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Li JZ, Ramalingam N, Li S. Targeting epigenetic mechanisms in amyloid-β-mediated Alzheimer's pathophysiology: unveiling therapeutic potential. Neural Regen Res 2025; 20:54-66. [PMID: 38767476 PMCID: PMC11246147 DOI: 10.4103/nrr.nrr-d-23-01827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/07/2024] [Indexed: 05/22/2024] Open
Abstract
Alzheimer's disease is a prominent chronic neurodegenerative condition characterized by a gradual decline in memory leading to dementia. Growing evidence suggests that Alzheimer's disease is associated with accumulating various amyloid-β oligomers in the brain, influenced by complex genetic and environmental factors. The memory and cognitive deficits observed during the prodromal and mild cognitive impairment phases of Alzheimer's disease are believed to primarily result from synaptic dysfunction. Throughout life, environmental factors can lead to enduring changes in gene expression and the emergence of brain disorders. These changes, known as epigenetic modifications, also play a crucial role in regulating the formation of synapses and their adaptability in response to neuronal activity. In this context, we highlight recent advances in understanding the roles played by key components of the epigenetic machinery, specifically DNA methylation, histone modification, and microRNAs, in the development of Alzheimer's disease, synaptic function, and activity-dependent synaptic plasticity. Moreover, we explore various strategies, including enriched environments, exposure to non-invasive brain stimulation, and the use of pharmacological agents, aimed at improving synaptic function and enhancing long-term potentiation, a process integral to epigenetic mechanisms. Lastly, we deliberate on the development of effective epigenetic agents and safe therapeutic approaches for managing Alzheimer's disease. We suggest that addressing Alzheimer's disease may require distinct tailored epigenetic drugs targeting different disease stages or pathways rather than relying on a single drug.
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Affiliation(s)
- Jennie Z Li
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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2
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Qiu L, Liang C, Kochunov P, Hutchison KE, Sui J, Jiang R, Zhi D, Vergara VM, Yang X, Zhang D, Fu Z, Bustillo JR, Qi S, Calhoun VD. Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging. Transl Psychiatry 2024; 14:326. [PMID: 39112461 PMCID: PMC11306356 DOI: 10.1038/s41398-024-03035-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
People affected by psychotic, depressive and developmental disorders are at a higher risk for alcohol and tobacco use. However, the further associations between alcohol/tobacco use and symptoms/cognition in these disorders remain unexplored. We identified multimodal brain networks involving alcohol use (n = 707) and tobacco use (n = 281) via supervised multimodal fusion and evaluated if these networks affected symptoms and cognition in people with psychotic (schizophrenia/schizoaffective disorder/bipolar, n = 178/134/143), depressive (major depressive disorder, n = 260) and developmental (autism spectrum disorder/attention deficit hyperactivity disorder, n = 421/346) disorders. Alcohol and tobacco use scores were used as references to guide functional and structural imaging fusion to identify alcohol/tobacco use associated multimodal patterns. Correlation analyses between the extracted brain features and symptoms or cognition were performed to evaluate the relationships between alcohol/tobacco use with symptoms/cognition in 6 psychiatric disorders. Results showed that (1) the default mode network (DMN) and salience network (SN) were associated with alcohol use, whereas the DMN and fronto-limbic network (FLN) were associated with tobacco use; (2) the DMN and fronto-basal ganglia (FBG) related to alcohol/tobacco use were correlated with symptom and cognition in psychosis; (3) the middle temporal cortex related to alcohol/tobacco use was associated with cognition in depression; (4) the DMN related to alcohol/tobacco use was related to symptom, whereas the SN and limbic system (LB) were related to cognition in developmental disorders. In summary, alcohol and tobacco use were associated with structural and functional abnormalities in DMN, SN and FLN and had significant associations with cognition and symptoms in psychotic, depressive and developmental disorders likely via different brain networks. Further understanding of these relationships may assist clinicians in the development of future approaches to improve symptoms and cognition among psychotic, depressive and developmental disorders.
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Affiliation(s)
- Ling Qiu
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
- Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Chuang Liang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
- Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kent E Hutchison
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
- Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Juan R Bustillo
- Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA.
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
- Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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3
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Feng Y, Zhi D, Zhu Y, Guo X, Luo X, Dang C, Liu L, Sui J, Sun L. Symptom-guided multimodal neuroimage fusion patterns in children with attention-deficit/hyperactivity disorder and its potential "brain structure-function-cognition-behavior" pathological pathways. Eur Child Adolesc Psychiatry 2024; 33:2141-2152. [PMID: 37777608 DOI: 10.1007/s00787-023-02303-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/14/2023] [Indexed: 10/02/2023]
Abstract
The "brain-cognition-behavior" process is an important pathological pathway in children with attention-deficit/hyperactivity disorder (ADHD). Symptom guided multimodal neuroimaging fusion can capture behaviorally relevant and intrinsically linked structural and functional features, which can help to construct a systematic model of the pathology. Analyzing the multimodal neuroimage fusion pattern and exploring how these brain features affect executive function (EF) and leads to behavioral impairment is the focus of this study. Based on gray matter volume (GMV) and fractional amplitude of low frequency fluctuation (fALFF) for 152 ADHD and 102 healthy controls (HC), the total symptom score (TO) was set as a reference to identify co-varying components. Based on the correlation between the identified co-varying components and EF, further mediation analysis was used to explore the relationship between brain image features, EF and clinical symptoms. This study found that the abnormalities of GMV and fALFF in ADHD are mainly located in the default mode network (DMN) and prefrontal-striatal-cerebellar circuits, respectively. GMV in ADHD influences the TO through Metacognition Index, while fALFF in HC mediates the TO through behavior regulation index (BRI). Further analysis revealed that GMV in HC influences fALFF, which further modulates BRI and subsequently affects hyperactivity-impulsivity score. To conclude, structural brain abnormalities in the DMN in ADHD may affect local brain function in the prefrontal-striatal-cerebellar circuit, making it difficult to regulate EF in terms of inhibit, shift, and emotional control, and ultimately leading to hyperactive-impulsive behavior.
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Affiliation(s)
- Yuan Feng
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No.19, Xinjiekouwai Street, Haidian District, Beijing, 100088, China
| | - Yu Zhu
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Xiaojie Guo
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Xiangsheng Luo
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Chen Dang
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No.19, Xinjiekouwai Street, Haidian District, Beijing, 100088, China.
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China.
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Han S, Zheng Q, Zheng Z, Su J, Liu X, Shi C, Li B, Zhang X, Zhang M, Yu Q, Hou Z, Li T, Zhang B, Lin Y, Wen G, Deng Y, Liu K, Xu K. Exosomal miR-1202 mediates Brodmann Area 44 functional connectivity changes in medication-free patients with major depressive disorder: An fMRI study. J Affect Disord 2024; 356:470-476. [PMID: 38608766 DOI: 10.1016/j.jad.2024.04.042] [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: 12/27/2023] [Revised: 03/28/2024] [Accepted: 04/09/2024] [Indexed: 04/14/2024]
Abstract
Previous large-sample postmortem study revealed that the expression of miR-1202 in brain tissues from Brodmann area 44 (BA44) was dysregulated in patients with major depressive disorder (MDDs). However, the specific in vivo neuropathological mechanism of miR-1202 as well as its interplay with BA44 circuits in the depressed brain are still unclear. Here, we performed a case-control study with imaging-genetic approach based on resting-state functional magnetic resonance imaging (MRI) data and miR-1202 quantification from 110 medication-free MDDs and 102 healthy controls. Serum-derived circulating exosomes that readily cross the blood-brain barrier were isolated to quantify miR-1202. For validation, repeated MR scans were performed after a six-week follow-up of antidepressant treatment on a cohort of MDDs. Voxelwise factorial analysis revealed two brain areas (including the striatal-thalamic region) in which the effect of depression on the functional connectivity with BA44 was significantly dependent on the expression level of exosomal miR-1202. Moreover, longitudinal change of the BA44 connectivity with the striatal-thalamic region in MDDs after antidepressant treatment was found to be significantly related to the level of miR-1202 expression. These findings revealed that the in vivo neuropathological effect of miR-1202 dysregulation in depression is possibly exerted by mediating neural functional abnormalities in BA44-striatal-thalamic circuits.
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Affiliation(s)
- Shuguang Han
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China; Research Center for Psychological Crisis Prevention and Intervention of College Students in Jiangsu Province, Jiangsu, China; Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China
| | - Qingtong Zheng
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Zixuan Zheng
- School of Anesthesiology, Xuzhou Medical University, Xuzhou, China
| | - Jie Su
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Xiaohua Liu
- Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China
| | - Changzhou Shi
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Bo Li
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Xuanxuan Zhang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Minghao Zhang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Qian Yu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Ziwei Hou
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Ting Li
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Bin Zhang
- Department of Psychiatry, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yong Lin
- The Fifth Affiliated Hospital of Sun-Yat Sen University, Sun-Yat Sen University, Zhuhai, China; The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Ge Wen
- Medical Imaging Department, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanjia Deng
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China; Research Center for Psychological Crisis Prevention and Intervention of College Students in Jiangsu Province, Jiangsu, China.
| | - Kai Liu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China; Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China.
| | - Kai Xu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China; Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China.
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Chen T, Lin Q, Gong C, Zhao H, Peng R. Research Progress on Micro (Nano)Plastics Exposure-Induced miRNA-Mediated Biotoxicity. TOXICS 2024; 12:475. [PMID: 39058127 PMCID: PMC11280978 DOI: 10.3390/toxics12070475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 06/28/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024]
Abstract
Micro- and nano-plastics (MNPs) are ubiquitously distributed in the environment, infiltrate organisms through multiple pathways, and accumulate, thus posing potential threats to human health. MNP exposure elicits changes in microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), thereby precipitating immune, neurological, and other toxic effects. The investigation of MNP exposure and its effect on miRNA expression has garnered increasing attention. Following MNP exposure, circRNAs serve as miRNA sponges by modulating gene expression, while lncRNAs function as competing endogenous RNAs (ceRNAs) by fine-tuning target gene expression and consequently impacting protein translation and physiological processes in cells. Dysregulated miRNA expression mediates mitochondrial dysfunction, inflammation, and oxidative stress, thereby increasing the risk of neurodegenerative diseases, cardiovascular diseases, and cancer. This tract, blood, urine, feces, placenta, and review delves into the biotoxicity arising from dysregulated miRNA expression due to MNP exposure and addresses the challenges encountered in this field. This study provides novel insights into the connections between MNPs and disease risk.
