Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression

There is increasing focus on use of resting-state functional connectivity (RSFC) analyses to subtype depression and to predict treatment response. To date, identification of RSFC patterns associated with response to electroconvulsive therapy (ECT) remain limited, and focused on interactions between dorsal prefrontal and regions of the limbic or default-mode networks. Deficits in visual processing are reported in depression, however, RSFC with or within the visual network have not been explored in recent models of depression. Here, we support prior studies showing in a sample of 18 patients with depression that connectivity between dorsal prefrontal and regions of the limbic and default-mode networks serves as a significant predictor. In addition, however, we demonstrate that including visual connectivity measures greatly increases predictive power of the RSFC algorithm (>80% accuracy of remission). These exploratory results encourage further investigation into visual dysfunction in depression, and use of RSFC algorithms incorporating the visual network in prediction of response to both ECT and transcranial magnetic stimulation (TMS), offering a new framework for the development of RSFC-guided TMS interventions in depression.


Partial correlation analyses
. Connectivity measures at baseline and change in depression scores. The nomenclature used in this table and along the results section is based on the HCP's multimodal parcellation, each parcel used for analyses appears in parenthesis preceded by the region to which it pertains; e.g., aDMN(10r) refers to parcels 10r within the anterior medial locus of DMN (or aDMN), DLPFC (46) refers to parcel 46 within the DLPF, etc. For visual system (or VIS) analyses, 2 levels of analyses were explored (VIS region vs. parcel within region); e.g., VIS(MT+) refers to the MT+ region within the visual system. In follow up VIS analyses, e.g., MT+ (FST) refers to the parcel FST within the MT+ region, etc. Initial DLPFC neg analyses tested 8 correlations involving RSFC between DLPFC neg (46, p9-46v, a9-46v, 9-46d) and sgACC (25) or rostral ACC(a24) to choose significant (uncorrected p values) DLPFC neg parcels for subsequent network analyses. Subsequent network analyses tested 38 correlations involving RSFC within and between DLPFC neg (46, p9-46v), DMN or VIS networks. Follow-up VIS analyses queried which parcels within MT+ (p = 9) and ventral (p = 7) were driving the observed regional comparisons. These analyses used all parcels within each region and were therefore FDR corrected for 16 comparisons each. We include in Table 1 all significant (uncorrected p values) connections from correlation analyses; those that survived multiple comparison correction controlling for FDR are highlighted.
www.nature.com/scientificreports www.nature.com/scientificreports/ Two Factor Models for prediction of magnitude of improvement. No significant additivity was observed for models involving only DLPFC and DMN regions. Specifically, the combination of sgACC (25) and aDMN (24) did not show significantly increased predictive ability vs. DLPFC (46) connectivity to either region alone (Supplementary Table 1).
By contrast, when either intrinsic ventral VIS connectivity or pairwise connectivity between VIS regions and DLPFC or DMN were included, significant additive predictive values were obtained. Thus, the combination of intrinsic ventral VIS connectivity with either intrinsic aDMN(10r) connectivity ( Table 2, Model 1), pairwise connectivity between DLPFC(46)-aDMN(s32) (Table 2, Model 2) or DLPFC(p9-46v)-VIS(MT+) ( Table 2, Model 3), all resulted in significant improvements vs. the corresponding one-predictor models (R 2 change ≥ 0.3, p < 0.05). Each of these models accounted for ~60% of the variance in treatment response. two factor models for prediction of remission. Logistic regression analyses and LOOCV were used in order to assess the degree to which the models shown above predicted remission. Intrinsic ventral VIS connectivity alone predicted 77.8% of remitters and non-remitters. The addition of intrinsic aDMN(10r) connectivity in Model 1 (Table 3), increased predictive value to 100% accuracy for both remitters and non-remitters. In Model 2 (Table 3), the addition of DLPFC(46)-DMN(s32) connectivity, also improved predictive value, with 94% accuracy of remission.
