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. 2025 Aug 28;48:103874. doi: 10.1016/j.nicl.2025.103874

Brain changes associated with depression treatment: a meta-analysis

Gianna M Perez a,⁎, Benjamin M Rosenberg b, Wenyi Xu c,1, Desmond J Oathes a
PMCID: PMC12410565  PMID: 40886591

Graphical abstract

Utilizing coordinate based meta-analysis of depression treatment neuroimaging studies to investigate common brain changes following a variety of treatments revealed a change region across 302 patients with a peak in the right amygdala. These results suggest promise in using treatment outcome neuroimaging data to apply neuroimaging to psychiatric research.

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Keywords: Meta-analysis, Depression, fMRI, Depression treatment, Amygdala, Biomarkers

Highlights

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    Meta-analysis of patient brain activity changes following depression treatment.

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    Focused on within-patient task-based neuroimaging data.

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    Revealed common change area in the right amygdala across treatments.

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    Treatment outcome neuroimaging data could aid understanding of biomarkers.

Abstract

Background

Understanding changes in brain activity following treatment is critical to advancing the use of neuroimaging in psychiatric research. Previous work has focused on defining abnormalities (biomarkers) between patients and healthy controls but there has been less investigation of brain changes in depression patients following treatment. To boost sample size and explore the possibility of common treatment mechanisms, we considered results from standard and emerging forms of treatment. In order to investigate and synthesize findings of brain changes, we conducted a coordinate based meta-analysis of depression treatment studies reporting pre- and post-treatment task-based neuroimaging data to determine if there were common brain regions that changed with effective depression treatment across treatment types.

Methods

Activation likelihood estimation was performed to synthesize the imaging results. The meta-analysis included data from 302 depressed subjects yielding 87 foci across 18 experiments. The studies examined various depression treatments including pharmacology, psychotherapy, electroconvulsive therapy, psilocybin, and ketamine with brain activity measures in response to emotion tasks in the scanner.

Results

Across studies, the right amygdala (peak MNI coordinates [30, 2, –22]) was a region of convergence, reflecting a consistent change in activity following depression treatment. Follow-up analyses suggested that this finding was driven by right amygdala activity decreasing with treatment.

Conclusions

Our result, focusing on within-patient changes associated with treatment, highlights the right amygdala as a brain area especially relevant to depression treatment measured with fMRI. This finding provides a lens to focus depression biomarker research based on imaging measures that track depression treatment effects.

1. Introduction

Neuroimaging has opened new avenues for research across psychiatric diagnoses including major depressive disorder. Early and recent work focused on using neuroimaging to characterize brain abnormalities through comparison of patients with depression to healthy controls with no history of serious mental illness. There have been abnormalities reported in brain structure (Drevets et al., 2008, Fitzgerald et al., 2008, Gray et al., 2020, Palmer et al., 2015, Schmaal et al., 2020) function (Gray et al., 2020, Palmer et al., 2015) and circuitry (Cash et al., 2023, McTeague et al., 2020, Williams, 2017) for patients with depression as compared to healthy controls. More recent work has focused on using neuroimaging to develop biomarkers for possible relevance to depression diagnosis and treatments (Dunlop and Mayberg, 2014, Waters and Mayberg, 2017). Nonetheless, for all the insights that neuroimaging research provides, there are still concerns that the applications and benefits of using neuroimaging have yet to be realized clinically to benefit patients (Abi-Dargham et al., 2023, Mayberg, 2014). Given the scale of suffering that depression causes, with an estimated 5% of adults in the world experiencing depression (WHO Depression Fact Sheet, 2024) there is more need than ever to find innovative ways to advance depression treatments. Thus, it is imperative to leverage insights from neuroimaging to inform treatments to ultimately help as many patients as possible. Considering the amount of neuroimaging research that has been done to learn about depression, there is now an opportunity to combine smaller studies into a larger analysis and to ask new questions possible with larger sample sizes.