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Affiliation(s)
| | | | | | - Haiyang Zhao
- Institute of Life Sciences & Biomedicine Collaborative Innovation Center of Zhejiang Province, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China; (T.C.); (Q.L.); (C.G.)
| | - Renyi Peng
- Institute of Life Sciences & Biomedicine Collaborative Innovation Center of Zhejiang Province, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China; (T.C.); (Q.L.); (C.G.)
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Fouladivanda M, Iraji A, Wu L, van Erp TG, Belger A, Hawamdeh F, Pearlson GD, Calhoun VD. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.13.553101. [PMID: 38853973 PMCID: PMC11160563 DOI: 10.1101/2023.08.13.553101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
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Affiliation(s)
- Mahshid Fouladivanda
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
| | - Lei Wu
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Theodorus G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior School of Medicine, University of California, Irvine, CA, USA
| | - Aysenil Belger
- Department of Psychiatry Director, Neuroimaging Research in Psychiatry Director, Clinical Translational Core, UNC Intellectual and Developmental Disabilities Research Center, University of North Carolina, Chapel Hill, NC, USA
| | - Faris Hawamdeh
- Center for Disaster Informatics and Computational Epidemiology (DICE), Georgia State University, Atlanta, GA, USA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, Department of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, USA
| | - Vince D. Calhoun
- Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Georgia State University, Atlanta, GA, USA
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Liu X, Dong L, Jiang Z, Song M, Yan P. Identifying the differentially expressed peripheral blood microRNAs in psychiatric disorders: a systematic review and meta-analysis. Front Psychiatry 2024; 15:1390366. [PMID: 38827444 PMCID: PMC11140110 DOI: 10.3389/fpsyt.2024.1390366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/25/2024] [Indexed: 06/04/2024] Open
Abstract
Background Evidence has suggested that microRNAs (miRNAs) may play an important role in the pathogenesis of psychiatric disorders (PDs), but the results remain inconclusive. We aimed to identify specific differentially expressed miRNAs and their overlapping miRNA expression profiles in schizophrenia (SZ), major depression disorder (MDD), and bipolar disorder (BD), the three major PDs. Methods The literatures up to September 30, 2023 related to peripheral blood miRNAs and PDs were searched and screened from multiple databases. The differences in miRNA levels between groups were illustrated by the standardized mean difference (SMD) and 95% confidence interval (95% CI). Results In total, 30 peripheral blood miRNAs were included in the meta-analysis, including 16 for SZ, 12 for MDD, and 2 for BD, each was reported in more than 3 independent studies. Compared with the control group, miR-181b-5p, miR-34a-5p, miR-195-5p, miR-30e-5p, miR-7-5p, miR-132-3p, miR-212-3p, miR-206, miR-92a-3p and miR-137-3p were upregulated in SZ, while miR-134-5p, miR-107 and miR-99b-5p were downregulated. In MDD, miR-124-3p, miR-132-3p, miR-139-5p, miR-182-5p, miR-221-3p, miR-34a-5p and miR-93-5p were upregulated, while miR-144-5p and miR-135a-5p were downregulated. However, we failed to identify statistically differentially expressed miRNAs in BD. Interestingly, miR-132-3p and miR-34a-5p were upregulated in both SZ and MDD. Conclusions Our study identified 13 differentially expressed miRNAs in SZ and 9 in MDD, among which miR-132-3p and miR-34a-5p were upregulated in both SZ and MDD by systematically analyzing qualified studies. These miRNAs may be used as potential biomarkers for the diagnosis of SZ and MDD in the future. Systematic Review Registration http://www.crd.york.ac.uk/PROSPERO, identifier CRD42023486982.
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Affiliation(s)
- Xiaoyan Liu
- Department of Psychiatry, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Liying Dong
- Internal Medicine of Traditional Chinese Medicine, The 4th Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhaowei Jiang
- Internal Medicine of Traditional Chinese Medicine, The 4th Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Mingfen Song
- Molecular Biology Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Pan Yan
- Molecular Biology Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Li YT, Zhang C, Han JC, Shang YX, Chen ZH, Cui GB, Wang W. Neuroimaging features of cognitive impairments in schizophrenia and major depressive disorder. Ther Adv Psychopharmacol 2024; 14:20451253241243290. [PMID: 38708374 PMCID: PMC11070126 DOI: 10.1177/20451253241243290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 03/14/2024] [Indexed: 05/07/2024] Open
Abstract
Cognitive dysfunctions are one of the key symptoms of schizophrenia (SZ) and major depressive disorder (MDD), which exist not only during the onset of diseases but also before the onset, even after the remission of psychiatric symptoms. With the development of neuroimaging techniques, these non-invasive approaches provide valuable insights into the underlying pathogenesis of psychiatric disorders and information of cognitive remediation interventions. This review synthesizes existing neuroimaging studies to examine domains of cognitive impairment, particularly processing speed, memory, attention, and executive function in SZ and MDD patients. First, white matter (WM) abnormalities are observed in processing speed deficits in both SZ and MDD, with distinct neuroimaging findings highlighting WM connectivity abnormalities in SZ and WM hyperintensity caused by small vessel disease in MDD. Additionally, the abnormal functions of prefrontal cortex and medial temporal lobe are found in both SZ and MDD patients during various memory tasks, while aberrant amygdala activity potentially contributes to a preference to negative memories in MDD. Furthermore, impaired large-scale networks including frontoparietal network, dorsal attention network, and ventral attention network are related to attention deficits, both in SZ and MDD patients. Finally, abnormal activity and volume of the dorsolateral prefrontal cortex (DLPFC) and abnormal functional connections between the DLPFC and the cerebellum are associated with executive dysfunction in both SZ and MDD. Despite these insights, longitudinal neuroimaging studies are lacking, impeding a comprehensive understanding of cognitive changes and the development of early intervention strategies for SZ and MDD. Addressing this gap is critical for advancing our knowledge and improving patient prognosis.
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Affiliation(s)
- Yu-Ting Li
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Chi Zhang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
- Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Jia-Cheng Han
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Yu-Xuan Shang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Zhu-Hong Chen
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Guang-Bin Cui
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi’an 710038, Shaanxi, China
| | - Wen Wang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi’an 710038, Shaanxi, China
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9
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He C, Wang Q, Fan D, Liu X, Bai Y, Zhang H, Zhang H, Yao H, Zhang Z, Xie C. MicroRNA-124 influenced depressive symptoms via large-scale brain connectivity in major depressive disorder patients. Asian J Psychiatr 2024; 95:104025. [PMID: 38522164 DOI: 10.1016/j.ajp.2024.104025] [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: 08/21/2023] [Revised: 03/17/2024] [Accepted: 03/21/2024] [Indexed: 03/26/2024]
Abstract
This study aimed to investigate the neurobiological mechanisms by which microRNA 124 (miR-124) is involved in major depressive disorder (MDD). We enrolled 53 untreated MDD patients and 38 healthy control (HC) subjects who completed behavior assessments and resting-state functional MRI (rs-fMRI) scans. MiR-124 expression levels were detected in the peripheral blood of all participants. We determined that miR-124 levels could influence depressive symptoms via disrupted large-scale intrinsic intra- and internetwork connectivity, including the default mode network (DMN)-DMN, dorsal attention network (DAN)-salience network (SN), and DAN-cingulo-opercular network (CON). This study deepens our understanding of how miR-124 dysregulation contributes to depression.
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Affiliation(s)
- Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China
| | - Xinyi Liu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China
| | - Ying Bai
- Department of Pharmacology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Life Sciences, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China
| | - Haisan Zhang
- Department of Radiology, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, China; Xinxiang Key Laboratory of Multimodal Brain Imaging, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, China
| | - Hongxing Zhang
- Department of Psychiatry, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002, China; Psychology School of Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Honghong Yao
- Department of Pharmacology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Life Sciences, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210096, China.
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Wang K, Fu Y, Li L, Zhang L, Huang M, Yan W, Shan X, Yan Z, Lu Y. Gut Microbiota Moderates Multimodal Brain Structure-Function Integration and Behavioral Cognition in Growth Hormone Deficient Children. Neuroendocrinology 2024; 114:698-708. [PMID: 38679006 DOI: 10.1159/000539097] [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: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
INTRODUCTION Previous brain studies of growth hormone deficiency (GHD) often used single-modal neuroimaging, missing the complexity captured by multimodal data. Growth hormone affects gut microbiota and metabolism in GHD. However, from a gut-brain axis (GBA) perspective, the relationship between abnormal GHD brain development and microbiota alterations remains unclear. The ultimate goal is to uncover the manifestations underlying GBA abnormalities in GHD and idiopathic short stature (ISS). METHODS Participants included 23 GHD and 25 ISS children. The fusion independent component analysis was applied to integrate multimodal brain data (high-resolution structural, diffusion tensor, and resting-state functional MRI) covering regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and white matter fractional anisotropy (FA). Gut microbiome diversity and metabolites were analyzed using 16S sequencing and proton nuclear magnetic resonance (1H-NMR). Associations between multimodal neuroimaging and cognition were assessed using moderation analysis. RESULTS Six independent components (IC) of ReHo, ALFF, and FA differed significantly between GHD and ISS patients, with three functional components linked to the processing speed index. GHD individuals showed higher levels of acetate, nicotinate, and lysine in microbiota metabolism. Higher alpha diversity in GHD strengthened connections between ReHo-IC1, ReHo-IC5, ALFF-IC1, and the processing speed index, while increasing agathobacter levels in ISS weakened the link between ALFF-IC1 and the speech comprehension index. CONCLUSIONS Our findings uncover differing brain structure and functional fusion in GHD, alongside microbiota metabolism of short-chain fatty acids. Additionally, microbiome influences connections between neuroimaging and cognition, offering insight into diverse GBA patterns in GHD and ISS, enhancing our understanding of the disease's pathophysiology and interventions.
