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Zhang W, Zhai X, Zhang C, Cheng S, Zhang C, Bai J, Deng X, Ji J, Li T, Wang Y, Tong HHY, Li J, Li K. Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder. Psychiatry Res Neuroimaging 2024; 344:111862. [PMID: 39153232 DOI: 10.1016/j.pscychresns.2024.111862] [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: 10/31/2023] [Revised: 03/22/2024] [Accepted: 07/31/2024] [Indexed: 08/19/2024]
Abstract
Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.
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Affiliation(s)
- Wenjie Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xiaobing Zhai
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Chan Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Song Cheng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Chaoqing Zhang
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Jinji Bai
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Xuan Deng
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Junjun Ji
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Ting Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Yu Wang
- Department of Psychiatry, Changzhi Mental Health Center, Changzhi, Shanxi, China
| | - Henry H Y Tong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Junfeng Li
- Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China; Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China
| | - Kefeng Li
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.
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2
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Chu T, Si X, Xie H, Ma H, Shi Y, Yao W, Xing D, Zhao F, Dong F, Gai Q, Che K, Guo Y, Chen D, Ming D, Mao N. Regional structural-functional connectivity coupling in major depressive disorder is associated with neurotransmitter and genetic profiles. Biol Psychiatry 2024:S0006-3223(24)01555-5. [PMID: 39218135 DOI: 10.1016/j.biopsych.2024.08.022] [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: 04/29/2024] [Revised: 08/03/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms underlying regional SC-FC coupling patterns are not well understood. METHODS We enrolled 182 patients with MDD and 157 healthy control (HC) subjects, quantifying the intergroup differences in regional SC-FC coupling. The extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression. RESULTS We observed increased regional SC-FC coupling in default mode network (T = 3.233) and decreased coupling in frontoparietal network (T = -3.471) in MDD relative to HC. XGBoost (AUC = 0.853), SVM (AUC = 0.832) and RF (p < 0.05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of four neurotransmitters (p < 0.05) and expression maps of specific genes. These genes were strongly enriched in genes implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on two brain atlases. CONCLUSIONS This work enhances our understanding of MDD and pave the way for the development of additional targeted therapeutic interventions.
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Affiliation(s)
- Tongpeng Chu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, 300072; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China, 300072; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China, 300392; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China, 300072; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital); Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, 300072; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China, 300072; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China, 300392; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China, 300072
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Wei Yao
- Department of Neurology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, Shandong, P. R. China, 253000
| | - Dong Xing
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, P. R. China, 264000
| | - Fanghui Dong
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Qun Gai
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Yuting Guo
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Danni Chen
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, 300072; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China, 300072; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China, 300392; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China, 300072
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital); Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, P. R. China, 264000.
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3
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Qu J, Qu Y, Zhu R, Wu Y, Xu G, Wang D. Transcriptional expression patterns of the cortical morphometric similarity network in progressive supranuclear palsy. CNS Neurosci Ther 2024; 30:e14901. [PMID: 39097922 PMCID: PMC11298202 DOI: 10.1111/cns.14901] [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: 06/10/2024] [Revised: 07/09/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND It has been demonstrated that progressive supranuclear palsy (PSP) correlates with structural abnormalities in several distinct regions of the brain. However, whether there are changes in the morphological similarity network (MSN) and the relationship between changes in brain structure and gene expression remain largely unknown. METHODS We used two independent cohorts (discovery dataset: PSP: 51, healthy controls (HC): 82; replication dataset: PSP: 53, HC: 55) for MSN analysis and comparing the longitudinal changes in the MSN of PSP. Then, we applied partial least squares regression to determine the relationships between changes in MSN and spatial transcriptional features and identified specific genes associated with MSN differences in PSP. We further investigated the biological processes enriched in PSP-associated genes and the cellular characteristics of these genes, and finally, we performed an exploratory analysis of the relationship between MSN changes and neurotransmitter receptors. RESULTS We found that the MSN in PSP patients was mainly decreased in the frontal and temporal cortex but increased in the occipital cortical region. This difference is replicable. In longitudinal studies, MSN differences are mainly manifested in the frontal and parietal regions. Furthermore, the expression pattern associated with MSN changes in PSP involves genes implicated in astrocytes and excitatory and inhibitory neurons and is functionally enriched in neuron-specific biological processes related to synaptic signaling. Finally, we found that the changes in MSN were mainly negatively correlated with the levels of serotonin, norepinephrine, and opioid receptors. CONCLUSIONS These results have enhanced our understanding of the microscale genetic and cellular mechanisms responsible for large-scale morphological abnormalities in PSP patients, suggesting potential targets for future therapeutic trials.
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Affiliation(s)
- Junyu Qu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Yancai Qu
- Department of NeurosurgeryTraditional Chinese Medicine Hospital of Muping DistrictYantaiChina
| | - Rui Zhu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Yongsheng Wu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Guihua Xu
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong University, Qilu Medical Imaging Institute of Shandong UniversityJinanChina
- Magnetic Field‐free Medicine & Functional ImagingResearch Institute of Shandong UniversityJinanChina
- Magnetic Field‐free Medicine & Functional Imaging (MF)Shandong Key LaboratoryJinanChina
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4
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Al-Sharif NB, Zavaliangos-Petropulu A, Narr KL. A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response. Neuropsychopharmacology 2024:10.1038/s41386-024-01894-3. [PMID: 38902355 DOI: 10.1038/s41386-024-01894-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/22/2024]
Abstract
By measuring the molecular diffusion of water molecules in brain tissue, diffusion MRI (dMRI) provides unique insight into the microstructure and structural connections of the brain in living subjects. Since its inception, the application of dMRI in clinical research has expanded our understanding of the possible biological bases of psychiatric disorders and successful responses to different therapeutic interventions. Here, we review the past decade of diffusion imaging-based investigations with a specific focus on studies examining the mechanisms and predictors of therapeutic response in people with mood disorders. We present a brief overview of the general application of dMRI and key methodological developments in the field that afford increasingly detailed information concerning the macro- and micro-structural properties and connectivity patterns of white matter (WM) pathways and their perturbation over time in patients followed prospectively while undergoing treatment. This is followed by a more in-depth summary of particular studies using dMRI approaches to examine mechanisms and predictors of clinical outcomes in patients with unipolar or bipolar depression receiving pharmacological, neurostimulation, or behavioral treatments. Limitations associated with dMRI research in general and with treatment studies in mood disorders specifically are discussed, as are directions for future research. Despite limitations and the associated discrepancies in findings across individual studies, evolving research strongly indicates that the field is on the precipice of identifying and validating dMRI biomarkers that could lead to more successful personalized treatment approaches and could serve as targets for evaluating the neural effects of novel treatments.
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Affiliation(s)
- Noor B Al-Sharif
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Artemis Zavaliangos-Petropulu
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Katherine L Narr
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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5
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Singh P, Vasundhara B, Das N, Sharma R, Kumar A, Datusalia AK. Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences. Mol Neurobiol 2024:10.1007/s12035-024-04302-5. [PMID: 38898199 DOI: 10.1007/s12035-024-04302-5] [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: 10/28/2023] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.
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Affiliation(s)
- Pooja Singh
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India
| | - Boosani Vasundhara
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India
| | - Nabanita Das
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India
| | - Ruchika Sharma
- Centre for Precision Medicine and Centre, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, 110017, India
| | - Anoop Kumar
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, 110017, India
| | - Ashok Kumar Datusalia
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India.
- Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India.
