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Zhong X, Chen X, Liu Y, Gui S, Pu J, Wang D, Tao W, Chen Y, Chen X, Chen W, Chen X, Qiao R, Tao X, Li Z, Xie P. Integrated analysis of transcriptional changes in major depressive disorder: Insights from blood and anterior cingulate cortex. Heliyon 2024; 10:e28960. [PMID: 38628773 PMCID: PMC11019182 DOI: 10.1016/j.heliyon.2024.e28960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 02/22/2024] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
Background Major depressive disorder (MDD) was involved in widely transcriptional changes in central and peripheral tissues. While, previous studies focused on single tissues, making it difficult to represent systemic molecular changes throughout the body. Thus, there is an urgent need to explore the central and peripheral biomarkers with intrinsic correlation. Methods We systematically retrieved gene expression profiles of blood and anterior cingulate cortex (ACC). 3 blood datatsets (84 MDD and 88 controls) and 6 ACC datasets (100 MDD and 100 controls) were obtained. Differential expression analysis, RobustRankAggreg (RRA) analysis, functional enrichment analysis, immune associated analysis and protein-protein interaction networks (PPI) were integrated. Furthermore, the key genes were validated in an independent ACC dataset (12 MDD and 15 controls) and a cohort with 120 MDD and 117 controls. Results Differential expression analysis identified 2211 and 2021 differential expressed genes (DEGs) in blood and ACC, respectively. RRA identified 45 and 25 robust DEGs in blood and ACC based on DEGs, and all of them were closely associated with immune cells. Functional enrichment results showed both the robust DEGs in blood and ACC were enriched in humoral immune response. Furthermore, PPI identified 8 hub DEGs (CD79A, CD79B, CD19, MS4A1, PLP1, CLDN11, MOG, MAG) in blood and ACC. Independent ACC dataset showed the area under the curve (AUC) based on these hub DEGs was 0.77. Meanwhile, these hub DEGs were validated in the serum of MDD patients, and also showed a promising diagnostic power. Conclusions The biomarker panel based on hub DEGs yield a promising diagnostic efficacy, and all of these hub DEGs were strongly correlated with immunity. Humoral immune response may be the key link between the brain and blood in MDD, and our results may provide further understanding for MDD.
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Affiliation(s)
- Xiaogang Zhong
- College of Basic Medicine, Chongqing Medical University, Chongqing, 400016, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
| | - Xiangyu Chen
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yiyun Liu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
| | - Siwen Gui
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
| | - Juncai Pu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Dongfang Wang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
| | - Wei Tao
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
| | - Yue Chen
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiang Chen
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Weiyi Chen
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiaopeng Chen
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Renjie Qiao
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Xiangkun Tao
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Zhuocan Li
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Peng Xie
- College of Basic Medicine, Chongqing Medical University, Chongqing, 400016, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- The Jin Feng Laboratory, Chongqing, 401329, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
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Wang X, Xue L, Shao J, Dai Z, Hua L, Yan R, Yao Z, Lu Q. Distinct MRI-based functional and structural connectivity for antidepressant response prediction in major depressive disorder. Clin Neurophysiol 2024; 160:19-27. [PMID: 38367310 DOI: 10.1016/j.clinph.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/28/2023] [Accepted: 02/06/2024] [Indexed: 02/19/2024]
Abstract
OBJECTIVE Emerging studies have identified treatment-related connectome predictors in major depressive disorder (MDD). However, quantifying treatment-responsive patterns in structural connectivity (SC) and functional connectivity (FC) simultaneously remains underexplored. We aimed to evaluate whether spatial distributions of FC and SC associated treatment responses are shared or unique. METHODS Diffusion tensor imaging and resting-state functional magnetic resonance imaging were collected from 210 patients with MDD at baseline. We separately developed connectome-based prediction models (CPM) to predict reduction of depressive severity after 6-week monotherapy based on structural, functional, and combined connectomes, then validated them on the external dataset. We identified the predictive SC and FC from CPM with high occurrence frequencies during the cross-validation. RESULTS Structural connectomes (r = 0.2857, p < 0.0001), functional connectomes (r = 0.2057, p = 0.0025), and their combined CPM (r = 0.4, p < 0.0001) can significantly predict a reduction of depressive severity. We didn't find shared connectivity between predictive FC and SC. Specifically, the most predictive FC stemmed from the default mode network, while predictive SC was mainly characterized by within-network SC of fronto-limbic networks. CONCLUSIONS These distinct patterns suggest that SC and FC capture unique connectivity concerning the antidepressant response. SIGNIFICANCE Our findings provide comprehensive insights into the neurophysiology of antidepressants response.