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Affiliation(s)
- Keren Wang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuchuan Fu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lan Li
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lingfeng Zhang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mei Huang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weihao Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaoou Shan
- Department of Pediatric Endocrinology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Structural Malformations in Children of Zhejiang Province, Wenzhou, China
- Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China
| | - Yi Lu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Structural Malformations in Children of Zhejiang Province, Wenzhou, China
- Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China
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11
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Xu M, Li X, Teng T, Huang Y, Liu M, Long Y, Lv F, Zhi D, Li X, Feng A, Yu S, Calhoun V, Zhou X, Sui J. Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression. JAMA Netw Open 2024; 7:e241933. [PMID: 38470418 PMCID: PMC10933730 DOI: 10.1001/jamanetworkopen.2024.1933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/13/2024] Open
Abstract
Importance Adolescent major depressive disorder (MDD) is associated with serious adverse implications for brain development and higher rates of self-injury and suicide, raising concerns about its neurobiological mechanisms in clinical neuroscience. However, most previous studies regarding the brain alterations in adolescent MDD focused on single-modal images or analyzed images of different modalities separately, ignoring the potential role of aberrant interactions between brain structure and function in the psychopathology. Objective To examine alterations of structural and functional connectivity (SC-FC) coupling in adolescent MDD by integrating both diffusion magnetic resonance imaging (MRI) and resting-state functional MRI data. Design, Setting, and Participants This cross-sectional study recruited participants aged 10 to 18 years from January 2, 2020, to December 28, 2021. Patients with first-episode MDD were recruited from the outpatient psychiatry clinics at The First Affiliated Hospital of Chongqing Medical University. Healthy controls were recruited by local media advertisement from the general population in Chongqing, China. The sample was divided into 5 subgroup pairs according to different environmental stressors and clinical characteristics. Data were analyzed from January 10, 2022, to February 20, 2023. Main Outcomes and Measures The SC-FC coupling was calculated for each brain region of each participant using whole-brain SC and FC. Primary analyses included the group differences in SC-FC coupling and clinical symptom associations between SC-FC coupling and participants with adolescent MDD and healthy controls. Secondary analyses included differences among 5 types of MDD subgroups: with or without suicide attempt, with or without nonsuicidal self-injury behavior, with or without major life events, with or without childhood trauma, and with or without school bullying. Results Final analyses examined SC-FC coupling of 168 participants with adolescent MDD (mean [mean absolute deviation (MAD)] age, 16.0 [1.7] years; 124 females [73.8%]) and 101 healthy controls (mean [MAD] age, 15.1 [2.4] years; 61 females [60.4%]). Adolescent MDD showed increased SC-FC coupling in the visual network, default mode network, and insula (Cohen d ranged from 0.365 to 0.581; false discovery rate [FDR]-corrected P < .05). Some subgroup-specific alterations were identified via subgroup analyses, particularly involving parahippocampal coupling decrease in participants with suicide attempt (partial η2 = 0.069; 90% CI, 0.025-0.121; FDR-corrected P = .007) and frontal-limbic coupling increase in participants with major life events (partial η2 ranged from 0.046 to 0.068; FDR-corrected P < .05). Conclusions and Relevance Results of this cross-sectional study suggest increased SC-FC coupling in adolescent MDD, especially involving hub regions of the default mode network, visual network, and insula. The findings enrich knowledge of the aberrant brain SC-FC coupling in the psychopathology of adolescent MDD, underscoring the vulnerability of frontal-limbic SC-FC coupling to external stressors and the parahippocampal coupling in shaping future-minded behavior.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yicheng Long
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Hunan, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dongmei Zhi
- International Data Group (IDG)/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Sui
- International Data Group (IDG)/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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12
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Wei J, Wang M, Dou Y, Wang Y, Du Y, Zhao L, Ni R, Yang X, Ma X. Dysconnectivity of the brain functional network and abnormally expressed peripheral transcriptional profiles in patients with anxious depression. J Psychiatr Res 2024; 171:316-324. [PMID: 38340698 DOI: 10.1016/j.jpsychires.2024.01.021] [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: 10/26/2023] [Revised: 12/18/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is a heterogeneous mental disorder, and accompanying anxiety symptoms, known as anxious depression (AD), are the most common subtype. However, the pathophysiology of AD may be distinct in depressed patients without anxiety (NAD) and remains unknown. This study aimed to investigate the relationship between functional connectivity and peripheral transcriptional profiles in patients with AD and NAD. METHODS Functional imaging data were collected to identify differences in functional networks among patients with AD (n = 66), patients with NAD (n = 115), and healthy controls (HC, n = 200). The peripheral transcriptional data were clustered as co-expression modules, and their associations with AD, AND, and HC were analyzed. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses of the genes in the significant module were performed. Correlation analysis was performed to identify functional network-associated gene co-expression modules. RESULTS A network was identified which consisted of 23 nodes and 28 edges that were significantly different among three sample groups. The regions of the network were located in temporal and occipital lobe. Two gene co-expression modules were shown to be associated with NAD, and one of which was correlated with the disrupted network in the AD group. The biological function of this module was enriched in immune regulation pathways. CONCLUSION The results suggested that immune-related mechanisms were associated with functional networks in AD.
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Affiliation(s)
- Jinxue Wei
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Yikai Dou
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Yue Du
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Rongjun Ni
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Xiao Yang
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China.
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13
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Musazzi L, Mingardi J, Ieraci A, Barbon A, Popoli M. Stress, microRNAs, and stress-related psychiatric disorders: an overview. Mol Psychiatry 2023; 28:4977-4994. [PMID: 37391530 DOI: 10.1038/s41380-023-02139-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 05/23/2023] [Accepted: 06/16/2023] [Indexed: 07/02/2023]
Abstract
Stress is a major risk factor for psychiatric disorders. During and after exposure to stressors, the stress response may have pro- or maladaptive consequences, depending on several factors related to the individual response and nature of the stressor. However, the mechanisms mediating the long-term effects of exposure to stress, which may ultimately lead to the development of stress-related disorders, are still largely unknown. Epigenetic mechanisms have been shown to mediate the effects of the environment on brain gene expression and behavior. MicroRNAs, small non-coding RNAs estimated to control the expression of about 60% of all genes by post-transcriptional regulation, are a fundamental epigenetic mechanism. Many microRNAs are expressed in the brain, where they work as fine-tuners of gene expression, with a key role in the regulation of homeostatic balance, and a likely influence on pro- or maladaptive brain changes. Here we have selected a number of microRNAs, which have been strongly implicated as mediators of the effects of stress in the brain and in the development of stress-related psychiatric disorders. For all of them recent evidence is reported, obtained from rodent stress models, manipulation of microRNAs levels with related behavioral changes, and clinical studies of stress-related psychiatric disorders. Moreover, we have performed a bioinformatic analysis of the predicted brain-expressed target genes of the microRNAs discussed, and found a central role for mechanisms involved in the regulation of synaptic function. The complex regulatory role of microRNAs has suggested their use as biomarkers for diagnosis and treatment response, as well as possible therapeutic drugs. While, microRNA-based diagnostics have registered advancements, particularly in oncology and other fields, and many biotech companies have launched miRNA therapeutics in their development pipeline, the development of microRNA-based tests and drugs for brain disorders is comparatively slower.
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Affiliation(s)
- Laura Musazzi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Jessica Mingardi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Alessandro Ieraci
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Italy
- Molecular Pharmacology, Cellular and Behavioral Physiology; Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy
| | - Alessandro Barbon
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Maurizio Popoli
- Laboratory of Neuropsychopharmacology and Functional Neurogenomics, Dipartimento di Scienze Farmaceutiche, Università Degli Studi di Milano, Milano, Italy.
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. PSYCHORADIOLOGY 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States
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Shuai J, Gao M, Zou Q, He Y. Association between vitamin D, depression, and sleep health in the National Health and Nutrition Examination Surveys: a mediation analysis. Nutr Neurosci 2023:1-8. [PMID: 37962262 DOI: 10.1080/1028415x.2023.2279363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
OBJECTIVE This study aimed to assess the association between vitamin D and sleep health and to investigate whether depression could mediate this relationship. METHODS A cross-sectional analysis was performed using the 2005-2014 US National Health and Nutrition Examination Survey (NHANES) data. The logistic regression models were conducted to evaluate association of serum vitamin D concentrations with sleep health and depression. Mediation analyses were conducted to investigate the mediated effects of depression on the association of vitamin D with sleep health. RESULTS In multivariate logistic models, vitamin D was found to be negatively associated with an increased risk of poor sleep health, with an odds ratio (OR) of vitamin D deficiency versus sufficiency was 1.256 (95% CI = 1.084-1.455). Additionally, univariate logistic models showed that vitamin D was also negatively associated with depression risk (vitamin D deficiency vs. sufficiency: OR = 1.699, 95% CI = (1.373-2.103). Further mediation analyses showed that the association of vitamin D with sleep health was mediated by depression, with the mediating effects of depression accounted for 44.56% of the total effects. CONCLUSION Vitamin D affects sleep health directly and indirectly through depression. The results suggest that interventions increasing intake of vitamin D should be prioritized to promote sleep health of persons with or at risk of depression.