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6
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Irastorza-Valera L, Soria-Gómez E, Benitez JM, Montáns FJ, Saucedo-Mora L. Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM). Biomimetics (Basel) 2024; 9:362. [PMID: 38921242 PMCID: PMC11202129 DOI: 10.3390/biomimetics9060362] [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: 05/06/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- PIMM Laboratory, ENSAM–Arts et Métiers ParisTech, 151 Bd de l’Hôpital, 75013 Paris, France
| | - Edgar Soria-Gómez
- Achúcarro Basque Center for Neuroscience, Barrio Sarriena, s/n, 48940 Leioa, Spain;
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain
- Department of Neurosciences, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
| | - José María Benitez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
| | - Francisco J. Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave, Cambridge, MA 02139, USA
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7
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Flinkenflügel K, Gruber M, Meinert S, Thiel K, Winter A, Goltermann J, Usemann P, Brosch K, Stein F, Thomas-Odenthal F, Wroblewski A, Pfarr JK, David FS, Beins EC, Grotegerd D, Hahn T, Leehr EJ, Dohm K, Bauer J, Forstner AJ, Nöthen MM, Jamalabadi H, Straube B, Alexander N, Jansen A, Witt SH, Rietschel M, Nenadić I, van den Heuvel MP, Kircher T, Repple J, Dannlowski U. The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression. Mol Psychiatry 2024:10.1038/s41380-024-02577-7. [PMID: 38693319 DOI: 10.1038/s41380-024-02577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 04/15/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.
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Grants
- DA1151/5-1, DA1151/5-2, DA1151/11‑1 DA1151/6-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- HA7070/2-2, HA7070/3, HA7070/4 Deutsche Forschungsgemeinschaft (German Research Foundation)
- NO 246/10-1, NO 246/10-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- STR 1146/18-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- JA 1890/7-1, JA 1890/7-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- WI 3439/3-1, WI 3439/3-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- RI 908/11-1, RI 908/11-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- KI 588/14-1, KI 588/14-2, KI 588/22-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- ERC-COG 101001062, VIDI-452-16-015 Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)
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Affiliation(s)
- Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Friederike S David
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Eva C Beins
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, Münster, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
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Dam S, Batail JM, Robert GH, Drapier D, Maurel P, Coloigner J. Structural Brain Connectivity and Treatment Improvement in Mood Disorder. Brain Connect 2024; 14:239-251. [PMID: 38534988 DOI: 10.1089/brain.2023.0063] [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] [Indexed: 04/25/2024] Open
Abstract
Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.
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Affiliation(s)
- Sébastien Dam
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Jean-Marie Batail
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Gabriel H Robert
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Dominique Drapier
- Academic Psychiatry Department, Centre Hospitalier Guillaume Régnier, Rennes, France
- CIC 1414, CHU de Rennes, INSERM, Rennes, France
| | - Pierre Maurel
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Julie Coloigner
- Univ Rennes, Inria, CNRS, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
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9
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Stein F, Gruber M, Mauritz M, Brosch K, Pfarr JK, Ringwald KG, Thomas-Odenthal F, Wroblewski A, Evermann U, Steinsträter O, Grumbach P, Thiel K, Winter A, Bonnekoh LM, Flinkenflügel K, Goltermann J, Meinert S, Grotegerd D, Bauer J, Opel N, Hahn T, Leehr EJ, Jansen A, de Lange SC, van den Heuvel MP, Nenadić I, Krug A, Dannlowski U, Repple J, Kircher T. Brain Structural Network Connectivity of Formal Thought Disorder Dimensions in Affective and Psychotic Disorders. Biol Psychiatry 2024; 95:629-638. [PMID: 37207935 DOI: 10.1016/j.biopsych.2023.05.010] [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: 11/03/2022] [Revised: 04/14/2023] [Accepted: 05/04/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND The psychopathological syndrome of formal thought disorder (FTD) is not only present in schizophrenia (SZ), but also highly prevalent in major depressive disorder and bipolar disorder. It remains unknown how alterations in the structural white matter connectome of the brain correlate with psychopathological FTD dimensions across affective and psychotic disorders. METHODS Using FTD items of the Scale for the Assessment of Positive Symptoms and Scale for the Assessment of Negative Symptoms, we performed exploratory and confirmatory factor analyses in 864 patients with major depressive disorder (n= 689), bipolar disorder (n = 108), or SZ (n = 67) to identify psychopathological FTD dimensions. We used T1- and diffusion-weighted magnetic resonance imaging to reconstruct the structural connectome of the brain. To investigate the association of FTD subdimensions and global structural connectome measures, we employed linear regression models. We used network-based statistic to identify subnetworks of white matter fiber tracts associated with FTD symptomatology. RESULTS Three psychopathological FTD dimensions were delineated, i.e., disorganization, emptiness, and incoherence. Disorganization and incoherence were associated with global dysconnectivity. Network-based statistics identified subnetworks associated with the FTD dimensions disorganization and emptiness but not with the FTD dimension incoherence. Post hoc analyses on subnetworks did not reveal diagnosis × FTD dimension interaction effects. Results remained stable after correcting for medication and disease severity. Confirmatory analyses showed a substantial overlap of nodes from both subnetworks with cortical brain regions previously associated with FTD in SZ. CONCLUSIONS We demonstrated white matter subnetwork dysconnectivity in major depressive disorder, bipolar disorder, and SZ associated with FTD dimensions that predominantly comprise brain regions implicated in speech. Results open an avenue for transdiagnostic, psychopathology-informed, dimensional studies in pathogenetic research.
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Affiliation(s)
- Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany.
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Marco Mauritz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Kai G Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Pascal Grumbach
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Linda M Bonnekoh
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich Schiller University Jena, Jena, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Siemon C de Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands; Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, the Netherlands
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
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10
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Mecklenbrauck F, Gruber M, Siestrup S, Zahedi A, Grotegerd D, Mauritz M, Trempler I, Dannlowski U, Schubotz RI. The significance of structural rich club hubs for the processing of hierarchical stimuli. Hum Brain Mapp 2024; 45:e26543. [PMID: 38069537 PMCID: PMC10915744 DOI: 10.1002/hbm.26543] [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: 06/09/2023] [Revised: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 03/07/2024] Open
Abstract
The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.
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Affiliation(s)
- Falko Mecklenbrauck
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Marius Gruber
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department for Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital Frankfurt, Goethe UniversityFrankfurtGermany
| | - Sophie Siestrup
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Anoushiravan Zahedi
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Marco Mauritz
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Ima Trempler
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Udo Dannlowski
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ricarda I. Schubotz
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
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11
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Li J, Long Z, Sheng W, Du L, Qiu J, Chen H, Liao W. Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes. Biol Psychiatry 2024; 95:414-425. [PMID: 37573006 DOI: 10.1016/j.biopsych.2023.08.003] [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: 03/14/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.
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Affiliation(s)
- Jiao Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Zhiliang Long
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Wei Sheng
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Lian Du
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Wei Liao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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12
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Huang Y, Zhang J, He K, Mo X, Yu R, Min J, Zhu T, Ma Y, He X, Lv F, Lei D, Liu M. Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models. Diagnostics (Basel) 2024; 14:389. [PMID: 38396428 PMCID: PMC10888009 DOI: 10.3390/diagnostics14040389] [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: 01/10/2024] [Revised: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.
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Affiliation(s)
- Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jingbo Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Kewei He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xue Mo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jing Min
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Tong Zhu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Yunfeng Ma
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xiangqian He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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13
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Udayakumar P, Subhashini R. Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN). JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:1041-1059. [PMID: 38820060 DOI: 10.3233/xst-230426] [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: 06/02/2024]
Abstract
BACKGROUND Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients.
