
==== Front
Transl Psychiatry
Transl Psychiatry
Translational Psychiatry
2158-3188
Nature Publishing Group UK London

3062
10.1038/s41398-024-03062-z
Article
Transcriptional patterns of amygdala functional connectivity in first-episode, drug-naïve major depressive disorder
Liu Yuan 1
Li Meijuan 1
http://orcid.org/0000-0002-9280-8247
Zhang Bin 1
http://orcid.org/0000-0002-9121-8296
Qin Wen 2
Gao Ying 1
Jing Yifan 1
http://orcid.org/0000-0001-5408-8413
Li Jie jieli@tmu.edu.cn

1
1 grid.265021.2 0000 0000 9792 1228 Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222 China
2 https://ror.org/003sav965 grid.412645.0 0000 0004 1757 9434 Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052 China
31 8 2024
31 8 2024
2024
14 35126 4 2024
20 8 2024
22 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ 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/.
Previous research has established associations between amygdala functional connectivity abnormalities and major depressive disorder (MDD). However, inconsistencies persist due to limited sample sizes and poorly elucidated transcriptional patterns. In this study, we aimed to address these gaps by analyzing a multicenter magnetic resonance imaging (MRI) dataset consisting of 210 first-episode, drug-naïve MDD patients and 363 age- and sex-matched healthy controls (HC). Using Pearson correlation analysis, we established individualized amygdala functional connectivity patterns based on the Automated Anatomical Labeling (AAL) atlas. Subsequently, machine learning techniques were employed to evaluate the diagnostic utility of amygdala functional connectivity for identifying MDD at the individual level. Additionally, we investigated the spatial correlation between MDD-related amygdala functional connectivity alterations and gene expression through Pearson correlation analysis. Our findings revealed reduced functional connectivity between the amygdala and specific brain regions, such as frontal, orbital, and temporal regions, in MDD patients compared to HC. Importantly, amygdala functional connectivity exhibited robust discriminatory capability for characterizing MDD at the individual level. Furthermore, we observed spatial correlations between MDD-related amygdala functional connectivity alterations and genes enriched for metal ion transport and modulation of chemical synaptic transmission. These results underscore the significance of amygdala functional connectivity alterations in MDD and suggest potential neurobiological mechanisms and markers for these alterations.

Subject terms

Depression
Human behaviour
https://doi.org/10.13039/501100001809 National Natural Science Foundation of China (National Science Foundation of China) 62027812 Li Jie 1. Tianjin Key Medical Discipline (Specialty) Construction Project (Grant number: TJYXZDXK-033A); 2. Beijing Tianjin Hebei Basic Research Cooperation Project (Grant number: 23JCZXJC00230; J230011)issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Major depressive disorder (MDD) is a prevalent psychiatric illness on a global scale, marked by symptoms such as depressed mood, anhedonia, and decreased energy, which may culminate in suicidal behavior [1]. This condition not only significantly impacts the general well-being of individuals, but also has substantial repercussions on global human rights and economic conditions, leading to considerable burdens worldwide [2]. Despite extensive research, the underlying pathophysiological mechanisms of MDD remain elusive, largely due to variability in brain structure and function abnormalities, as well as diverse treatment responses [3, 4].

Understanding brain dysfunction in MDD is crucial for elucidating the disorder’s pathophysiological mechanisms and developing more effective treatments [5, 6]. The emergence of resting-state functional magnetic resonance imaging (rs-fMRI) has greatly expanded our insight into the functional organization of the brain in both healthy individuals and clinical populations [7, 8]. Insights emerging from mapping intrinsic brain connectivity networks provide a potentially mechanistic framework for understanding aspects of human behavior [9, 10]. A multitude of functional brain imaging studies focusing on depressive patients have consistently revealed significant alterations within the limbic regions, notably the amygdala, throughout the various stages of depression including onset, progression, remission, and recurrence [11, 12].

The amygdala is a pivotal brain region responsible for the processing and regulation of emotions [13]. Recent research indicates a correlation between the severity of depression and abnormalities in amygdala structure and function [14, 15]. Notably, hyperactivity of the amygdala following negative emotional processing is a prominent characteristic of depressive disorders, suggesting heightened bottom-up processing in affected individuals [16]. Moreover, the amygdala establishes reciprocal connections with various cortical areas implicated in social, cognitive, and affective processing [17–19]. Specifically, bidirectional connections have been observed between the amygdala and different regions within the prefrontal cortex, such as the dorsolateral and dorsomedial areas, which play roles in cognitive and threat regulation, respectively [20–22]. Recent clinical studies have provided some evidence of reduced connectivity between the basolateral amygdala and prefrontal cortex in individuals diagnosed with MDD [23]. While previous studies have highlighted abnormalities in amygdala functional connectivity in MDD patients [23, 24], these conclusions rely on limited data and may be influenced by medication, leading to inconsistent and contested findings. Thus, it is important to validate amygdala functional connectivity alterations among first-episode, drug-naïve MDD patients in a multicenter dataset.

Recent studies have highlighted the substantial influence of genetic factors on the development of human brain networks [25, 26]. Multiple lines of evidence indicate that the onset of MDD is intricately linked to a complex interplay of genetic and epigenetic elements [27, 28]. Genome-wide association studies (GWAS) have also identified numerous genetic loci that are correlated with MDD [29]. Nevertheless, the precise mechanisms through which genetic factors modulate brain activity in MDD remain elusive. The advent of a comprehensive whole-brain atlas of gene expression, derived from the Allen Human Brain Atlas (AHBA) database, has opened new avenues for probing the intricate relationship between disease-related gene expression at the micro-level and broader brain alterations observed across diverse psychiatric conditions [30–32]. Furthermore, neuroimaging traits, serving as intermediate phenotypes, are believed to be closer to the genetic underpinnings of MDD [33]. Consequently, there has been a growing body of imaging transcriptomics research aiming to elucidate the complex mechanisms contributing to MDD by linking brain structural and functional changes to gene expression data [31, 34, 35]. Numerous studies have pinpointed genes linked to anomalies in resting-state brain function, shifts in cerebral blood flow, and modifications in structural brain networks in individuals with MDD [31, 35–37]. Nevertheless, there are currently no relevant studies to establish the association between gene expression and amygdala functional connectivity alterations in MDD.

In this study, we aimed to identify amygdala functional connectivity patterns linked with MDD and explore associated transcriptional profiles (Fig. 1). Using a multicenter neuroimaging dataset of 573 individuals, we first compared amygdala functional connectivity between first-episode, drug-naïve MDD patients and healthy controls (HC). Next, we developed individual-level machine learning models utilizing amygdala functional connectivity to diagnose MDD. Lastly, we investigated connectome-transcriptome associations using AHBA. Our hypotheses were: (1) amygdala functional connectivity abnormalities would be evident in MDD patients; (2) amygdala functional connectivity could effectively diagnose MDD at the individual level; (3) MDD-related amygdala connectivity alterations would correlate with gene expression profiles in relevant biological pathways, cell types, and developmental genes.Fig. 1 Overview of the study design.