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Affiliation(s)
- Jingliang Shuai
- Department of Epidemiology and Health Statistics, School of Public Health, Xiangya School of Public Health, Central South University, Changsha, People's Republic of China
| | - Mengqi Gao
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Qi Zou
- Medical Department, The First Hospital of Nanchang, Nanchang, People's Republic of China
| | - Youming He
- Department of Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, People's Republic of China
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Wang Y, Yan Y, Wei J, Yang X, Wang M, Zhao L, Dou Y, Du Y, Wang Q, Ma X. Down-regulated miR-16-2 in peripheral blood is positively correlated with decreased bilateral insula volume in patients with major depressive disorder. J Affect Disord 2023; 338:137-143. [PMID: 37245547 DOI: 10.1016/j.jad.2023.05.068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 05/30/2023]
Abstract
BACKGROUND The downregulated microRNA-16-2-3p (miR-16-2) had been believed to be associated with major depressive disorder (MDD). This study aimed to investigate the potential of miR-16-2 as a biomarker for MDD by analysing its expression levels, furthermore, to explore the relationship between miR-16-2, clinical symptoms and alterations in grey matter volume (GMV) in MDD patients. METHODS Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression level of miR-16-2 in 48 drug-naïve patients with MDD and 50 healthy controls (HCs). We conducted ROC curve analysis to assess the diagnostic value of miR-16-2 in MDD, and evaluated its ability to predict antidepressant response by reassessing depressive and anxiety symptoms after treatment. Voxel-based morphometry was carried out to explore alterations in regional GMV that may be associated with MDD. Pearson analysis was used to explore the relationship between miR-16-2 expression, clinical symptoms, and altered GMV in the brains of MDD patients. RESULTS We found that MDD patients had significantly downregulated miR-16-2 expression, which was negatively correlated with HAMD-17 and HAMA-14 scores, and had great diagnostic value for MDD (AUC = 0.806, 95 % CI: 0.721-0.891). In addition, MDD patients had significantly lower GMV in the bilateral insula and left superior temporal gyrus (STG_L) than HCs. GMV reduction in the bilateral insula was found to be correlated with miR-16-2 expression. CONCLUSIONS Our findings support the potential value of miRNA-16-2 as a biomarker for MDD. It also suggests that miRNA-16-2 may be associated with abnormal insula and involved in pathophysiological mechanisms of MDD.
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Affiliation(s)
- Yu Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yushun Yan
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jinxue Wei
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China; National Clinical Research Center on Mental Disorders (Changsha) of China, Changsha, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Min Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China; National Clinical Research Center on Mental Disorders (Changsha) of China, Changsha, China
| | - Yikai Dou
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yue Du
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China; National Clinical Research Center on Mental Disorders (Changsha) of China, Changsha, China.
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17
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Ma X, Li Q, Chen G, Xie J, Wu M, Meng F, Liu J, Liu Y, Zhao D, Wang W, Wang D, Liu C, Dai J, Li C, Cui M. Role of Hippocampal miR-132-3p in Modifying the Function of Protein Phosphatase Mg2+/Mn2+-dependent 1 F in Depression. Neurochem Res 2023:10.1007/s11064-023-03926-8. [PMID: 37036545 DOI: 10.1007/s11064-023-03926-8] [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: 09/13/2022] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 04/11/2023]
Abstract
Depression is a common, severe, and debilitating psychiatric disorder of unclear etiology. Our previous study has shown that protein phosphatase Mg2+/Mn2+-dependent 1F (PPM1F) in the hippocampal dentate gyrus (DG) displays significant regulatory effects in depression-related behaviors. miR-132-3p plays a potential role in the etiology of depression. This study explored the effect of miR-132-3p on the onset of depression and the possible underlying mechanism for modulating PPM1F expression during the pathology of depression. We found that miR-132-3p levels in the hippocampus of depressed mice subjected to chronic unpredictable stress (CUS) were dramatically reduced, which were correlated with depression-related behaviors. Knockdown of miR-132-3p in hippocampal DG resulted in depression-related phenotypes and increased susceptibility to stress. miR-132-3p overexpression in hippocampal DG alleviated CUS-induced depression-related performance. We then screened out the potential target genes of miR-132-3p, and we found that the expression profiles of sterol regulatory element-binding transcription factor 1 (Srebf1) and forkhead box protein O3a (FOXO3a) were positively correlated with PPM1F under the condition of miR-132-3p knockdown. Finally, as anticipated, we revealed that the activities of Ca2+/calmodulin-dependent protein kinase II (CAMKII) and adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) were reduced, which underlies the target signaling pathway of PPM1F. In conclusion, our study suggests that miR-132-3p was designed to regulate depression-related behaviors by indirectly regulating PPM1F and targeting Srebf1 and FOXO3a, which have been linked to the pathogenesis and treatment of depression.
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Affiliation(s)
- Xiangxian Ma
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Qiongyu Li
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Guanhong Chen
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
- The first clinical medical college, Binzhou Medical University, Yantai, Shandong, China
| | - Junjie Xie
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
- The first clinical medical college, Binzhou Medical University, Yantai, Shandong, China
| | - Min Wu
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
- Department of Neurosurgery, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Fantao Meng
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Jing Liu
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Yong Liu
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
- Department of Physiology, Binzhou Medical University, Shandong, China
| | - Di Zhao
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Wentao Wang
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Dan Wang
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Cuilan Liu
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Juanjuan Dai
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Chen Li
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China.
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China.
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China.
| | - Minghu Cui
- Department of Psychology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China.
- Medical research center, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Binzhou, Shandong, 256603, China.
- Institute for Metabolic & Neuropsychiatric Disorders, Binzhou Medical University Hospital, Binzhou, Shandong, China.
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18
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Yang X, Kumar P, Wang M, Zhao L, Du Y, Zhang BY, Qi S, Sui J, Li T, Ma X. Antidepressant treatment-related brain activity changes in remitted major depressive disorder. Psychiatry Res Neuroimaging 2023; 330:111601. [PMID: 36724678 DOI: 10.1016/j.pscychresns.2023.111601] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 12/21/2022] [Accepted: 01/24/2023] [Indexed: 01/29/2023]
Abstract
Recent evidence has shown that some brain regions are core hubs and play a key role in the treatment of depression. Twenty-five unmedicated patients with major depressive disorder (MDD) were included, and telephone follow-up was performed at 8, 24, and 48 weeks after enrollment. After reaching clinical remission, they were scheduled for a second magnetic resonance imaging scan and clinical evaluation. Thirty-one healthy controls were also investigated. The intrinsic functional connectivity (degree centrality) of each participant was mapped using a computationally efficient approach. Then, functional connectivity of patients was calculated between the identified regions of interest by degree centrality analysis and every voxel. Later, linear regression analysis was used to identify potential variables predictive of an improvement in disease severity. The prominent hubs identified by degree centrality analysis included the cerebellum, inferior temporal gyrus, lingual gyrus, dorsal medial prefrontal cortex (DMPFC), and dorsal lateral prefrontal cortex. We also found that the increased degree centrality of DMPFC was associated with improvement in depressive symptoms. The brain activity associated with antidepressant effects, especially brain connectivity changes in the left DMPFC, can potentially be used to monitor treatment response and predict treatment outcomes.
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Affiliation(s)
- Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Poornima Kumar
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, United States of America; Department of Psychiatry, Harvard Medical School, United States of America
| | - Min Wang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Yue Du
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Belinda Y Zhang
- School of Nursing and Health Professions, University of San Francisco, CA, United States
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], 30303, Atlanta, GA, United States
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, 100190, Beijing, China
| | - Tao Li
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China.
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19
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Ding R, Su D, Zhao Q, Wang Y, Wang JY, Lv S, Ji X. The role of microRNAs in depression. Front Pharmacol 2023; 14:1129186. [PMID: 37063278 PMCID: PMC10090555 DOI: 10.3389/fphar.2023.1129186] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/21/2023] [Indexed: 03/31/2023] Open
Abstract
Major depressive disorder (MDD) is a psychiatric disorder with increasing prevalence worldwide. It is a leading cause of disability and suicide, severely affecting physical and mental health. However, the study of depression remains at an exploratory stage in terms of diagnostics and treatment due to the complexity of its pathogenesis. MicroRNAs are endogenous short-stranded non-coding RNAs capable of binding to the 3’untranslated region of mRNAs. Because of their ability to repress translation process of genes and are found at high levels in brain tissues, investigation of their role in depression has gradually increased recently. This article summarizes recent research progress on the relationship between microRNAs and depression. The microRNAs play a regulatory role in the pathophysiology of depression, involving dysregulation of monoamines, abnormalities in neuroplasticity and neurogenesis, hyperactivity of the HPA axis, and dysregulation of inflammatory responses. These microRNAs might provide new clue for the diagnosis and treatment of MDD, and the development of antidepressant drugs.
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Affiliation(s)
- Ruidong Ding
- Institute of Molecular Medicine, Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Dingyuan Su
- Institute of Molecular Medicine, Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Qian Zhao
- Institute of Molecular Medicine, Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Yu Wang
- Institute of Molecular Medicine, Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Jia-Yi Wang
- San-Quan College, Xinxiang Medical University, Xinxiang, Henan, China
| | - Shuangyu Lv
- Institute of Molecular Medicine, Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
- *Correspondence: Shuangyu Lv, ; Xinying Ji,
| | - Xinying Ji
- Institute of Molecular Medicine, Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
- Kaifeng Key Laboratory for Infectious Diseases and Biosafety, Kaifeng, Henan, China
- Faculty of Basic Medical Subjects, Shu-Qing Medical College of Zhengzhou, Zhengzhou, Henan, China
- *Correspondence: Shuangyu Lv, ; Xinying Ji,
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20
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Gong W, Bai S, Zheng YQ, Smith SM, Beckmann CF. Supervised Phenotype Discovery From Multimodal Brain Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:834-849. [PMID: 36318559 DOI: 10.1109/tmi.2022.3218720] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects' demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.