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Affiliation(s)
- P Udayakumar
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
| | - R Subhashini
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
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14
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Winter A, Thiel K, Meinert S, Lemke H, Waltemate L, Breuer F, Culemann R, Pfarr JK, Stein F, Brosch K, Meller T, Ringwald KG, Thomas-Odenthal F, Jansen A, Nenadić I, Krug A, Repple J, Opel N, Dohm K, Leehr EJ, Grotegerd D, Kugel H, Hahn T, Kircher T, Dannlowski U. Familial risk for major depression: differential white matter alterations in healthy and depressed participants. Psychol Med 2023; 53:4933-4942. [PMID: 36052484 PMCID: PMC10476061 DOI: 10.1017/s003329172200188x] [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: 12/20/2021] [Revised: 04/13/2022] [Accepted: 06/06/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) has been associated with alterations in brain white matter (WM) microstructure. However, diffusion tensor imaging studies in biological relatives have presented contradicting results on WM alterations and their potential as biomarkers for vulnerability or resilience. To shed more light on associations between WM microstructure and resilience to familial risk, analyses including both healthy and depressed relatives of MDD patients are needed. METHODS In a 2 (MDD v. healthy controls, HC) × 2 (familial risk yes v. no) design, we investigated fractional anisotropy (FA) via tract-based spatial statistics in a large well-characterised adult sample (N = 528), with additional controls for childhood maltreatment, a potentially confounding proxy for environmental risk. RESULTS Analyses revealed a significant main effect of diagnosis on FA in the forceps minor and the left superior longitudinal fasciculus (ptfce-FWE = 0.009). Furthermore, a significant interaction of diagnosis with familial risk emerged (ptfce-FWE = 0.036) Post-hoc pairwise comparisons showed significantly higher FA, mainly in the forceps minor and right inferior fronto-occipital fasciculus, in HC with as compared to HC without familial risk (ptfce-FWE < 0.001), whereas familial risk played no role in MDD patients (ptfce-FWE = 0.797). Adding childhood maltreatment as a covariate, the interaction effect remained stable. CONCLUSIONS We found widespread increased FA in HC with familial risk for MDD as compared to a HC low-risk sample. The significant effect of risk on FA was present only in HC, but not in the MDD sample. These alterations might reflect compensatory neural mechanisms in healthy adults at risk for MDD potentially associated with resilience.
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Affiliation(s)
- Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute of Translational Neuroscience, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Regina Culemann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Kai Gustav Ringwald
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J. Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Harald Kugel
- University Clinic for Radiology, University of Muenster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
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15
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Li K, Lu X, Xiao C, Zheng K, Sun J, Dong Q, Wang M, Zhang L, Liu B, Liu J, Zhang Y, Guo H, Zhao F, Ju Y, Li L. Aberrant Resting-State Functional Connectivity in MDD and the Antidepressant Treatment Effect-A 6-Month Follow-Up Study. Brain Sci 2023; 13:brainsci13050705. [PMID: 37239177 DOI: 10.3390/brainsci13050705] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/10/2023] [Accepted: 04/19/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND The mechanism by which antidepressants normalizing aberrant resting-state functional connectivity (rsFC) in patients with major depressive disorder (MDD) is still a matter of debate. The current study aimed to investigate aberrant rsFC and whether antidepressants would restore the aberrant rsFC in patients with MDD. METHODS A total of 196 patients with MDD and 143 healthy controls (HCs) received the resting-state functional magnetic resonance imaging and clinical assessments at baseline. Patients with MDD received antidepressant treatment after baseline assessment and were re-scanned at the 6-month follow-up. Network-based statistics were employed to identify aberrant rsFC and rsFC changes in patients with MDD and to compare the rsFC differences between remitters and non-remitters. RESULTS We identified a significantly decreased sub-network and a significantly increased sub-network in MDD at baseline. Approximately half of the aberrant rsFC remained significantly different from HCs after 6-month treatment. Significant overlaps were found between baseline reduced sub-network and follow-up increased sub-network, and between baseline increased sub-network and follow-up decreased sub-network. Besides, rsFC at baseline and rsFC changes between baseline and follow-up in remitters were not different from non-remitters. CONCLUSIONS Most aberrant rsFC in patients with MDD showed state-independence. Although antidepressants may modulate aberrant rsFC, they may not specifically target these aberrations to achieve therapeutic effects, with only a few having been directly linked to treatment efficacy.
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Affiliation(s)
- Kangning Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Xiaowen Lu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Chuman Xiao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Kangning Zheng
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Jinrong Sun
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Qiangli Dong
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Mi Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Liang Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Jin Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Yan Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Futao Zhao
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Yumeng Ju
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
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16
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de Lange SC, Helwegen K, van den Heuvel MP. Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox. Neuroimage 2023; 273:120108. [PMID: 37059156 DOI: 10.1016/j.neuroimage.2023.120108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/13/2023] [Accepted: 04/11/2023] [Indexed: 04/16/2023] Open
Abstract
We describe a Connectivity Analysis TOolbox (CATO) for the reconstruction of structural and functional brain connectivity based on diffusion weighted imaging and resting-state functional MRI data. CATO is a multimodal software package that enables researchers to run end-to-end reconstructions from MRI data to structural and functional connectome maps, customize their analyses and utilize various software packages to preprocess data. Structural and functional connectome maps can be reconstructed with respect to user-defined (sub)cortical atlases providing aligned connectivity matrices for integrative multimodal analyses. We outline the implementation and usage of the structural and functional processing pipelines in CATO. Performance was calibrated with respect to simulated diffusion weighted imaging from the ITC2015 challenge, test-retest diffusion weighted imaging data and resting-state functional MRI data from the Human Connectome Project. CATO is open-source software distributed under the MIT License and available as a MATLAB toolbox and as a stand-alone application at www.dutchconnectomelab.nl/CATO.
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Affiliation(s)
- Siemon C de Lange
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands; Department of Child Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam
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17
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Helwegen K, Libedinsky I, van den Heuvel MP. Statistical power in network neuroscience. Trends Cogn Sci 2023; 27:282-301. [PMID: 36725422 DOI: 10.1016/j.tics.2022.12.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/31/2023]
Abstract
Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.
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Affiliation(s)
- Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ilan Libedinsky
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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18
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Hahn T, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Fisch L, Leenings R, Sarink K, Blanke J, Holstein V, Emden D, Beisemann M, Opel N, Grotegerd D, Meinert S, Heindel W, Witt S, Rietschel M, Nöthen MM, Forstner AJ, Kircher T, Nenadic I, Jansen A, Müller-Myhsok B, Andlauer TFM, Walter M, van den Heuvel MP, Jamalabadi H, Dannlowski U, Repple J. Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder. Mol Psychiatry 2023; 28:1057-1063. [PMID: 36639510 PMCID: PMC10005934 DOI: 10.1038/s41380-022-01936-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023]
Abstract
Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.
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Affiliation(s)
- Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marco J Mauritz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Julian Blanke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vincent Holstein
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marie Beisemann
- Department of Statistics, TU Dortmund University, Dortmund, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Interdisciplinary Centre for Clinical Research IZKF, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Walter Heindel
- Institute of Clinical Radiology, University of Münster, Münster, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany.,Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | | | - Till F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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19
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Gruber M, Mauritz M, Meinert S, Grotegerd D, de Lange SC, Grumbach P, Goltermann J, Winter NR, Waltemate L, Lemke H, Thiel K, Winter A, Breuer F, Borgers T, Enneking V, Klug M, Brosch K, Meller T, Pfarr JK, Ringwald KG, Stein F, Opel N, Redlich R, Hahn T, Leehr EJ, Bauer J, Nenadić I, Kircher T, van den Heuvel MP, Dannlowski U, Repple J. Cognitive performance and brain structural connectome alterations in major depressive disorder. Psychol Med 2023; 53:1-12. [PMID: 36752136 PMCID: PMC10600941 DOI: 10.1017/s0033291722004007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 12/02/2022] [Accepted: 12/23/2022] [Indexed: 02/09/2023]
Abstract
BACKGROUND Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks. METHODS Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength. RESULTS All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course. CONCLUSIONS Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.