A Brain imaging analysis. Amygdala functional connectivity was determined by extracting mean BOLD signals utilizing the AAL atlas and computing Pearson correlation coefficients between the bilateral amygdala and other brain regions. Amygdala functional connectivity was used for group-level comparison and individual-level classification. B Gene expression. The gene expression data was obtained from the AHBA and underwent comprehensive preprocessing for analysis across the entire brain using the AAL atlas, resulting in a regional gene expression matrix. C Transcriptional analysis. Pearson’s correlation analysis was conducted to establish connections between MDD-related abnormalities in amygdala functional connectivity and gene expression data. Subsequently, enrichment analyses were performed on significant gene lists to reveal relevant biological pathways, cell types, and developmental genes. AAL Automated Anatomical Labeling, AHBA Allen Human Brain Atlas, BOLD blood oxygen level dependent, MDD major depressive disorders, HC healthy controls.

Materials and methods

Phenotypic and imaging dataset

The study comprised 210 drug-naïve first-episode MDD patients and 363 age- and sex-matched HC who were part of the DIRECT Consortium [38, 39] (Supplementary Table 1; Supplementary Fig. 1), utilizing publicly available brain imaging data of depression (http://rfmri.org/REST-meta-MDD). Patients met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) or International Classification of Diseases 10 (ICD-10) criteria for MDD, with a Hamilton Rating Scale for Depression (HAMD) score ≥14 points and ages between 18 and 65 years. Exclusion criteria included missing demographic information, poor image quality, and excessive head motion. Detailed criteria were provided in the Supplementary Materials. Ethics approval was obtained from respective site committees, and written consent was obtained from all participants before testing. Brain image preprocessing followed a standardized protocol using DPARSF software (http://www.rfmri.org/), involving removal of the first 10 volumes, slice timing correction, head motion realignment, covariate regression, normalization to the Montreal Neurological Institute (MNI) template, and application of a bandpass filter (0.01–0.1 Hz), as outlined in the Supplementary Materials (Supplementary Table 2).

Brain imaging analysis

Region of interest (ROI) based amygdala functional connectivity construction

In this study, the whole brain was parcellated into 116 regions using the Automated Anatomical Labeling (AAL) atlas [40]. Based on prior literature highlighting the amygdala’s significance in MDD [41], it was selected as a key region for connectivity analysis. To establish ROI based amygdala functional connectivity, mean blood oxygen level dependent (BOLD) signals were extracted from preprocessed fMRI data for each of the 116 ROIs. Pearson correlation coefficients were then, respectively computed between the mean time series of the bilateral amygdala and other whole-brain ROIs. Subsequently, Fisher r-to-z transformation was applied to identify functional connections between the bilateral amygdala and other whole-brain ROIs. Finally, we could obtain the functional connectivity map (2 × 114) in the left and right amygdala.

Group differences in amygdala functional connectivity

We utilized the GRETNA toolbox to assess group differences in amygdala functional connectivity [42]. Specifically, we separately compared the functional connectivity of the left and right amygdala between HC and MDD, while controlling for sex, age, and education level. Benjamini–Hochberg False Discovery Rate (BH-FDR) was applied to adjust the P-values, with significance set at PBH-FDR < 0.05.

Classification performance based on amygdala functional connectivity

The classification task aimed to distinguish between MDD patients and HC using a Gaussian support vector machine (SVM) model trained on significantly different amygdala functional connectivity between the two groups. Initially, the datasets were divided into training and test sets (7:3 ratio). Subsequently, within the training set, a 10-fold cross-validation method was employed to split the data into internal training and validation sets (9:1 ratio) to optimize the training model. Finally, the model’s performance was evaluated using the test set, with the final area under the receiver operating characteristic curve (AUC) and accuracy serving as metrics to assess classification performance. The detailed steps are described in the Supplementary Materials.

Gene expression dataset and preprocessing

The gene expression data utilized in this study was sourced from six neurotypical adult donors in AHBA (http://human.brain-map.org) (Supplementary Table 3) [43]. The gene microarray data from brain tissue samples underwent preprocessing using the abagen (https://www.github.com/netneurolab/abagen) toolbox following a recommended pipeline [44, 45]. Specifically, reannotation of genetic probes was conducted based on established guidelines, and intensity-based filtering was applied to exclude probes with values below the background noise threshold, set at 50%. For genes indexed by multiple probes, we selected the probe that demonstrated the most consistent regional variation across donors, ensuring differential stability. Each tissue sample was spatially registered to MNI (https://github.com/chrisfilo/alleninf) coordinate space based on the T1-weighted images of individual donors, and subsequently assigned to specific brain regions according to their MNI coordinates. The microarray data was then integrated with the parcellation consisting of 116 brain regions defined by the AAL atlas, allowing for gene expression analysis to be conducted across the entire brain. Gene expression values were normalized for each donor using the scaled robust sigmoid (SRS) method and then averaged across brain regions. Finally, a gene expression map (112 regions × 15,633 genes) was obtained for further transcription-neuroimaging association analysis. Of note, four regions were discarded because no tissue samples were assigned to these regions.

Transcription-neuroimaging association analysis

To ascertain variations in amygdala functional connectivity among distinct groups within a specified brain tissue sample, we analyzed the discrepancies in amygdala functional connectivity between cases and controls, utilizing T-values derived from 114 regions. It is important to note that self-connections of the amygdala were not included in the analysis. Pearson’s correlations were utilized to assess the relationship between the gene expression matrix and the amygdala functional connectivity case-control T-vector. Correction for multiple comparisons was conducted using BH-FDR method. To further test whether the number of the identified genes was significantly greater than the random level, a spatially-constrained permutation test (i.e., spin test, n = 1000) was conducted to establish the significance of our results. The detailed steps are described in the Supplementary Materials.

Enrichment analysis was performed to uncover the biological pathways linked to MDD. The Gene Ontology (GO), Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases integrated into Metascape were utilized to examine significant gene sets (https://metascape.org/gp/index.html#/main/step1) [46]. Additionally, CSEA tool was employed to investigate the expression profiles of identified genes in different cell types, brain regions, and developmental stages, with the objective of identifying specific patterns of overexpression in MDD (http://doughertytools.wustl.edu/CSEAtool.html) [47]. All enrichment analyses were adjusted for the false discovery rate using PBH-FDR < 0.05 to ensure statistical rigor.

Validation analyses

We implemented a leave-one-site-out cross-validation approach to assess the potential impact of specific sites on our findings. This involved systematically excluding one dataset at a time and utilizing the remaining three datasets as a subset for statistical analysis. In each subset, we performed T-tests across 114 regions to generate T-value maps, assessing whole-brain case-control differences. Pearson correlation was used to examine the spatial congruence of regional differences between each subset and the full dataset.