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21
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Ryan KM, Smyth P, Blackshields G, Kranaster L, Sartorius A, Sheils O, McLoughlin DM. Electroconvulsive Stimulation in Rats Induces Alterations in the Hippocampal miRNome: Translational Implications for Depression. Mol Neurobiol 2023; 60:1150-1163. [PMID: 36414911 DOI: 10.1007/s12035-022-03131-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 11/09/2022] [Indexed: 11/24/2022]
Abstract
MicroRNAs (miRNAs) may contribute to the development of depression and its treatment. Here, we used the hypothesis-neutral approach of next-generation sequencing (NGS) to gain comprehensive understanding of the effects of a course of electroconvulsive stimulation (ECS), the animal model equivalent of electroconvulsive therapy (ECT), on rat hippocampal miRNAs. Significant differential expression (p < 0.001) of six hippocampal miRNAs was noted following NGS, after correcting for multiple comparisons. Three of these miRNAs were upregulated (miR-132, miR-212, miR-331) and three downregulated (miR-204, miR-483, miR-301a). qRT-PCR confirmed significant changes in four of the six miRNAs (miR-132, miR-212, miR-204, miR-483). miR-483 was also significantly reduced in frontal cortex, though no other significant alterations were noted in frontal cortex, cerebellum, or whole blood. Assessing the translatability of the results, miR-132 and miR-483 were significantly reduced in whole blood samples from medicated patients with depression (n = 50) compared to healthy controls (n = 45), though ECT had no impact on miRNA levels. Notably, pre-ECT miR-204 levels moderately positively correlated with depression severity at baseline and moderately negatively correlated with mood score reduction post-ECT. miRNAs were also examined in cerebrospinal fluid and serum from a separate cohort of patients (n = 8) treated with ECT; no significant changes were noted post-treatment. However, there was a large positive correlation between changes in miR-212 and mood score post-ECT in serum. Though replication studies using larger sample sizes are required, alterations in miRNA expression may be informative about the mechanism of action of ECS/ECT and in turn might give insight into the neurobiology of depression.
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Affiliation(s)
- Karen M Ryan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland. .,Department of Psychiatry, Trinity College Dublin, St Patrick's University Hospital, Dublin 8, Ireland.
| | - Paul Smyth
- Department of Histopathology, Trinity Translational Medicine Institute, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Gordon Blackshields
- Department of Histopathology, Trinity Translational Medicine Institute, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Laura Kranaster
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty, Mannheim/Heidelberg University, Mannheim, Germany
| | - Alexander Sartorius
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty, Mannheim/Heidelberg University, Mannheim, Germany
| | - Orla Sheils
- Department of Histopathology, Trinity Translational Medicine Institute, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Declan M McLoughlin
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.,Department of Psychiatry, Trinity College Dublin, St Patrick's University Hospital, Dublin 8, Ireland
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22
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Liang C, Pearlson G, Bustillo J, Kochunov P, Turner JA, Wen X, Jiang R, Fu Z, Zhang X, Li K, Xu X, Zhang D, Qi S, Calhoun VD. Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders. Schizophr Bull 2023; 49:172-184. [PMID: 36305162 PMCID: PMC9810025 DOI: 10.1093/schbul/sbac158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective.
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Affiliation(s)
- Chuang Liang
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Godfrey Pearlson
- Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Juan Bustillo
- Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Xuyun Wen
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rongtao Jiang
- Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Xiao Zhang
- Department of Psychiatry, Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Daoqiang Zhang
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Shile Qi
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA
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23
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Matraszek-Gawron R, Chwil M, Terlecki K, Skoczylas MM. Current Knowledge of the Antidepressant Activity of Chemical Compounds from Crocus sativus L. Pharmaceuticals (Basel) 2022; 16:58. [PMID: 36678554 PMCID: PMC9860663 DOI: 10.3390/ph16010058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 01/03/2023] Open
Abstract
Psychotropic effect of Crocus sativus L. (family Iridaceae) biologically active chemical compounds are quite well documented and they can therefore be used in addition to the conventional pharmacological treatment of depression. This systematic review on antidepressant compounds in saffron crocus and their mechanisms of action and side effects is based on publications released between 1995−2022 and data indexed in 15 databases under the following search terms: antidepressant effect, central nervous system, Crocus sativus, cognitive impairement, crocin, crocetin, depression, dopamine, dopaminergic and serotonergic systems, picrocrocin, phytotherapy, neurotransmitters, safranal, saffron, serotonin, and biologically active compounds. The comparative analysis of the publications was based on 414 original research papers. The investigated literature indicates the effectiveness and safety of aqueous and alcoholic extracts and biologically active chemical compounds (alkaloids, anthocyanins, carotenoids, flavonoid, phenolic, saponins, and terpenoids) isolated from various organs (corms, leaves, flower petal, and stigmas) in adjuvant treatment of depression and anxiety. Monoamine reuptake inhibition, N-methyl-d-aspartate (NMDA) receptor antagonism, and gamma-aminobutyric acid (GABA)-α agonism are the main proposed mechanism of the antidepressant action. The antidepressant and neuroprotective effect of extract components is associated with their anti-inflammatory and antioxidant activity. The mechanism of their action, interactions with conventional drugs and other herbal preparations and the safety of use are not fully understood; therefore, further detailed research in this field is necessary. The presented results regarding the application of C. sativus in phytotherapy are promising in terms of the use of herbal preparations to support the treatment of depression. This is particularly important given the steady increase in the incidence of this disease worldwide and social effects.
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Affiliation(s)
- Renata Matraszek-Gawron
- Department of Botany and Plant Physiology, University of Life Sciences in Lublin, Akademicka 15 Street, 20-950 Lublin, Poland
| | - Mirosława Chwil
- Department of Botany and Plant Physiology, University of Life Sciences in Lublin, Akademicka 15 Street, 20-950 Lublin, Poland
| | - Karol Terlecki
- Department of Vascular Surgery and Angiology, Medical University of Lublin, Racławickie 1 Street, 20-059 Lublin, Poland
| | - Michał Marian Skoczylas
- Department of Diagnostic Imaging and Interventional Radiology, Pomeranian Medical University in Szczecin, Unii Lubelskiej 1 Street, 71-252 Szczecin, Poland
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24
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Qi S, Calhoun VD, Zhang D, Miller J, Deng ZD, Narr KL, Sheline Y, McClintock SM, Jiang R, Yang X, Upston J, Jones T, Sui J, Abbott CC. Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder. BMC Med 2022; 20:477. [PMID: 36482369 PMCID: PMC9733153 DOI: 10.1186/s12916-022-02678-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Although electroconvulsive therapy (ECT) is an effective treatment for depression, ECT cognitive impairment remains a major concern. The neurobiological underpinnings and mechanisms underlying ECT antidepressant and cognitive impairment effects remain unknown. This investigation aims to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks and assesses whether they are associated with the ECT-induced electric field (E-field) with an optimal pulse amplitude estimation. METHODS A single site clinical trial focused on amplitude (600, 700, and 800 mA) included longitudinal multimodal imaging and clinical and cognitive assessments completed before and immediately after the ECT series (n = 54) for late-life depression. Another two independent validation cohorts (n = 84, n = 260) were included. Symptom and cognition were used as references to supervise fMRI and sMRI fusion to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks. Correlations between ECT-induced E-field within these two networks and clinical and cognitive outcomes were calculated. An optimal pulse amplitude was estimated based on E-field within antidepressant-response and cognitive-impairment networks. RESULTS Decreased function in the superior orbitofrontal cortex and caudate accompanied with increased volume in medial temporal cortex showed covarying functional and structural alterations in both antidepressant-response and cognitive-impairment networks. Volume increases in the hippocampal complex and thalamus were antidepressant-response specific, and functional decreases in the amygdala and hippocampal complex were cognitive-impairment specific, which were validated in two independent datasets. The E-field within these two networks showed an inverse relationship with HDRS reduction and cognitive impairment. The optimal E-filed range as [92.7-113.9] V/m was estimated to maximize antidepressant outcomes without compromising cognitive safety. CONCLUSIONS The large degree of overlap between antidepressant-response and cognitive-impairment networks challenges parameter development focused on precise E-field dosing with new electrode placements. The determination of the optimal individualized ECT amplitude within the antidepressant and cognitive networks may improve the treatment benefit-risk ratio. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02999269.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Yvette Sheline
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Shawn M McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rongtao Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Tom Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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25
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Tsujimura K, Shiohama T, Takahashi E. microRNA Biology on Brain Development and Neuroimaging Approach. Brain Sci 2022; 12:brainsci12101366. [PMID: 36291300 PMCID: PMC9599180 DOI: 10.3390/brainsci12101366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/22/2022] Open
Abstract
Proper brain development requires the precise coordination and orchestration of various molecular and cellular processes and dysregulation of these processes can lead to neurological diseases. In the past decades, post-transcriptional regulation of gene expression has been shown to contribute to various aspects of brain development and function in the central nervous system. MicroRNAs (miRNAs), short non-coding RNAs, are emerging as crucial players in post-transcriptional gene regulation in a variety of tissues, such as the nervous system. In recent years, miRNAs have been implicated in multiple aspects of brain development, including neurogenesis, migration, axon and dendrite formation, and synaptogenesis. Moreover, altered expression and dysregulation of miRNAs have been linked to neurodevelopmental and psychiatric disorders. Magnetic resonance imaging (MRI) is a powerful imaging technology to obtain high-quality, detailed structural and functional information from the brains of human and animal models in a non-invasive manner. Because the spatial expression patterns of miRNAs in the brain, unlike those of DNA and RNA, remain largely unknown, a whole-brain imaging approach using MRI may be useful in revealing biological and pathological information about the brain affected by miRNAs. In this review, we highlight recent advancements in the research of miRNA-mediated modulation of neuronal processes that are important for brain development and their involvement in disease pathogenesis. Also, we overview each MRI technique, and its technological considerations, and discuss the applications of MRI techniques in miRNA research. This review aims to link miRNA biological study with MRI analytical technology and deepen our understanding of how miRNAs impact brain development and pathology of neurological diseases.
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Affiliation(s)
- Keita Tsujimura
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Group of Brain Function and Development, Nagoya University Neuroscience Institute of the Graduate School of Science, Nagoya 4648602, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya 4648602, Japan
- Correspondence: (K.T.); (E.T.)
| | - Tadashi Shiohama
- Department of Pediatrics, Chiba University Hospital, Chiba 2608677, Japan
| | - Emi Takahashi
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Correspondence: (K.T.); (E.T.)
