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Rosovsky RP, Mezue K, Gharios C, Civieri G, Cardeiro A, Zureigat H, Lau HC, Pitman RK, Shin L, Abohashem S, Osborne MT, Jaffer FA, Tawakol A. Anxiety and depression are associated with heightened risk of incident deep vein thrombosis: Mediation through stress-related neural mechanisms. Am J Hematol 2024. [PMID: 38965839 DOI: 10.1002/ajh.27427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/08/2024] [Accepted: 06/19/2024] [Indexed: 07/06/2024]
Abstract
Controversy exists as to whether anxiety and depression increase deep vein thrombosis (DVT) risk, and the mechanisms mediating potential links remain unknown. We aimed to evaluate the association between anxiety and depression and DVT risk and determine whether upregulated stress-related neural activity (SNA), which promotes chronic inflammation, contributes to this link. Our retrospective study included adults (N = 118 871) enrolled in Mass General Brigham Biobank. A subset (N = 1520) underwent clinical 18F-FDG-PET/CT imaging. SNA was measured as the ratio of amygdalar to cortical activity (AmygAC). High-sensitivity C-reactive protein (hs-CRP) and heart rate variability (HRV) were also obtained. Median age was 58 [interquartile range (IQR) 42-70] years with 57% female participants. DVT occurred in 1781 participants (1.5%) over median follow-up of 3.6 years [IQR 2.1-5.2]. Both anxiety and depression independently predicted incident DVT risk after robust adjustment (HR [95% CI]: 1.53 [1.38-1.71], p < .001; and 1.48 [1.33-1.65], p < .001, respectively). Additionally, both anxiety and depression associated with increased AmygAC (standardized beta [95% CI]: 0.16 [0.04-0.27], p = .007, and 0.17 [0.05-0.29], p = .006, respectively). Furthermore, AmygAC associated with incident DVT (HR [95% CI]: 1.30 [1.07-1.59], p = .009). Mediation analysis demonstrated that the link between anxiety/depression and DVT was mediated by: (1) higher AmygAC, (2) higher hs-CRP, and (3) lower HRV ( < .05 for each). Anxiety and depression confer an attributable risk of DVT similar to other traditional DVT risk factors. Mechanisms appear to involve increased SNA, autonomic system activity, and inflammation. Future studies are needed to determine whether treatment of anxiety and depression can reduce DVT risk.
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Affiliation(s)
- Rachel P Rosovsky
- Department of Medicine, Division of Hematology/Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kenechukwu Mezue
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Charbel Gharios
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Giovanni Civieri
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Cardeiro
- Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Hadil Zureigat
- Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Hui Chong Lau
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Roger K Pitman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Lisa Shin
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Department of Psychology, Tufts University, Medford, Massachusetts, USA
| | - Shady Abohashem
- Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael T Osborne
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Farouc A Jaffer
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ahmed Tawakol
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Guo H, Huang X, Wang C, Wang H, Bai X, Li Y. High-Order line graphs of fMRI data in major depressive disorder. Med Phys 2024. [PMID: 38767470 DOI: 10.1002/mp.17119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 02/24/2024] [Accepted: 04/19/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. PURPOSE This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. METHODS Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. RESULTS The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy = 92.42%, sensitivity = 92.86%, specificity = 92.11%, precision = 89.66%, F1 = 91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. CONCLUSIONS This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.