Results

Brain imaging analysis

Group differences in amygdala functional connectivity

Compared to HC, individuals with MDD demonstrated reduced FC between the right amygdala and various brain regions, including the left orbital parts of the middle frontal gyrus (MFG), left orbital parts of the superior frontal gyrus (SFG), bilateral orbital parts of the inferior frontal gyrus (IFG), right medial SFG, right medial orbital parts of the SFG, bilateral gyrus rectus, and left temporal pole (comprising the superior temporal gyrus and middle temporal gyrus) (PBH-FDR < 0.05) (Fig. 2A). Only reduced FC between the left amygdala and left temporal pole (i.e., superior temporal gyrus) was observed in MDD compared to HC (PBH-FDR < 0.05) (Fig. 2B).Fig. 2 Differences in amygdala functional connectivity between MDD and HC.

A Brain regions showing significant differences in left amygdala-based functional connectivity between MDD and HC. B Brain regions showing significant differences in right amygdala-based functional connectivity between MDD and HC. Non-significant regions are depicted in green. Blue spheres represent significant decreases (MDD < HC), while red spheres represent significant increases (MDD > HC). MDD major depressive disorders, HC healthy controls.

Classification performance based on amygdala functional connectivity

The classification performance of the model, utilizing amygdala functional connectivity to distinguish MDD patients from HC, was evaluated using a 10-fold cross-validation method. The classifier achieved an accuracy of 72% (Fig. 3A) and exhibited an AUC of 0.74 (Fig. 3B). The 10-fold cross-validation method was employed to ensure the robustness of the classifier by reducing overfitting and providing a reliable estimate of the model’s performance, enhancing the generalizability and stability of the classification results.Fig. 3 Classification performance for MDD identification.

A The confusion matrices for the classifier. B The receiver operating characteristic curve for the classifier. ACC accuracy, AUC area under the receiver operating characteristic curve, MDD major depressive disorder, HC healthy controls.

Imaging transcriptomics analysis

Transcriptional profiles related to altered amygdala functional connectivity in MDD

Gene-wise cross-region spatial correlation analyses were performed between gene expression data and amygdala functional connectivity difference maps for both the right and left amygdala. Using a significance threshold of PBH-FDR < 0.05, a total of 3624 genes were found to be associated with case-control amygdala functional connectivity alterations in the right amygdala, while 2419 genes were associated with alterations in the left amygdala. Of these, 2059 genes showed consistent spatial correlations with amygdala functional connectivity alterations in both the right and left amygdala. The reliability of this association was confirmed through spatially-constrained permutation tests.

Enrichment analyses

Enrichment analyses using GO biological process, Reactome, and KEGG were performed to elucidate the biological functions of genes reliably linked to alterations in amygdala functional connectivity in MDD. Following the removal of discrete clusters, these genes exhibited significant enrichment in various biological processes (PBH-FDR < 0.05). The most prominent processes were related to metal ion transport and modulation of chemical synaptic transmission, both of which had the highest gene counts and significance (Fig. 4A, B).Fig. 4 Transcriptional patterns associated with MDD-related amygdala functional connectivity alterations.

A Enrichment for the significant genes. Bubble size represents the number of overlapping genes with each annotation (GO term, Reactome gene set, or KEGG pathway), while color indicates significance level (BH-FDR corrected). B Enrichment network shows intra-cluster and inter-cluster similarities of enriched annotations. Each term is represented by a node, with size indicating the number of input genes and color representing cluster identity. The most prominent processes—related to metal ion transport and modulation of chemical synaptic transmission—are highlighted in bold and marked with an asterisk (*), as they had the highest gene counts and significance. C CSEA of the significant genes list (BH-FDR corrected). D Developmental gene expression enrichment analysis of the significant genes list. Bubble size and color both represent significance level (BH-FDR corrected). GO gene ontology, R-HAS Reactome-Homo sapiens, KEGG Kyoto Encyclopedia of Genes and Genomes, CSEA cell-type single-cell expression analysis, BH-FDR Benjamini–Hochberg false discovery rate, MO myelinating oligodendrocyte, OPC oligodendrocyte progenitor cells.

CSEA indicated that the 2059 identified genes showed significant enrichment in Glt25d2 neurons (P = 1.17 × 10−4) and Ntsr+ neurons (P = 1.47 × 10−4) within the cortex (Fig. 4C). Subsequently, we explored whether these genes displayed enrichment in specific human brain regions or developmental stages. Analysis of developmental gene expression revealed that these genes were expressed in the brain starting from early/mid fetal development, encompassing various brain regions such as the cortex and subcortex (including the thalamus, striatum, and amygdala) (Fig. 4D). Notably, their expression was particularly pronounced during neonatal and early infancy, adolescence, and young adulthood stages.

Validation analysis

Overall, the patterns of amygdala functional connectivity differences remained similar even after removing any one of the four datasets (Supplementary Fig. 2). Specifically, both the right and left amygdala functional connectivity T-maps from the validation analysis showed significant correlations with the corresponding T-value maps from the main analysis (all P < 0.001).

Discussion

Utilizing a multicenter MRI dataset, this study represents the first to unveil the transcriptional pattern of amygdala functional connectivity disruption in first-episode, drug-naïve major depressive disorder. Consistently with our hypothesis, significant amygdala functional connectivity abnormalities were observed in MDD patients compared to HC. Notably, at the individual level, features of amygdala functional connectivity demonstrated considerable efficacy in distinguishing between MDD patients and HC. Moreover, by investigating the relationship between amygdala functional connectivity abnormalities and brain gene expression patterns, we discovered that amygdala functional connectivity abnormalities associated with MDD were linked to biologically relevant pathways. These prominent pathways were related to metal ion transport and modulation of chemical synaptic transmission. Furthermore, we observed preferential expression of these genes in distinct cell types and brain regions. These findings underscore the robustness of amygdala functional connectivity signatures in individuals with MDD, establishing a crucial link between neuroimaging and transcriptome data. This integration offers novel insights into the neurobiological mechanisms underlying MDD.

Regional abnormalities in amygdala functional connectivity were identified in the orbitofrontal cortex (OFC) (including left orbital parts of MFG, left orbital parts of SFG, bilateral orbital parts of IFG, and right medial orbital parts of the SFG), medial prefrontal cortex (PFC) (i.e., right medial SFG), bilateral gyrus rectus, and left temporal pole. Functional connectivity of the amygdala with these regions have been widely reported in MDD [23, 48–50]. OFC and medial PFC, pivotal regions within the PFC, played crucial roles in the onset and progression of depression [51]. They were involved in various functions including reward processing, attention, perception, emotional processing, and executive function. Numerous preclinical and clinical studies have indicated that the consistently impairments in PFC was one of the most important characteristic in MDD [51]. Compared to healthy children, children with a higher risk of MDD, as well as those currently diagnosed with MDD, exhibited reduced functional connectivity between the right dorsolateral prefrontal cortex and the amygdala [52]. This observed alteration in connectivity corresponds to a pathway previously associated with the regulation of emotional responses in adult MDD [53]. Furthermore, there is evidence suggesting that the temporal pole and gyrus rectus are implicated in various high-level cognitive processes, including language and semantic processing, socio-emotional processing, autobiographical memory, facial recognition, and analysis and recognition of complex objects [54–56]. Our previous study results indicated that these cognitive functions were somewhat compromised in MDD [57]. Therefore, we speculate that the disrupted functional connectivity between the amygdala and the temporal pole and gyrus rectus may heighten susceptibility to depressive symptoms, potentially by affecting cognitive processes. Crucially, these aberrant amygdala functional connectivity patterns hold potential for effectively identifying individuals with MDD at the individual level. This further emphasizes the diagnostic value of these specific amygdala functional connectivity patterns for MDD identification.

