Subcallosal cingulate functional connectivity in depression: a systematic review of brain stimulation-induced changes and pretreatment connectivity predictors.

Subcallosal cingulate functional connectivity in depression: a systematic review of brain stimulation–induced changes and pretreatment connectivity predictors - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Transl Psychiatry . 2026 Apr 21;16:294. doi: 10.1038/s41398-026-03999-3 Subcallosal cingulate functional connectivity in depression: a systematic review of brain stimulation–induced changes and pretreatment connectivity predictors Adèle Henensal 1, 2 , David Attali 2, 3 , Jean-François Aubry 3 , Marion Plaze 1, 2, ✉ Author information Article notes Copyright and License information 1 Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, IMA-Brain, 75014 Paris, France 2 GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, Unité de Neuropsychiatrie interventionnelle, 75014 Paris, France 3 Institute Physics for Medicine Paris, Inserm U1273, ESPCI Paris, PSL University, CNRS UMR 8063, 75015 Paris, France ✉ Corresponding author. Received 2025 Sep 9; Revised 2026 Feb 20; Accepted 2026 Mar 16; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13230609  PMID: 42014673 Abstract Major depressive episode (MDE) is a common mental disorder that severely impacts patients’ lives and carries a high suicide risk. About 30% of patients are resistant to oral antidepressants, necessitating alternative approaches such as neurostimulation. The subcallosal cingulate (SCC), characterized by hyperactivity and altered functional connectivity (FC) in MDE, has emerged as a key target for interventions like deep brain stimulation (DBS) and transcranial focused ultrasound stimulation (TUS). Other techniques, including repetitive transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT), may influence SCC activity indirectly via network-level effects. We searched multiple databases for studies investigating changes in SCC connectivity following various neurostimulation techniques in depression. We included 28 studies using resting-state functional MRI to investigate SCC FC changes following neurostimulation for depression with different modalities: ECT, DBS, rTMS, tDCS, TUS. Results showed high variability across studies. rTMS targeting the DLPFC modulated SCC FC with the OFC, precuneus, and default mode network (DMN), while other targets affected connectivity with the hippocampus and supramarginal gyrus. Stronger baseline anticorrelation between SCC and DLPFC predicted better rTMS response. ECT studies reported heterogeneous effects, involving the temporal pole and vmPFC. TUS targeting SCC increased FC with the DLPFC, whereas TUS to the DLPFC enhanced FC between SCC subregions and mPFC or cerebellum. Findings remain heterogeneous, likely due to methodological inconsistencies, including variability in SCC ROI definitions and stimulation protocols. Standardizing analysis methods is essential to improve reproducibility and clarify the SCC’s role in treatment response. Subject terms: Depression, Neuroscience, Predictive markers Highlights The subcallosal cingulate has emerged as a key hub in the pathophysiology of depression. Changes in subcallosal cingulate functional connectivity following brain stimulation differ across brain stimulation treatment modalities. Pre-treatment brain connectivity patterns could predict clinical response to brain stimulation therapies. Methodological standardization is needed to improve reproducibility and consistency across brain connectivity studies. Introduction Major depressive episode (MDE), one of the most common mental disorders with a lifetime prevalence of 20% [ 1 ], is classified as a leading cause of disability worldwide [ 2 ]. MDE not only significantly reduces quality of life but is also associated with increased disability, frequent physical and psychiatric comorbidities, and heightened mortality—particularly due to medical complications and suicide, which accounts for over 700,000 deaths annually [ 3 , 4 ]. For moderate-to-severe MDE, first-line treatments include antidepressant medication, psychotherapy (e.g., CBT), or their combination [ 5 – 7 ]. 30–48% of MDE are treatment-resistant, commonly defined as the non-response to at least two different lines of pharmacological treatment [ 4 , 8 ]. This highlights the need for alternative treatments such as brain stimulation. Several methods, including electroconvulsive therapy (ECT) and invasive deep brain stimulation (DBS), have been developed and shown efficacy but come with significant side effects [ 9 , 10 ]. Other non-invasive techniques such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have emerged and demonstrated efficacy in reducing symptoms of depression [ 11 – 13 ]. These last decades, transcranial focused ultrasound stimulation (TUS), a non-invasive brain stimulation technique, has become an established treatment for pharmacoresistant major depressive disorder (MDE), demonstrating efficacy in symptom reduction [ 14 – 17 ]. Although evidence of efficacy has been reported for these brain stimulation approaches, their mechanisms of action remain unclear, with the main hypothesis involving modulation of brain activity and functional connectivity (FC) of the targeted regions. Many neural networks are involved in depression, but one region in particular has been highlighted in recent years: the subcallosal cingulate (SCC) [ 18 , 19 ]. SCC is recognized as a key structure that plays a critical role in depression symptoms and treatment response [ 20 ]. Indeed, this region exhibits structural and functional connections with both the prefrontal cortex and