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Affiliation(s)
- Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Lingling Hua
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China.
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3
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Gonsalves MA, White TL, Barredo J, DeMayo MM, DeLuca E, Harris AD, Carpenter LL. Cortical glutamate, Glx, and total N-acetylaspartate: potential biomarkers of repetitive transcranial magnetic stimulation treatment response and outcomes in major depression. Transl Psychiatry 2024; 14:5. [PMID: 38184652 PMCID: PMC10771455 DOI: 10.1038/s41398-023-02715-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/08/2024] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for individuals with major depressive disorder (MDD) who have not improved with standard therapies. However, only 30-45% of patients respond to rTMS. Predicting response to rTMS will benefit both patients and providers in terms of prescribing and targeting treatment for maximum efficacy and directing resources, as individuals with lower likelihood of response could be redirected to more suitable treatment alternatives. In this exploratory study, our goal was to use proton magnetic resonance spectroscopy to examine how glutamate (Glu), Glx, and total N-acetylaspartate (tNAA) predict post-rTMS changes in overall MDD severity and symptoms, and treatment response. Metabolites were measured in a right dorsal anterior cingulate cortex voxel prior to a standard course of 10 Hz rTMS to the left DLPFC in 25 individuals with MDD. MDD severity and symptoms were evaluated via the Inventory of Depression Symptomatology Self-Report (IDS-SR). rTMS response was defined as ≥50% change in full-scale IDS-SR scores post treatment. Percent change in IDS-SR symptom domains were evaluated using principal component analysis and established subscales. Generalized linear and logistic regression models were used to evaluate the relationship between baseline Glu, Glx, and tNAA and outcomes while controlling for age and sex. Participants with baseline Glu and Glx levels in the lower range had greater percent change in full scale IDS-SR scores post-treatment (p < 0.001), as did tNAA (p = 0.007). Low glutamatergic metabolite levels also predicted greater percent change in mood/cognition symptoms (p ≤ 0.001). Low-range Glu, Glx, and tNAA were associated with greater improvement on the immuno-metabolic subscale (p ≤ 0.003). Baseline Glu predicted rTMS responder status (p = 0.025) and had an area under the receiving operating characteristic curve of 0.81 (p = 0.009), demonstrating excellent discriminative ability. Baseline Glu, Glx, and tNAA significantly predicted MDD improvement after rTMS; preliminary evidence also demonstrates metabolite association with symptom subdomain improvement post-rTMS. This work provides feasibility for a personalized medicine approach to rTMS treatment selection, with individuals with Glu levels in the lower range potentially being the best candidates.
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Affiliation(s)
- Meghan A Gonsalves
- Neuroscience Graduate Program, Brown University, Providence, RI, USA.
- Butler Hospital Neuromodulation Research Facility, Providence, RI, USA.
- Center of Biomedical Research Excellence (COBRE) for Neuromodulation, Butler Hospital, Providence, RI, USA.