DLPFC(p9-46v)-VIS(MT+) connectivity alone predicted 83.3% (88.9% of remitters and 77.8% of non-remitters). The addition of ventral VIS connectivity in Model 3 (Table 3), also increased predictive value to 94% accuracy of remission. In ROC analyses, Models 1-3 ( Fig. 5B) accounted for AUC values of 100%. In LOOCV analyses, Models 1-3 showed 0.83-0.89 accuracy (Table 4). parcelwise analysis. Given the overall significant involvement of reduced connectivity with the MT+ complex and within the ventral VIS region in prediction of ECT response, follow-up analyses explored connectivity related to individual parcels of these regions.
Connectivity measures, motion displacement regressors and multiple comparison correction at baseline. No significant correlation between the mean relative motion displacement from frame-to-frame and percentage change in depression scores (r = −0.15, p = 0.55), nor between the mean absolute motion displacement from the first fMRI frame and percentage change in depression scores (r = 0.04, p = 0.87), were observed.
Further inspection of the association between baseline functional connections and change in depression scores revealed no significant effects of motion displacement (mean absolute or mean relative) on correlation values (Table 1). After FDR controlled multiple comparison correction, connectivity within aDMN(10r), or between DLPFC(46) and aDMN(s32), or DLPFC(p9-46v) and VIS(MT+), remained significant (Table 1). Of note, DLPFC(p9-46v)-VIS(MT+) survived multiple comparison correction after adjusting by motion displacement regressors, in line with the notion of distance-dependence bias 58 . Within the MT+ region, connectivity between FST and DLPFC(p9-46v), adjusted by motion displacement regressors, also remained significant after FDR correction (Table 1).
Cortical Thickness and RSFC. No significant effect of treatment response on cortical thickness was found.
Cortical thickness was not associated with remission nor with treatment response.

Discussion
This study evaluated RSFC patterns prior to and following ECT, with the goals of both identifying individuals who would benefit maximally from ECT treatment and evaluating RSFC changes associated with successful ECT. As in prior studies of this type 23,59-61 , pretreatment reduced connectivity (decreased negative correlation) within the fronto-limbic network, i.e., between DLPFC(46) and area 25, correlated with the magnitude of subsequent response and significantly predicted which subjects would enter remission. Similar relationships were observed for connectivity between DLPFC and DMN, also consistent with prior observations 59,62-66 ; our findings regarding the fronto-limbic and DMN networks are further discussed in Supplementary Discussion.
A novel finding of the present study is that baseline reductions in RSFC between DLPFC and VIS regions, as well as reduced local connectivity within VIS, also significantly predicted subsequent treatment response, and changed along with improvement in depression. Moreover, inclusion of pretreatment connectivity reductions within the ventral VIS region along with other measures in the two-factor prediction algorithm significantly improved the accuracy of prediction and permitted ~100% discrimination between remitters and non-remitters within this sample, and >80% accuracy in a confirmatory LOOCV analysis.
VIS disturbances in TRD have previously been demonstrated using task-based activation and functional connectivity 45,56,67,68 , or metabolite concentration approaches 69,70 , but RSFC with or within VIS regions and treatment response in TRD has not been previously investigated. These findings thus provide strong evidence both for VIS cortical involvement in the pathophysiology of TRD and for the utility of RSFC as a predictor of ECT response in depression.