the limbic system [ 18 , 21 , 22 ]. In particular, using resting-state functional MRI (rs-fMRI), it has been demonstrated an increased SCC activity in MDE, which tends to normalize following effective pharmacological or brain stimulation treatments [ 18 , 23 , 24 ]. Subsequent studies found that alterations in FC of this region were associated with depression [ 25 – 28 ]. SCC has thus been identified as a first-choice target candidate for deep neuromodulation using DBS [ 20 , 29 , 30 ] or TUS [ 14 , 16 ]. Furthermore, numerous studies have shown that the efficacy of rTMS or intermittent theta burst stimulation (iTBS) is associated with the degree of anti-correlation between a superficial cortical target within the dorsolateral prefrontal cortex (DLPFC) and the SCC—an effect that may be partly mediated by the inhibitory FC of the left DLPFC on the SCC [ 31 – 33 ]. More recently, a study demonstrated that a five-day course of TUS targeting the left SCC significantly reduced depression severity in five patients with TRD [ 14 ]. But despite extensive research on this topic and related hypotheses, the specific changes in SCC connectivity following neurostimulation treatments remain unclear. This review aims to synthesize current findings on changes in connectivity of the SCC following various forms of brain stimulation such as ECT, DBS, rTMS, tDCS and TUS, in order to better understand the neural mechanisms underlying observed therapeutic effects. By highlighting converging evidence and discrepancies across studies, we seek to identify emerging trends and gaps in the literature. Additionally, we explored the potential of SCC FC as a predictive biomarker of efficacy of brain stimulation for depression. Methods Literature search A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) criteria [ 34 ]. This systematic review was pre-registered in PROSPERO (CRD420250550271). Articles were searched using PubMed, Cochrane Library and Web of Science search engines. The search criteria were: ((antidepress*[Title/Abstract]) OR (Depress*[Title/Abstract])) AND (BA25[Title/Abstract] OR subgenual[Title/Abstract] OR subcallosal[Title/Abstract] OR SCC[Title/Abstract] OR OR SCG[Title/Abstract]) AND (resting[Title/Abstract] OR (“functional MRI” [Title/Abstract]) OR (“functional connectivity” [Title/Abstract]) OR (“effective connectivity” [Title/Abstract]) OR (fMRI[Title/Abstract])) AND ((TMS[Title/Abstract]) OR (“magnetic stimulation”[Title/Abstract]) OR (rTMS[Title/Abstract]) OR (iTBS[Title/Abstract]) OR (ECT[Title/Abstract]) OR (electroconvulsive[Title/Abstract]) OR (DBS[Title/Abstract]) OR (neuromodulation[Title/Abstract]) OR (“brain stimulation”[Title/Abstract]) OR (TUS[Title/Abstract]) OR (FUS[Title/Abstract]) OR (“focused ultrasound” [Title/Abstract]) OR (“transcranial ultrasound” [Title/Abstract]) OR (tDCS[Title/Abstract]) OR (“transcranial direct current stimulation”[Title/Abstract])). The search process was conducted for articles published until 15 December 2025. Study selection criteria All results were screened. Relevant articles were further assessed through full-text review and had to meet the following criteria: Inclusion criteria: - Studies with adult patients with MDE as part of a major depressive disorder (MDD) or a bipolar disorder (BP) - Studies assessing one of the five treatment methods: rTMS, ECT, tDCS, DBS, TUS - Studies using resting-state functional magnetic resonance imaging (rs-fMRI) conducted on patients before and after treatment, or just before treatment - Studies reporting SCC functional connectivity results, including those that used other seed regions, provided they reported connectivity changes involving the SCC - Studies reporting original data - Studies available in full text in English or French - Studies published in peer-reviewed journals Exclusion criteria: - Studies focused on children or adolescents - Studies about other psychiatric diagnoses - Studies assessing other treatments, such as pharmacological treatments or psychotherapies - Studies using task-fMRI - Studies with animal model - Review or meta-analysis - Full text not in English or French A total of 442 potential articles were identified: 131 from PubMed, 229 from Web of Science, 78 from the Cochrane Library, and 4 additional articles through manual screening of reference lists (Supplementary Table S1 ). Out of the 442 articles, 123 were duplicates and were subsequently removed. The remaining 319 articles were screened by their titles and abstracts, and 140 were excluded. The remaining 179 articles were assessed for eligibility. Based on the inclusion criteria, 28 articles were included in the final review (Fig. 1 ). Fig. 1. PRISMA Flow chart of the screening process. Open in a new tab ECT : electroconvulsive therapy; FC : functional connectivity; rs-fMRI : resting-state functional MRI; SCC: subcallosal cingulate; TMS : transcranial magnetic stimulation; TUS : transcranial ultrasound stimulation. Data collection Data were extracted from full-text articles by two authors (A.H., M.P.) independently; data were then reviewed together. Collected data included author, year, brain stimulation technique, sample size, demographics, study design, anatomical target, SCC FC changes after treatment, and SCC FC prediction data. Studies were grouped by brain stimulation techniques. Studies outcomes and missing data are reported in the main text and the summary table. No sensitivity analysis, risk of bias analysis, statistical analysis or meta-analysis were performed due to the small number of studies included per technique and therefore the lack of direct comparisons of outcomes common to these studies. Figures Figures were drawn by