled trials of ketamine in the rapid treatment of major depressive episodes. Psychol Med 45:693–704. [DOI] [PubMed] [Google Scholar]
  16. McIntyre RS, Filteau MJ, Martin L, Patry S, Carvalho A, Cha DS, Barakat M, Miguelez M (2014) Treatment-resistant depression: definitions, review of the evidence, and algorithmic approach. J Affect Disord 156:1–7. [DOI] [PubMed] [Google Scholar]
  17. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44:893–905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Murrough JW, Collins KA, Fields J, DeWilde KE, Phillips ML, Mathew SJ, Wong E, Tang CY, Charney DS, Iosifescu DV (2015) Regulation of neural responses to emotion perception by ketamine in individuals with treatment-resistant major depressive disorder. Transl Psychiatry 5:e509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Naismith SL, Hickie IB, Ward PB, Scott E, Little C (2006) Impaired implicit sequence learning in depression: a probe for frontostriatal dysfunction? Psychol Med 36:313–323. [DOI] [PubMed] [Google Scholar]
  20. Niciu MJ, Luckenbaugh DA, Ionescu DF, Guevara S, Machado-Vieira R, Richards EM, Brutsche NE, Nolan NM, Zarate CA Jr (2014) Clinical predictors of ketamine response in treatment-resistant major depression. J Clin Psychiatry 75:e417–e423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Otte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M, Mohr DC, Schatzberg AF (2016) Major depressive disorder. Nat Rev Dis Primers 2:16065. [DOI] [PubMed] [Google Scholar]
  22. Parkes L, Fulcher B, Yücel M, Fornito A (2018) An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage 171:415–436. [DOI] [PubMed] [Google Scholar]
  23. Porter RJ, Bourke C, Gallagher P (2007) Neuropsychological impairment in major depression: its nature, origin and clinical significance. Aust N Z J Psychiatry 41:115–128. [DOI] [PubMed] [Google Scholar]
  24. Power JD, Plitt M, Laumann TO, Martin A (2017) Sources and implications of whole-brain fMRI signals in humans. Neuroimage 146:609–625. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Rogers MA, Bradshaw JL, Pantelis C, Phillips JG (1998) Frontostriatal deficits in unipolar major depression. Brain Res Bull 47:297–310. [DOI] [PubMed] [Google Scholar]
  26. Rong C, Park C, Rosenblat JD, Subramaniapillai M, Zuckerman H, Fus D, Lee YL, Pan Z, Brietzke E, Mansur RB, Cha DS, Lui LMW, McIntyre RS (2018) Predictors of response to ketamine in treatment resistant major depressive disorder and bipolar disorder. Int J Environ Res Public Health 15:771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Rutherford BR, Roose SP (2013) A model of placebo response in antidepressant clinical trials. Am J Psychiatry 170:723–733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Salvadore G, Cornwell BR, Colon-Rosario V, Coppola R, Grillon C, Zarate CA Jr, Manji HK (2009) Increased anterior cingulate cortical activity in response to fearful faces: a neurophysiological biomarker that predicts rapid antidepressant response to ketamine. Biol Psychiatry 65:289–295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, Wolf DH (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64:240–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Segarra N, Metastasio A, Ziauddeen H, Spencer J, Reinders NR, Dudas RB, Arrondo G, Robbins TW, Clark L, Fletcher PC, Murray GK (2016) Abnormal frontostriatal activity during unexpected reward receipt in depression and schizophrenia: relationship to anhedonia. Neuropsychopharmacology 41:2001–2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Sinyor M, Schaffer A, Levitt A (2010) The sequenced treatment alternatives to relieve depression (STAR*D) trial: a review. Can J Psychiatry 55:126–135. [DOI] [PubMed] [Google Scholar]
  32. Sterpenich V, Vidal S, Hofmeister J, Michalopoulos G, Bancila V, Warrot D, Dayer A, Desseilles M, Aubry JM, Kosel M, Schwartz S, Vutskits L (2019) Increased reactivity of the mesolimbic reward system after ketamine injection in patients with treatment-resistant major depressive disorder. Anesthesiology 130:923–935. [DOI] [PubMed] [Google Scholar]
  33. Su TP, Chen MH, Li CT, Lin WC, Hong CJ, Gueorguieva R, Tu PC, Bai YM, Cheng CM, Krystal JH (2017) Dose-related effects of adjunctive ketamine in Taiwanese patients with treatment-resistant depression. Neuropsychopharmacology 42:2482–2492. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Tu PC, Bai YM, Li CT, Chen MH, Lin WC, Chang WC, Su TP (2019) Identification of common thalamocortical dysconnectivity in four major psychiatric disorders. Schizophr Bull 45:1143–1151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Tziortzi AC, Haber SN, Searle GE, Tsoumpas C, Long CJ, Shotbolt P, Douaud G, Jbabdi S, Behrens TE, Rabiner EA, Jenkinson M, Gunn RN (2013) Connectivity-based functional analysis of dopamine release in the striatum using diffusion-weighted MRI and positron emission tomography. Cereb Cortex 24:1165–1177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Walsh E, Carl H, Eisenlohr-Moul T, Minkel J, Crowther A, Moore T, Gibbs D, Petty C, Bizzell J, Smoski MJ, Dichter GS (2017) Attenuation of frontostriatal connectivity during reward processing predicts response to psychotherapy in major depressive disorder. Neuropsychopharmacology 42:831–843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Yan CG, Wang XD, Zuo XN, Zang YF (2016) DPABI: data processing and analysis for (resting-state) brain imaging. Neuroinformatics 14:339–351. [DOI] [PubMed] [Google Scholar]
  38. Ye T, Bartlett MJ, Schmit MB, Sherman SJ, Falk T, Cowen SL (2018) Ten-hour exposure to low-dose ketamine enhances corticostriatal cross-frequency coupling and hippocampal broad-band gamma oscillations. Front Neural Circuits 12:61. [DOI] [PMC free article] [PubMed] [Google Scholar]

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pyaa056_suppl_Supplementary_Table_1

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