To our knowledge, this is the first study to evaluate RSFC involving VIS cortex as a predictor of treatment response. Although sensory functions were once considered to be intact in neuropsychiatric disorders, over recent years there has been increased documentation of VIS cortical dysfunction in conditions such as schizophrenia 74 , autism spectrum disorder 75 and aging 76 . Higher order VIS 2 areas are divided into 3 regions, i.e., dorsal, ventral and MT+ complex. Consistent with our a priori hypothesis, pretreatment reduced connectivity (reduced positive correlation) between DLPFC neg and VIS was highly predictive of ECT treatment response including remission (Fig. 2C). Within DLPFC neg , parcel DLPFC(p9-46v) showed the highest correlation, while within MT+ complex, parcels FST and LO1 were most involved. Accuracy of models based on VIS connectivity were similar to models involving DLPFC and DMN.  www.nature.com/scientificreports www.nature.com/scientificreports/ Furthermore, when RSFC involving ventral VIS regions was included in two-predictor models, significant added predictive value was obtained over and above contributions of DLPFC and DMN (Fig. 5B). Thus, reduced positive RSFC within ventral VIS (Fig. 4C) and aDMN(10r) (Fig. 3C), or between DLPFC(p9-46v) and VIS(MT+) (Fig. 2C), or negative RSFC between DLPFC(46) and aDMN(s32) (Fig. 1C), at baseline significantly predicted ECT response.
Pretreatment DMN-VIS reduced connectivity (reduced negative correlation) also significantly predicted remission, although to a lesser extent than DLPFC-VIS connectivity; i.e., reduced connectivity between aDMN(10r) and MT+ VIS (e.g., parcels FST, LO1). After treatment, increased positive RSFC between VIS and DLPFC, or increase negative between VIS and DMN, were highly correlated to symptom improvement (Fig. 6B).
Although the physiological basis for the correlations between VIS connectivity and ECT response are unknown, regions such as MT+ complex are directly or indirectly coupled to parietal and frontal regions associated with sensory-motor integration 77 . Multisynaptic inputs in non-human primates from the frontal cortex to MT+ complex and ventral stream are derived from ventral area 46 78 , which is possibly involved in carrying functionally diverse top-down signals to each VIS region (i.e., dorsal, ventral and MT+). www.nature.com/scientificreports www.nature.com/scientificreports/ The MT+ complex incorporates areas in lateral occipital and posterior temporal cortex, and is extensively activated during both motion and social cognition tasks 2 . Within MT+, the connectivity in non-human primates between area FST (dorsal) and the dorsal processing stream directed to posterior parietal cortex is important in spatial aspects of vision, while FST (ventral) connectivity to the ventral processing stream is related to object vision 79 . Thus, the MT+ parcels FST and LO might function as an intermediary of top-down regulation of dorsal and ventral streams of VIS.
The ventral stream incorporates face-sensitive regions such as FFC 2 and PIT 80 and color sensitive regions such as V8 81 . Patients with MDD show increased FFC activity to sad faces and decreased activation to happy faces 82 , and slowed face emotion recognition 83 . Moreover, alterations in color processing are known both scientifically 53,84-87 and colloquially ("seeing the world through grey tinted glasses") 88 in depression. More basic deficits in VIS function, such as alterations in sensory event-related potentials or center-surround inhibition of motion processing 89 are also reported, consistent with our VIS findings.
A surprising result of our study is that the strongest model for prediction of ECT response involved connectivity within ventral VIS by itself, rather than connectivity between VIS and other brain regions, in combination with connectivity within aDMN. Although the basis for the intraregional dysconnectivity is unknown, both serotonergic and GABAergic theories can be considered. In humans, VIS cortex receives dense serotonergic innervation. Serotonergic fibers arise in the raphe nuclei and project anteriorly to innervate inferior and medial prefrontal cortex along with looping posteriorly to terminate in VIS regions. Treatment of depression with the serotonin 5-HT2A receptor agonist psilocybin improves emotional processing 83 , suggesting potential serotonergic mediation of the effects. Additionally, ECT-induced changes in several 5-HT-receptor subtypes 18,90,91 in the central nervous system, have also been reported.
Occipital GABA levels are also reduced in MDD 92 , consistent with center-surround inhibition abnormalities 89 . Furthermore, such levels increase following ECT 93 . Both serotonin 94 and GABA [95][96][97] , may modulate RSFC in healthy individuals. The present study suggests that disruption of intrinsic connectivity within both VIS and DMN may indicate both need for and successful response to treatments such as ECT that may function in part through modulation of serotonergic and GABAergic activity. Thus, to the extent that serotonergic and GABAergic dysfunction is involved in TRD, disturbances in RSFC between VIS cortex and DLPFC/DMN are expected as part of a larger RSFC disturbance.