While depression has been associated with various biomarkers that differentiate depressed from healthy populations, such results do not reveal whether identified differences are mechanistically essential for the occurrence of depression, are only tangentially relevant, or are potentially compensatory processes developed by depressed individuals. These specific mechanisms are essential to understanding how neuroimaging results may bridge to depression treatments. In addition, there has been debate about the extent to which brain abnormalities can be reliably detected using current standard neuroimaging measures in the context of depression (Müller et al., 2017, Scheepens et al., 2020, Winter et al., 2022) which may reflect heterogeneity within the disorder itself (Drysdale et al., 2017, Fried and Nesse, 2015). Furthermore, it could be the case that certain neurobiological correlates of depression remain even after effective treatment. For example, recent work identified an enlarged salience network as a “trait like” feature of depression that did not change in size over time or after a rapid acting antidepressant treatment (Lynch et al., 2024). Similarly, psychological research has shown that certain cognitive deficits persist in patients with depression even after successful remission from their mood disturbance (Bora et al., 2013). Taken together, these findings suggest that both biological and cognitive indices of depression remain detectable even after successful treatment, raising new questions about the neurobiology of depression treatment, such as whether treatment induces compensatory or normalizing changes, that have yet to be fully addressed by prior studies. These questions may be addressable, especially as neuroimaging technologies continue to advance (Roalf et al., 2024). From a clinical perspective, there is a pressing need for research that can circumvent inherent challenges in biomarker research to provide insights that are reliable and translatable.

Given the pressing clinical need, treatment outcome and treatment mechanism-based work is emerging as a priority in psychiatric neuroimaging (Drysdale et al., 2017, Dunlop and Mayberg, 2014). Thus far, the primary focus of many imaging studies has been biomarker work that attempts to better characterize differences in brain structure or function (Cash et al., 2023, Drevets et al., 2008, Fitzgerald et al., 2008, Gray et al., 2020, Lynch et al., 2024, McTeague et al., 2020, Palmer et al., 2015, Schmaal et al., 2020, Williams, 2017) as well as predictors related to depression treatment (Goldstein-Piekarski et al., 2018, Lai, 2019, Williams and Yesavage, 2024). What have been less prioritized are studies that utilize pre- and post-treatment neuroimaging data to look at which patient brain changes are associated with positive clinical outcomes. Incorporating evidence from treatment studies may prove a crucial step in the future of validating biomarkers. Specifically, by focusing on pre- versus post-treatment brain activity changes in response to depression treatment, such evidence might synergize with abnormality and biomarker findings to filter which brain measures may be most mechanistically relevant to drive clinical decision making.

Some existing neuroimaging studies have focused on pre-to-post brain changes associated with depression treatment. However, these studies are often conducted with relatively small sample sizes, and the findings are best interpreted with respect to whichever treatment was used. These treatment-related imaging studies may be useful preliminary indicators (Gratton et al., 2022), but they are also subject to the same limitations associated with neuroimaging in general, including low statistical power and reproducibility concerns (Botvinik-Nezer and Wager, 2023, Marek et al., 2022, Vogt, 2023). Common changes in brain connectivity amongst some treatments (Dunlop et al., 2023, Gudayol-Ferré et al., 2015) have been reported, and common changes in brain activity were reported even in a study focused primarily on contrasting the effects of psychopharmacotherapy and psychotherapy (Nord et al., 2021).

Coordinate based meta-analysis helps to increase both the confidence and reliability of neuroimaging findings by pooling together data to identify regions of convergence across studies (Eickhoff et al., 2012, Turkeltaub et al., 2002). Though there is recognized heterogeneity in the clinical presentation of depression (Buch and Liston, 2021, Fried and Nesse, 2015), which may be important in predicting and measuring treatment success (Siddiqi et al., 2020, Trivedi et al., 2016), there may nonetheless be common features across patients (Drysdale et al., 2017, Winter et al., 2022) that change during treatment (e.g. depressed mood) (Administration, 2016) and coincide with neural changes regardless of treatment type. The common element may also be easier to establish since it allows all patients to be included in defining biomarkers and treatment-induced brain changes, boosting statistical power, compared with patient subtyping based on symptom and/or brain measures. Identifying voxel-based commonalities can also help to enhance clarity in understanding relevant brain areas that may otherwise be obscured by the variety of brain labels reported in the literature for the same regions across investigative teams. Brain coordinate-based meta-analysis allows researchers to use full brain data, regardless of labels, from a variety of depression treatment studies to ask questions about possible common brain activity changes across studies and across treatments.