using pictures of Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License ( https://creativecommons.org/licenses/by/ 3.0/). Results We identified 28 articles that met the inclusion criteria (Fig. 1 ), 21 about rTMS, 4 about ECT and 3 about TUS. No studies involving tDCS or DBS met the inclusion criteria.The main characteristics of the included studies involving rTMS, ECT and TUS are summarized in Table 1 , and Figs. 2 , 3 and 4 , respectively. Table 1. Summary of rTMS/iTBS, ECT and TUS studies. Authors Group Treatment Changes after treatment Outcome predictions from baseline rs-fMRI rTMS/iTBS studies Rajasekharan et al. [ 48 ] Brain stimulation 46 MDD rTMS 30 sessions Targeting the L-DLPFC (Beam/F3 method with electric-field modeling) / The stronger the anticorrelation between SCC and L-DLPFC (defined by the E-field-modeled stimulation site) at baseline, the greater clinical improvement Zhao et al. [ 39 ] Cell reports medecine Active group: 26 MDD Sham group: 26 MDD rTMS 16 sessions Targeting both the DLPFC and the dmPFC Decreased FC between the dmPFC and the SCC The stronger the positive correlation between SCC and dmPFC at baseline, the greater clinical improvement Hearne et al. [ 41 ] Psychotherapy and Psychosomatics 61 TRD (28 after rs-fMRI) rTMS 20–30 sessions Targeting the L-DLPFC (point more anticorrelated with the R-SCC) Decreased anticorrelation between the L-DLPFC and R-SCC / Duprat et al. [ 50 ] NeuroImage 24 MDD 84 HC TMS single pulse interleaved fMRI 1 session of 71 stimulations Targeting the L-PFC (point more correlated with the L-SCC) / The stronger the positive correlation between the stimulation site (PFC) and the L-SCC at baseline, the stronger the TMS-induced SCC inhibition, which is associated with lower reported negative affect in patients Cui et al. [ 37 ] Psychological Medicine 68 MDD Dual group ( n = 23) Single group ( n = 22) Sham group ( n = 23) Dual group :cTBS of the R- lOFC followed by 20 Hz rTMS of the L-DLPFC Single group : Sham cTBS of the R-lOFC and active 20 Hz rTMS of the L-DLPFC Sham group 2 : Sham cTBS of the R-lOFC and sham rTMS of the L-DLPFC 20 sessions Decreased positive FC between L-SCC and R-lOFC (in the dual group) (FC positive to negative) / Pan et al. [ 46 ] Journal of Psychiatric Research 28 MDD iTBS 10 sessions Targeting the L-DLPFC Decreased positive FC between R-SCC and L-DLPFC / Li et al. [ 44 ] Translational psych 26 MDD iTBS 50 sessions Targeting the L-DLPFC Decreased FC between L-SCC and L-hippocampus / Tan et al. [ 43 ] Brain stimulation 34 MDD (TRD) 1 Hz rTMS 20 sessions Targeting the R-DLPFC Decreased anticorrelation between L-SCC and R-supramarginal gyrus / Elbau et al. [ 58 ] Am J Psychiatry 295 MDD 10 Hz rTMS or iTBS 20 sessionsTargeting the L-DLPFC / Stronger anticorrelation between the L-DLPFC stimulation site and the SCC predicted better clinical response. Hopman et al. [ 47 ] Journal of aff disorder 70 MDD HF rTMS 20 sessions Targeting the L-DLPFC / Long-term responders showed stronger positive correlation between SCC and L-frontal pole, L-superior parietal lobule and L-occipital cortex Cash et al. [ 71 ] JAMA Psychiatry 26 MDD 10 Hz rTMS 15 sessions Targeting the L-DLPFC / Stronger anticorrelation between SCC and L-DLPFC predicted better improvement. Persson et al. [ 42 ] Behavioural Brain Research 23 MDD OR BP iTBS 20 sessions Targeting the dmPFC (bil) sham and active / Stronger anticorrelation between SCC and bilateral post precuneus predicted better improvement Ge et al. [ 26 ] Brain Stimulation 32 MDD 24 HC 10 Hz rTMS or iTBS 20–30 sessions Targeting the L-DLPFC Decreased positive FC between SCC and L-fusiform The stronger anticorrelation between SCC and R-DLPFC predicted greater improvement on HRSD immediately and 3 months after treatment Cash et al. [ 59 ] Biol Psychiatry 47 MDD 1020 HC 10 Hz rTMS Targeting the L-DLPFC / Stronger anticorrelation between SCC and L-DLPFCpredicted better clinical outcome Williams et al. [ 40 ] Brain 5 MDD or BP II iTBS 50 sessions Targeting the L-DLPFC Increased anticorrelation between SCC and L-DLPFC / Taylor et al. [ 36 ] Journal of aff disorder 32 MDD 10 Hz rTMS 20 sessions Targeting the L-DLPFC with rTMS active and rTMS sham Decreased FC between SCC and L-IPL and also L-OFC in responders (including sham responders) (FC positive to negative) / Weigand et al. [ 45 ] Biol Psychiatry 25 MDD (TRD) 16 MDD 10 Hz rTMS mean of 28.5 sessions Targeting the L-DLPFC / A stronger anticorrelation between SCC and L-DLPFC predicted a greater clinical improvement Baeken et al. [ 35 ] Biol Psychiatry 44 MDD (TRD) 44 HC accelerated iTBS 20 sessions Targeting the L-DLPFC Increased positive FC between SCC and bilateral mOFC / Liston et al. [ 38 ] Biol Psychiatry 17 MDD OR BP II 35 HC 10 Hz rTMS 25 sessions Targeting the L-DLPFC Decreased positive FC between SCC and bilateral vmPFC and pgACC Decreased FC between SCC and L-precuneus - Responders exhibited higher baseline positive correlations between SCC and multiple nodes of the DMN: vmPFC, dmPFC, pgACC and mOFC - Responders exhibited higher baseline positive correlations between SCC and areas of the CEN: R-DLPFC and PPC Baeken et al. [ 31 ] The world Journal of Biological psy 20 MDD (TRD) 20 Hz rTMS 20 sessions Targeting the L-DLPFC Increased FC between SCC and L-superior frontal gyrus (FC negative to positive) Responders showed higher anticorrelation between SCC and L-superior frontal gyrus Salomons et al. [ 49 ] Neuropsychopharmacology 25 MDD or BP I or II 10 Hz rTMS 20 sessions Targeting the right and left dmPFC Decreased FC between SCC and Caudate Better response to treatment was predicted by: - A higher positive correlation between SCC and DLPFC - A higher