The findings of impaired connectivity within ventral VIS also converge with reported alterations in attention to happy vs. sad faces in depression. Connectivity within VIS cortex, including ventral VIS, may be modulated by top-down connections from DLPFC and limbic regions such as amygdala or sgACC [98][99][100][101] . Thus, even though connectivity was obtained during the resting state, it is possible that decreased top-down DLPFC or increased top-down limbic modulation of VIS may have contributed to the present results. Future studies evaluating modulation of RSFC as a function of attention to affective stimuli are therefore required.
Alternatively, intrinsic abnormalities of ventral VIS connectivity may lead to intrinsic biases in VIS function that cannot be overcome through usual top-down control mechanisms. VIS connectivity may also be indexed www.nature.com/scientificreports www.nature.com/scientificreports/ by generation of the posterior alpha rhythm, with reduced connectivity associated to an alpha synchronization deficit 102 . Alpha rhythms in parieto-occipital cortex are controlled by top-down signals 103 and their disruption might lead to a suboptimal state of neural synchronization. Future studies would thus be needed to evaluate the potential utility of resting posterior alpha for prediction of ECT response.
Based upon the present pattern of results, we propose a dysfunction in the hierarchical organization within and between VIS, DMN, and DLPFC neg subnetworks as a new biomarker of ECT response, which also sheds light on underlying mechanisms. Specifically, we observed that TRD patients who show the best response to ECT show a pretreatment pattern characterized by reduced anti-correlation between DLPFC neg -DMN and DMN-VIS, as well as reduced positive correlation between VIS-DLPFC neg or within VIS and DMN (Fig. 6A).
Moreover, effective ECT increases the strength of anti-correlations between DMN and DLPFC or VIS, and of the positive correlation between VIS and DLPFC neg (Fig. 6B), or within VIS and DMN. The resting anticorrelation between DLPFC and DMN is extensively studied and serves as the basis for suppression of DMN during active task conditions. Our results suggest that failures of deactivation may result in part from reduced connectivity both within DMN and between DMN and DLPFC. The VIS-DLPFC correlation is also studied in the context of attentional filtering of distracting information (bottom-up/top-down inhibitory control of the distractor related processing). The VIS-DMN anti-correlation is less studied, but may underlie the ability of VIS stimuli also to suppress DMN activity 104 .
Aberrant DMN connectivity is well established in MDD 29,105,106 , including TRD 32,107 . The present findings suggest that ECT may function primarily to restore the normal 108 anti-correlation of DMN with both "top down" and "bottom up" networks, in order to suppress ongoing DMN hyperactivity that may be associated with increased "mind wandering" relative to goal-directed activity. Positive connectivity between DLPFC and VIS regions is critical for normal goal-directed VIS activity 109 . ECT-induced changes in connectivity may therefore restore both baseline VIS biases and top-down VIS control. Interestingly, our study implicates both face-(e.g. FFC, PIT) and color-(e.g. V8) parcels within ventral VIS, consistent with popular depictions of the disorder.
The only model to obtain 100% accuracy in both logistic regression and ROC analyses involved connectivity within ventral VIS and aDMN(10r) (Model 1, Table 3), or aDMN(s32/10r) (Model 5, Supplementary Table 3). The rostral MPFC (i.e., 10r) is considered a focal point of neural communication, participating in multiple functional networks and enabling high levels of functional diversity (functional hub) 110 . Disturbances of this hub region and their interconnections are likely to cause severe impairments due to their influential role in global integrative processes.
Our results showed abnormal functioning of this hub region was associated to disturbances in the pattern of dynamic interactions (outgoing projections) with central regions of attentional networks, such as DLPFC and VIS, with decreased connectivity intra-aDMN(10r) associated to decreased connectivity intra-DLPFC or DLPFC-VIS, and DLPFC-DMN or DMN-VIS (connected components) (Fig. 5A), reinforcing the central role for 10r hub node in the overall network structure and dynamical organization. Depression has also been associated with abnormal topological organization of brain networks, including disrupted global integrity and regional connectivity 111 .