In transcription-neuroimaging association analysis, we identified alterations in amygdala functional connectivity associated with changes in gene expression enriched in metal ion transport and modulation of chemical synaptic transmission. Metal ion transport is crucial for regulating cell metabolism and influencing disease progression. [58]. Specifically, mitochondrial metal ion channels and transporters could serve as potential therapeutic targets for MDD and metabolic diseases [58, 59]. Dysregulation of metal ion homeostasis can impact neurotransmitter systems and synaptic plasticity, contributing to the cognitive and emotional symptoms of MDD [60–63]. Synaptic transmission, essential for synaptic plasticity, is also implicated in the neurobiology of depression [64, 65] Disruptions in synaptic transmission, critical for neuronal communication and plasticity, likely contribute to the observed neural connectivity changes in MDD [65]. This supports the effectiveness of synaptic modulators in antidepressant treatments, as many antidepressants function by modulating serotonergic neurotransmission at the synaptic level [66]. Our findings suggest that targeting both metal ion transport channels and synaptic transmission mechanisms could be promising therapeutic strategies for MDD. Further investigation into drugs that modulate these pathways may lead to the development of novel, more effective treatments.

Utilizing single-cell expression data, our investigation revealed that genes consistently associated with amygdala functional connectivity alterations in MDD exhibited significant expression in Glt25d2 neurons and Ntsr+ neurons. This specific neuronal expression pattern aligns with previous research underscoring the significant role of neurons in mediating the genetic influence on functional connectivity [67–69]. Evidence suggests that Glt25d2 and Ntsr+ neurons are constituents of the pyramidal neuron population [70, 71], and their neuropathological abnormalities are intricately linked to the pathophysiology of MDD [72]. Moreover, developmental enrichment analyses revealed that these genes were expressed not only in the cortex but also in several subcortical regions in MDD. The enriched time window for the expression of these genes spanned a broad range of developmental stages, from early/mid fetal development to young adulthood. This suggests that the susceptibility to MDD may potentially manifest during earlier developmental stages than previously anticipated, extending beyond young adulthood [31].

Several limitations warrant consideration in our study. Firstly, the gene expression data were obtained from six healthy adult donors without MDD. Differences in gene expression between depressed patients and healthy individuals, as well as sex variations, may impact the interpretation of connectome-transcriptome associations. Then, our study employed a cross-sectional design, which restricts our ability to establish causal relationships between amygdala functional connectivity and MDD. Future prospective longitudinal studies are needed to provide deeper insights into causal relationships and a more comprehensive understanding.

In summary, our findings confirm our hypothesis of amygdala functional connectivity alterations in MDD, facilitating individual-level identification of the disorder. Additionally, our investigation into connectome-transcriptome associations revealed MDD-related genes enriched for metal ion transport and modulation of chemical synaptic transmission. These insights significantly contribute to our understanding of the neurobiological mechanisms in MDD and offer novel avenues for prevention and intervention strategies.

Supplementary information

Supplemental Material

Supplementary information

The online version contains supplementary material available at 10.1038/s41398-024-03062-z.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant number: 62027812), Tianjin Key Medical Discipline (Specialty) Construction Project (Grant number: TJYXZDXK-033A), and Beijing Tianjin Hebei Basic Research Cooperation Project (Grant number: 23JCZXJC00230; J230011). The authors thank the DIRECT consortium for collecting and sharing the data.

Author contributions

YL: Conceptualization, Methodology, Formal Analysis, Writing–Original draft preparation & Editing. ML: Formal Analysis, Review & Editing. BZ, WQ, YG and YJ: Review & Editing. JL: Conceptualization, Methodology, Supervision, Resources, Writing–Review & Editing.

Data availability

Phenotypic and imaging datasets were sourced from the publicly available brain imaging depression consortium (http://rfmri.org/REST-meta-MDD). Human gene expression data supporting this study’s findings are accessible in the Allen Human Brain Atlas database (http://human.brain-map.org/static/download).