anticorrelation between SCC and insula, putamen, and parahippocampus/amygdala - A higher positive correlation between SCC and dmPFC ECT studies Moreno-Ortega et al. [ 54 ] Scientific reports 18 MDD Right unilateral ECT / A lower anticorrelation between SCC and DLPFC predicted better clinical outcome (results didn’t survive to multiple comparison correction) Cano et al. [ 53 ] Brain Stimulation 15 MDD (TRD) 10 HC Bifrontotemporal ECT Decreased positive FC between left SCC and right superficial centromedial amygdala / Argyelan et al. [ 51 ] Transl Psychiatry 16 MDD 10 HC Bifrontal ECT - Decreased positive FC between SCC and vmPFC, bilateral parahippocampal gyrus and bilateral temporal pole - Decreased anticorrelation between SCC and right supramarginal gyrus / Liu et al. [ 52 ] Medicine (Baltimore) 23 MDD Bitemporal ECT, Increased positive FC between L-SCC and L-parahippocampal gyrus, L-pgACC, R-middle temporal pole and R-OFC / TUS studies Cai et al. [ 55 ] Journal of Affective Disorders Active group : 24 MDD Sham group : 12 MDD HC : 11 TUS 10 sessions Targeting the L-DLPFC Decreased FC between the L-DLPFC and the L-SCC / Attali et al. [ 14 ] Brain stimulation 5 MDD (TRD) mTUS 25 sessions Targeting the L-SCC - Increased FC between the L-SCC and the L-DLPFC (FC negative to positive), SMA, PostCG - Decreased FC between the L-SCC and the R-hippocampal and parahippocampal region / Oh et al. [ 15 ] Psychiatry Investig . 23 MDD 12 sham 11 active low intensity tFUS 6 sessions Targeting the L-DLPFC Increased positive FC between the LS-SCC and bilateral mPFC, between the RS-SCC and L-mPFC, between LI-SCC and cerebellum in active group / Open in a new tab Bil bilateral, BP I or II bipolar I or II disorder, CEN central executive network, cTBS continuous theta burst transcranial magnetic stimulation, DMN default mode network, DLPFC dorsolateral prefrontal cortex, dmPFC dorsomedial prefrontal cortex, ECT electroconvulsive therapy, FC functional connectivity, HC healthy controls, HF high frequency, HRSD hamilton rating scale for depression, IPL inferior parietal lobule, iTBS intermittent theta burst transcranial magnetic stimulation, MDD major depressive disorder, mOFC medical orbitofrontal, mPFC medial prefrontal cortex, mTUS metalens-based transcranial ultrasound stimulation, lOFC lateral orbitofrontal cortex, L left, LI left inferior, LS left superior, OFC orbitofrontal cortex, pgACC pregenual anterior cingulate cortex, PostCG postcentral gyrus, PPC posterior parietal cortex, R right, RS right superior, r-TMS repetitive transcranial magnetic stimulation, SCC subcallosal cingulate, SMA supplementary motor area, tFUS transcranial focused ultrasound stimulation, TRD treatment resistant depression, vmPFC ventromedial prefrontal cortex. We reported SCC laterality only when it was mentioned by the authors. Fig. 2. Changes in SCC functional connectivity after rTMS and its predictive value on rTMS efficacy in depression. Open in a new tab A Representations of regions whose functional connectivity with the SCC (in red) changes after rTMS treatment. In dark green an increase of correlation, in light green a decrease, in orange an increase of anticorrelation, in yellow a decrease of anticorrelation, in black a change between anticorrelation/correlation or vice versa. B Representations of regions whose functional connectivity with the SCC (in red) predicts rTMS clinical response. In dark blue a higher anticorrelation at baseline, in light blue a lower anticorrelation, in purple a higher positive correlation and in pink a lower positive correlation. AMG: amygdale; DLPFC: dorsolateral prefrontal cortex; dmPFC: dorsomedial cortex prefrontal; Hipp: hippocampus; mOFC: medial orbitofrontal cortex; L: left; pgACC: pregenual anterior cingulate cortex; R: right; SCC: subcallosal cingulate; vmPFC: ventromedial prefrontal cortex. Fig. 3. Changes in SCC functional connectivity after ECT and its predictive value on ECT efficacy in depression. Open in a new tab A Representations of regions whose functional connectivity with the SCC (in red) changes after ECT treatment. In dark green an increase of correlation, in light green a decrease, in orange an increase of anticorrelation, in yellow a decrease of anticorrelation, in black a change between anticorrelation/correlation or vice versa. B Representations of regions whose functional connectivity with the SCC (in red) predicts ECT clinical response. In light blue a lower anticorrelation. AMG: amygdale; DLPFC: dorsolateral prefrontal cortex; L : left; OFC: orbitofrontal cortex; pgACC: pregenual anterior cingulate cortex; R : right; SCC: subcallosal cingulate; vmPFC: ventromedial prefrontal cortex. Fig. 4. Changes in SCC functional connectivity after TUS in depression. Open in a new tab Representations of regions whose functional connectivity with the SCC (in red) changes after TUS treatment. In dark green an increase of correlation, in light green a decrease. bil : bilateral; DLPFC: dorsolateral prefrontal cortex; mPFC: medial prefrontal cortex; L: left; PostCG : postcentral gyrus; R : right; SCC: subcallosal cingulate; SMA : supplementary motor area. Changes in functional connectivity of the SCC after rTMS Many studies have investigated changes of SCC FC after rTMS, yet the identified regions reveal considerable variability and the direction of these changes also varies significantly across studies (Table 1 , Fig. 2 A ). For instance, following L-DLPFC rTMS, Baeken et al., [ 35 ] reported an increase in the positive correlation between the SCC and the bilateral orbitofrontal cortex (OFC), which was correlated with a decrease in feelings of hopelessness, according to the Beck Hopelessness