On the other hand, decreased intra-VIS connectivity was only associated to decreased intra-DLPFC connectivity (Fig. 5A), consistent with the observation of primary regions (e.g., VIS) to participate in small number of functional networks compared to the participation of 10r in multiple functional networks 110 . Consistent with current results, Zhang et al. 112 showed decreased regional connectivity (degree efficiency and betweenness) in the DLPFC and occipital regions. As such, the model combining both, RSFC within 10r and ventral VIS, includes two core non-overlapping components of the overall network dysfunction in TRD.
The posterior alpha network comprises the occipital lobe and parts of the temporal and medial posterior-parietal cortex (including posterior cingulate gyrus and precuneus) and temporal lobes 113 . After effective treatment, connectivity changes within (increased correlation) and between VIS and pDMN (increased anticorrelation) were associated with good response. Connectivity changes propagated through the entire VIS network including MT+ complex (e.g., FST, L03, V3CD), dorsal stream (e.g., V3A, V3B, V6A, V7) and ventral stream (e.g., PIT), extending to pDMN (e.g., 31pv, v23ab), suggesting modulatory effects in the intrinsic activity of VIS system.
In addition to predicting ECT response, the present results argue for a move in scalp location of the standard TMS probe placement ≥ 20-50 mm of geodesic distance (dg) (defined as the length of the shortest directed path from the current canonical location) from the present location which is defined based upon the 5cm-rule (corresponding to vertex 30641) (Fig. 7A). Furthermore, it argues strongly for individual targeting of rTMS to areas 46 or p9-46v, which correspond to the parcels of DLPFC region that are anti-correlated to sgACC (DLPFC neg ). These findings are consistent with those of Fox et al. 15,71,72 , who also suggested that the DLPFC neg (BA46) region may serve as a critical treatment target in depression, and support the role of DLPFC-sgACC, DLPFC-aDMN, and DLPFC-VIS RSFC both as predictors of need for ECT, and correlates of change.
There are several limitations of the present study. First, the sample size is relatively small, necessitating replication in larger cohorts. There were also no cohorts receiving other treatment types (e.g. antidepressants, ketamine) so that it is unknown whether the network effects predict general treatment response or response specifically to ECT.
Second, in order to obtain 100% correct classification, we required use of two independent predictors. Because the model was not pre-specified, the present results must be considered exploratory and must be confirmed in a future sample. Nevertheless, analyses were constrained using a pre-planned parcellation scheme, which limited the number of comparisons. Specifically, DLPFC neg , sgACC, aDMN, ventral VIS and MT+ complex were all identified a priori. DLPFC neg -sgACC and DLPFC neg were selected for analyses based upon prior literature. Although 3 VIS regions were used in analyses, the findings regarding DLPFC-MT+ connectivity were sufficiently strong to survive FDR correction for multiple comparisons. The findings with intra-ventral connectivity were weaker, www.nature.com/scientificreports www.nature.com/scientificreports/ but were additive to several other connectivities (DLPFC-sgACC, DLPFC-aDMN, DLPFC-VIS, intra-aDMN), providing convergent support. Predictive models showed 0.83-0.89 accuracy in LOOCV analyses even following control by motion regressors and FDR correction, and thus support further studies with this algorithm.
As opposed to the present study, a recent study 114 evaluated changes in patients with schizophrenia treated with combined antipsychotics and ECT, and showed a five-predictor algorithm (i.e., DMN, temporal lobe network, language network, cortico-striatal network and fronto-parietal network) that correctly classified 83.8% of 13 patients, suggesting that multiple predictors do not, of necessity, produce high predictive values. Here, by uniquely incorporating VIS RSFC measures in our algorithm, we obtain 100% correct classification in a sample of comparable size and with only one-or two-predictors.