| | - Tara L White
- Center for Alcohol and Addiction Studies, Brown University, Providence, RI, USA
- Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, RI, USA
- Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Jennifer Barredo
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI, USA
- Providence VA Medical Center, Providence, RI, USA
- Clinical Neuroimaging Research Core, Brown University, Providence, RI, USA
| | - Marilena M DeMayo
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Emily DeLuca
- Clinical Neuroimaging Research Core, Brown University, Providence, RI, USA
| | - Ashley D Harris
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Linda L Carpenter
- Butler Hospital Neuromodulation Research Facility, Providence, RI, USA
- Center of Biomedical Research Excellence (COBRE) for Neuromodulation, Butler Hospital, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI, USA
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Matar D, Serhan A, El Bilani S, Faraj RA, Hadi BA, Fakhoury M. Psychopharmacological Approaches for Neural Plasticity and Neurogenesis in Major Depressive Disorders. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1456:27-48. [PMID: 39261422 DOI: 10.1007/978-981-97-4402-2_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Major depressive disorder (MDD) is a mental health disorder associated with cognitive impairment, dysregulated appetite, fatigue, insomnia or hypersomnia, and severe mood changes that significantly impact the ability of the affected individual to perform day-to-day tasks, leading to suicide in the worst-case scenario. As MDD is becoming more prevalent, affecting roughly 300 million individuals worldwide, its treatment has become a major point of interest. Antidepressants acting as selective serotonin reuptake inhibitors (SSRIs) are currently used as the first line of treatment for MDD. Other antidepressants currently used for the treatment of MDD include the serotonin and norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), and monoamine oxidase inhibitors (MAOIs). However, although effective in alleviating symptoms of MDD, most antidepressants require weeks or even months of regular administration prior to eliciting a rational clinical effect. Owing to the strong evidence showing a relationship between neural plasticity, neurogenesis, and MDD, researchers have also looked at the possibility of using treatment modalities that target these processes in an attempt to improve clinical outcome. The overarching aim of this chapter is to highlight the role of neural plasticity and neurogenesis in the pathophysiology of MDD and discuss the most recently studied treatment strategies that target these processes by presenting supporting evidence from both animal and human studies.
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Affiliation(s)
- Dina Matar
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon
| | - Aya Serhan
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon
| | - Sabah El Bilani
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon
| | - Rashel Abi Faraj
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon
| | - Bayan Ali Hadi
- School of Pharmacy, Lebanese American University, Beirut, Lebanon
| | - Marc Fakhoury
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon.
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5
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Wang X, Xia Y, Yan R, Sun H, Huang Y, Zou H, Du Y, Hua L, Tang H, Zhou H, Yao Z, Lu Q. The sex differences in anhedonia in major depressive disorder: A resting-state fMRI study. J Affect Disord 2023; 340:555-566. [PMID: 37591350 DOI: 10.1016/j.jad.2023.08.083] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/23/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVE The external behavioural manifestations and internal neural mechanisms of anhedonia are sexually dimorphic. This study aimed to explore the sex differences in the regional brain neuroimaging features of anhedonia in the context of major depressive disorder (MDD). METHOD The resting-fMRI by applying amplitude of low-frequency fluctuation (ALFF) method was estimated in 414 patients with MDD (281 high anhedonia [HA], 133 low anhedonia [LA]) and 213 healthy controls (HC). The effects of two factors in patients with MDD were analysed using a 2 (sex: male, female) × 2 (group: HA, LA) ANOVA concerning the brain regions in which statistical differences were identified between patients with MDD and HC. We followed up with patients with HA at baseline, and 43 patients completed a second fMRI scan in remission. Paired t-test was performed to compare the ALFF values of anhedonia-related brain regions between the baseline and remission periods. RESULTS For the sex-by-group interaction, the bilateral insula, right hippocampus, right post cingulum cortex, and left putamen showed significant differences. Furthermore, the abnormally elevated ALFF values in anhedonia-related brain regions at baseline decreased in remission. CONCLUSION Our findings point to the fact that the females showed unique patterns of anhedonia-related brain activity compared to males, which may have clinical implications for interfering with the anhedonia symptoms in MDD. Using task fMRI, we can further examine the distinct characteristics between consumption anhedonia and anticipation anhedonia in MDD.
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Affiliation(s)
- Xiaoqin Wang
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China
| | - Yi Xia
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China
| | - Rui Yan
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China
| | - Hao Sun
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, 22 Hankou Road, Nanjing 210093, China
| | - Yinghong Huang
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, 22 Hankou Road, Nanjing 210093, China
| | - Haowen Zou
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, 22 Hankou Road, Nanjing 210093, China
| | - Yishan Du
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China
| | - Lingling Hua
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China
| | - Hao Tang
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China
| | - Hongliang Zhou
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China
| | - Zhijian Yao
- The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, 22 Hankou Road, Nanjing 210093, China; School of Biological Sciences and Medical Engineering, Southeast University, 2 sipailou, Nanjing 210096, China.