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC: American Psychiatric Association; 2013.
2. Dakić T Mental health burden and unmet needs for treatment: a call for justice Br J Psychiatry 2020 216 241 42 10.1192/bjp.2019.254 31791425
Dakić T. Mental health burden and unmet needs for treatment: a call for justice. Br J Psychiatry. 2020;216:241–42.31791425 10.1192/bjp.2019.254
3. Zhuo C Li G Lin X Jiang D Xu Y Tian H The rise and fall of MRI studies in major depressive disorder Transl Psychiatry 2019 9 335 10.1038/s41398-019-0680-6 31819044
Zhuo C, Li G, Lin X, Jiang D, Xu Y, Tian H, et al. The rise and fall of MRI studies in major depressive disorder. Transl Psychiatry. 2019;9:335.31819044 10.1038/s41398-019-0680-6
4. Cipriani A Furukawa TA Salanti G Chaimani A Atkinson LZ Ogawa Y Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis Lancet 2018 391 1357 66 10.1016/S0140-6736(17)32802-7 29477251
Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391:1357–66.29477251 10.1016/S0140-6736(17)32802-7
5. Williams LM Korgaonkar MS Song YC Paton R Eagles S Goldstein-Piekarski A Amygdala Reactivity to Emotional Faces in the Prediction of General and Medication-Specific Responses to Antidepressant Treatment in the Randomized iSPOT-D Trial Neuropsychopharmacology 2015 40 2398 408 10.1038/npp.2015.89 25824424
Williams LM, Korgaonkar MS, Song YC, Paton R, Eagles S, Goldstein-Piekarski A, et al. Amygdala Reactivity to Emotional Faces in the Prediction of General and Medication-Specific Responses to Antidepressant Treatment in the Randomized iSPOT-D Trial. Neuropsychopharmacology. 2015;40:2398–408.25824424 10.1038/npp.2015.89
6. Xue L Pei C Wang X Wang H Tian S Yao Z Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression J Magn Reson Imaging 2021 54 551 59 10.1002/jmri.27577 33634921
Xue L, Pei C, Wang X, Wang H, Tian S, Yao Z, et al. Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression. J Magn Reson Imaging. 2021;54:551–59.33634921 10.1002/jmri.27577
7. Sun H He Y Cao H Functional magnetic resonance imaging research in China CNS Neurosci Therapeutics 2021 27 1259 67 10.1111/cns.13725
Sun H, He Y, Cao H. Functional magnetic resonance imaging research in China. CNS Neurosci Therapeutics. 2021;27:1259–67.10.1111/cns.13725
8. Woodward ND Cascio CJ Resting-State Functional Connectivity in Psychiatric Disorders JAMA Psychiatry 2015 72 743 4 10.1001/jamapsychiatry.2015.0484 26061674
Woodward ND, Cascio CJ. Resting-State Functional Connectivity in Psychiatric Disorders. JAMA Psychiatry. 2015;72:743–4.26061674 10.1001/jamapsychiatry.2015.0484
9. Zhang X Xu R Ma H Qian Y Zhu J Brain Structural and Functional Damage Network Localization of Suicide Biol Psychiatry 2024 95 1091 99 10.1016/j.biopsych.2024.01.003 38215816
Zhang X, Xu R, Ma H, Qian Y, Zhu J. Brain Structural and Functional Damage Network Localization of Suicide. Biol Psychiatry. 2024;95:1091–99.38215816 10.1016/j.biopsych.2024.01.003
10. Mo F, Zhao H, Li Y, Cai H, Song Y, Wang R, et al. Network Localization of State and Trait of Auditory Verbal Hallucinations in Schizophrenia. Schizophrenia Bulletin. 2024.
11. Murray EA Wise SP Drevets WC Localization of dysfunction in major depressive disorder: prefrontal cortex and amygdala Biol Psychiatry 2011 69 e43 54 10.1016/j.biopsych.2010.09.041 21111403
Murray EA, Wise SP, Drevets WC. Localization of dysfunction in major depressive disorder: prefrontal cortex and amygdala. Biol Psychiatry. 2011;69:e43–54.21111403 10.1016/j.biopsych.2010.09.041
12. Pizzagalli DA Frontocingulate dysfunction in depression: toward biomarkers of treatment response Neuropsychopharmacolgy 2011 36 183 206 10.1038/npp.2010.166
Pizzagalli DA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacolgy. 2011;36:183–206.10.1038/npp.2010.166
13. Murray EA The amygdala, reward and emotion Trends Cogn Sci 2007 11 489 97 10.1016/j.tics.2007.08.013 17988930
Murray EA. The amygdala, reward and emotion. Trends Cogn Sci. 2007;11:489–97.17988930 10.1016/j.tics.2007.08.013
14. Cullen KR Westlund MK Klimes-Dougan B Mueller BA Houri A Eberly LE Abnormal amygdala resting-state functional connectivity in adolescent depression JAMA Psychiatry 2014 71 1138 47 10.1001/jamapsychiatry.2014.1087 25133665
Cullen KR, Westlund MK, Klimes-Dougan B, Mueller BA, Houri A, Eberly LE, et al. Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry. 2014;71:1138–47.25133665 10.1001/jamapsychiatry.2014.1087
15. Wu H Sun H Wang C Yu L Li Y Peng H Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder J Psychiatr Res 2017 84 237 42 10.1016/j.jpsychires.2016.10.001 27770743
Wu H, Sun H, Wang C, Yu L, Li Y, Peng H, et al. Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder. J Psychiatr Res. 2017;84:237–42.27770743 10.1016/j.jpsychires.2016.10.001
16. Li X Wang J Abnormal neural activities in adults and youths with major depressive disorder during emotional processing: a meta-analysis Brain Imaging Behav 2021 15 1134 54 10.1007/s11682-020-00299-2 32710330
Li X, Wang J. Abnormal neural activities in adults and youths with major depressive disorder during emotional processing: a meta-analysis. Brain Imaging Behav. 2021;15:1134–54.32710330 10.1007/s11682-020-00299-2
17. Uchida M Biederman J Gabrieli JD Micco J de Los Angeles C Brown A Emotion regulation ability varies in relation to intrinsic functional brain architecture Soc Cogn Affect Neurosci 2015 10 1738 48 10.1093/scan/nsv059 25999363
Uchida M, Biederman J, Gabrieli JD, Micco J, de Los Angeles C, Brown A, et al. Emotion regulation ability varies in relation to intrinsic functional brain architecture. Soc Cogn Affect Neurosci. 2015;10:1738–48.25999363 10.1093/scan/nsv059
18. Buhle JT Silvers JA Wager TD Lopez R Onyemekwu C Kober H Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies Cereb Cortex 2014 24 2981 90 10.1093/cercor/bht154 23765157
Buhle JT, Silvers JA, Wager TD, Lopez R, Onyemekwu C, Kober H, et al. Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cereb Cortex. 2014;24:2981–90.23765157 10.1093/cercor/bht154
19. Bickart KC Dickerson BC Barrett LF The amygdala as a hub in brain networks that support social life Neuropsychologia 2014 63 235 48 10.1016/j.neuropsychologia.2014.08.013 25152530
Bickart KC, Dickerson BC, Barrett LF. The amygdala as a hub in brain networks that support social life. Neuropsychologia. 2014;63:235–48.25152530 10.1016/j.neuropsychologia.2014.08.013
20. Marek R Strobel C Bredy TW Sah P The amygdala and medial prefrontal cortex: partners in the fear circuit J Physiol 2013 591 2381 91 10.1113/jphysiol.2012.248575 23420655
Marek R, Strobel C, Bredy TW, Sah P. The amygdala and medial prefrontal cortex: partners in the fear circuit. J Physiol. 2013;591:2381–91.23420655 10.1113/jphysiol.2012.248575