Previous meta-analytic work has looked for structural brain changes after treatment (Enneking et al., 2020b) or compared brain activity changes across two treatment types (Nord et al., 2021). Systematic review has also been used to measure resting functional connectivity brain changes comparing two treatments (Gudayol-Ferré et al., 2015). These studies offer a useful start to investigating treatment outcomes, but there are areas where the work can be expanded. More recent meta-analytic work has investigated treatment effects across large numbers of patients and incorporating both traditional and emerging treatment options for patients. One study searching for the conjunction of treatment effects in brain structure and function in patients with depression found significant conjunction in the affective control network (Li et al., 2022). Another meta-analysis searching for convergence of treatment reported effects for antidepressants found a significant region of convergence only when comparing when treated patients had greater activation compared to untreated patients (Saberi et al., 2025).

We conducted a coordinate based meta-analysis of task-based fMRI studies evaluating changes in brain activity following a variety of depression treatments. Prior meta-analyses contained fMRI, PET, and resting state data as well as responders and non-responders to treatment in initial analyses (Li et al., 2022, Saberi et al., 2025). By focusing solely on task-based imaging studies and treatment effects, we aimed to better understand brain activity changes during tasks designed to be especially relevant to depression pathology (Pilmeyer et al., 2022) and therefore possibly more sensitive to depression treatment changes. Consistent with this hypothesis, task fMRI data have yielded higher effect sizes to detect brain-behavior relationships compared to resting state or structural MRI data (Gratton et al., 2022, Marek et al., 2022). This sensitivity likely depends on the relevance of the fMRI protocol to the behavior of interest. Here, we found that most examples of task-fMRI treatment studies in depression used tasks in which patients were asked to engage with stimuli designed to include emotional contents. The goal of this analysis was to determine if there is evidence for common brain regions that change in depressed patients following effective treatment. To this end, the present study focused on within-patients longitudinal brain changes associated with depression treatment.

2. Methods and materials

2.1. Paper identification

A PubMed search was conducted using the keywords “MDD OR Depression OR Depressive OR dysthymia AND prospective OR longitudinal OR cohort OR predict OR outcome AND fMRI OR neuroimaging OR functional MRI OR MRI AND treatment OR therapy” for papers published between 2005–2022, when the paper search concluded. The papers then underwent abstract and subsequently full text reviews to ensure adherence to the meta-analysis inclusion criteria described below. Fig. 1 is a PRISMA flow diagram (Page et al., 2021) outlining the paper selection process. One paper found in the search was published in 2003 but was included in final analyses as it met all inclusion criteria.

Fig. 1.

Fig. 1

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PRISMA diagram. A PRISMA flow diagram that details the paper identification and screening process for studies included in the meta-analysis.

2.2. Inclusion criteria

In order to be included in the meta-analysis, a study must have recruited patients diagnosed with unipolar major depressive disorder between the ages of 18 and 65 years old. The study must have involved treatment for depression and reported a significant effect for treatment (typically characterized as greater than 50% symptom reduction or a significant change in pre- to post- treatment depression questionnaire symptom scores) for the intervention. Studies must have applied whole-brain functional MRI activation analysis and provided neuroimaging results encompassing pre- to post-treatment changes in brain activity. For inclusion, all brain coordinates must have been provided in either Montreal Neurological Institute (MNI) (Mazziotta et al., 1995) or Talairach (Lancaster et al., 2000) atlas space. Supplementary Table A.1 lists the characteristics of the included studies: treatment types and lengths, variety of task types, patient counts and sex breakdown, and reported changes in pre to post treatment depression symptom scores. All studies passed a basic quality assessment if sample characteristics, task type, imaging acquisition details, and image processing details were reported. Covidence software (Covidence − Better Systematic Review Management, 2022) was used to sort and review studies for inclusion in the meta-analysis. Papers were initially identified and screened by two members of the research team (GMP and WX). Final papers to be included were identified by one member of the research team (GMP) with input from the senior author (DJO).