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, 2 sipailou, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China.
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6
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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7
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Shen H, Ge L, Cao B, Wei GX, Zhang X. The contribution of the cingulate cortex: treating depressive symptoms in first-episode drug naïve schizophrenia. Int J Clin Health Psychol 2023; 23:100372. [PMID: 36793339 PMCID: PMC9922813 DOI: 10.1016/j.ijchp.2023.100372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/17/2023] [Indexed: 01/29/2023] Open
Abstract
Background Our previous study has shown the cingulate cortex abnormalities in first-episode drug naïve (FEDN) schizophrenia patients with comorbid depressive symptoms. However, it remains largely unknown whether antipsychotics may induce morphometric change in cingulate cortex and its relationship with depressive symptoms. The purpose of this study was to further clarify the important role of cingulate cortex in the treatment on depressive symptoms in FEDN schizophrenia patients. Method In this study, 42 FEDN schizophrenia patients were assigned into depressed patients group (DP, n = 24) and non-depressed patients group (NDP, n = 18) measured by the 24-item Hamilton Depression Rating Scale (HAMD). Clinical assessments and anatomical images were obtained from all patients before and after 12-week treatment with risperidone. Results Although risperidone alleviated psychotic symptoms in all patients, depressive symptoms were decreased only in DP. Significant group by time interaction effects were found in the right rostral anterior cingulate cortex (rACC) and other subcortical regions in the left hemisphere. After risperidone treatment, the right rACC were increased in DP. Further, the increasing volume of right rACC was negatively associated with improvement in depressive symptoms. Conclusion These findings suggested that the abnormality of the rACC is the typical characteristics in schizophrenia with depressive symptoms. It's likely key region contributing to the neural mechanisms underlying the effects of risperidone treatment on depressive symptoms in schizophrenia.
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Affiliation(s)
- Haoran Shen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Likun Ge
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Alberta, Canada
| | - Gao-Xia Wei
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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8
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Chai Y, Gehrman P, Yu M, Mao T, Deng Y, Rao J, Shi H, Quan P, Xu J, Zhang X, Lei H, Fang Z, Xu S, Boland E, Goldschmied JR, Barilla H, Goel N, Basner M, Thase ME, Sheline YI, Dinges DF, Detre JA, Zhang X, Rao H. Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation. Proc Natl Acad Sci U S A 2023; 120:e2214505120. [PMID: 37339227 PMCID: PMC10293819 DOI: 10.1073/pnas.2214505120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 05/08/2023] [Indexed: 06/22/2023] Open
Abstract
Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai201620, China
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Philip Gehrman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Meichen Yu
- Indiana Alzheimer’s Disease Research Center, School of Medicine, Indiana University, Indianapolis, IN46202
- Indiana University Network Science Institute, Bloomington, IN47408
| | - Tianxin Mao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai201620, China
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yao Deng
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai201620, China
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Joy Rao
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Hui Shi
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Beijing An Zhen Hospital, Capital Medical University, Beijing100029, China
| | - Peng Quan
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Research Center for Quality of Life and Applied Psychology, Guangdong Medical University, Dongguan, Guangdong524023, China
| | - Jing Xu
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai201620, China
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Xiaocui Zhang
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan410017, China
| | - Hui Lei
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- College of Education, Hunan Agricultural University, Changsha, Hunan410127, China
| | - Zhuo Fang
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Sihua Xu
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai201620, China
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Elaine Boland
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Mental Illness Research Education and Clinical Center, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA19104
| | - Jennifer R. Goldschmied
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Holly Barilla
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Namni Goel
- Biological Rhythms Research Laboratory, Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL60612
| | - Mathias Basner