Finally, since there is not a multi-modal subcortical parcellation available at this time, we considered only cortical structures in the present study. By contrast, a recent study demonstrated that DLPFC-striatal connectivity significantly predicted TMS response 115  . Patients with comorbid other Axis I or Axis II psychiatric disorders were excluded. All subjects were right-handed and without severe medical conditions. The NYSPI/CUMC Institutional Review Board approved this study. All participants provided written informed consent. This study was performed in accordance with all relevant guidelines and regulations. electroconvulsive therapy. Participants received a full course of right unilateral, frontal ECT. ECT and anesthesia procedures complied with APA Guidelines 116 . Blood pressure, pulse, ECG, and pulse oximetry were monitored prior to anesthetic induction and continuously during the procedure. Seizure manifestations were recorded with two frontal-mastoid EEG channels, as well as motor manifestations with the cuff technique. Using conservative criteria (≥15 seconds), generalized seizures of adequate duration were elicited at each treatment. Ultra-brief right unilateral (RUL) ECT was given with a Thymatron System IV brief pulse device. Seizure threshold (ST) was determined at the first treatment using the dose titration method 117 . Dose at subsequent treatments was at 6xST or maxim output, whichever was lower. Patients remained on pre-existing antidepressant medication without alteration over the course of the study.
The Hamilton 118 rating scale (HDRS, 24 items) was obtained pre/post ECT and used for outcome analyses.
fMRI Data Acquisition and Processing. High resolution anatomical images and resting-state functional MRI were collected pre/post ECT. Post study imaging was performed approximately 48-72 hours after the last treatment to allow sufficient time for recovery from treatment. Anatomical and functional imaging data from each subject were collected and processed using acquisition guidelines and processing pipelines provided by the Human Connectome Project (HCP) 119 . Collected data was also compared to the healthy individuals from the HCP database (https://www.humanconnectome.org/study/hcp-young-adult/data-releases). High-resolution functional imaging scans were completed at the MRI facility at NYSPI using a GE Discovery MR750 3.0 Tesla full body MR system equipped with a 32-channel phased array head coil (Nova Medical, Wilmington MA). Subjects were placed in the scanner with head cushioning to restrict head movement. Following localizer scans (5 minutes), distortion correction scans (B0 fieldmap), a pair of T1-weighted images, and a T2-weighted image are acquired over 25 minutes, followed by one 10-minute resting state fMRI scan. Total scan time was 45 min. Sessions were conducted prior to and following completion of the course of ECT. T1-weighted images acquired for anatomical co-registration were transverse T1-weighted BRAVO sequence with the following acquisition parameters: 3D sagittal, 0.8 mm isotropic, matrix size = 300 × 300, slices = 220, TR = 7.856 ms, TE = 3.108 ms, flip angle = 12°, TI = 450 ms and CUBE T2-weighted image was acquired with these parameters; 3D sagittal, 0.8 mm isotropic, matrix = 320 × 320, # of slices = 220, TR = 2500 ms, TE = 95.708 ms, flip angle = 90°.
Functional images were acquired with a GE-EPI sequence (2.5 mm isotopic, slice plane = transverse, TR = 2500 ms, SENSE factor = 2, TE = 22 ms, matrix = 96 × 96, slices = 54, phase encode = A −> P); an instruction was given to all patients before the functional MRI sequence to have their eyes opened. Images were transferred to a workstation with the HCP processing pipeline version 3.4 installed. The pipeline implements standard automated structural and fMRI processing (movement correction, atlas realignment, creation of cortical surface model etc.) with FSL 5.0.6, FreeSurfer v5.3.0-HCP, and Connectome Workbench v1.0, and two additional procedures that improve upon standard preprocessing: required use of field-mapping sequences to "undistort" GE-EPI images and creation of a "gray-ordinate" CIFTI-format files that only contained data from cortical and subcortical gray matter. The HCP pipelines were modified to a) process the B0 fieldmaps produced by the GE MR scanner and b) to implement slice-time correction (fMRIVolume processing pipeline set to ODD for interleaved).