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Affiliation(s)
- Hao Guo
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Xiaoyan Huang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Chunyan Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Hao Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Xiaohe Bai
- School of Software, Taiyuan University of Technology, Taiyuan, China
| | - Yao Li
- School of Software, Taiyuan University of Technology, Taiyuan, China
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Li YT, Zhang C, Han JC, Shang YX, Chen ZH, Cui GB, Wang W. Neuroimaging features of cognitive impairments in schizophrenia and major depressive disorder. Ther Adv Psychopharmacol 2024; 14:20451253241243290. [PMID: 38708374 PMCID: PMC11070126 DOI: 10.1177/20451253241243290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 03/14/2024] [Indexed: 05/07/2024] Open
Abstract
Cognitive dysfunctions are one of the key symptoms of schizophrenia (SZ) and major depressive disorder (MDD), which exist not only during the onset of diseases but also before the onset, even after the remission of psychiatric symptoms. With the development of neuroimaging techniques, these non-invasive approaches provide valuable insights into the underlying pathogenesis of psychiatric disorders and information of cognitive remediation interventions. This review synthesizes existing neuroimaging studies to examine domains of cognitive impairment, particularly processing speed, memory, attention, and executive function in SZ and MDD patients. First, white matter (WM) abnormalities are observed in processing speed deficits in both SZ and MDD, with distinct neuroimaging findings highlighting WM connectivity abnormalities in SZ and WM hyperintensity caused by small vessel disease in MDD. Additionally, the abnormal functions of prefrontal cortex and medial temporal lobe are found in both SZ and MDD patients during various memory tasks, while aberrant amygdala activity potentially contributes to a preference to negative memories in MDD. Furthermore, impaired large-scale networks including frontoparietal network, dorsal attention network, and ventral attention network are related to attention deficits, both in SZ and MDD patients. Finally, abnormal activity and volume of the dorsolateral prefrontal cortex (DLPFC) and abnormal functional connections between the DLPFC and the cerebellum are associated with executive dysfunction in both SZ and MDD. Despite these insights, longitudinal neuroimaging studies are lacking, impeding a comprehensive understanding of cognitive changes and the development of early intervention strategies for SZ and MDD. Addressing this gap is critical for advancing our knowledge and improving patient prognosis.
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Affiliation(s)
- Yu-Ting Li
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Chi Zhang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
- Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Jia-Cheng Han
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Yu-Xuan Shang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Zhu-Hong Chen
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Guang-Bin Cui
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi’an 710038, Shaanxi, China
| | - Wen Wang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi’an 710038, Shaanxi, China
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Zhou Y, Zhu Y, Ye H, Jiang W, Zhang Y, Kong Y, Yuan Y. Abnormal changes of dynamic topological characteristics in patients with major depressive disorder. J Affect Disord 2024; 345:349-357. [PMID: 37884195 DOI: 10.1016/j.jad.2023.10.143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND Most studies have detected abnormalities of static topological characteristics in major depressive disorder (MDD). However, whether dynamic alternations in brain topology are influenced by MDD remains unknown. METHODS An approach was proposed to capture the dynamic topological characteristics with sliding-window and graph theory for a large data sample from the REST-meta-MDD project. RESULTS It was shown that patients with MDD were characterized by decreased nodal efficiency of the left orbitofrontal cortex. The temporal variability of topological characteristics was focused on the left opercular part of inferior frontal gyrus, and the right part of middle frontal gyrus, inferior parietal gyrus, precuneus and thalamus. LIMITATIONS Future studies need larger and diverse samples to explore the relationship between dynamic topological network characteristics and MDD symptoms. CONCLUSIONS The results support that the altered dynamic topology in cortex of frontal and parietal lobes and thalamus during resting-state activity may be involved in the neuropathological mechanism of MDD.
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Affiliation(s)
- Yue Zhou
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Yihui Zhu
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China
| | - Hongting Ye
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Yubo Zhang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Youyong Kong
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China.
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China; Jiangsu Provincial Key Laboratory of Critical Care Medicine, Southeast University, Nanjing 210009, China.