21. Banks SJ Eddy KT Angstadt M Nathan PJ Phan KL Amygdala-frontal connectivity during emotion regulation Soc Cogn Affect Neurosci 2007 2 303 12 10.1093/scan/nsm029 18985136
Banks SJ, Eddy KT, Angstadt M, Nathan PJ, Phan KL. Amygdala-frontal connectivity during emotion regulation. Soc Cogn Affect Neurosci. 2007;2:303–12.18985136 10.1093/scan/nsm029
22. Alexandra Kredlow M Fenster RJ Laurent ES Ressler KJ Phelps EA Prefrontal cortex, amygdala, and threat processing: implications for PTSD Neuropsychopharmacology 2022 47 247 59 10.1038/s41386-021-01155-7 34545196
Alexandra Kredlow M, Fenster RJ, Laurent ES, Ressler KJ, Phelps EA. Prefrontal cortex, amygdala, and threat processing: implications for PTSD. Neuropsychopharmacology. 2022;47:247–59.34545196 10.1038/s41386-021-01155-7
23. Hossein S Cooper JA DeVries BAM Nuutinen MR Hahn EC Kragel PA Effects of acute stress and depression on functional connectivity between prefrontal cortex and the amygdala Mol Psychiatry 2023 28 4602 12 10.1038/s41380-023-02056-5 37076616
Hossein S, Cooper JA, DeVries BAM, Nuutinen MR, Hahn EC, Kragel PA, et al. Effects of acute stress and depression on functional connectivity between prefrontal cortex and the amygdala. Mol Psychiatry. 2023;28:4602–12.37076616 10.1038/s41380-023-02056-5
24. Wen X Han B Li H Dou F Wei G Hou G Unbalanced amygdala communication in major depressive disorder J Affect Disord 2023 329 192 206 10.1016/j.jad.2023.02.091 36841299
Wen X, Han B, Li H, Dou F, Wei G, Hou G, et al. Unbalanced amygdala communication in major depressive disorder. J Affect Disord. 2023;329:192–206.36841299 10.1016/j.jad.2023.02.091
25. Arnatkeviciute A Fulcher BD Oldham S Tiego J Paquola C Gerring Z Genetic influences on hub connectivity of the human connectome Nat Commun 2021 12 4237 10.1038/s41467-021-24306-2 34244483
Arnatkeviciute A, Fulcher BD, Oldham S, Tiego J, Paquola C, Gerring Z, et al. Genetic influences on hub connectivity of the human connectome. Nat Commun. 2021;12:4237.34244483 10.1038/s41467-021-24306-2
26. Alex AM Buss C Davis EP Campos GL Donald KA Fair DA Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders Biol Psychiatry 2023 93 905 20 10.1016/j.biopsych.2023.01.013 36932005
Alex AM, Buss C, Davis EP, Campos GL, Donald KA, Fair DA, et al. Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders. Biol Psychiatry. 2023;93:905–20.36932005 10.1016/j.biopsych.2023.01.013
27. Palazidou E The neurobiology of depression Br Med Bull 2012 101 127 45 10.1093/bmb/lds004 22334281
Palazidou E. The neurobiology of depression. Br Med Bull. 2012;101:127–45.22334281 10.1093/bmb/lds004
28. Tozzi L Farrell C Booij L Doolin K Nemoda Z Szyf M Epigenetic Changes of FKBP5 as a Link Connecting Genetic and Environmental Risk Factors with Structural and Functional Brain Changes in Major Depression Neuropsychopharmacology 2018 43 1138 45 10.1038/npp.2017.290 29182159
Tozzi L, Farrell C, Booij L, Doolin K, Nemoda Z, Szyf M, et al. Epigenetic Changes of FKBP5 as a Link Connecting Genetic and Environmental Risk Factors with Structural and Functional Brain Changes in Major Depression. Neuropsychopharmacology. 2018;43:1138–45.29182159 10.1038/npp.2017.290
29. Wray NR Ripke S Mattheisen M Trzaskowski M Byrne EM Abdellaoui A Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression Nat Genet 2018 50 668 81 10.1038/s41588-018-0090-3 29700475
Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.29700475 10.1038/s41588-018-0090-3
30. Rasero J Jimenez-Marin A Diez I Toro R Hasan MT Cortes JM The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals Biol Psychiatry 2023 94 804 13 10.1016/j.biopsych.2023.04.014 37088169
Rasero J, Jimenez-Marin A, Diez I, Toro R, Hasan MT, Cortes JM. The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals. Biol Psychiatry. 2023;94:804–13.37088169 10.1016/j.biopsych.2023.04.014
31. Xue K Guo L Zhu W Liang S Xu Q Ma L Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder Neuropsychopharmacology 2023 48 518 28 10.1038/s41386-022-01474-3 36253546
Xue K, Guo L, Zhu W, Liang S, Xu Q, Ma L, et al. Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder. Neuropsychopharmacology. 2023;48:518–28.36253546 10.1038/s41386-022-01474-3
32. Cai M Ji Y Zhao Q Xue H Sun Z Wang H Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression NeuroImage 2024 289 120551 10.1016/j.neuroimage.2024.120551 38382862
Cai M, Ji Y, Zhao Q, Xue H, Sun Z, Wang H, et al. Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression. NeuroImage. 2024;289:120551.38382862 10.1016/j.neuroimage.2024.120551
33. Gottesman II Gould TD The endophenotype concept in psychiatry: etymology and strategic intentions Am J Psychiatry 2003 160 636 45 10.1176/appi.ajp.160.4.636 12668349
Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160:636–45.12668349 10.1176/appi.ajp.160.4.636
34. Fang Q Cai H Jiang P Zhao H Song Y Zhao W Transcriptional substrates of brain structural and functional impairments in drug-naive first-episode patients with major depressive disorder J Affect Disord 2023 325 522 33 10.1016/j.jad.2023.01.051 36657492
Fang Q, Cai H, Jiang P, Zhao H, Song Y, Zhao W, et al. Transcriptional substrates of brain structural and functional impairments in drug-naive first-episode patients with major depressive disorder. J Affect Disord. 2023;325:522–33.36657492 10.1016/j.jad.2023.01.051
35. Xue K Liang S Yang B Zhu D Xie Y Qin W Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles Psychological Med 2022 52 2052 61 10.1017/S0033291720003876
Xue K, Liang S, Yang B, Zhu D, Xie Y, Qin W, et al. Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles. Psychological Med. 2022;52:2052–61.10.1017/S0033291720003876
36. Sun X, Huang W, Wang J, Xu R, Zhang X, Zhou J, et al. Cerebral blood flow changes and their genetic mechanisms in major depressive disorder: a combined neuroimaging and transcriptome study. Psychological Med. 2023:1–13.
37. Xia M Liu J Mechelli A Sun X Ma Q Wang X Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes Mol Psychiatry 2022 27 1384 93 10.1038/s41380-022-01519-5 35338312
Xia M, Liu J, Mechelli A, Sun X, Ma Q, Wang X, et al. Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes. Mol Psychiatry. 2022;27:1384–93.35338312 10.1038/s41380-022-01519-5
38. Chen X Lu B Li H-X Li X-Y Wang Y-W Castellanos FX The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder Psychoradiology 2022 2 32 42 10.1093/psyrad/kkac005 38665141
Chen X, Lu B, Li H-X, Li X-Y, Wang Y-W, Castellanos FX, et al. The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder. Psychoradiology. 2022;2:32–42.38665141 10.1093/psyrad/kkac005
39. Yan CG Chen X Li L Castellanos FX Bai TJ Bo QJ Reduced default mode network functional connectivity in patients with recurrent major depressive disorder Proc Natl Acad Sci USA 2019 116 9078 83 10.1073/pnas.1900390116 30979801