2.3. Coordinate extraction

Brain coordinates were extracted for change contrasts (i.e., pre- to post-treatment comparisons of brain activity during the study task). Coordinates were extracted by one member of the research team (GMP) with input from the senior author (DJO). The extracted coordinates came from a variety of emotional response tasks conducted before and after treatment and included the pre < post (increase) and pre > post (decrease) coordinates. Studies by the same research groups were assessed using characteristics of treatment type, patient gender, patient age, and change in depression symptom scores to avoid the inclusion of duplicate patients and experiments. Review of these characteristics revealed two pairs of studies that were likely to have sample overlap, as a result coordinates from these studies were merged to prevent duplicate inclusions of the same experiments and/or patient samples. Tasks included presentations of various emotional stimuli, emotional faces, recall of negative autobiographical memories, dysfunctional attitude reporting, self-referential judgments, and personal relevance rating tasks.

2.4. Activation likelihood estimation

Peak coordinates were taken from each study and run through Activation Likelihood Estimation (ALE), an algorithm that detects converging foci across neuroimaging studies by treating each reported peak as its own gaussian distribution and then testing the null hypothesis that peak coordinates are spread uniformly throughout the brain (Eickhoff et al., 2012, Turkeltaub et al., 2012). Sample sizes of studies are used as a factor in estimating the width of the gaussian probability distributions (i.e., larger sample sizes get a taller, tighter gaussian) (Eickhoff et al., 2009, Eickhoff et al., 2012, Turkeltaub et al., 2012). Regions that are found to be significant in the ALE analysis are interpreted as regions of convergence across studies.

Activation likelihood estimation was conducted using GingerALE 3.0.2 (Eickhoff et al., 2009, Eickhoff et al., 2012, Turkeltaub et al., 2012). Foci were taken from MNI space and input into the ALE analysis software. All foci originally reported in Talairach space were converted into MNI space using the GingerALE ‘icbm2tal’ function (Laird et al., 2010, Lancaster et al., 2007). We applied the following default standard GingerALE statistical parameters: cluster-level family-wise error threshold < 0.01, number of threshold permutations = 1000, and p-value < 0.001 (uncorrected). Separate follow-up ALE analyses were performed for increase or decrease in brain activity (change in response to treatment) coordinates to clarify directional effects. Results were overlaid on standard MNI brain templates (Fonov et al., 2009, Fonov et al., 2011).

3. Results

3.1. Study sample

ALE was conducted using data from 16 studies with N = 302 depressed patients across 18 different experiments, yielding 87 total foci for analysis (Davidson et al., 2003, Dichter et al., 2010, Enneking et al., 2020a, Fu et al., 2007, Fu et al., 2008, Fu et al., 2015, Redlich et al., 2017, Reed et al., 2018, Reed et al., 2019, Roseman et al., 2018, Rubin-Falcone et al., 2018, Sankar et al., 2015, Victor et al., 2010, Victor et al., 2013, Yoshimura et al., 2014, Young et al., 2020). The included treatment types were psychopharmacological medications (n = 104 patients), psychotherapies (n = 90 patients), electroconvulsive therapy (n = 56 patients), ketamine (n = 33 patients), and psilocybin (n = 19 patients).

3.2. Main analysis

After applying standard ALE parameters described above, we discovered a significant cluster of convergence across studies in the right amygdala with peak MNI coordinates [30, 2, –22] (Fig. 2, pre-threshold ALE results shown in Supplementary Fig. A.2). This cluster (z = 6.27) survived correction after a family wise error threshold was applied (FWE < 0.01, z = 5.87).

Fig. 2.