- Division of Sleep and Chronobiology, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Michael E. Thase
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Mental Illness Research Education and Clinical Center, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA19104
| | - Yvette I. Sheline
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Center for Neuromodulation in Depression and Stress, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - David F. Dinges
- Division of Sleep and Chronobiology, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - John A. Detre
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Xiaochu Zhang
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai201620, China
- Department of Radiology, the First Affiliated Hospital of University of Science and Technology of China, School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui230026, China
- Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Anhui230026, China
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai201620, China
- Center for Functional Neuroimaging and Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
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9
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Abnormal dynamic functional network connectivity in first-episode, drug-naïve patients with major depressive disorder. J Affect Disord 2022; 319:336-343. [PMID: 36084757 DOI: 10.1016/j.jad.2022.08.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/25/2022] [Accepted: 08/22/2022] [Indexed: 11/20/2022]
Abstract
Dynamic functional network connectivity (dFNC) could capture temporal features of spontaneous brain activity during MRI scanning, and it might be a powerful tool to examine functional brain network alters in major depressive disorder (MDD). Therefore, this study investigated the changes in temporal properties of dFNC of first-episode, drug-naïve patients with MDD. A total of 48 first-episode, drug-naïve MDD patients and 46 age- and gender-matched healthy controls were recruited in this study. Sliding windows were implied to construct dFNC. We assessed the relationships between altered dFNC temporal properties and depressive symptoms. Receiver operating characteristic (ROC) curve analyses were used to examine the diagnostic performance of these altered temporal properties. The results showed that patients with MDD have more occurrences and spent more time in a weak connection state, but with fewer occurrences and shorter dwell time in a strong connection state. Importantly, the fractional time and mean dwell time of state 2 was negatively correlated with Hamilton Depression Rating Scale (HDRS) scores. ROC curve analysis demonstrated that these temporal properties have great identified power including the fractional time and mean dwell time in state 2, and the AUC is 0.872, 0.837, respectively. The AUC of the combination of fractional time and mean dwell time in state 2 with age, gender is 0.881. Our results indicated the temporal properties of dFNC are altered in first-episode, drug-naïve patients with MDD, and these changes' properties could serve as a potential biomarker in MDD.
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10
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He J, Wang D, Ban M, Kong L, Xiao Q, Yuan F, Zhu X. Regional metabolic heterogeneity in anterior cingulate cortex in major depressive disorder: A multi-voxel 1H magnetic resonance spectroscopy study. J Affect Disord 2022; 318:263-271. [PMID: 36087788 DOI: 10.1016/j.jad.2022.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/29/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Previous studies have shown major depressive disorder (MDD) is associated with altered neuro-metabolites in the anterior cingulate cortex (ACC). However, the regional metabolic heterogeneity in the ACC in individuals with MDD remains unclear. METHODS We recruited 59 first-episode, treatment-naive young adults with MDD and 50 healthy controls who underwent multi-voxel 1H-MRS scanning at 3 T (Tesla) with voxels placed in the ACC, which was divided into two subregions, pregenual ACC (pACC) and anterior midcingulate cortex (aMCC). Between and within-subjects metabolite concentration variations were analyzed with SPSS. RESULTS Compared with control subjects, patients with MDD exhibited higher glutamate (Glu) and glutamine (Gln) levels in the pACC and higher myo-inositol (MI) level in the aMCC. We observed higher Glu and Gln levels and lower N-acetyl-aspartate (NAA) level in the pACC than those in the aMCC in both MDD and healthy control (HC) groups. More importantly, the metabolite concentration gradients of Glu, Gln and NAA were more pronounced in MDD patients relative to HCs. In the MDD group, the MI level in the aMCC positively correlated with the age of onset. LIMITATIONS The use of the relative concentration of metabolites constitutes a key study limitation. CONCLUSIONS We observed inconsistent alterations and distribution of neuro-metabolites concentration in the pACC and aMCC, revealing regional metabolic heterogeneity of ACC in first-episode, treatment-naive young individuals with MDD. These results provided new evidence for abnormal neuro-metabolites of ACC in the pathophysiology of MDD and suggested that pACC and aMCC might play different roles in MDD.