Yan CG, Chen X, Li L, Castellanos FX, Bai TJ, Bo QJ, et al. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci USA. 2019;116:9078–83.30979801 10.1073/pnas.1900390116
40. Tzourio-Mazoyer N Landeau B Papathanassiou D Crivello F Etard O Delcroix N Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain NeuroImage 2002 15 273 89 10.1006/nimg.2001.0978 11771995
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 2002;15:273–89.11771995 10.1006/nimg.2001.0978
41. Grogans SE Fox AS Shackman AJ The Amygdala and Depression: A Sober Reconsideration Am J Psychiatry 2022 179 454 57 10.1176/appi.ajp.20220412 35775156
Grogans SE, Fox AS, Shackman AJ. The Amygdala and Depression: A Sober Reconsideration. Am J Psychiatry. 2022;179:454–57.35775156 10.1176/appi.ajp.20220412
42. Wang J Wang X Xia M Liao X Evans A He Y GRETNA: a graph theoretical network analysis toolbox for imaging connectomics Front Hum Neurosci 2015 9 386 26175682
Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci. 2015;9:386.26175682
43. Hawrylycz MJ Lein ES Guillozet-Bongaarts AL Shen EH Ng L Miller JA An anatomically comprehensive atlas of the adult human brain transcriptome Nature 2012 489 391 99 10.1038/nature11405 22996553
Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489:391–99.22996553 10.1038/nature11405
44. Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife. 2021;10:e72129.
45. Arnatkeviciute A Fulcher BD Fornito A A practical guide to linking brain-wide gene expression and neuroimaging data NeuroImage 2019 189 353 67 10.1016/j.neuroimage.2019.01.011 30648605
Arnatkeviciute A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage. 2019;189:353–67.30648605 10.1016/j.neuroimage.2019.01.011
46. Zhou Y Zhou B Pache L Chang M Khodabakhshi AH Tanaseichuk O Metascape provides a biologist-oriented resource for the analysis of systems-level datasets Nat Commun 2019 10 1523 10.1038/s41467-019-09234-6 30944313
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.30944313 10.1038/s41467-019-09234-6
47. Dougherty JD Schmidt EF Nakajima M Heintz N Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells Nucleic Acids Res 2010 38 4218 30 10.1093/nar/gkq130 20308160
Dougherty JD, Schmidt EF, Nakajima M, Heintz N. Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Res. 2010;38:4218–30.20308160 10.1093/nar/gkq130
48. Tassone VK Demchenko I Salvo J Mahmood R Di Passa AM Kuburi S Contrasting the amygdala activity and functional connectivity profile between antidepressant-free participants with major depressive disorder and healthy controls: A systematic review of comparative fMRI studies Psychiatry Res Neuroimaging 2022 325 111517 10.1016/j.pscychresns.2022.111517 35944425
Tassone VK, Demchenko I, Salvo J, Mahmood R, Di Passa AM, Kuburi S, et al. Contrasting the amygdala activity and functional connectivity profile between antidepressant-free participants with major depressive disorder and healthy controls: A systematic review of comparative fMRI studies. Psychiatry Res Neuroimaging. 2022;325:111517.35944425 10.1016/j.pscychresns.2022.111517
49. Tang Y Kong L Wu F Womer F Jiang W Cao Y Decreased functional connectivity between the amygdala and the left ventral prefrontal cortex in treatment-naive patients with major depressive disorder: a resting-state functional magnetic resonance imaging study Psychological Med 2013 43 1921 7 10.1017/S0033291712002759
Tang Y, Kong L, Wu F, Womer F, Jiang W, Cao Y, et al. Decreased functional connectivity between the amygdala and the left ventral prefrontal cortex in treatment-naive patients with major depressive disorder: a resting-state functional magnetic resonance imaging study. Psychological Med. 2013;43:1921–7.10.1017/S0033291712002759
50. Satyshur MD Layden EA Gowins JR Buchanan A Gollan JK Functional connectivity of reflective and brooding rumination in depressed and healthy women Cogn Affect Behav Neurosci 2018 18 884 901 10.3758/s13415-018-0611-7 29949111
Satyshur MD, Layden EA, Gowins JR, Buchanan A, Gollan JK. Functional connectivity of reflective and brooding rumination in depressed and healthy women. Cogn Affect Behav Neurosci. 2018;18:884–901.29949111 10.3758/s13415-018-0611-7
51. Pizzagalli DA Roberts AC Prefrontal cortex and depression Neuropsychopharmacology 2022 47 225 46 10.1038/s41386-021-01101-7 34341498
Pizzagalli DA, Roberts AC. Prefrontal cortex and depression. Neuropsychopharmacology. 2022;47:225–46.34341498 10.1038/s41386-021-01101-7
52. Singh MK Leslie SM Packer MM Weisman EF Gotlib IH Limbic Intrinsic Connectivity in Depressed and High-Risk Youth J Am Acad Child Adolesc Psychiatry 2018 57 775 85.e3 10.1016/j.jaac.2018.06.017 30274652
Singh MK, Leslie SM, Packer MM, Weisman EF, Gotlib IH. Limbic Intrinsic Connectivity in Depressed and High-Risk Youth. J Am Acad Child Adolesc Psychiatry. 2018;57:775–85.e3.30274652 10.1016/j.jaac.2018.06.017
53. Lu Q Li H Luo G Wang Y Tang H Han L Impaired prefrontal-amygdala effective connectivity is responsible for the dysfunction of emotion process in major depressive disorder: a dynamic causal modeling study on MEG Neurosci Lett 2012 523 125 30 10.1016/j.neulet.2012.06.058 22750155
Lu Q, Li H, Luo G, Wang Y, Tang H, Han L, et al. Impaired prefrontal-amygdala effective connectivity is responsible for the dysfunction of emotion process in major depressive disorder: a dynamic causal modeling study on MEG. Neurosci Lett. 2012;523:125–30.22750155 10.1016/j.neulet.2012.06.058
54. Herlin B Navarro V Dupont S The temporal pole: From anatomy to function—A literature appraisal J Chem Neuroanat 2021 113 101925 10.1016/j.jchemneu.2021.101925 33582250
Herlin B, Navarro V, Dupont S. The temporal pole: From anatomy to function—A literature appraisal. J Chem Neuroanat. 2021;113:101925.33582250 10.1016/j.jchemneu.2021.101925
55. Ballmaier M Toga AW Blanton RE Sowell ER Lavretsky H Peterson J Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients: an MRI-based parcellation of the prefrontal cortex Am J Psychiatry 2004 161 99 108 10.1176/appi.ajp.161.1.99 14702257
Ballmaier M, Toga AW, Blanton RE, Sowell ER, Lavretsky H, Peterson J, et al. Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients: an MRI-based parcellation of the prefrontal cortex. Am J Psychiatry. 2004;161:99–108.14702257 10.1176/appi.ajp.161.1.99
56. Li W Lou W Zhang W Tong RK-Y Jin R Peng W Gyrus rectus asymmetry predicts trait alexithymia, cognitive empathy, and social function in neurotypical adults Cereb Cortex 2023 33 1941 54 10.1093/cercor/bhac184 35567793
Li W, Lou W, Zhang W, Tong RK-Y, Jin R, Peng W. Gyrus rectus asymmetry predicts trait alexithymia, cognitive empathy, and social function in neurotypical adults. Cereb Cortex. 2023;33:1941–54.35567793 10.1093/cercor/bhac184