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Xiao Y, Zhao L, Zang X, Xue S. Compressed primary-to-transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder. Hum Brain Mapp 2023; 44:5919-5935. [PMID: 37688552 PMCID: PMC10619397 DOI: 10.1002/hbm.26485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 08/18/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
Major depressive disorder (MDD) has been shown to involve widespread changes in low-level sensorimotor and higher-level cognitive functions. Recent research found that a primary-to-transmodal gradient could capture a cortical hierarchical organization ranging from perception and action to cognition in healthy subjects, but a prominent gradient dysfunction in MDD patients. However, whether and how this cortical gradient is linked to subcortical impairments and whether it is reflected in the microscale neurotransmitter systems and cell type-specific transcriptional signatures remain largely unknown. Data were acquired from 323 MDD patients and 328 sex- and age-matched healthy controls derived from the REST-meta-MDD project, and the human brain neurotransmitter systems density maps and gene expression data were drawn from two publicly available datasets. We investigated alterations of the primary-to-transmodal gradient in MDD patients and their correlations with clinical symptoms of depression and anxiety, as well as their paralleled subcortical impairments. The correlations between MDD-related gradient alterations and densities of the neurotransmitter systems and gene expression information were assessed, respectively. The results demonstrated that MDD patients had a compressed primary-to-transmodal gradient accompanied by paralleled alterations in subcortical regions including the caudate, amygdala, and thalamus. The case-control gradient differences were spatially correlated with the densities of the neurotransmitter systems including the serotonin and dopamine receptors, and meanwhile with gene expression enriched in astrocytes, excitatory and inhibitory neuronal cells. These findings mapped the paralleled subcortical impairments in cortical hierarchical organization and also helped us understand the possible molecular and cellular substrates of the co-occurrence of high-level cognitive impairments with low-level sensorimotor abnormalities in MDD.
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Affiliation(s)
- Yang Xiao
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
| | - Lei Zhao
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
| | - Xuelian Zang
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
| | - Shao‐Wei Xue
- Center for Cognition and Brain DisordersThe Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Institute of Psychological ScienceHangzhou Normal UniversityHangzhouZhejiang ProvincePR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouZhejiang ProvincePR China
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Dunlop BW, Cha J, Choi KS, Nemeroff CB, Craighead WE, Mayberg HS. Functional connectivity of salience and affective networks among remitted depressed patients predicts episode recurrence. Neuropsychopharmacology 2023; 48:1901-1909. [PMID: 37491672 PMCID: PMC10584833 DOI: 10.1038/s41386-023-01653-w] [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: 04/03/2023] [Revised: 06/09/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023]
Abstract
Recurrent episodes in major depressive disorder (MDD) are common but the neuroimaging features predictive of recurrence are not established. Participants in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study who achieved remission after 12 weeks of treatment withcognitive behavior therapy, duloxetine, or escitalopram were prospectively monitored for up to 21 months for recurrence. Neuroimaging markers predictive of recurrence were identified from week 12 functional magnetic resonance imaging scans by analyzing whole-brain resting state functional connectivity (RSFC) using seeds for four brain networks that are altered in MDD. Neuroimaging correlates of established clinical predictors of recurrence, including the magnitude of depressive (Hamilton Depression Rating Scale), anxiety (Hamilton Anxiety Rating Scale) symptom severity at time of remission, and a comorbid anxiety disorder were examined for their similarity to the neuroimaging predictors of recurrence. Of the 344 patients randomized in PReDICT, 61 achieved remission and had usable scans for analysis, 9 of whom experienced recurrence during follow-up. Recurrence was predicted by: 1) increased RSFC between subcallosal cingulate cortex (SCC) and right anterior insula, 2) decreased RSFC between SCC and bilateral primary visual cortex, and 3) decreased RSFC between insula and bilateral caudate. Week 12 depression and anxiety scores were negatively correlated with RSFC strength between executive control and default mode networks, but they were not correlated with the three RSFC patterns predicting recurrence. We conclude that altered RSFC in SCC and anterior insula networks are prospective risk factors associated with MDD recurrence, reflecting additional sources of risk beyond clinical measures.