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Affiliation(s)
- Marius Gruber
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, 60528 Frankfurt, Germany
| | - Marco Mauritz
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
- Institute of Translational Neuroscience, University of Münster, 48149 Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Siemon C. de Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV Amsterdam, The Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, The Netherlands
| | - Pascal Grumbach
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Nils Ralf Winter
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Tiana Borgers
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Verena Enneking
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Melissa Klug
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, 35032 Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, 35032 Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, 35032 Marburg, Germany
| | - Kai Gustav Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, 35032 Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, 35032 Marburg, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
- Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, 07743 Jena, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
- Institute of Psychology, University of Halle, 06108 Halle (Saale), Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Elisabeth J. Leehr
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, 48149 Münster, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, 35032 Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, 35032 Marburg, Germany
| | - Martijn P. van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, 1105 AZ Amsterdam, The Netherlands
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, 60528 Frankfurt, Germany
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20
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Zhou J, Jiang X, Zhou Y, Zhu Y, Jia L, Sun T, Liu L, Sun Q, Ren L, Guo Y, Wu F, Kong L, Tang Y. Distinguishing major depressive disorder from bipolar disorder in remission: A brain structural network analysis. J Affect Disord 2022; 319:8-14. [PMID: 36058360 DOI: 10.1016/j.jad.2022.08.102] [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: 12/18/2021] [Revised: 05/18/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND It is challenging to differentiate major depressive disorder (MDD) from bipolar disorder (BD) in depression and remission. To exclude the potential influence of depressive episodes, we compared the white matter (WM) network between MDD and BD patients in remission to find disease-specific alterations in MDD and BD, and then distinguish these two affective disorders. METHODS We recruited 33 patients with remitted MDD (rMDD), 54 patients with remitted BD (rBD), and 60 healthy controls (HCs). Diffusion tensor imaging and high-resolution 3D T1-weighted image were acquired. Global and nodal topological parameters were used to depict the alterations of the whole-brain WM network. RESULTS We found that rMDD displayed increased global network efficiency (Eglob) and local network efficiency (Eloc) compared with HCs, whereas we found no significance between rBD and HCs. Compared with rBD and HCs, patients in the rMDD group showed increased nodal degree and nodal efficiency, and decreased nodal shortest path length in the four cerebral regions, including the right calcarine fissure (CAL.R), right cuneus (CUN.R), left lingual gyrus (LING.L), and left middle occipital gyrus (MOG.L). We did not find any rBD specific changes of nodal topological metrics. LIMITATIONS The main limitation is the possible effects of medication and BD subtypes on the results. CONCLUSIONS Our findings indicate that rMDD exhibited elevated global properties compared with HCs group, and increased nodal properties in the CAL.R, CUN.R, LING.L, and MOG.L specifically compared with rBD and HCs, which may underlie the distinction of the two affective disorders in remission.
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Affiliation(s)
- Jian Zhou
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Xiaowei Jiang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yifang Zhou
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yue Zhu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Linna Jia
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Ting Sun
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Linzi Liu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Qikun Sun
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Luyu Ren
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yanan Guo
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Feng Wu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Lingtao Kong
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Brain Function Research Section, The First Hospital of China Medical University, Shenyang, China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China; Department of Geriatric Medicine, The First Hospital of China Medical University, Shenyang, China.
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21
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Sammer G, Neumann E, Blecker C, Pedraz-Petrozzi B. Fractional anisotropy and peripheral cytokine concentrations in outpatients with depressive episode: a diffusion tensor imaging observational study. Sci Rep 2022; 12:17450. [PMID: 36261698 PMCID: PMC9582033 DOI: 10.1038/s41598-022-22437-0] [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: 02/16/2022] [Accepted: 10/14/2022] [Indexed: 01/12/2023] Open
Abstract
Over the past few years, evidence of a positive relationship between inflammation and depression has grown steadily. The aim of the current study was to investigate whether such depression-related inflammation could also be associated with altered microstructural changes in the white matter. FA and serum cytokines (IL-1β, IL-6, TNF-α, and IFN-γ) were measured in 25 patients with depression (DE) and 24 healthy controls (HC). Diffusion tensor imaging was performed. Fractional anisotropy (FA) was calculated using the FSL pipeline for Tract-Based Spatial Statistics (TBSS). Both voxelwise and mean whole-brain FA were analyzed using general linear models (GLM). Higher concentrations of IL-1β were associated with lower whole-brain fractional anisotropy, particularly in people with depression (ρ = - 0.67; p < 0.001). TNF-α shared some variance with IL-1β and also showed a negative relationship between TNF-α concentrations and FA in depression (F1,46 = 11.13, p = 0.002, η2p = 0.21). In detail, the voxelwise analysis showed that the regression slopes of IL-1β on FA were more negative in the DE group than in the HC group, mainly in the corpus callosum (cluster statistics: genu corpus callosum, p = 0.022; splenium of corpus callosum, p = 0.047). Similar effects were not found for the other remaining cytokines. This study clearly demonstrated an association between peripherally measured IL-1β and white matter integrity in depression as assessed by DTI. The results suggest that microstructural changes in the corpus callosum are associated with increased peripheral IL-1β concentrations in depression.
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Affiliation(s)
- Gebhard Sammer
- grid.8664.c0000 0001 2165 8627Psychiatry, Justus Liebig University Giessen, Klinikstrasse 36, 35392 Giessen, Hessen Germany ,grid.8664.c0000 0001 2165 8627Faculty of Psychology and Sports Science, Justus Liebig University Giessen, Giessen, Hessen Germany ,grid.8664.c0000 0001 2165 8627Bender Institute of Neuroimaging (BION), Faculty of Psychology and Sports Science, Justus Liebig University Giessen, Giessen, Hessen Germany
| | - Elena Neumann
- grid.8664.c0000 0001 2165 8627Internal Medicine and Rheumatology, Campus Kerckhoff, Justus Liebig University Giessen, Giessen, Hessen Germany
| | - Carlo Blecker
- grid.8664.c0000 0001 2165 8627Faculty of Psychology and Sports Science, Justus Liebig University Giessen, Giessen, Hessen Germany ,grid.8664.c0000 0001 2165 8627Bender Institute of Neuroimaging (BION), Faculty of Psychology and Sports Science, Justus Liebig University Giessen, Giessen, Hessen Germany
| | - Bruno Pedraz-Petrozzi
- grid.413757.30000 0004 0477 2235Central Institute of Mental Health, Mannheim, Germany
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22
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Altered hippocampus and amygdala subregion connectome hierarchy in major depressive disorder. Transl Psychiatry 2022; 12:209. [PMID: 35589678 PMCID: PMC9120054 DOI: 10.1038/s41398-022-01976-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/04/2022] [Accepted: 05/10/2022] [Indexed: 01/04/2023] Open
Abstract
The hippocampus and amygdala limbic structures are critical to the etiology of major depressive disorder (MDD). However, there are no high-resolution characterizations of the role of their subregions in the whole brain network (connectome). Connectomic examination of these subregions can uncover disorder-related patterns that are otherwise missed when treated as single structures. 38 MDD patients and 40 healthy controls (HC) underwent anatomical and diffusion imaging using 7-Tesla MRI. Whole-brain segmentation was performed along with hippocampus and amygdala subregion segmentation, each representing a node in the connectome. Graph theory analysis was applied to examine the importance of the limbic subregions within the brain network using centrality features measured by node strength (sum of weights of the node's connections), Betweenness (number of shortest paths that traverse the node), and clustering coefficient (how connected the node's neighbors are to one another and forming a cluster). Compared to HC, MDD patients showed decreased node strength of the right hippocampus cornu ammonis (CA) 3/4, indicating decreased connectivity to the rest of the brain, and decreased clustering coefficient of the right dentate gyrus, implying it is less embedded in a cluster. Additionally, within the MDD group, the greater the embedding of the right amygdala central nucleus (CeA) in a cluster, the greater the severity of depressive symptoms. The altered role of these limbic subregions in the whole-brain connectome is related to diagnosis and depression severity, contributing to our understanding of the limbic system involvement in MDD and may elucidate the underlying mechanisms of depression.