Fig. 2

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Main Result Summary: The meta-analysis identified region of convergence across all (both post treatment activity increase and decrease) coordinates (N = 302) overlaid on a standard MNI 152 T1 template brain (65–66). This area has a peak in the right amygdala [MNI Coordinates (30, 2, –22)]. This region was identified after performing activation likelihood estimation with the following parameters: cluster-level family-wise error threshold < 0.01, number of threshold permutations = 1000, and p-value < 0.001 (uncorrected).

3.3. Follow-up analyses

3.3.1. Post < Pre

Follow-up analyses were conducted for decrease (post < pre) coordinates using data from n = 166 depressed patients across 9 different experiments where activity decreases were reported after treatment, yielding 53 foci for analysis. In this analysis, after applying the same ALE parameters, there was a significant cluster of convergence across studies in the right amygdala with the same peak MNI coordinates [30, 2, –22] (Fig. 3, pre-threshold ALE results shown in Supplementary figure A.3). This cluster (z = 5.63) survived correction after a family wise error threshold was applied (FWE < 0.01, z = 5.51).

Fig. 3.

Fig. 3

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Follow-Up Analysis Summary: The meta-analysis identified region of convergence across post-treatment decrease coordinates (n = 166) overlaid on a standard MNI 152 T1 template brain (65–66). This area has a peak in the right amygdala [MNI Coordinates (30, 2, –22)]. This region was identified after performing activation likelihood estimation with the following parameters: cluster-level family-wise error threshold < 0.01, number of threshold permutations = 1000, and p-value < 0.001 (uncorrected).

3.3.2. Post > Pre

Follow-up analyses using data from n = 215 depressed patients revealed no significant clusters of convergence across 40 foci from 11 different experiments for the increase (post > pre) coordinates, suggesting that the effect across the full sample may be attributable primarily to a decrease in right amygdala activity with effective depression treatment.

3.3.3. Isolating pharmacological interventions

Follow-up analyses using data from n = 104 depressed patients revealed no significant clusters of convergence across 45 foci from 8 experiments from studies utilizing pharmacological treatment, suggesting the effect across the full sample may not be driven specifically by pharmacological interventions.

3.3.4. Isolating fMRI tasks with facial stimuli

Follow-up analyses utilizing data from n = 229 depressed patients across 14 different experiments with 76 foci in studies that used a facial stimuli task revealed a significant cluster of convergence across studies in the right amygdala with the same peak MNI coordinates [30, 2, –22]. This cluster (z = 6.41) survived correction after a family wise error threshold was applied (FWE < 0.01, z = 5.53). See supplemental figures A.4 and A.5.

4. Discussion

This coordinate based meta-analysis evaluated changes in brain activity associated with response to treatment for major depression. The studies included in this analysis spanned a variety of depression treatments such as traditional treatments like pharmacological treatments, psychotherapy, electroconvulsive therapy, and emerging therapies like ketamine and psilocybin. Included studies also spanned a variety of fMRI emotion-based tasks such as processing of sad and happy faces, reacting to negative autobiographical memories, and self-referential judgment tasks, although the majority of tasks used facial stimuli. Our result demonstrated that across a variety of treatment types, there was one convergent area of brain activity change in the right amygdala following successful depression treatment. Follow-up analyses indicated that this effect was 1) specifically driven by treatment-related decreases in right amygdala activation during affective tasks, 2) not specifically driven by pharmacological intervention effects, and 3) robust to the exclusion of studies that did not use facial stimuli.

Early work suggested that amygdala dysfunction, typically measured by elevated blood flow during neuroimaging tasks, is related to depression (Whalen et al., 2002). Subsequent studies have established links between amygdala hyperactivity and risk for developing depression (Barbour et al., 2020, Kaczkurkin et al., 2016, Shackman et al., 2016a, Shackman et al., 2016b, Swartz et al., 2015). Although some have called for reconsidering the amygdala’s role in depression (Grogans et al., 2022, Tamm et al., 2022), this push for reconsideration is based on data from attempting to associate amygdala activity with depression symptoms in a large cohort of older adults not selected based on a clinical diagnosis. Our meta-analysis was conducted on data from patients with a confirmed depression diagnosis that received one of several forms of treatment with the goal of elucidating a common brain region of change in response to depression treatment. Our patient-focused approach revealed that the right amygdala activity during affective tasks changed following treatment. As our approach was methodologically distinct from other recent large-scale meta-analyses (Li et al., 2022, Saberi et al., 2025), this unique finding may point to the particular relevance of changes in task-related activation of the right amygdala during treatment.