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Affiliation(s)
- Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA.
| | - Jungho Cha
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Ki Sueng Choi
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences, Institute for Early Life Adversity Research, University of Texas at Austin Dell Medical School, Austin, USA
| | - W Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
- Department of Psychology, Emory University, Atlanta, USA
| | - Helen S Mayberg
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
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Zhan Q, Kong F. Mechanisms associated with post-stroke depression and pharmacologic therapy. Front Neurol 2023; 14:1274709. [PMID: 38020612 PMCID: PMC10651767 DOI: 10.3389/fneur.2023.1274709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Stroke is one of the most common cerebrovascular diseases, which is the cause of long-term mental illness and physical disability, Post-stroke depression (PSD) is the most common neuropsychiatric complication after stroke, and its mechanisms are characterized by complexity, plurality, and diversity, which seriously affects the quality of survival and prognosis of patients. Studies have focused on and recognized neurotransmitter-based mechanisms and selective serotonin-reuptake inhibitors (SSRIs) can be used to treat PSD. Neuroinflammation, neuroendocrinology, neurotrophic factors, and the site of the stroke lesion may affect neurotransmitters. Thus the mechanisms of PSD have been increasingly studied. Pharmacological treatment mainly includes SSRIs, noradrenergic and specific serotonergic antidepressant (NaSSA), anti-inflammatory drugs, vitamin D, ect, which have been confirmed to have better efficacy by clinical studies. Currently, there is an increasing number of studies related to the mechanisms of PSD. However, the mechanisms and pharmacologic treatment of PSD is still unclear. In the future, in-depth research on the mechanisms and treatment of PSD is needed to provide a reference for the prevention and treatment of clinical PSD.
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Affiliation(s)
- Qingyang Zhan
- Institute of Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Fanyi Kong
- Neurosurgery, Affiliated First Hospital, Harbin Medical University, Harbin, China
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Zavaliangos-Petropulu A, McClintock SM, Joshi SH, Taraku B, Al-Sharif NB, Espinoza RT, Narr KL. Hippocampal subfield volumes in treatment resistant depression and serial ketamine treatment. Front Psychiatry 2023; 14:1227879. [PMID: 37876623 PMCID: PMC10590913 DOI: 10.3389/fpsyt.2023.1227879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 10/26/2023] Open
Abstract
Introduction Subanesthetic ketamine is a rapidly acting antidepressant that has also been found to improve neurocognitive performance in adult patients with treatment resistant depression (TRD). Provisional evidence suggests that ketamine may induce change in hippocampal volume and that larger pre-treatment volumes might be related to positive clinical outcomes. Here, we examine the effects of serial ketamine treatment on hippocampal subfield volumes and relationships between pre-treatment subfield volumes and changes in depressive symptoms and neurocognitive performance. Methods Patients with TRD (N = 66; 31M/35F; age = 39.5 ± 11.1 years) received four ketamine infusions (0.5 mg/kg) over 2 weeks. Structural MRI scans, the National Institutes of Health Toolbox (NIHT) Cognition Battery, and Hamilton Depression Rating Scale (HDRS) were collected at baseline, 24 h after the first and fourth ketamine infusion, and 5 weeks post-treatment. The same data was collected for 32 age and sex matched healthy controls (HC; 17M/15F; age = 35.03 ± 12.2 years) at one timepoint. Subfield (CA1/CA3/CA4/subiculum/molecular layer/GC-ML-DG) volumes corrected for whole hippocampal volume were compared across time, between treatment remitters/non-remitters, and patients and HCs using linear regression models. Relationships between pre-treatment subfield volumes and clinical and cognitive outcomes were also tested. All analyses included Bonferroni correction. Results Patients had smaller pre-treatment left CA4 (p = 0.004) and GC.ML.DG (p = 0.004) volumes compared to HC, but subfield volumes remained stable following ketamine treatment (all p > 0.05). Pre-treatment or change in hippocampal subfield volumes over time showed no variation by remission status nor correlated with depressive symptoms (p > 0.05). Pre-treatment left CA4 was negatively correlated with improved processing speed after single (p = 0.0003) and serial ketamine infusion (p = 0.005). Left GC.ML.DG also negatively correlated with improved processing speed after single infusion (p = 0.001). Right pre-treatment CA3 positively correlated with changes in list sorting working memory at follow-up (p = 0.0007). Discussion These results provide new evidence to suggest that hippocampal subfield volumes at baseline may present a biomarker for neurocognitive improvement following ketamine treatment in TRD. In contrast, pre-treatment subfield volumes and changes in subfield volumes showed negligible relationships with ketamine-related improvements in depressive symptoms.