Scale (BHS). Conversely, Taylor et al. [ 36 ] observed that the FC between the SCC and both the left OFC and left inferior parietal lobule (IPL) transitioned from initially positive to negative after sessions of rTMS on the left dlLPFC. Similarly, Cui [ 37 ] found a shift from a positive to negative FC between left SCC and right lateral OFC; but with cTBS targeting the right OFC and iTBS targeting the left DLPFC. Ge et al. [ 26 ] also observed a decrease in positive SCC FC with the left fusiform gyrus which was associated with clinical improvement as measured by the HRSD. In contrast, other studies have reported changes in connectivity between the SCC and medial prefrontal regions. Liston [ 38 ] reported a decrease in the positive correlation between the SCC and some regions of the Default Mode Network (DMN), such as the ventromedial prefrontal cortex (vmPFC), posterior cingulate cortex (PCC), and pregenual anterior cingulate cortex (pgACC), which were abnormally high in depressed patients prior to rTMS. This same team also noted that a positive correlation between the SCC and the precuneus became negative after rTMS on the left DLPFC. Similarly, Zhao et al. [ 39 ] reported a decrease in FC between the SCC and the dorsomedial prefrontal cortex (dmPFC) associated with MADRS improvement following rTMS applied both on the DLPFC and the dmPFC. Moreover, Salomons et al. found that after rTMS on the right and left dmPFC, functional connectivity between the SCC and the caudate decreased, which was correlated with clinical improvement. Moreover, there are reports of increased anticorrelation after rTMS in specific regions. Williams et al. [ 40 ] found that iTBS over the left DLPFC enhanced anticorrelation between this region and the SCC. Similarly Pan et al. [ 33 ], also using iTBS on the same target, observed a reduction in the positive correlation between the left DLPFC and the right SCC, whereas Hearne et al. [ 41 ], using conventional rTMS, reported a decrease in anticorrelation between these areas. In both studies, changes in functional connectivity were not associated with clinical changes. In contrast, iTBS applied to the left dorsomedial prefrontal cortex (dmPFC) resulted in a stronger anticorrelation between the SCC and the precuneus [ 42 ]. Additionally, the authors reported an increased positive correlation between the right SCC and the left DLPFC. Tan [ 43 ] reported a decrease in anticorrelation between the left SCC and the right supramarginal gyrus. After several sessions of rTMS, Baeken et al. [ 31 ] observed that the FC between the SCC and the left superior frontal gyrus shifted from a negative to a positive correlation. Li et al. [ 44 ] demonstrated that, following rTMS sessions, the FC between the SCC and the left hippocampus became negative. Predictive value of baseline SCC FC on rTMS efficacy In 2014 Baeken [ 31 ] observed that responders to rTMS exhibited a stronger anticorrelation of the SCC with the left superior frontal gyrus at baseline compared to nonresponders. Weigand [ 45 ] found that a stronger baseline anticorrelation between the left DLPFC and SCC was associated with greater reductions in depression symptoms following rTMS. This was further supported by several studies, reporting that rTMS clinical outcomes are predicted by the strength of anticorrelation between the target DLPFC and the SCC [ 43 , 46 , 47 ]. Rajasekharan et al. [ 48 ] also reported that stronger baseline anticorrelation between the SCC and the left DLPFC stimulation site, defined using electric-field modeling, was associated with greater clinical improvement following rTMS, whereas no significant association was observed when the stimulation site was defined using the anatomical Beam/F3 target alone. However, Salomons [ 49 ] and Duprat [ 50 ] found that, contrary to previous results, a stronger positive correlation -rather than anticorrelation- predicted rTMS efficacy. On the other hand, Ge demonstrated that a stronger anticorrelation between the right (and not left) DLPFC and the SCC predicted greater improvements with left-DLPFC TMS [ 26 ]. Liston [ 38 ] noted that higher baseline functional connectivity between the SCC and several regions of the DMN, such as the vmPFC, PCC, and pgACC, predicted better outcomes after rTMS sessions. They observed a similar pattern for the medial orbitofrontal cortex (mOFC), a region also identified by Baeken, in 2017 [ 35 ], to distinguish responders from non-responders, and for the dmPFC, as reported by Salomons in 2014 [ 42 ] and Zhao in 2025 [ 39 ]. Salomon’s team also identified anticorrelations in regions of the limbic system as predictors. They reported that the greater the anticorrelation between the SCC and the amygdala, insula, and hippocampus, the better the clinical outcomes for patients [ 49 ]. Another region identified as a potential predictor is the precuneus. Indeed, Persson reported better clinical improvement when patients exhibited a stronger anticorrelation between the SCC and the precuneus at baseline [ 42 ]. Hopman et al., in comparing responders to non-responders, observed that long-term responders exhibited a higher positive correlation between the SCC and the left frontal pole, left superior parietal lobule, and left occipital cortex at baseline [ 47 ]. Changes in functional connectivity of SCC after ECT Regarding changes in SCC FC after ECT, the results remain highly diverse in the limited number of included studies (Table 1 , Fig. 3 A ). Two teams identified common brain regions, but opposite directions of changes [ 51 , 52 ]. Liu reported an increase in positive correlation between the left SCC and the temporal pole and gyrus parahippocampal, which