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Affiliation(s)
- Jincheng He
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Meiting Ban
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Lingyu Kong
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Qian Xiao
- Mental Health Centre, Xiangya Hospital, Central South University, Changsha, China
| | - Fulai Yuan
- Health Management Center, Xiangya Hospital, Central South University, Changsha, China
| | - Xueling Zhu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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11
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Sun J, Du Z, Ma Y, Guo C, Gao S, Luo Y, Chen Q, Hong Y, Xiao X, Yu X, Fang J. Characterization of Resting-State Striatal Differences in First-Episode Depression and Recurrent Depression. Brain Sci 2022; 12:brainsci12121603. [PMID: 36552063 PMCID: PMC9776048 DOI: 10.3390/brainsci12121603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/19/2022] [Accepted: 11/19/2022] [Indexed: 11/24/2022] Open
Abstract
The presence of reward deficits in major depressive disorder is associated with abnormal striatal function. However, differences in striatal whole-brain functional between recurrent depressive episode (RDE) and first-episode depression (FDE) have not been elucidated. Thirty-three patients with RDE, 27 with FDE, and 35 healthy controls (HCs) were recruited for this study. A seed-based functional connectivity (FC) method was used to analyze abnormalities in six predefined striatal subregion circuits among the three groups of subjects and to further explore the correlation between abnormal FC and clinical symptoms. The results revealed that compared with the FDE group, the RDE group showed higher FC of the striatal subregion with the left middle occipital gyrus, left orbital area of the middle frontal gyrus, and bilateral posterior cerebellar gyrus, while showing lower FC of the striatal subregion with the right thalamus, left inferior parietal lobule, left middle cingulate gyrus, right angular gyrus, right cerebellum anterior lobe, and right caudate nucleus. In the RDE group, the HAMD-17 scores were positively correlated with the FC between the left dorsal rostral putamen and the left cerebellum posterior lobe. This study provides new insights into understanding the specificity of striatal circuits in the RDE group.
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Affiliation(s)
- Jifei Sun
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Zhongming Du
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yue Ma
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Chunlei Guo
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Shanshan Gao
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Yi Luo
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Qingyan Chen
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Yang Hong
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Xue Xiao
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing 100026, China
| | - Xue Yu
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing 100026, China
| | - Jiliang Fang
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
- Correspondence: ; Tel.: +86-010-88001493
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12
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Ma Y, Wang Z, He J, Sun J, Guo C, Du Z, Chen L, Luo Y, Gao D, Hong Y, Zhang L, Liu Y, Fang J. Transcutaneous auricular vagus nerve immediate stimulation treatment for treatment-resistant depression: A functional magnetic resonance imaging study. Front Neurol 2022; 13:931838. [PMID: 36119681 PMCID: PMC9477011 DOI: 10.3389/fneur.2022.931838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/19/2022] [Indexed: 11/29/2022] Open
Abstract
Objective Transcutaneous auricular vagus nerve stimulation (taVNS) is effective for treatment-resistant depression (TRD). In the current study, we observed the immediate modulating brain effect of taVNS in patients with TRD using rest-state functional magnetic resonance imaging (rs-fMRI). Method Forty patients with TRD and forty healthy controls (HCs) were recruited. Rs-fMRI was performed before and after 30 min of taVNS at baseline. The brain regions that presented significantly different the Regional Homogeneity (ReHo) between the TRD patients and HCs were selected as the ROI to calculate the functional connectivity (FC) of full brain. The correlations were estimated between the clinical scales' score and the functional brain changes. Results Following taVNS stimulation treatment, TRD patients showed significantly reduced ReHo in the medial orbital frontal cortex (mOFC) (F = 18.06, P < 0.0001), ANCOVA of the mOFC-Based FC images revealed a significant interaction effect on the left inferior parietal gyrus (IPG) and left superior marginal gyrus (SMG) (F = 11.6615, P<0.001,F = 16.7520, P<0.0001). Among these regions, the HAMD and HAMA scores and ReHo/FC changes were not correlated. Conclusion This study applied rs-fMRI technology to examine the effect of taVNS stimulation treatment on the brain activity of TRD. These results suggest that the brain response of TRD patients to taVNS treatment may be associated with the functional modulation of cortical regions including the medial orbital frontal cortex, the left inferior parietal gyrus, and the left superior marginal regions. Changes in these neuroimaging indices may represent the neural mechanisms underlying taVNS Immediate Stimulation treatment in TRD.