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23
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Meinert S, Leehr EJ, Grotegerd D, Repple J, Förster K, Winter NR, Enneking V, Fingas SM, Lemke H, Waltemate L, Stein F, Brosch K, Schmitt S, Meller T, Linge A, Krug A, Nenadić I, Jansen A, Hahn T, Redlich R, Opel N, Schubotz RI, Baune BT, Kircher T, Dannlowski U. White matter fiber microstructure is associated with prior hospitalizations rather than acute symptomatology in major depressive disorder. Psychol Med 2022; 52:1166-1174. [PMID: 32921338 DOI: 10.1017/s0033291720002950] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Eighty percent of all patients suffering from major depressive disorder (MDD) relapse at least once in their lifetime. Thus, understanding the neurobiological underpinnings of the course of MDD is of utmost importance. A detrimental course of illness in MDD was most consistently associated with superior longitudinal fasciculus (SLF) fiber integrity. As similar associations were, however, found between SLF fiber integrity and acute symptomatology, this study attempts to disentangle associations attributed to current depression from long-term course of illness. METHODS A total of 531 patients suffering from acute (N = 250) or remitted (N = 281) MDD from the FOR2107-cohort were analyzed in this cross-sectional study using tract-based spatial statistics for diffusion tensor imaging. First, the effects of disease state (acute v. remitted), current symptom severity (BDI-score) and course of illness (number of hospitalizations) on fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity were analyzed separately. Second, disease state and BDI-scores were analyzed in conjunction with the number of hospitalizations to disentangle their effects. RESULTS Disease state (pFWE < 0.042) and number of hospitalizations (pFWE< 0.032) were associated with decreased FA and increased MD and RD in the bilateral SLF. A trend was found for the BDI-score (pFWE > 0.067). When analyzed simultaneously only the effect of course of illness remained significant (pFWE < 0.040) mapping to the right SLF. CONCLUSIONS Decreased FA and increased MD and RD values in the SLF are associated with more hospitalizations when controlling for current psychopathology. SLF fiber integrity could reflect cumulative illness burden at a neurobiological level and should be targeted in future longitudinal analyses.
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Affiliation(s)
- Susanne Meinert
- Department of Psychiatry, University of Münster, Münster, Germany
| | | | | | - Jonathan Repple
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Katharina Förster
- Department of Psychiatry, University of Münster, Münster, Germany
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Nils R Winter
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Verena Enneking
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Stella M Fingas
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Anna Linge
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Core-Unit Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Tim Hahn
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Ronny Redlich
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Department of Psychiatry, University of Münster, Münster, Germany
- Interdisciplinary Centre for Clinical Research (IZKF) Münster, University of Münster, Münster, Germany
| | | | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
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24
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Hengartner MP, Plöderl M. Estimates of the minimal important difference to evaluate the clinical significance of antidepressants in the acute treatment of moderate-to-severe depression. BMJ Evid Based Med 2022; 27:69-73. [PMID: 33593736 DOI: 10.1136/bmjebm-2020-111600] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/22/2021] [Indexed: 12/27/2022]
Abstract
The efficacy of antidepressants in the acute treatment of moderate-to-severe depression remains a controversial issue. The minimal important difference (MID) is relevant to judge the clinical significance of treatment effects. In this analysis paper, we discuss estimates of the MID for common depression outcome measures.For the Hamilton Depression Rating Scale 17-item Version (HDRS-17), according to both anchor-based and distribution-based approaches, MID estimates range from 3 to 8 points, and the most accurate values are likely between 3 and 5 points. For the 6-item version (HDRS-6), MID estimates range between 2 and 4 points. For both the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Beck Depression Inventory II (BDI-II), MID estimates range between 3 and 9 points, with estimates of 3-6 points likely being the most accurate. Quality of life appears to be more important to patients than core depression symptoms. We thus also evaluated the Short-Form 36 (SF-36) mental component score, a popular mental-health-related quality of life measure. Its MID estimate is likely about 5 points. By contrast, the average treatment effects of antidepressants on the HDRS-17, HDRS-6, MADRS, BDI-II and SF-36 are 2 points, 1.5 points, 3 points, 2 points and 3-5 points, respectively.In conclusion, the efficacy of antidepressants in the acute treatment of moderate-to-severe depression consistently fails to exceed the lower bound of the MID estimates for common depression outcome measures. The clinical significance of antidepressants thus remains uncertain and we call for more research on quality of life measures, which are the patients' most valued outcome domains.
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Affiliation(s)
- Michael P Hengartner
- Department of Applied Psychology, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Martin Plöderl
- Department of Crisis Intervention and Suicide Prevention, Paracelsus Medical University Salzburg, Salzburg, Salzburg, Austria
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25
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Disrupted default mode network and executive control network are associated with depression severity on structural network. Neuroreport 2022; 33:227-235. [PMID: 35287146 DOI: 10.1097/wnr.0000000000001773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Major depressive disorder (MDD) is a psychiatric disorder with a relatively limited response to treatment. It is necessary to better understand the neuroanatomical mechanisms of structural networks. METHODS The current study recruited 181 first-onset, untreated adult MDD patients: slight MDD (SD, N = 23), moderate MDD (MD, N = 77), Heavy MDD (HD, N = 81) groups; along with a healthy control group (HC, N = 81) with matched general clinical data. FreeSurfer was used to preprocess T1 images for gray matter volume (GMV), and the default mode network (DMN) and the execution control network (ECN) were analyzed by structural covariance network (SCN). RESULTS Present study found that the GMV of brain regions reduced with the severity of the disease. Specifically, the GMV of the left anterior cingulate gyrus (ACC.L) is negatively correlated with MDD severity. In addition, the SCN connectivity of the whole-brain network increases with the increase of severity in MDD. ACC.L is a key brain region with increased connectivity between the left orbitofrontal in DMN and between the right orbitofrontal in ECN, which leads to damage to the balance of neural circuits. CONCLUSIONS Patients with smaller GMV of ACC.L are more likely to develop severe MDD, and as a key region in both networks which have distinct structural network models in DMN and ECN. MDD patients with different severity have different neuroimaging changes in DMN and ECN.
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26
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He M, Ping L, Chu Z, Zeng C, Shen Z, Xu X. Identifying Changes of Brain Regional Homogeneity and Cingulo-Opercular Network Connectivity in First-Episode, Drug-Naïve Depressive Patients With Suicidal Ideation. Front Neurosci 2022; 16:856366. [PMID: 35310111 PMCID: PMC8924659 DOI: 10.3389/fnins.2022.856366] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 01/31/2022] [Indexed: 11/15/2022] Open
Abstract
Objective Adult patients with major depressive disorder (MDD) may not actively reveal their suicidal ideation (SI). Therefore, this study is committed to finding the alterations in the cingulo-opercular network (CON) that are closely related to SI with multi-imaging methods, thus providing neuroimaging basis for SI. Method A total of 198 participants (129 MDD patients and 69 healthy controls) were recruited and evaluated with the Montgomery–Asberg Depression Rating Scale (MADRS). The healthy individuals formed the HC group, while the MDD patients were subdivided into no SI MDD (NSI, n = 32), mild SI MDD (MSI, n = 64), and severe SI MDD (SSI, n = 33) according to their MADRS item 10. We obtained MRI data of all participants and applied regional homogeneity (ReHo) analysis to verify a previous finding that links CON abnormality to SI. In addition, we employed the structural covariance network (SCN) analysis to investigate the correlation between abnormal structural connectivity of CON and SI severity. Results Compared to those of the HC group, MDD ReHo values and gray matter volume (GMV) were consistently found abnormal in CON. ReHo values and GMV of the right orbital inferior frontal gyrus (ORBinf.R) in the MDD group decreased with the increase of SI. Compared to the HC group, the MDD patients showed enhanced structural connectivity of three pairs of brain regions in CON [ACC.L–left superior frontal gyrus (SFG.L), SFG.L–left middle temporal gyrus (MTG.L), and the SFG.L–left post-central gyrus (PoCG.L)]. Compared with that of the NSI and MSI groups, the structural connectivity of three pairs of brain regions in CON is enhanced in the SSI groups [ORBinf.L–right ventral posterior cingulate gyrus (VPCC.R), VPCC.R–SFG.R, and SFG.R–PoCG.R]. Conclusion Our findings showed the distinctive ReHo, GMV, and SCN pattern of CON in MDD patients with SI; and with the severity of suicide, abnormal brain regions increased. Our finding suggested that MDD patients with different severity of SI have different neuroimaging changes.