Resulting structural and functional data were aligned in volume space to the MNI152 atlas and in surface space to the HCP-generated Conte69 surface atlas based on cortical folding patterns. Resting-state data files have undergone removal by regression of CSF, white matter, whole brain, and movement parameters (6 translation/ rotation parameters + quadratic combinations of the 6 parameters + derivatives of these 12 parameters); to avoid slice-time correction affecting the movement parameters, movement parameters were derived from the data before HCP processing 120 . Frames with FD >0.2 mm and/or DVARS with >(75percentile) + (1.5) × (interquartile range) (fsl defaults) were then censored and interpolated 121,122 , and then a bandpass filter with a low cutoff of www.nature.com/scientificreports www.nature.com/scientificreports/ 0.0005 Hz and high cutoff of 0.2 Hz was applied. Subsequent analyses only used the non-censored frames (they did not include the interpolated frames).
Even though the most common approach for removing global structured noise is to remove the mean (across space) fMRI timecourse from the data using global signal regression 123 , there are some limitations associated to this approach given that global signal may differ between patients with psychiatric disorders 124,125 , and because the removal of neural signal may distort the resulting connectivity measures in network-specific ways 126 .

Definition of ROIs.
Regions of interest (ROI) for these analyses were defined using the multi-modal parcellation 2 of human cortex (Supplementary Table 5); all ROIs used for analyses were bilateral. DLPFC, DMN and VIS networks were created manually following grouped regions from the multi-modal parcellation ( Supplementary  Fig. 1A-C).
The sgACC ROI (area 25) was first used as a seed on an independent cohort of 1200 healthy subjects from the HCP database. The sgACC RSFC map generated across 1200 healthy subjects from the HCP dataset was used to find parcels within the DLPFC region 2 with highest negative (DLPFC neg ) correlation with the sgACC (Supplementary Fig. 2), based on previous findings 15 . These negative areas (46, p9-46v, a9-46v, 9-46d) within the DLPFC were used to create our DLPFC neg masks.
The VIS network has been divided into primary VIS (V1), early VIS (V2-V3), dorsal stream, ventral stream and MT+ complex regions. We exclusively focused on higher order VIS regions, grouped into three separate subsystems: dorsal and ventral streams and MT+ complex. These separate subsystems were used to create our VIS masks.

Seed-based RSFC analyses.
Baseline RSFC maps of areas within the DLPFC neg (46, p9-46v, a9-46v, 9-46d) were first created for each TRD patient to explore patterns of connectivity with sgACC (25) and rostral ACC (a24) as predictors of treatment response. Cortical areas within the DLPFC neg with predictive power (46, p9-46v) were then selected to further explore patterns of connectivity with DMN and VIS networks.
RSFC maps of areas within the aDMN (s32, 10r) and pDMN (31pv, v23ab) were created for each TRD patient to further explore patterns of connectivity with VIS network.
RSFC maps of each separate region of VIS network (dorsal, ventral and MT+) were created for each TRD patient to explore patterns of intra-VIS connectivity (intra-regional connectivity). First, the spatial map of each VIS region was used as the predefined mask. Then, RSFC of each VIS region to all voxels in each predefined mask was computed and averaged as the intra-regional connectivity (intra-ventral, intra-dorsal and intra-MT+). Following detection of significant within regional correlations, secondary analyses evaluated the relative contribution of individual parcels within each region by computing parcel-to-region connectivity within region (e.g., FFC to ventral). Network analyses. Initial RSFC analyses explored patterns of connectivity between 4 cortical areas within the DLPFC neg (46, p9-46v, a9-46v, 9-46d) and 2 cortical areas within the sgACC (25) or the rostral ACC (a24); 8 correlation analyses were conducted. Subsequent RSFC analyses were based on a network configuration of 9 ROIs: 2 cortical areas within the DLPFC neg (46, p9-46v), 2 cortical regions within aDMN (s32, 10r), 2 cortical regions within pDMN (31pv, v23ab), and 3 VIS subsystems focused on higher order VIS regions (dorsal, ventral and MT+). Using the RSFC measures obtained from this network, we analyzed (1) within-network RSFC, averaged across hemispheres, and (2) between-network RSFC, averaged across hemispheres; 38 correlation analyses were conducted. Analyses focused first on prediction of the degree of treatment response and remission across individuals; and subsequently on correlates of change. Follow-up VIS analyses focused on significant patterns of connectivity between DLPFC(p9-46v) or DMN(31pv) and VIS(MT+, dorsal), or within VIS(ventral), to evaluate the relative contribution of individual parcels within each VIS region (7 parcels for ventral, 6 parcels for dorsal, 9 parcels for MT+).