Our results support the relevance of the right amygdala for future treatment-oriented depression neuroimaging research. Future research may explore task-related activation of the right amygdala as an important factor related to efficacious (greater than 50% symptom reduction) depression treatment. For example, task-based activation of the right amygdala may offer a brain target that can be used to filter brain-based depression research prioritizing patient treatment outcomes. It may also serve as a lens through which to interpret biomarker findings, only some of which will be relevant for tracking treatment outcome. Despite historical challenges for the successful application of neuroimaging to psychiatry, our work leveraged a reasonably large database of existing studies that had pre- and post-treatment neuroimaging data and found a common result that could guide future efforts to enhance the clinical applicability of fMRI research among patients with depression.

4.1. Limitations

Our study was a whole-brain analysis focused on identifying consistent changes in task-related fMRI activity following a variety of depression treatments. The meta-analysis included a variety of emotion-based task studies, although the majority of studies used facial stimuli. The choice of task likely contributed to our result, as expected, given the role of the amygdala in emotion-based processing (Hariri et al., 2002, Phelps and LeDoux, 2005, Sergerie et al., 2008). Future work is needed to explore how depression treatments are associated with changes in brain activity, possibly in other brain regions, across a wider variety of fMRI paradigms (e.g. cognitive tasks, motor tasks, resting connectivity, etc.).

Our result represents a common change area across all treatment types. However, it does not preclude the possibility that there are unique mechanisms contributing to clinical benefits specific to individual treatments included. Future work involving larger datasets for each individual treatment modality and direct comparisons between treatment types with randomized controlled trials may differentiate mechanistic differences between treatment types.

One limitation of using meta-analysis to detect a brain activity change in response to an intervention is ALE is blind to direction of change. In order to overcome this interpretive challenge and to determine if an increase or decrease in amygdala activity drove our effect, we ran follow-up analyses separately for increase and decrease contrasts. These analyses revealed a convergent area of activity in the right amygdala for the decrease but not for the increase direction. However, because follow-up analyses were done with fewer studies than a suggested threshold needed for robust ALE (Eickhoff et al., 2016), the specificity of the directional results remains preliminary. While ALE was utilized in the current study to make use of available coordinate data and best identify convergent brain region activity change across tasks, future meta-analyses could make use of alternate methods, such as Seed Based D Mapping (Radua et al., 2012) which include the addition of statistical maps to uncover additional details about directionality. In addition, the ALE analysis cannot easily explain the laterality of our finding. Previous work in high density electroencephalogram recordings demonstrated differences in right amygdala connectivity in patients with depression (Damborská et al., 2020), but future work is needed to determine whether amygdala lateralization is important in depression treatment. Another limitation of meta-analysis is that individual patient characteristics that may be useful for understanding or predicting treatment effects are not evaluated. Future work may investigate the value of individual characteristics in guiding treatment.

Lastly, relevant to all such literature-based meta-analyses, publication bias is a potential influence on our results. Selective reporting of results favoring significant findings in specific brain regions could skew the findings of a meta-analysis (Lin et al., 2018). Despite these concerns, others demonstrate that the effects of publication bias on meta-analysis are not estimated to be particularly influential (van Aert et al., 2019). By adhering to meta-analytic best practice (Müller et al., 2018) that calls for only including studies reporting whole-brain results (versus region-of-interest investigations), we sought to reduce confirmation bias to the extent possible in the present study.