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Affiliation(s)
- Mengxin He
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Mental Health Institute of Yunnan, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Liangliang Ping
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zhaosong Chu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chunqiang Zeng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Mental Health Institute of Yunnan, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Yunnan Clinical Research Center for Mental Disorders, First Affiliated Hospital of Kunming Medical University, Kunming, China
- *Correspondence: Zonglin Shen,
| | - Xiufeng Xu
- Mental Health Institute of Yunnan, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Yunnan Clinical Research Center for Mental Disorders, First Affiliated Hospital of Kunming Medical University, Kunming, China
- Xiufeng Xu,
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27
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Association of brain white matter microstructure with cognitive performance in major depressive disorder and healthy controls: a diffusion-tensor imaging study. Mol Psychiatry 2022; 27:1103-1110. [PMID: 34697453 PMCID: PMC9054669 DOI: 10.1038/s41380-021-01330-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 09/10/2021] [Accepted: 09/28/2021] [Indexed: 11/30/2022]
Abstract
Cognitive deficits are central attendant symptoms of major depressive disorder (MDD) with a crucial impact in patients' everyday life. Thus, it is of particular clinical importance to understand their pathophysiology. The aim of this study was to investigate a possible relationship between brain structure and cognitive performance in MDD patients in a well-characterized sample. N = 1007 participants (NMDD = 482, healthy controls (HC): NHC = 525) were selected from the FOR2107 cohort for this diffusion-tensor imaging study employing tract-based spatial statistics. We conducted a principal component analysis (PCA) to reduce neuropsychological test results, and to discover underlying factors of cognitive performance in MDD patients. We tested the association between fractional anisotropy (FA) and diagnosis (MDD vs. HC) and cognitive performance factors. The PCA yielded a single general cognitive performance factor that differed significantly between MDD patients and HC (P < 0.001). We found a significant main effect of the general cognitive performance factor in FA (Ptfce-FWE = 0.002) in a large bilateral cluster consisting of widespread frontotemporal-association fibers. In MDD patients this effect was independent of medication intake, the presence of comorbid diagnoses, the number of previous hospitalizations, and depressive symptomatology. This study provides robust evidence that white matter disturbances and cognitive performance seem to be associated. This association was independent of diagnosis, though MDD patients show more pronounced deficits and lower FA values in the global white matter fiber structure. This suggests a more general, rather than the depression-specific neurological basis for cognitive deficits.
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28
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Bas‐Hoogendam JM, Groenewold NA, Aghajani M, Freitag GF, Harrewijn A, Hilbert K, Jahanshad N, Thomopoulos SI, Thompson PM, Veltman DJ, Winkler AM, Lueken U, Pine DS, van der Wee NJA, Stein DJ. ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders. Hum Brain Mapp 2022; 43:83-112. [PMID: 32618421 PMCID: PMC8805695 DOI: 10.1002/hbm.25100] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/09/2020] [Accepted: 06/08/2020] [Indexed: 12/11/2022] Open
Abstract
Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.
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Affiliation(s)
- Janna Marie Bas‐Hoogendam
- Department of Developmental and Educational PsychologyLeiden University, Institute of PsychologyLeidenThe Netherlands
- Department of PsychiatryLeiden University Medical CenterLeidenThe Netherlands
- Leiden Institute for Brain and CognitionLeidenThe Netherlands
| | - Nynke A. Groenewold
- Department of Psychiatry & Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Moji Aghajani
- Department of PsychiatryAmsterdam UMC / VUMCAmsterdamThe Netherlands
- Department of Research & InnovationGGZ inGeestAmsterdamThe Netherlands
| | - Gabrielle F. Freitag
- National Institute of Mental Health, Emotion and Development BranchBethesdaMarylandUSA
| | - Anita Harrewijn
- National Institute of Mental Health, Emotion and Development BranchBethesdaMarylandUSA
| | - Kevin Hilbert
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Neda Jahanshad
- University of Southern California Keck School of MedicineImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteLos AngelesCaliforniaUSA
| | - Sophia I. Thomopoulos
- University of Southern California Keck School of MedicineImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- University of Southern California Keck School of MedicineImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteLos AngelesCaliforniaUSA
| | - Dick J. Veltman
- Department of PsychiatryAmsterdam UMC / VUMCAmsterdamThe Netherlands
| | - Anderson M. Winkler
- National Institute of Mental Health, Emotion and Development BranchBethesdaMarylandUSA
| | - Ulrike Lueken
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Daniel S. Pine
- National Institute of Mental Health, Emotion and Development BranchBethesdaMarylandUSA
| | - Nic J. A. van der Wee
- Department of PsychiatryLeiden University Medical CenterLeidenThe Netherlands
- Leiden Institute for Brain and CognitionLeidenThe Netherlands
| | - Dan J. Stein
- Department of Psychiatry & Mental HealthUniversity of Cape TownCape TownSouth Africa
- University of Cape TownSouth African MRC Unit on Risk & Resilience in Mental DisordersCape TownSouth Africa
- University of Cape TownNeuroscience InstituteCape TownSouth Africa
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29
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Li R, Zhan W, Huang X, Liu Z, Lv S, Wang J, Liang L, Ma Y. Association of Dietary Inflammatory Index (DII) and Depressive Disorders. J Inflamm Res 2021; 14:6959-6973. [PMID: 34949933 PMCID: PMC8691198 DOI: 10.2147/jir.s344002] [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: 10/12/2021] [Accepted: 12/03/2021] [Indexed: 11/23/2022] Open
Abstract
A lot of evidence shows that inflammation is related to the development of depression. However, the heterogeneity of depression hinders efforts to understand, prevent and treat this disease. The purpose of this comprehensive review is to summarize the links between inflammation and the established core features of depression, which show more homogeneity than the syndrome itself: overreaction to negative information, changes in reward processing, and cognitive control decline, and somatic syndrome. For each core feature, we first briefly outline its relevance to depression and neurobiological basis, and then review the evidence to investigate the potential role of inflammation. We mainly focus on the discovery of the experimental paradigm of exogenous inflammation. We concluded that inflammation may play a role in overreaction to negative information, altered reward responses, and physical symptoms. There is less evidence to support the effect of inflammation on cognitive control by standard neuropsychological measures. Finally, we discussed the implications for future research and recommendations on how to test the role of inflammation in the pathogenesis of heterogeneous mental illness.
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Affiliation(s)
- Ruiqiang Li
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
| | - Wenqiang Zhan
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China
| | - Xin Huang
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
| | - Zhan Liu
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
| | - Shuaishuai Lv
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
| | - Jiaqi Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
| | - Luyao Liang
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
| | - Yuxia Ma
- Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
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30
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Xu SX, Deng WF, Qu YY, Lai WT, Huang TY, Rong H, Xie XH. The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics. J Affect Disord 2021; 295:759-770. [PMID: 34517250 DOI: 10.1016/j.jad.2021.08.120] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/26/2021] [Accepted: 08/28/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies. METHODS Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis. RESULTS Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively). CONCLUSION Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration. LIMITATIONS Included number of trials was low and heterogeneity should be noted.
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Affiliation(s)
- Shu-Xian Xu
- Brain Function and Psychosomatic Medicine Institute, Second People's Hospital of Huizhou, Huizhou, Guangdong, China; Department of Psychiatry, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wen-Feng Deng
- Huizhou Center for Disease Control and Prevention, Huizhou, Guangdong, China
| | - Ying-Ying Qu
- Center of Acute Psychiatry Service, Second People's Hospital of Huizhou, Huizhou, Guangdong, China
| | - Wen-Tao Lai
- Department of Radiology, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China
| | - Tan-Yu Huang
- Department of Radiology, Second People's Hospital of Huizhou, Huizhou, Guangdong, China
| | - Han Rong
- Department of Psychiatry, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China; Affiliated Shenzhen Clinical College of Psychiatry, Jining Medical University, Jining, Shandong, China
| | - Xin-Hui Xie
- Brain Function and Psychosomatic Medicine Institute, Second People's Hospital of Huizhou, Huizhou, Guangdong, China; Department of Psychiatry, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China; Center of Acute Psychiatry Service, Second People's Hospital of Huizhou, Huizhou, Guangdong, China.