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Affiliation(s)
- Yue Ma
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School of China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhi Wang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School of China Academy of Chinese Medical Sciences, Beijing, China
| | - Jiakai He
- Graduate School of China Academy of Chinese Medical Sciences, Beijing, China
- Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jifei Sun
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School of China Academy of Chinese Medical Sciences, Beijing, China
| | - Chunlei Guo
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School of China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhongming Du
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Limei Chen
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School of China Academy of Chinese Medical Sciences, Beijing, China
| | - Yi Luo
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School of China Academy of Chinese Medical Sciences, Beijing, China
| | - Deqiang Gao
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yang Hong
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Lei Zhang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yong Liu
- Affiliated Hospital of Traditional Chinese Medicine, Southwest Medical University, Luzhou, China
- *Correspondence: Yong Liu
| | - Jiliang Fang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Jiliang Fang
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13
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Gerlach AR, Karim HT, Peciña M, Ajilore O, Taylor WD, Butters MA, Andreescu C. MRI predictors of pharmacotherapy response in major depressive disorder. Neuroimage Clin 2022; 36:103157. [PMID: 36027717 PMCID: PMC9420953 DOI: 10.1016/j.nicl.2022.103157] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/11/2022] [Accepted: 08/15/2022] [Indexed: 02/08/2023]
Abstract
Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.
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Affiliation(s)
- Andrew R Gerlach
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Peciña
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois-Chicago, Chicago, IL, USA
| | - Warren D Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carmen Andreescu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
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14
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Zhang Y, Shao J, Wang X, Chen Z, Liu H, Pei C, Zhang S, Yao Z, Lu Q. Functional impairment-based segmentation of anterior cingulate cortex in depression and its relationship with treatment effects. Hum Brain Mapp 2021; 42:4035-4047. [PMID: 34008911 PMCID: PMC8288091 DOI: 10.1002/hbm.25537] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/09/2021] [Accepted: 05/11/2021] [Indexed: 11/09/2022] Open
Abstract
In major depressive disorder (MDD), the anterior cingulate cortex (ACC) is widely related to depression impairment and antidepressant treatment response. The multiplicity of ACC subdivisions calls for a fine‐grained investigation of their functional impairment and recovery profiles. We recorded resting state fMRI signals from 59 MDD patients twice before and after 12‐week antidepressant treatment, as well as 59 healthy controls (HCs). With functional connectivity (FC) between each ACC voxel and four regions of interests (bilateral dorsolateral prefrontal cortex [DLPFC] and amygdalae), subdivisions with variable impairment were identified based on groups' dissimilarity values between MDD patients before treatment and HC. The ACC was subdivided into three impairment subdivisions named as MedialACC, DistalACC, and LateralACC according to their dominant locations. Furthermore, the impairment pattern and the recovery pattern were measured based on group statistical analyses. DistalACC impaired more on its FC with left DLPFC, whereas LateralACC showed more serious impairment on its FC with bilateral amygdalae. After treatment, FCs between DistalACC and left DLPFC, and between LateralACC and right amygdala were normalized while impaired FC between LateralACC and left amygdala kept dysfunctional. Subsequently, FC between DistalACC and left DLPFC might contribute to clinical outcome prediction. Our approach could provide an insight into how the ACC was impaired in depression and partly restored after antidepressant treatment, from the perspective of the interaction between ACC subregions and critical frontal and subcortical regions.
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Affiliation(s)
- Yujie Zhang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Ministry of Education, Research Center for Learning Science, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Ministry of Education, Research Center for Learning Science, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Ministry of Education, Research Center for Learning Science, Nanjing, China
| | - Zhilu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Haiyan Liu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Cong Pei
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Ministry of Education, Research Center for Learning Science, Nanjing, China
| | - Shuqiang Zhang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Ministry of Education, Research Center for Learning Science, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Ministry of Education, Research Center for Learning Science, Nanjing, China
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