57. Liu Y Li M Gao Y Zhang C Wang Y Liu X Specific correlation between childhood trauma and social cognition in Chinese Han first-episode, drug-naïve major depressive disorder J Affect Disord 2023 333 51 57 10.1016/j.jad.2023.04.059 37084962
Liu Y, Li M, Gao Y, Zhang C, Wang Y, Liu X, et al. Specific correlation between childhood trauma and social cognition in Chinese Han first-episode, drug-naïve major depressive disorder. J Affect Disord. 2023;333:51–57.37084962 10.1016/j.jad.2023.04.059
58. Wang X, An P, Gu Z, Luo Y, Luo J. Mitochondrial Metal Ion Transport in Cell Metabolism and Disease. International journal of molecular sciences. Int J Mol Sci. 2021;22:7525.
59. Du J Zhu M Bao H Li B Dong Y Xiao C The Role of Nutrients in Protecting Mitochondrial Function and Neurotransmitter Signaling: Implications for the Treatment of Depression, PTSD, and Suicidal Behaviors Crit Rev food Sci Nutr 2016 56 2560 78 10.1080/10408398.2013.876960 25365455
Du J, Zhu M, Bao H, Li B, Dong Y, Xiao C, et al. The Role of Nutrients in Protecting Mitochondrial Function and Neurotransmitter Signaling: Implications for the Treatment of Depression, PTSD, and Suicidal Behaviors. Crit Rev food Sci Nutr. 2016;56:2560–78.25365455 10.1080/10408398.2013.876960
60. Neher E Sakaba T Multiple roles of calcium ions in the regulation of neurotransmitter release Neuron 2008 59 861 72 10.1016/j.neuron.2008.08.019 18817727
Neher E, Sakaba T. Multiple roles of calcium ions in the regulation of neurotransmitter release. Neuron. 2008;59:861–72.18817727 10.1016/j.neuron.2008.08.019
61. Sapolsky RM Krey LC McEwen BS The neuroendocrinology of stress and aging: the glucocorticoid cascade hypothesis Endocr Rev 1986 7 284 301 10.1210/edrv-7-3-284 3527687
Sapolsky RM, Krey LC, McEwen BS. The neuroendocrinology of stress and aging: the glucocorticoid cascade hypothesis. Endocr Rev. 1986;7:284–301.3527687 10.1210/edrv-7-3-284
62. Su L Faluyi YO Hong YT Fryer TD Mak E Gabel S Neuroinflammatory and morphological changes in late-life depression: the NIMROD study Br J Psychiatry 2016 209 525 26 10.1192/bjp.bp.116.190165 27758838
Su L, Faluyi YO, Hong YT, Fryer TD, Mak E, Gabel S, et al. Neuroinflammatory and morphological changes in late-life depression: the NIMROD study. Br J Psychiatry. 2016;209:525–26.27758838 10.1192/bjp.bp.116.190165
63. Dantzer R O’Connor JC Freund GG Johnson RW Kelley KW From inflammation to sickness and depression: when the immune system subjugates the brain Nat Rev Neurosci 2008 9 46 56 10.1038/nrn2297 18073775
Dantzer R, O’Connor JC, Freund GG, Johnson RW, Kelley KW. From inflammation to sickness and depression: when the immune system subjugates the brain. Nat Rev Neurosci. 2008;9:46–56.18073775 10.1038/nrn2297
64. Marsden WN Synaptic plasticity in depression: molecular, cellular and functional correlates Prog Neuro Psychopharmacol Biol Psychiatry 2013 43 168 84 10.1016/j.pnpbp.2012.12.012
Marsden WN. Synaptic plasticity in depression: molecular, cellular and functional correlates. Prog Neuro Psychopharmacol Biol Psychiatry. 2013;43:168–84.10.1016/j.pnpbp.2012.12.012
65. Klempan TA Sequeira A Canetti L Lalovic A Ernst C ffrench-Mullen J Altered expression of genes involved in ATP biosynthesis and GABAergic neurotransmission in the ventral prefrontal cortex of suicides with and without major depression Mol Psychiatry 2009 14 175 89 10.1038/sj.mp.4002110 17938633
Klempan TA, Sequeira A, Canetti L, Lalovic A, Ernst C, ffrench-Mullen J, et al. Altered expression of genes involved in ATP biosynthesis and GABAergic neurotransmission in the ventral prefrontal cortex of suicides with and without major depression. Mol Psychiatry. 2009;14:175–89.17938633 10.1038/sj.mp.4002110
66. Feighner JP Mechanism of action of antidepressant medications J Clin Psychiatry 1999 60 4 11 10086478
Feighner JP. Mechanism of action of antidepressant medications. J Clin Psychiatry. 1999;60:4–11.10086478
67. Anderson KM Krienen FM Choi EY Reinen JM Yeo BTT Holmes AJ Gene expression links functional networks across cortex and striatum Nat Commun 2018 9 1428 10.1038/s41467-018-03811-x 29651138
Anderson KM, Krienen FM, Choi EY, Reinen JM, Yeo BTT, Holmes AJ. Gene expression links functional networks across cortex and striatum. Nat Commun. 2018;9:1428.29651138 10.1038/s41467-018-03811-x
68. Chen J Zhang C Wang R Jiang P Cai H Zhao W Molecular basis underlying functional connectivity of fusiform gyrus subregions: A transcriptome-neuroimaging spatial correlation study Cortex J Devoted Study Nerv Syst Behav 2022 152 59 73 10.1016/j.cortex.2022.03.016
Chen J, Zhang C, Wang R, Jiang P, Cai H, Zhao W, et al. Molecular basis underlying functional connectivity of fusiform gyrus subregions: A transcriptome-neuroimaging spatial correlation study. Cortex J Devoted Study Nerv Syst Behav. 2022;152:59–73.10.1016/j.cortex.2022.03.016
69. Richiardi J Altmann A Milazzo AC Chang C Chakravarty MM Banaschewski T BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks Science 2015 348 1241 4 10.1126/science.1255905 26068849
Richiardi J, Altmann A, Milazzo AC, Chang C, Chakravarty MM, Banaschewski T, et al. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science. 2015;348:1241–4.26068849 10.1126/science.1255905
70. Kim EJ Juavinett AL Kyubwa EM Jacobs MW Callaway EM Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function Neuron 2015 88 1253 67 10.1016/j.neuron.2015.11.002 26671462
Kim EJ, Juavinett AL, Kyubwa EM, Jacobs MW, Callaway EM. Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function. Neuron. 2015;88:1253–67.26671462 10.1016/j.neuron.2015.11.002
71. Gong S Doughty M Harbaugh CR Cummins A Hatten ME Heintz N Targeting Cre recombinase to specific neuron populations with bacterial artificial chromosome constructs J Neurosci 2007 27 9817 23 10.1523/JNEUROSCI.2707-07.2007 17855595
Gong S, Doughty M, Harbaugh CR, Cummins A, Hatten ME, Heintz N, et al. Targeting Cre recombinase to specific neuron populations with bacterial artificial chromosome constructs. J Neurosci. 2007;27:9817–23.17855595 10.1523/JNEUROSCI.2707-07.2007
72. Oh DH Son H Hwang S Kim SH Neuropathological abnormalities of astrocytes, GABAergic neurons, and pyramidal neurons in the dorsolateral prefrontal cortices of patients with major depressive disorder Eur Neuropsychopharmacol 2012 22 330 8 10.1016/j.euroneuro.2011.09.001 21962915
Oh DH, Son H, Hwang S, Kim SH. Neuropathological abnormalities of astrocytes, GABAergic neurons, and pyramidal neurons in the dorsolateral prefrontal cortices of patients with major depressive disorder. Eur Neuropsychopharmacol. 2012;22:330–8.21962915 10.1016/j.euroneuro.2011.09.001