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Affiliation(s)
- Artemis Zavaliangos-Petropulu
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Shawn M. McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States
| | - Shantanu H. Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Brandon Taraku
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Noor B. Al-Sharif
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Randall T. Espinoza
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L. Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
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Dai P, Zhou X, Xiong T, Ou Y, Chen Z, Zou B, Li W, Huang Z. Altered Effective Connectivity Among the Cerebellum and Cerebrum in Patients with Major Depressive Disorder Using Multisite Resting-State fMRI. CEREBELLUM (LONDON, ENGLAND) 2023; 22:781-789. [PMID: 35933493 DOI: 10.1007/s12311-022-01454-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
Major depressive disorder (MDD) is a serious and widespread psychiatric disorder. Previous studies mainly focused on cerebrum functional connectivity, and the sample size was relatively small. However, functional connectivity is undirected. And, there is increasing evidence that the cerebellum is also involved in emotion and cognitive processing and makes outstanding contributions to the symptomology and pathology of depression. Therefore, we used a large sample size of resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate the altered effective connectivity (EC) among the cerebellum and other cerebral cortex in patients with MDD. Here, from the perspective of data-driven analysis, we used two different atlases to divide the whole brain into different regions and analyzed the alterations of EC and EC networks in the MDD group compared with healthy controls group (HCs). The results showed that compared with HCs, there were significantly altered EC in the cerebellum-neocortex and cerebellum-basal ganglia circuits in MDD patients, which implied that the cerebellum may be a potential biomarker of depressive disorders. And, the alterations of EC brain networks in MDD patients may provide new insights into the pathophysiological mechanisms of depression.
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Affiliation(s)
- Peishan Dai
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
| | - Xiaoyan Zhou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Tong Xiong
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Yilin Ou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Zailiang Chen
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Beiji Zou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Weihui Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhongchao Huang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
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Hu YT, Tan ZL, Hirjak D, Northoff G. Brain-wide changes in excitation-inhibition balance of major depressive disorder: a systematic review of topographic patterns of GABA- and glutamatergic alterations. Mol Psychiatry 2023; 28:3257-3266. [PMID: 37495889 DOI: 10.1038/s41380-023-02193-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 07/28/2023]
Abstract
The excitation-inhibition (E/I) imbalance is an important molecular pathological feature of major depressive disorder (MDD) as altered GABA and glutamate levels have been found in multiple brain regions in patients. Healthy subjects show topographic organization of the E/I balance (EIB) across various brain regions. We here raise the question of whether such EIB topography is altered in MDD. Therefore, we systematically review the gene and protein expressions of inhibitory GABAergic and excitatory glutamatergic signaling-related molecules in postmortem MDD brain studies as proxies for EIB topography. Searches were conducted through PubMed and 45 research articles were finally included. We found: i) brain-wide GABA- and glutamatergic alterations; ii) attenuated GABAergic with enhanced glutamatergic signaling in the cortical-subcortical limbic system; iii) that GABAergic signaling is decreased in regions comprising the default mode network (DMN) while it is increased in lateral prefrontal cortex (LPFC). These together demonstrate abnormal GABA- and glutamatergic signaling-based EIB topographies in MDD. This enhances our pathophysiological understanding of MDD and carries important therapeutic implications for stimulation treatment.
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Affiliation(s)
- Yu-Ting Hu
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada.
| | - Zhong-Lin Tan
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dusan Hirjak
- Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Georg Northoff
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada.
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Li G, Yap PT. From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Front Hum Neurosci 2022; 16:940842. [PMID: 36061504 PMCID: PMC9428697 DOI: 10.3389/fnhum.2022.940842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/28/2022] [Indexed: 01/28/2023] Open
Abstract
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.
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Affiliation(s)
- Guoshi Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States,*Correspondence: Guoshi Li,
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States
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