4.2. Future directions

There are multiple future lines of research recommended given our result. First, the rise of neuromodulation-based interventions offers opportunities to develop treatments that specifically target the amygdala. For example, the amygdala has been shown to play an important role in patient-specific deep brain stimulation treatment for depression (Scangos et al., 2021). There are additional studies that reported deep brain stimulation treatment altering aberrant amygdala activity in treating depression (Fan et al., 2024, Runia et al., 2023). More recent, non-invasive work using low intensity focused ultrasound found that targeting the amygdala with sonication may alter subjective emotional experiences (Hoang-Dang et al., 2024). It is also possible to indirectly engage the amygdala non-invasively with transcranial magnetic stimulation through functionally connected cortical circuits (Sydnor et al., 2022). Taken with these recent studies, our finding encourages continued focus on the amygdala for establishing novel treatments for depression as well as understanding mechanisms of existing treatments.

Second, our findings can also aid in the interpretation and development of depression biomarker studies. Identifying a brain area that changes with depression treatment is useful to filter biomarker studies that attempt to predict treatment outcomes. Our work also demonstrates the value of including patient treatment outcome neuroimaging data in neuroimaging-based neuropsychiatry studies. Future biomarker studies should prioritize pre- to post-treatment neuroimaging results, when possible, to further aid in the interpretation of biomarkers most relevant to successful depression treatment.

Third, the communication between the amygdala and the rest of the brain can be further characterized especially as a contributor to depression symptom improvement. As noted in the Limitations, the right amygdala cluster identified in our result does not reflect a comprehensive measure of the biological basis of depression or its improvement in isolation from the rest of the brain. Prior work highlights abnormal functional brain circuits and networks in depression including distributed brain regions (Cash et al., 2023, McTeague et al., 2020, Williams, 2017). One promising avenue to characterize this network-level context would be through studying the functional connectivity of this right amygdala region to other parts of the brain and how it participates in brain networks relevant to depression improvement. Fully understanding the role of this region as a function of these network interactions may reveal richer detail about specific mechanisms involved in depression treatment response. While our work focused on depression, there may also be transdiagnostic relevance for the right amygdala, and it has been broadly implicated across other psychiatric disorders (McTeague et al., 2020). As there are increasing efforts to characterize psychopathologies of mental health disorders in transdiagnostic ways (Kotov et al., 2017), future work could investigate possible connections between change in right amygdala activity after treatment in other psychiatric disorders.

4.3. Conclusion

Our work evaluates pre- and post-treatment neuroimaging data from a variety of depression treatment studies to explore common changes in functional brain activity associated with effective treatment. We found that, across treatment types, there was a consistent change in right amygdala activity within depression patients following treatment. This finding supports the importance of the amygdala in depression neuroimaging research and offers a new lens with which to interpret the ongoing search for depression biomarkers.

Funding sources

This work was supported by the National Institutes of Health [MH111886, MH135428]; and the Hart Fund in Cognitive Neuroscience, Philadelphia, PA [Oathes].

CRediT authorship contribution statement

Gianna M. Perez: Writing – original draft, Visualization, Validation, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Benjamin M. Rosenberg: Writing – review & editing, Supervision, Conceptualization. Wenyi Xu: Validation, Resources, Investigation, Data curation. Desmond J. Oathes: Writing – review & editing, Supervision, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors wish to acknowledge the work of previous researchers that make meta-analysis possible.

Glossary

fMRI

functional magnetic resonance imaging, a technique to indirectly measure brain activity during a specific task

Coordinate

spatial coordinates from a 3D plane used to indicate the location of an area in the brain in neuroimaging

MNI

Montreal Neurological Institute, a standardized coordinate system used in neuroimaging

Talairach

a standardized coordinate system used in neuroimaging

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2025.103874.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.docx (1.3MB, docx)

Data availability

All data collected from the studies in the meta-analysis are available in the Supplementary Table A.1.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data 1
mmc1.docx (1.3MB, docx)

Data Availability Statement

All data collected from the studies in the meta-analysis are available in the Supplementary Table A.1.

Articles from NeuroImage : Clinical are provided here courtesy of Elsevier

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