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26
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Qi S, Sui J, Pearlson G, Bustillo J, Perrone-Bizzozero NI, Kochunov P, Turner JA, Fu Z, Shao W, Jiang R, Yang X, Liu J, Du Y, Chen J, Zhang D, Calhoun VD. Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network. Nat Commun 2022; 13:4929. [PMID: 35995794 PMCID: PMC9395379 DOI: 10.1038/s41467-022-32513-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 08/03/2022] [Indexed: 12/23/2022] Open
Abstract
Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Godfrey Pearlson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Juan Bustillo
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Nora I Perrone-Bizzozero
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Yuhui Du
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
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27
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Chu C, Zhang Y, Liu Q, Pang Y, Niu Y, Zhang R. Identification of ceRNA network to explain the mechanism of cognitive dysfunctions induced by PS NPs in mice. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 241:113785. [PMID: 35753268 DOI: 10.1016/j.ecoenv.2022.113785] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/11/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Plastics breaking down of larger plastics into smaller ones (microplastics and nanoplastic) as potential threats to the ecosystem. Previous studies demonstrate that the central nervous system (CNS) is a vulnerable target of nanoplastics. However, the potentially epigenetic biomarkers of nanoplastic neurotoxicity in rodent models are still unknown. The present research aimed to determine the role of competing endogenous RNA (ceRNA) in the process of polystyrene nanoplastics (PS NPs) exposure-induced nerve injury. The study was designed to investigate whether 25 nm PS NPs could cause learning dysfunction and to elucidate the underlying mechanisms in mice. A total of 40 mice were divided into 4 groups and were exposed to PS NPs (0, 10, 25, 50 mg/kg). Chronic toxicity was introduced in mice by administration of oral gavage for 6 months. The evaluation included assessment of their behavior, pathological investigation and determination of the levels of reactive oxygen species (ROS) and DNA damage. RNA-Seq was performed to detect the expression levels of circRNAs, miRNAs and mRNAs in PFC samples of mice treated with 0 and 50 mg/kg PS NPs. The results indicated that exposure of mice to PS NPs caused a dose-dependent cognitive decline. ROS levels and DNA damage were increased in the PFC following exposure of the mice to PS NPs. A total of 987 mRNAs, 29 miRNAs and 67 circRNAs demonstrated significant differences between the 0 and 50 mg/kg PS NPs groups. Functional enrichment analyses indicated that PS NPs may induce major injury in the synaptic function. A total of 96 mRNAs, which were associated with synaptic dysfunction were identified. A competing endogenous RNA (ceRNA) network containing 27 circRNAs, 19 miRNAs and 35 synaptic dysfunction-related mRNAs was constructed. The present study provided insight into the molecular events associated with nanoplastic toxicity and induction of cognitive dysfunction.
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Affiliation(s)
- Chen Chu
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, China; Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, 200040, China
| | - Yaling Zhang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, China
| | - Qingping Liu
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, China
| | - Yaxian Pang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, China
| | - Yujie Niu
- Deportment Occupational Health and Environmental Health, School of Public Health, Hebei Medical University, Shijiazhuang 050017, China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China
| | - Rong Zhang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, China.
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28
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Qi S, Fu Z, Wu L, Calhoun VD, Zhang D, Daughters SB, Hsu PC, Jiang R, Vergara VM, Sui J, Addicott MA. Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation. Front Neurosci 2022; 16:923065. [PMID: 35968362 PMCID: PMC9363622 DOI: 10.3389/fnins.2022.923065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/20/2022] [Indexed: 02/04/2023] Open
Abstract
Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Stacey B. Daughters
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ping-Ching Hsu
- Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Victor M. Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Merideth A. Addicott
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
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29
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Tsermpini EE, Kalogirou CI, Kyriakopoulos GC, Patrinos GP, Stathopoulos C. miRNAs as potential diagnostic biomarkers and pharmacogenomic indicators in psychiatric disorders. THE PHARMACOGENOMICS JOURNAL 2022; 22:211-222. [PMID: 35725816 DOI: 10.1038/s41397-022-00283-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/23/2022] [Accepted: 06/08/2022] [Indexed: 12/11/2022]
Abstract
The heterogeneity of psychiatric disorders and the lack of reliable biomarkers for prediction and treatments follow-up pose difficulties towards recognition and understanding of the molecular basis of psychiatric diseases. However, several studies based on NGS approaches have shown that miRNAs could regulate gene expression during onset and disease progression and could serve as potential diagnostic and pharmacogenomics biomarkers during treatment. We provide herein a detailed overview of circulating miRNAs and their expression profiles as biomarkers in schizophrenia, bipolar disorder and major depressive disorder and their role in response to specific treatments. Bioinformatics analysis of miR-34a, miR-106, miR-134 and miR-132, which are common among SZ, BD and MDD patients, showed brain enrichment and involvement in the modulation of critical signaling pathways, which are often deregulated in psychiatric disorders. We propose that specific miRNAs support accurate diagnosis and effective precision treatment of psychiatric disorders.
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Affiliation(s)
- Evangelia Eirini Tsermpini
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Christina I Kalogirou
- Department of Biochemistry, School of Medicine, University of Patras, Patras, Greece
| | | | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, School of Health Sciences, Department of Pharmacy, University of Patras, Patras, Greece
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30
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Feng A, Luo N, Zhao W, Calhoun VD, Jiang R, Zhi D, Shi W, Jiang T, Yu S, Xu Y, Liu S, Sui J. Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage. Hum Brain Mapp 2022; 43:3486-3497. [PMID: 35388581 PMCID: PMC9248316 DOI: 10.1002/hbm.25862] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/10/2022] [Accepted: 03/23/2022] [Indexed: 11/25/2022] Open
Abstract
Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wentao Zhao
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiyang Shi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jing Sui
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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31
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Zhu J, Wang C, Qian Y, Cai H, Zhang S, Zhang C, Zhao W, Zhang T, Zhang B, Chen J, Liu S, Yu Y. Multimodal neuroimaging fusion biomarkers mediate the association between gut microbiota and cognition. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110468. [PMID: 34736997 DOI: 10.1016/j.pnpbp.2021.110468] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/25/2021] [Accepted: 10/29/2021] [Indexed: 02/06/2023]
Abstract
Background The field of microbiota-gut-brain research in animals has progressed, while the exact nature of gut microbiota-brain-cognition relationship in humans is not completely elucidated, likely due to small sample sizes and single neuroimaging modality utilized to delineate limited aspects of the brain. We aimed to comprehensively investigate such association in a large sample using multimodal MRI. Methods Fecal samples were collected from 157 healthy young adults and 16S sequencing was used to assess gut microbial diversity and enterotypes. Five brain imaging measures, including regional homogeneity (ReHo) and functional connectivity density (FCD) from resting-state functional MRI, cerebral blood flow (CBF) from arterial spin labeling, gray matter volume (GMV) from structural MRI, and fractional anisotropy (FA) from diffusion tensor imaging, were jointly analyzed with a data-driven multivariate fusion method. Cognition was evaluated by 3-back and digit span tasks. Results We found significant associations of gut microbial diversity with ReHo, FCD, CBF, and GMV within the frontoparietal, default mode and visual networks, as well as with FA in a distributed set of juxtacortical white matter regions. In addition, there were FCD, CBF, GMV, and FA differences between Prevotella- versus Bacteroides-enterotypes in females and between Prevotella- versus Ruminococcaceae-enterotypes in males. Moreover, the identified neuroimaging fusion biomarkers could mediate the associations between microbial diversity and cognition. Conclusions Our findings not only expand existing knowledge of the microbiota-gut-brain axis, but also have potential clinical and translational implications by exposing the gut microbiota as a promising treatment and prevention target for cognitive impairment and related brain disorders.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Chunli Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Shujun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Tingting Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Biao Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Jingyao Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China.
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Panariello F, Fanelli G, Fabbri C, Atti AR, De Ronchi D, Serretti A. Epigenetic Basis of Psychiatric Disorders: A Narrative Review. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2022; 21:302-315. [PMID: 34433406 DOI: 10.2174/1871527320666210825101915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/02/2021] [Accepted: 07/12/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Psychiatric disorders are complex, multifactorial illnesses with a demonstrated biological component in their etiopathogenesis. Epigenetic modifications, through the modulation of DNA methylation, histone modifications and RNA interference, tune tissue-specific gene expression patterns and play a relevant role in the etiology of psychiatric illnesses. OBJECTIVE This review aims to discuss the epigenetic mechanisms involved in psychiatric disorders, their modulation by environmental factors and their interactions with genetic variants, in order to provide a comprehensive picture of their mutual crosstalk. METHODS In accordance with the PRISMA guidelines, systematic searches of Medline, EMBASE, PsycINFO, Web of Science, Scopus, and the Cochrane Library were conducted. RESULTS Exposure to environmental factors, such as poor socio-economic status, obstetric complications, migration, and early life stressors, may lead to stable changes in gene expression and neural circuit function, playing a role in the risk of psychiatric diseases. The most replicated genes involved by studies using different techniques are discussed. Increasing evidence indicates that these sustained abnormalities are maintained by epigenetic modifications in specific brain regions and they interact with genetic variants in determining the risk of psychiatric disorders. CONCLUSION An increasing amount of evidence suggests that epigenetics plays a pivotal role in the etiopathogenesis of psychiatric disorders. New therapeutic approaches may work by reversing detrimental epigenetic changes that occurred during the lifespan.