was negatively correlated with decreased HAMD scores, whereas Argyelan noted a decrease in the positive correlation in the same regions and with the vmPFC after ECT. Additionally, Argyelan’s team found a decreased anticorrelation with the right supramarginal gyrus, indicating weaker SCC FC with these regions post-treatment. On their side, Liu et al additionally found an increase in functional connectivity between the SCC and the pgACC. In 2016, Cano et al. reported a decrease in the positive functional connectivity of the left SCC with the right superficial ventromedial amygdala after ECT [ 53 ]. Predictive value of baseline SCC FC on ECT efficacy Only one study has reported on the prediction of SCC FC on treatment response in ECT (Fig. 3 B ). Moreno-Ortega, in 2019, found that a lower anticorrelation at baseline between DLPFC and SCC was associated with treatment response. However, this result did not survive multiple comparison correction [ 54 ]. Changes in functional connectivity of the SCC after TUS To date, only three studies have investigated the effect of TUS on SCC FC. In a study by Attali et al., five consecutive daily sessions of deep TUS targeting directly the left SCC resulted in a shift in left SCC FC with the left DLPFC, from initially negative to positive [ 14 ]. Additionally, increased SCC FC was observed with both the left postcentral gyrus and the left supplementary motor cortex. In contrast, a decrease in FC was noted between the SCC and the right hippocampus (Fig. 4 ). Oh, in 2024, subdivided the SCC into left/right and superior/inferior regions for FC analysis. Following TUS targeting the left DLPFC, they reported increased positive correlations in the active group between the left superior SCC and the bilateral mPFC, between the right superior SCC and the left mPFC, and between the left inferior SCC and the cerebellum [ 12 ]. A third study [ 55 ], also targeting the left DLPFC with TUS, reported that a decrease in functional connectivity (FC) between the left DLPFC and the left sgACC in the active group correlated with clinical improvement, which contrasts with the increase observed by Attali et al. (Fig. 4 ). These studies focused exclusively on changes in functional connectivity and did not report predictive outcomes. Discussion Common patterns of SCC connectivity across modalities We reviewed 28 studies investigating the functional connectivity of the SCC in patients with depression undergoing three different non-invasive brain stimulation: ECT, rTMS and TUS. Across neuromodulation techniques, the SCC consistently interacts with prefrontal and limbic regions, forming circuits that appear central to treatment response. In particular connectivity between the SCC and the DLPFC is modulated following rTMS and TUS. Indeed, changes in SCC–DLPFC connectivity have been frequently observed after rTMS [ 40 , 41 , 46 ] and TUS [ 14 , 55 ], although the direction and magnitude of these effects vary across protocols. In addition to prefrontal connectivity, several studies have reported decreases in SCC functional connectivity with regions of the default mode network (DMN), including the vmPFC, precuneus, and inferior parietal lobule. This is particularly relevant, as the SCC and DMN are known to be abnormally hyperconnected in MDD [ 27 , 38 , 56 ]. Reductions between these regions have been observed following rTMS [ 36 , 38 ] and, to a lesser extent, ECT [ 51 ]. Given the DMN’s role in self-referential thought and rumination—core symptoms of depression—these findings suggest that part of the antidepressant effect of rTMS may involve normalization of pathological hyperconnectivity between the SCC and DMN regions. Beyond DMN-related regions, several studies have highlighted the involvement of limbic structures in the functional connectivity of the SCC in response to rTMS [ 44 , 49 ], sometimes associated with better clinical outcomes [ 49 ], supporting the hypothesis that antidepressant effect can be partly mediated by rebalancing of dysfunctional limbic circuits. Although ECT results in limbic changes less consistently, some studies report modifications in SCC–amygdala or SCC–parahippocampal connectivity [ 53 , 57 ]. Moreover, Attali et al. reported decreases in SCC-hippocampal connectivity following TUS [ 14 ], further reinforcing the importance of these regions in mood regulation. Treatment-specific SCC connectivity changes While common circuits can be identified, each neuromodulation modality produces distinct connectivity profiles. rTMS predominantly modulates SCC-DLPFC and SCC-DMN circuits, with some effects on the limbic regions. In contrast, ECT tends to induce changes in the temporal pole compared to rTMS [ 51 , 52 ], with more heterogeneous results of limbic structure. These inconsistencies may partly reflect differences in analytic approaches across studies : Liu et al. used a seed-based, whole-brain and voxel-wise analysis [ 57 ], Argyelan et al. defined their seed based on fALFF changes [ 51 ], and Cano et al. performed ROI-based analysis focusing on the SCC, amygdala and DLPFC [ 53 ]. Such methodological variability likely contributed to inconsistencies in reported effects. Meanwhile, TUS effects are highly dependent on the target: direct SCC stimulation increases SCC-DLPFC connectivity [ 14 ], while DLPFC-targeted TUS produces distinct effects, including SCC-mPFC and SCC-cerebellum connectivity [ 15 ]. Together, these findings suggest that each neuromodulation technique induces different connectivity changes that depend on both the anatomical target and the underlying mechanism of modulation. Treatment-specific predictive SCC connectivity All results in this section come