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31
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Pfarr JK, Brosch K, Meller T, Ringwald KG, Schmitt S, Stein F, Meinert S, Grotegerd D, Thiel K, Lemke H, Winter A, Waltemate L, Hahn T, Opel N, Repple J, Bauer J, Jansen A, Dannlowski U, Krug A, Kircher T, Nenadić I. Brain structural connectivity, anhedonia, and phenotypes of major depressive disorder: A structural equation model approach. Hum Brain Mapp 2021; 42:5063-5074. [PMID: 34302413 PMCID: PMC8449111 DOI: 10.1002/hbm.25600] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/10/2021] [Accepted: 07/12/2021] [Indexed: 12/22/2022] Open
Abstract
Aberrant brain structural connectivity in major depressive disorder (MDD) has been repeatedly reported, yet many previous studies lack integration of different features of MDD with structural connectivity in multivariate modeling approaches. In n = 595 MDD patients, we used structural equation modeling (SEM) to test the intercorrelations between anhedonia, anxiety, neuroticism, and cognitive control in one comprehensive model. We then separately analyzed diffusion tensor imaging (DTI) connectivity measures in association with those clinical variables, and finally integrated brain connectivity associations, clinical/cognitive variables into a multivariate SEM. We first confirmed our clinical/cognitive SEM. DTI analyses (FWE‐corrected) showed a positive correlation of anhedonia with fractional anisotropy (FA) in the right anterior thalamic radiation (ATR) and forceps minor/corpus callosum, while neuroticism was negatively correlated with axial diffusivity (AD) in the left uncinate fasciculus (UF) and inferior fronto‐occipital fasciculus (IFOF). An extended SEM confirmed the associations of ATR FA with anhedonia and UF/IFOF AD with neuroticism impacting on cognitive control. Our findings provide evidence for a differential impact of state and trait variables of MDD on brain connectivity and cognition. The multivariate approach shows feasibility of explaining heterogeneity within MDD and tracks this to specific brain circuits, thus adding to better understanding of heterogeneity on the biological level.
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Affiliation(s)
- Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Kai Gustav Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany.,Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
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32
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Dini H, Sendi MSE, Sui J, Fu Z, Espinoza R, Narr KL, Qi S, Abbott CC, van Rooij SJH, Riva-Posse P, Bruni LE, Mayberg HS, Calhoun VD. Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder. Front Hum Neurosci 2021; 15:689488. [PMID: 34295231 PMCID: PMC8291148 DOI: 10.3389/fnhum.2021.689488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/31/2021] [Indexed: 12/28/2022] Open
Abstract
Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03). Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.
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Affiliation(s)
- Hossein Dini
- Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Mohammad S E Sendi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Christopher C Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Luis Emilio Bruni
- Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Helen S Mayberg
- Departments of Neurology, Neurosurgery, Psychiatry and Neuroscience, Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Vince D Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
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33
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Goltermann J, Repple J, Redlich R, Dohm K, Flint C, Grotegerd D, Waltemate L, Lemke H, Fingas SM, Meinert S, Enneking V, Hahn T, Bauer J, Schmitt S, Meller T, Stein F, Brosch K, Steinsträter O, Jansen A, Krug A, Nenadić I, Baune BT, Rietschel M, Witt S, Forstner AJ, Nöthen M, Johnen A, Alferink J, Kircher T, Dannlowski U, Opel N. Apolipoprotein E homozygous ε4 allele status: Effects on cortical structure and white matter integrity in a young to mid-age sample. Eur Neuropsychopharmacol 2021; 46:93-104. [PMID: 33648793 DOI: 10.1016/j.euroneuro.2021.02.006] [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: 03/29/2020] [Revised: 01/04/2021] [Accepted: 02/09/2021] [Indexed: 11/16/2022]
Abstract
Apolipoprotein E (APOE) genotype is the strongest single gene predictor of Alzheimer's disease (AD) and has been frequently associated with AD-related brain structural alterations before the onset of dementia. While previous research has primarily focused on hippocampal morphometry in relation to APOE, sporadic recent findings have questioned the specificity of the hippocampus and instead suggested more global effects on the brain. With the present study we aimed to investigate associations between homozygous APOE ε4 status and cortical gray matter structure as well as white matter microstructure. In our study, we contrasted n = 31 homozygous APOE ε4 carriers (age=34.47 years, including a subsample of n = 12 subjects with depression) with a demographically matched sample without an ε4 allele (resulting total sample: N = 62). Morphometry analyses included a) Freesurfer based cortical segmentations of thickness and surface area measures and b) tract based spatial statistics of DTI measures. We found pronounced and widespread reductions in cortical surface area of ε4 homozygotes in 57 out of 68 cortical brain regions. In contrast, no differences in cortical thickness were observed. Furthermore, APOE ε4 homozygous carriers showed significantly lower fractional anisotropy in the corpus callosum, the right internal and external capsule, the left corona radiata and the right fornix. The present findings support a global rather than regionally specific effect of homozygous APOE ε4 allele status on cortical surface area and white matter microstructure. Future studies should aim to delineate the clinical implications of these findings.
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Affiliation(s)
- Janik Goltermann
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Ronny Redlich
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Claas Flint
- Department of Psychiatry, University of Münster, Münster, Germany; Department of Mathematics and Computer Science, University of Münster, Germany
| | | | - Lena Waltemate
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Department of Psychiatry, University of Münster, Münster, Germany
| | | | - Susanne Meinert
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Verena Enneking
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Institute of Clinical Radiology, University of Münster, Germany
| | - Simon Schmitt
- Department of Psychiatry, University of Marburg, Germany
| | - Tina Meller
- Department of Psychiatry, University of Marburg, Germany
| | | | | | | | - Andreas Jansen
- Department of Psychiatry, University of Marburg, Germany
| | - Axel Krug
- Department of Psychiatry, University of Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry, University of Marburg, Germany
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - Marcella Rietschel
- Department of Genetic Epidemiology, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, Department of Genomics, Life & Brain Center, University of Bonn, Germany; Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Markus Nöthen
- Institute of Human Genetics, Department of Genomics, Life & Brain Center, University of Bonn, Germany
| | | | - Judith Alferink
- Department of Psychiatry, University of Münster, Münster, Germany; Cells in Motion Interfaculty Centre, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Germany
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany.
| | - Nils Opel
- Department of Psychiatry, University of Münster, Münster, Germany
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34
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[Sport and physical exercise in unipolar depression : Prevention, therapy, and neurobiological mechanisms of action]. DER NERVENARZT 2021; 92:507-514. [PMID: 33847767 DOI: 10.1007/s00115-021-01113-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/05/2021] [Indexed: 10/21/2022]
Abstract
There is increasing consensus on the relevance of exercise interventions for the treatment and prevention of unipolar depression. The present review article aims to provide a practitioner-friendly overview of recent insights into the underlying neurobiological mechanisms of exercise interventions in depression in order to enhance their dissemination and acceptance. Exercise has proven antidepressive efficacy in major depressive disorders. Furthermore, it has demonstrated a protective effect on the development of depressive symptoms. Neurobiological research has shown that exercise increases the volume of gray matter in the brain, improves the microstructure of white matter and leads to a higher functional connectivity in brain regions implicated in major depressive disorders. On a molecular level, preliminary findings indicate that exercise has anti-inflammatory, neuroplastic and antioxidative effects, which could represent the basis for the effects observed on a brain structural and functional level. Exercise interventions should be recommended as an adjunct therapy for all patients with major depressive disorder.