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Affiliation(s)
- Fabio Panariello
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giuseppe Fanelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Anna Rita Atti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Diana De Ronchi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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Qi S, Silva RF, Zhang D, Plis SM, Miller R, Vergara VM, Jiang R, Zhi D, Sui J, Calhoun VD. Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data. Hum Brain Mapp 2021; 43:1280-1294. [PMID: 34811846 PMCID: PMC8837596 DOI: 10.1002/hbm.25720] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/17/2021] [Accepted: 11/07/2021] [Indexed: 01/24/2023] Open
Abstract
Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three‐way pGICA provides highly accurate cross‐modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode‐cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three‐way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rogers F Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Robyn Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
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34
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Ortega MA, Alvarez-Mon MA, García-Montero C, Fraile-Martinez O, Lahera G, Monserrat J, Muñoz-Merida L, Mora F, Rodríguez-Jiménez R, Fernandez-Rojo S, Quintero J, Álvarez-Mon M. MicroRNAs as Critical Biomarkers of Major Depressive Disorder: A Comprehensive Perspective. Biomedicines 2021; 9:biomedicines9111659. [PMID: 34829888 PMCID: PMC8615526 DOI: 10.3390/biomedicines9111659] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/23/2022] Open
Abstract
Major Depressive Disorder (MDD) represents a major global health concern, a body-mind malady of rising prevalence worldwide nowadays. The complex network of mechanisms involved in MDD pathophysiology is subjected to epigenetic changes modulated by microRNAs (miRNAs). Serum free or vesicles loaded miRNAs have starred numerous publications, denoting a key role in cell-cell communication, systematically and in brain structure and neuronal morphogenesis, activity and plasticity. Upregulated or downregulated expression of these signaling molecules may imply the impairment of genes implicated in pathways of MDD etiopathogenesis (neuroinflammation, brain-derived neurotrophic factor (BDNF), neurotransmitters, hypothalamic-pituitary-adrenal (HPA) axis, oxidative stress, circadian rhythms...). In addition, these miRNAs could serve as potential biomarkers with diagnostic, prognostic and predictive value, allowing to classify severity of the disease or to make decisions in clinical management. They have been considered as promising therapy targets as well and may interfere with available antidepressant treatments. As epigenetic malleable regulators, we also conclude emphasizing lifestyle interventions with physical activity, mindfulness and diet, opening the door to new clinical management considerations.
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Affiliation(s)
- Miguel A. Ortega
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
- Cancer Registry and Pathology Department, Hospital Universitario Principe de Asturias, 28806 Alcalá de Henares, Spain; (F.M.); (S.F.-R.); (J.Q.)
| | - Miguel Angel Alvarez-Mon
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain
- Correspondence:
| | - Cielo García-Montero
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
| | - Oscar Fraile-Martinez
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
| | - Guillermo Lahera
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
- Psychiatry Service, Center for Biomedical Research in the Mental Health Network, University Hospital Príncipe de Asturias, 28806 Alcalá de Henares, Spain
| | - Jorge Monserrat
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
| | - Luis Muñoz-Merida
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
| | - Fernando Mora
- Cancer Registry and Pathology Department, Hospital Universitario Principe de Asturias, 28806 Alcalá de Henares, Spain; (F.M.); (S.F.-R.); (J.Q.)
- Department of Legal Medicine and Psychiatry, Complutense University, 28040 Madrid, Spain;
| | - Roberto Rodríguez-Jiménez
- Department of Legal Medicine and Psychiatry, Complutense University, 28040 Madrid, Spain;
- Institute for Health Research Hospital 12 de Octubre (imas 12), CIBERSAM, 28041 Madrid, Spain
| | - Sonia Fernandez-Rojo
- Cancer Registry and Pathology Department, Hospital Universitario Principe de Asturias, 28806 Alcalá de Henares, Spain; (F.M.); (S.F.-R.); (J.Q.)
- Department of Legal Medicine and Psychiatry, Complutense University, 28040 Madrid, Spain;
| | - Javier Quintero
- Cancer Registry and Pathology Department, Hospital Universitario Principe de Asturias, 28806 Alcalá de Henares, Spain; (F.M.); (S.F.-R.); (J.Q.)
- Department of Legal Medicine and Psychiatry, Complutense University, 28040 Madrid, Spain;
| | - Melchor Álvarez-Mon
- Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcalá de Henares, Spain; (M.A.O.); (C.G.-M.); (O.F.-M.); (G.L.); (J.M.); (L.M.-M.); (M.Á.-M.)
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain
- Immune System Diseases-Rheumatology, Oncology Service an Internal Medicine, University Hospital Príncipe de Asturias, (CIBEREHD), 28806 Alcalá de Henares, Spain
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Dalvie S, Chatzinakos C, Al Zoubi O, Georgiadis F, Lancashire L, Daskalakis NP. From genetics to systems biology of stress-related mental disorders. Neurobiol Stress 2021; 15:100393. [PMID: 34584908 PMCID: PMC8456113 DOI: 10.1016/j.ynstr.2021.100393] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/22/2021] [Accepted: 09/08/2021] [Indexed: 01/20/2023] Open
Abstract
Many individuals will be exposed to some form of traumatic stress in their lifetime which, in turn, increases the likelihood of developing stress-related disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders (ANX). The development of these disorders is also influenced by genetics and have heritability estimates ranging between ∼30 and 70%. In this review, we provide an overview of the findings of genome-wide association studies for PTSD, depression and ANX, and we observe a clear genetic overlap between these three diagnostic categories. We go on to highlight the results from transcriptomic and epigenomic studies, and, given the multifactorial nature of stress-related disorders, we provide an overview of the gene-environment studies that have been conducted to date. Finally, we discuss systems biology approaches that are now seeing wider utility in determining a more holistic view of these complex disorders.
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Affiliation(s)
- Shareefa Dalvie
- South African Medical Research Council (SAMRC), Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SAMRC), Unit on Child & Adolescent Health, Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa
| | - Chris Chatzinakos
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Obada Al Zoubi
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Foivos Georgiadis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
| | | | - Lee Lancashire
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
- Department of Data Science, Cohen Veterans Bioscience, New York, USA
| | - Nikolaos P. Daskalakis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, USA
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The miRNome of Depression. Int J Mol Sci 2021; 22:ijms222111312. [PMID: 34768740 PMCID: PMC8582693 DOI: 10.3390/ijms222111312] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/10/2021] [Accepted: 10/18/2021] [Indexed: 02/07/2023] Open
Abstract
Depression is an effect of complex interactions between genetic, epigenetic and environmental factors. It is well established that stress responses are associated with multiple modest and often dynamic molecular changes in the homeostatic balance, rather than with a single genetic factor that has a strong phenotypic penetration. As depression is a multifaceted phenotype, it is important to study biochemical pathways that can regulate the overall allostasis of the brain. One such biological system that has the potential to fine-tune a multitude of diverse molecular processes is RNA interference (RNAi). RNAi is an epigenetic process showing a very low level of evolutionary diversity, and relies on the posttranscriptional regulation of gene expression using, in the case of mammals, primarily short (17–23 nucleotides) noncoding RNA transcripts called microRNAs (miRNA). In this review, our objective was to examine, summarize and discuss recent advances in the field of biomedical and clinical research on the role of miRNA-mediated regulation of gene expression in the development of depression. We focused on studies investigating post-mortem brain tissue of individuals with depression, as well as research aiming to elucidate the biomarker potential of miRNAs in depression and antidepressant response.
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Qi S, Schumann G, Bustillo J, Turner JA, Jiang R, Zhi D, Fu Z, Mayer AR, Vergara VM, Silva RF, Iraji A, Chen J, Damaraju E, Ma X, Yang X, Stevens M, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, Potkin SG, Preda A, Zhuo C, Xu Y, Chu C, Banaschewski T, Barker GJ, Bokde ALW, Quinlan EB, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Fröhner JH, Smolka MN, Walter H, Whelan R, Calhoun VD, Sui J. Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker. Biol Psychiatry 2021; 90:529-539. [PMID: 33875230 PMCID: PMC8322149 DOI: 10.1016/j.biopsych.2021.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/28/2020] [Accepted: 01/04/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. METHODS A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. RESULTS Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. CONCLUSIONS Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.
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Affiliation(s)
- Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Rongtao Jiang
- University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dongmei Zhi
- University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center and 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 State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Andrew R Mayer
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Rogers F Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Armin Iraji
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Eswar Damaraju
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | | | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California
| | - Judith M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, California
| | - James Voyvodic
- Department of Radiology, Duke University, Durham, North Carolina
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Steven G Potkin
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory, Nankai University Affiliated Anding Hospital, Tianjin, China
| | - Yong Xu
- Department of Humanities and Social Science, Shanxi Medical University, Taiyuan, China
| | - Congying Chu
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Erin Burke Quinlan
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Herta Flor
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - Robert Whelan
- PONS Research Group, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Humboldt University, Berlin, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; Department of Psychology, Georgia State University, Atlanta, Georgia.
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; University of Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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Sui J, Li X, Bell RP, Towe SL, Gadde S, Chen NK, Meade CS. Structural and Functional Brain Abnormalities in Human Immunodeficiency Virus Disease Revealed by Multimodal Magnetic Resonance Imaging Fusion: Association With Cognitive Function. Clin Infect Dis 2021; 73:e2287-e2293. [PMID: 32948879 PMCID: PMC8492163 DOI: 10.1093/cid/ciaa1415] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND Human immunodeficiency virus (HIV)-associated neurocognitive impairment remains a prevalent comorbidity that impacts daily functioning and increases morbidity. While HIV infection is known to cause widespread disruptions in the brain, different magnetic resonance imaging (MRI) modalities have not been effectively integrated. In this study, we applied 3-way supervised fusion to investigate how structural and functional coalterations affect cognitive function. METHODS Participants (59 people living with HIV and 58 without HIV) completed comprehensive neuropsychological testing and multimodal MRI scanning to acquire high-resolution anatomical, diffusion-weighted, and resting-state functional images. Preprocessed data were reduced using voxel-based morphometry, probabilistic tractography, and regional homogeneity, respectively. We applied multimodal canonical correlation analysis with reference plus joint independent component analysis using global cognitive functioning as the reference. RESULTS Compared with controls, participants living with HIV had lower global cognitive functioning. One joint component was both group discriminating and correlated with cognitive function. This component included the following covarying regions: fractional anisotropy in the corpus callosum, short and long association fiber tracts, and corticopontine fibers; gray matter volume in the thalamus, prefrontal cortex, precuneus, posterior parietal regions, and occipital lobe; and functional connectivity in frontoparietal and visual processing regions. Component loadings for fractional anisotropy also correlated with immunosuppression. CONCLUSIONS These results suggest that coalterations in brain structure and function can distinguish people with and without HIV and may drive cognitive impairment. As MRI becomes more commonplace in HIV care, multimodal fusion may provide neural biomarkers to support diagnosis and treatment of cognitive impairment.