primarily from rTMS studies, with one study examining ECT. Baseline SCC connectivity continues to show promise as a biomarker of treatment response. Moreno et al, reported that lower anticorrelation at baseline between the SCC and the left DLPFC was associated with greater clinical improvement following ECT [ 54 ]. In contrast, in rTMS studies, a strong anticorrelation between the SCC and the left DLPFC stimulation site has repeatedly been associated with better clinical outcomes [ 45 , 58 , 59 ], and was initially proposed by Fox et al. [ 60 ]. Moreover, two studies demonstrated that clinical response is enhanced when stimulation is applied closer to the DLPFC location showing the strongest group-average anticorrelation with the SCC [ 61 , 62 ]. However, several studies have failed to replicate these findings. For instance, Rajasekharan et al. [ 48 ], in a recent study suggested that baseline DLPFC–SCC connectivity predicts clinical improvement only when electric-field modeling is applied, demonstrating that scalp-based targeting can reliably identify DLPFC targets using combined neuroimaging and E-field methods across multiple sites. Gregory et al. [ 63 ] applied rTMS using individualized functional targets (iFTs) -defined as the DLPFC cluster showing the strongest anticorrelation with the SCC- and found that the distance between the stimulation site and each subject’s iFT was not associated with clinical improvement, with no significant correlations or differences between responders and non-responders or remitters and non-remitters. Khosravani et al. [ 64 ] examined a real-world cohort of MDD patients with multiple comorbidities undergoing rTMS. Functional connectivity between each patient’s DLPFC stimulation site and the SCC was assessed using a normative connectome derived from MRI data of healthy subjects. For this reason, their approach was not included in our study, although their findings remain interesting. Indeed, they found that the DLPFC–SCC anticorrelation was not a robust predictor of response across the full sample but became significant when clinical covariates were included or when the analysis was restricted to a small subset of patients meeting strict trial-like criteria. This highlights the influence of patient heterogeneity on connectivity-based predictions and suggests that results from highly selected research cohorts may not generalize to typical clinical populations. In addition to the DLPFC, the OFC has been identified as another relevant predictive region, with greater baseline positive SCC–OFC connectivity associated with improved response to rTMS [ 35 , 38 ]. Limitations of the study and future directions Contrary to the initial objectives and predefined inclusion criteria, no articles involving tDCS or DBS were included in this review. Most studies involving DBS do not include pre- and post-implantation resting-state fMRI due to the technical challenges associated with MRI acquisition in the presence of implanted electrodes. These challenges are well-documented, as implanted electrodes and extension wires produce localized artifacts that obscure both cortical and subcortical regions near the stimulation site, limiting the feasibility of resting-state functional connectivity analyses [ 65 , 66 ]. Consequently, a substantial portion of the DBS literature relies on alternative modalities, such as scalp EEG, intracranial local field potential (LFP) recordings from DBS electrodes, and PET imaging, to investigate SCC-related activity and network-level connectivity [ 67 ]. For example, historical studies by Mayberg et al. [ 23 ]have primarily used PET to assess the effects of DBS in treatment-resistant depression, highlighting both the methodological constraints of fMRI in this context and the relevance of PET for measuring network-level activity. Therefore, the apparent paucity of fMRI data in this area does not reflect a lack of research activity, but rather methodological differences that fell outside our inclusion criteria. Direct comparisons between fMRI and PET data were considered methodologically inappropriate due to their fundamentally different spatial, temporal, and physiological characteristics, especially the inability of PET data to inform on SCC connectivity with other brain regions. An exception is the study by Elias et al., [ 68 ] which successfully acquired resting-state fMRI data; however, their analyses focused on regional activity using the ALFF method rather than functional connectivity, and the study included a diagnostically heterogeneous patient group, and was therefore excluded. This review deliberately focused on functional connectivity measures, as our primary interest was in interregional brain interactions—particularly between the SCC and the rest of the brain—rather than region-specific measures of local activity. Consequently, resting-state fMRI metrics such as the amplitude of low-frequency fluctuations or regional homogeneity, which primarily capture within-region activity, were considered outside the scope of this review. Nevertheless, an important limitation of this review remains the methodological heterogeneity across included studies. This includes both differences in the brain stimulation methods and in the analytic strategies used to assess functional connectivity. For stimulation, there was considerable variability in target sites (e.g., left DLPFC, right lOFC for rTMS; SCC or DLPFC for TUS), protocols (e.g., standard low- or high-frequency rTMS, or iTBS), number and frequency of sessions, and clinical status of patients at the time of post-treatment assessment. Regarding connectivity analysis, studies differed in their use of