Fisher's z transform was applied to individual RSFC maps before group level analyses; uncorrected p-values were reported as only 8 (for initial analyses) and 38 (for network-analyses) comparisons were made; corrected p-values using FDR 129 controlled multiple comparison correction were computed to see whether any of the uncorrected p-values survived. Leave-one-out cross validation (LOOCV) analysis was conducted on FDR corrected predictive models to estimate potential predictive value when applied to larger samples.

Influence of motion regressors on depression scores and connectivity.
We evaluated if depression scores or connectivity measures were linked to motion estimators. The HCP pipeline provides an estimate of average displacement from initial frame (mean absolute) and frame-to-frame (mean relative) displacement for each run and fMRI session. We evaluated linear trends between percentage change in depression scores and subject's motion displacement values (Corr), or between percentage change in depression scores and baseline/ change in connectivity measures controlling by subject's motion displacement values (PCorr). Corrected p-values using FDR 129 were also computed to see whether any of the uncorrected p-values, adjusted by subject's motion displacement values, survived. (2019) 9:5071 | https://doi.org/10.1038/s41598-019-41175-4 www.nature.com/scientificreports www.nature.com/scientificreports/ statistics. All analyses were conducted using R-package (version 3.5.1). To investigate association between continuous variables, Pearson's correlation and partial correlation analyses were used. Partial correlation analyses were adjusted by motion displacement values (mean absolute and mean relative).
Initial analyses were performed using simple correlations between each pretreatment RSFC measure and percent change in depressive symptoms, defined as percentage change in HDRS-24 ([pre-post]/pre] × 100%). Multiple linear regression analyses were then conducted to evaluate the potential additive value of significant functional connections to identify best two-factor predictive models of percentage change in HDRS-24. The ability of pretreatment RSFC patterns to predict symptom remission, defined as final HDRS-24 score ≤ 7, was assessed using multiple logistic regression and the area under the receiver operating characteristic (ROC) curve. ROC analyses were conducted using R-package pROC (version 1.8) 130,131 ; confidence interval (95% CI) used bootstrapping (2000 stratified bootstrap replicates). Leave-one-out cross validation (LOOCV) analysis was conducted on FDR corrected predictive models; confidence interval (95% CI) of accuracy measure used bootstrapping (1000 iteration). Subsequent analyses used simple correlation analyses to asses ECT-induced changes on baseline connectivity measures by computing correlation between (post-pre) connectivity measures and percent change in depressive symptoms. For two group comparisons, two-sample t-test were used. Sample descriptive statistics for continuous variables are reported as mean ± s.e. All statistics are two-tailed, with preset alpha level for significance of p < 0.05, and n value of 18.
Cortical Thickness and RSFC. To confirm that changes in cortical thickness did not affect RSFC analyses, cortical thickness was evaluated (Supplementary Methods) as a function of both time (pre/post ECT) and group (remitter/non-remitter), using repeated ANOVA. In addition, we computed correlation analyses between changes in cortical thickness and improvement in depression. Across analyses, no significant relationships were observed between cortical thickness and ECT response.