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35
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Wang Y, Wang Q, Xie J, Zhu Y, Zhang D, Li G, Zhu X, Li Y. Mediation on the Association Between Stressful Life Events and Depression by Abnormal White Matter Microstructures. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:162-170. [PMID: 33775928 DOI: 10.1016/j.bpsc.2021.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/25/2021] [Accepted: 03/10/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Stressful life events (SLEs) are an important causal factor in depression; however, the mechanism by which SLEs cause depression remains unclear. Recent studies suggested that white matter (WM) microstructures might be a potential mediator between SLEs and depression. Hence, we aimed to investigate the concrete correspondence among them using mediation effect models. METHODS In participants (N = 194) with SLEs experience prospectively recruited from six residential communities, WM microstructures were detected with diffusion tensor imaging. The interrelationship among SLEs, WM microstructures, and depression was explored with multiple linear regression models and logistic regression models. Furthermore, the influence of WM microstructures on the association between SLEs and depression was tested with mediation effect models. RESULTS Successfully established mediation effect models showed the specific influence of fractional anisotropy of the corpus callosum and left uncinate fasciculus on the association between SLEs and depression onset (ab path = 0.032; ab path = 0.026, respectively) and between SLEs and depressive severity (ab path = 0.052; ab path = 0.067, respectively). In addition, significant total mediation effects on the association between SLEs and depression onset (ab path = 0.031) and severity (ab path = 0.075) through fractional anisotropy of the corpus callosum and left uncinate fasciculus were noted. CONCLUSIONS WM microstructure alterations impose a substantial mediation effect on the association between SLEs and depression, which suggest that changes in WM microstructure integrity might increase the risk of depression onset and unfavorable disease courses induced by the SLEs.
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Affiliation(s)
- Yun Wang
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China; Department of Clinical Psychology, Zhenjiang Mental Health Center, Zhenjiang, China
| | - Qi Wang
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Jie Xie
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Yan Zhu
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Danwei Zhang
- Department of Clinical Psychology, Zhenjiang Mental Health Center, Zhenjiang, China
| | - Guohai Li
- Department of Clinical Psychology, Zhenjiang Mental Health Center, Zhenjiang, China.
| | - Xiaolan Zhu
- Department of Central Laboratory, the Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Yuefeng Li
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China; Department of Clinical Psychology, Zhenjiang Mental Health Center, Zhenjiang, China.
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36
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Li J, Seidlitz J, Suckling J, Fan F, Ji GJ, Meng Y, Yang S, Wang K, Qiu J, Chen H, Liao W. Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nat Commun 2021; 12:1647. [PMID: 33712584 PMCID: PMC7955076 DOI: 10.1038/s41467-021-21943-5] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/12/2021] [Indexed: 01/08/2023] Open
Abstract
Major depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD. The correlation between brain structural changes in major depressive disorder (MDD) and gene expression is unclear. Here, the authors explore the correlation between cell type-specific gene expression changes and cortical structural difference in individuals with major depressive disorder.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Jakob Seidlitz
- Children's Hospital of Philadelphia, Department of Child and Adolescent Psychiatry and Behavioral Science, Philadelphia, PA, USA.,University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA
| | - John Suckling
- University of Cambridge, Department of Psychiatry, Cambridge, UK
| | - Feiyang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Gong-Jun Ji
- Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Kai Wang
- Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, P.R. China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China. .,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China. .,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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37
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Association Between Genetic Risk for Type 2 Diabetes and Structural Brain Connectivity in Major Depressive Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:333-340. [PMID: 33684623 DOI: 10.1016/j.bpsc.2021.02.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/20/2021] [Accepted: 02/18/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) and type 2 diabetes mellitus (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared genetics, both diseases seem to be associated with alterations in brain structural connectivity and impaired cognitive performance, but little is known about the mechanisms by which genetic risk of T2D might affect brain structure and function and if they do, how these effects could contribute to the disease course of MDD. METHODS This study explores the association of polygenic risk for T2D with structural brain connectome topology and cognitive performance in 434 nondiabetic patients with MDD and 539 healthy control subjects. RESULTS Polygenic risk score for T2D across MDD patients and healthy control subjects was found to be associated with reduced global fractional anisotropy, a marker of white matter microstructure, an effect found to be predominantly present in MDD-related fronto-temporo-parietal connections. A mediation analysis further suggests that this fractional anisotropy variation may mediate the association between polygenic risk score and cognitive performance. CONCLUSIONS Our findings provide preliminary evidence of a polygenic risk for T2D to be linked to brain structural connectivity and cognition in patients with MDD and healthy control subjects, even in the absence of a direct T2D diagnosis. This suggests an effect of T2D genetic risk on white matter integrity, which may mediate an association of genetic risk for diabetes and cognitive impairments.
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38
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Dai D, Lacadie CM, Holmes SE, Cool R, Anticevic A, Averill C, Abdallah C, Esterlis I. Ketamine Normalizes the Structural Alterations of Inferior Frontal Gyrus in Depression. CHRONIC STRESS 2021; 4:2470547020980681. [PMID: 33426409 PMCID: PMC7758564 DOI: 10.1177/2470547020980681] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 11/20/2020] [Indexed: 12/20/2022]
Abstract
Background Ketamine is a novel fast-acting antidepressant. Acute ketamine treatment can reverse microstructure deficits and normalize functional alterations in the brain, but little is known about the impacts of ketamine on brain volumes in individuals with depression. Methods We used 3 T magnetic resonance imaging (MRI) and tensorbased morphological methods to investigate the regional volume differences for 29 healthy control (HC) subjects and 21 subjects with major depressive disorder (MDD), including 10 subjects with comorbid post-traumatic stress disorder (PTSD). All the subjects participated in MRI scanning before and 24 h post intravenous ketamine infusion. The effects of acute ketamine administration on HC, MDD, and MDD/PTSD groups were examined separately by whole-brain voxel-wise t-tests. Results Our data showed smaller volume of inferior frontal gyrus (IFG, opercular part) in MDD and MDD/PTSD subjects compared to HC, and a significant correlation between opercular IFG volume and depressive severity in MDD subjects only. Ketamine administration normalized the structural alterations of opercular IFG in both MDD and MDD/PTSD groups, and significantly improved depressive and PTSD symptoms. Twenty-four hours after a single ketamine infusion, there were two clusters of voxels with volume changes in MDD subjects, including significantly increased volumes of opercular IFG. No significant structural alterations were found in the MDD/PTSD or HC groups. Conclusion These findings provide direct evidence that acute ketamine administration can normalize structural alterations associated with depression and highlight the importance of IFG in the guidance of future therapeutic targets.
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Affiliation(s)
- Dan Dai
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Cheryl M Lacadie
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Sophie E Holmes
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Ryan Cool
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Department of Psychology, Yale University School of Medicine, New Haven, Connecticut
| | - Chris Averill
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Clinical Neurosciences Division, National Center for PTSD, US Department of Veterans Affairs, West Haven, Connecticut
| | - Chadi Abdallah
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Clinical Neurosciences Division, National Center for PTSD, US Department of Veterans Affairs, West Haven, Connecticut.,Michael E. DeBakey, VA Medical Center, Houston, Texas.,Menninger Department of Psychiatry, Baylor College of Medicine, Houston, Texas
| | - Irina Esterlis
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Department of Psychology, Yale University School of Medicine, New Haven, Connecticut.,Clinical Neurosciences Division, National Center for PTSD, US Department of Veterans Affairs, West Haven, Connecticut
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Disrupted intrinsic functional brain topology in patients with major depressive disorder. Mol Psychiatry 2021; 26:7363-7371. [PMID: 34385597 PMCID: PMC8873016 DOI: 10.1038/s41380-021-01247-2] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 07/15/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023]
Abstract
Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.
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Licinio J, Wong ML. Molecular Psychiatry, August 2020: new impact factor, and highlights of recent advances in psychiatry, including an overview of the brain's response to stress during infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Mol Psychiatry 2020; 25:1606-1610. [PMID: 32724165 PMCID: PMC7385469 DOI: 10.1038/s41380-020-0845-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/10/2020] [Accepted: 07/10/2020] [Indexed: 11/17/2022]
Affiliation(s)
- Julio Licinio
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA.
| | - Ma-Li Wong
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA
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Licinio J, Wong ML. Advances in depression research: second special issue, 2020, with highlights on biological mechanisms, clinical features, co-morbidity, genetics, imaging, and treatment. Mol Psychiatry 2020; 25:1356-1360. [PMID: 32555341 DOI: 10.1038/s41380-020-0798-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Julio Licinio
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA.
| | - Ma-Li Wong
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA
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