seed-based whole-brain voxel-wise analyses, data-driven seed definitions based on ALFF changes, and ROI-based connectivity approaches involving partially overlapping brain regions. This variability may limit the direct comparability of findings and should be considered when interpreting convergent connectivity patterns reported across studies. These limitations highlight the need for future studies to adopt more standardized neuroimaging and reporting practices to improve interpretability, reproducibility, and comparability across studies. As Klooster et al. stated in their review on EEG and MRI biomarkers for brain stimulation, methodological details are important and must be reported [ 69 ]. Therefore, there are some recommendations for future research: Choice of ROI and method As Tura and Goya-Maldonado [ 70 ] concluded in their article, there is a need for standardization of the analysis method. Indeed, in this literature review, we observed significant variations in the regions of interest (ROIs) selected by the authors, even when referring to the same region. MNI coordinates and the size of the sphere used to define the SCC ROI varied across studies, which may have contributed to the discrepancies highlighted in this review. For example, one team used as SCC coordinates (2,18,−8) [ 26 ], another used (6,16,−10) with a 10 mm sphere [ 59 , 71 ], Baeken et al. used (1,25,−11) with a 6 mm sphere [ 31 ], Rajasekharan et al. used (4,26,−10) [ 48 ], and yet another study used (−4,21,−11) [ 43 ]. Additionally, many studies did not specify the laterality of the SCC ROI; only a few articles specified whether it was left, right for bilateral SCC [ 15 , 37 , 41 , 43 , 46 , 50 , 52 , 53 , 55 ]. Furthermore, regarding the ROI of the SCC, it was reported that the fMRI signal-to-noise ratio is low in this region, mainly due to susceptibility artifacts. Indeed, a study computed the signal-to-noise ratio of their ROI and reported that the SCC had the lowest one [ 72 ]. This could contribute to the poor reproducibility of SCC connectivity-based targets at the individual level [ 73 ]. Elbau et al. also reported that functional connectivity between the sgACC and the DLPFC was correlated with individual differences in treatment outcomes, but the association was generally weak and most pronounced only in a small subgroup of patients with distinct, respiration-related signal patterns, which were more common in male. In this large, representative sample, sgACC-DLPFC connectivity explained only a small portion of the variance in outcomes, suggesting that current sgACC-based targeting approaches may have limited predictive utility [ 58 ]. Standardization of terms related to functional connectivity An important issue identified in this review concerns the inconsistent use of terminology related to functional connectivity (FC). We observed substantial heterogeneity in the vocabulary employed to describe FC findings, which in some cases complicated the interpretation of results. In certain studies, the direction and nature of connectivity changes were clearly specified. For example, one team reported an increase in FC that corresponded to a decrease in anticorrelation, thus providing necessary clarification [ 43 ]. This observation highlights the need for future research to adopt standardized terminology when reporting functional connectivity findings. Improved consistency in language would enhance the interpretability and comparability of results across studies, ultimately contributing to a clearer understanding of brain network dynamics. Nevertheless, despite methodological heterogeneity, the decision to focus on SCC functional connectivity reflects its central role in depression across various treatment modalities, including pharmacotherapy [ 74 ] and CBT [ 75 ]. This transmodal approach supports the idea of SCC connectivity as a shared biomarker of treatment response. Conclusion This systematic review has identified as potential biomarkers of clinical response to brain stimulation the functional connectivity of the SCC with regions such as the DLPFC but also others, including parts of the DMN such as the vmPFC and precuneus, and the temporal pole. The variability in post-treatment changes underscores the necessity for standardization of methods and terminology in studies focusing on the functional connectivity of the SCC. Despite many articles aiming to demonstrate similar findings, discrepancies in identified regions persist across studies, even when examining the same stimulation paradigm. Supplementary information 41398_2026_3999_MOESM1_ESM.xlsx (57.2KB, xlsx) Table S1. Articles identified in the systematic review search. Table S1 - Legend (9.2KB, docx) Author contributions CRediT Adèle Henensal : Writing – original draft, Methodology, Investigation, Visualization. Marion Plaze : Writing – review&editing, Conceptualization, Methodology, Supervision. David Attali : Writing – review&editing, Methodology. Jean-François Aubry : Writing – review&editing. Funding The first author’s PhD is funded by the Focused Ultrasound Foundation. Competing interest MP has been invited to scientific meetings, consulted and served as a speaker, and received compensation from Janssen. JFA holds patents related to the technology described in one of the included studies, has co-founded a startup company related to this technology, and holds shares. The remaining authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Declaration of generative AI in scientific writing During the preparation of this work the authors used ChatGPT/OpenAI exclusively to improve the readability of the manuscript. 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