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Affiliation(s)
- Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiang Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ryan P Bell
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Sheri L Towe
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Syam Gadde
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
| | - Nan-kuei Chen
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
- Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona, USA
| | - Christina S Meade
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
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Li K, Fu Z, Qi S, Luo X, Zeng Q, Xu X, Huang P, Zhang M, Calhoun VD. Polygenic Hazard Score Associated Multimodal Brain Networks Along the Alzheimer's Disease Continuum. Front Aging Neurosci 2021; 13:725246. [PMID: 34539385 PMCID: PMC8446666 DOI: 10.3389/fnagi.2021.725246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/10/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Late-onset Alzheimer's disease (AD) is a polygenic neurodegenerative disease. Identifying the neuroimaging phenotypes behind the genetic predisposition of AD is critical to the understanding of AD pathogenesis. Two major questions which previous studies have led to are: (1) should the general "polygenic hazard score" (PHS) be a good choice to identify the individual genetic risk for AD; and (2) should researchers also include inter-modality relationships in the analyses considering these may provide complementary information about the AD etiology. METHODS We collected 88 healthy controls, 77 patients with mild cognitive impairment (MCI), and 22 AD patients to simulate the AD continuum included from the ADNI database. PHS-guided multimodal fusion was used to investigate the impact of PHS on multimodal brain networks in AD-continuum by maximizing both inter-modality association and reference-modality correlation. Fractional amplitude of low frequency fluctuations, gray matter (GM) volume, and amyloid standard uptake value ratios were included as neuroimaging features. Eventually, the changes in neuroimaging features along AD continuum were investigated, and relationships between cognitive performance and identified PHS associated multimodal components were established. RESULTS We found that PHS was associated with multimodal brain networks, which showed different functional and structural impairments under increased amyloid deposits. Notably, along with AD progression, functional impairment occurred before GM atrophy, amyloid deposition started from the MCI stage and progressively increased throughout the disease continuum. CONCLUSION PHS is associated with multi-facets of brain impairments along the AD continuum, including cognitive dysfunction, pathological deposition, which might underpin the AD pathogenesis.
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Affiliation(s)
- Kaicheng Li
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Shile Qi
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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40
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Meade CS, Li X, Towe SL, Bell RP, Calhoun VD, Sui J. Brain multimodal co-alterations related to delay discounting: a multimodal MRI fusion analysis in persons with and without cocaine use disorder. BMC Neurosci 2021; 22:51. [PMID: 34416865 PMCID: PMC8377830 DOI: 10.1186/s12868-021-00654-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/27/2021] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Delay discounting has been proposed as a behavioral marker of substance use disorders. Innovative analytic approaches that integrate information from multiple neuroimaging modalities can provide new insights into the complex effects of drug use on the brain. This study implemented a supervised multimodal fusion approach to reveal neural networks associated with delay discounting that distinguish persons with and without cocaine use disorder (CUD). METHODS Adults with (n = 35) and without (n = 37) CUD completed a magnetic resonance imaging (MRI) scan to acquire high-resolution anatomical, resting-state functional, and diffusion-weighted images. Pre-computed features from each data modality included whole-brain voxel-wise maps for gray matter volume, fractional anisotropy, and regional homogeneity, respectively. With delay discounting as the reference, multimodal canonical component analysis plus joint independent component analysis was used to identify co-alterations in brain structure and function. RESULTS The sample was 58% male and 78% African-American. As expected, participants with CUD had higher delay discounting compared to those without CUD. One joint component was identified that correlated with delay discounting across all modalities, involving regions in the thalamus, dorsal striatum, frontopolar cortex, occipital lobe, and corpus callosum. The components were negatively correlated with delay discounting, such that weaker loadings were associated with higher discounting. The component loadings were lower in persons with CUD, meaning the component was expressed less strongly. CONCLUSIONS Our findings reveal structural and functional co-alterations linked to delay discounting, particularly in brain regions involved in reward salience, executive control, and visual attention and connecting white matter tracts. Importantly, these multimodal networks were weaker in persons with CUD, indicating less cognitive control that may contribute to impulsive behaviors.
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Affiliation(s)
- Christina S Meade
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Box 102848, Durham, NC, 27708, USA.
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.
| | - Xiang Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Sheri L Towe
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Box 102848, Durham, NC, 27708, USA
| | - Ryan P Bell
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Box 102848, Durham, NC, 27708, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Atlanta, GA, USA.
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Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 1:135-145. [PMID: 36324992 PMCID: PMC9616319 DOI: 10.1016/j.bpsgos.2021.04.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 01/05/2023] Open
Abstract
Background Identifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD. Methods This study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes. Results Source-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2. Conclusions Overall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.
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Mizohata Y, Toda H, Koga M, Saito T, Fujita M, Kobayashi T, Hatakeyama S, Morimoto Y. Neural extracellular vesicle-derived miR-17 in blood as a potential biomarker of subthreshold depression. Hum Cell 2021; 34:1087-1092. [PMID: 34013455 DOI: 10.1007/s13577-021-00553-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/14/2021] [Indexed: 01/20/2023]
Abstract
Subthreshold depression (StD) is a depressive state that does not fulfil the criteria for major depressive disorder (MDD); however, StD has a risk for progression to MDD, and early intervention is therefore needed. Recently, a method for extracting neural extracellular vesicles (NEVs) excreted from neural cells of the brain from blood has been established, and microRNAs (miRNAs) encapsulated in NEVs are attracting interest because of their potential correlation to the pathogenesis of psychiatric disorders. However, miRNAs closely related to StD are still unknown. Therefore, to try to identify miRNAs closely related to the degree of StD, we examined the correlations between expression levels of some candidate miRNAs in NEVs and Patient Health Questionnaire-9 (PHQ-9) scores in subjects. Total RNAs in NEVs were extracted from serum of young adult males who had PHQ-9 scores of less than 10 (n = 9). Expression levels of eight miRNAs that were previously reported to be depression-associated miRNAs (let-7a-5p, miR-17-5p, miR-26b-5p, miR-34a-5p, miR-132-3p, miR-182-5p, miR-212-3p, and miR-1202) were measured using real-time PCR. Two of the eight miRNAs (miR-17-5p and miR-26b-5p) were stably detected. The relative expression levels of miR-17-5p showed a significant positive correlation with PHQ-9 scores (r = 0.85, p < 0.01), while those of miR-26b-5p showed no significance. Although a larger-scale analysis is needed due to the small number of subjects, these findings suggest that miR-17-5p in NEVs is a potential biomarker for StD.
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Affiliation(s)
- Yusuke Mizohata
- Department of Physiology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
- Aeromedical Laboratory, Japan Air Self-Defense Force, Iruma, Saitama, Japan
| | - Hiroyuki Toda
- Department of Psychiatry, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Minori Koga
- Department of Psychiatry, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Taku Saito
- Department of Psychiatry, National Defense Medical College, Tokorozawa, Saitama, Japan
| | - Masanori Fujita
- Division of Environmental Medicine, National Defense Medical College Research Institute, Tokorozawa, Saitama, Japan
| | - Tetsuya Kobayashi
- Course in Life Science, Graduate School of Science and Engineering, Saitama University, Saitama, Saitama, Japan
| | - Shin Hatakeyama
- Course in Life Science, Graduate School of Science and Engineering, Saitama University, Saitama, Saitama, Japan
| | - Yuji Morimoto
- Department of Physiology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan.
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Martins HC, Schratt G. MicroRNA-dependent control of neuroplasticity in affective disorders. Transl Psychiatry 2021; 11:263. [PMID: 33941769 PMCID: PMC8093191 DOI: 10.1038/s41398-021-01379-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/17/2021] [Accepted: 04/13/2021] [Indexed: 12/12/2022] Open
Abstract
Affective disorders are a group of neuropsychiatric disorders characterized by severe mood dysregulations accompanied by sleep, eating, cognitive, and attention disturbances, as well as recurring thoughts of suicide. Clinical studies consistently show that affective disorders are associated with reduced size of brain regions critical for mood and cognition, neuronal atrophy, and synaptic loss in these regions. However, the molecular mechanisms that mediate these changes and thereby increase the susceptibility to develop affective disorders remain poorly understood. MicroRNAs (miRNAs or miRs) are small regulatory RNAs that repress gene expression by binding to the 3'UTR of mRNAs. They have the ability to bind to hundreds of target mRNAs and to regulate entire gene networks and cellular pathways implicated in brain function and plasticity, many of them conserved in humans and other animals. In rodents, miRNAs regulate synaptic plasticity by controlling the morphology of dendrites and spines and the expression of neurotransmitter receptors. Furthermore, dysregulated miRNA expression is frequently observed in patients suffering from affective disorders. Together, multiple lines of evidence suggest a link between miRNA dysfunction and affective disorder pathology, providing a rationale to consider miRNAs as therapeutic tools or molecular biomarkers. This review aims to highlight the most recent and functionally relevant studies that contributed to a better understanding of miRNA function in the development and pathogenesis of affective disorders. We focused on in vivo functional studies, which demonstrate that miRNAs control higher brain functions, including mood and cognition, in rodents, and that their dysregulation causes disease-related behaviors.
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Affiliation(s)
- Helena Caria Martins
- Lab of Systems Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, 8057, Zurich, Switzerland
| | - Gerhard Schratt
- Lab of Systems Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, 8057, Zurich, Switzerland.
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Emerging role of microRNAs in major depressive disorder and its implication on diagnosis and therapeutic response. J Affect Disord 2021; 286:80-86. [PMID: 33714174 DOI: 10.1016/j.jad.2021.02.063] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 01/01/2021] [Accepted: 02/27/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a serious and common psychiatric disorder with a high prevalence in the population. Although great advances have been made, its pathogenesis is still unclear and a validated biomarker for diagnosis or therapeutic response remains unidentified. This review aims at summarizing the functional role of miRNAs in MDD pathogenesis and their potential as biomarkers for MDD diagnosis and antidepressant response. METHODS We performed a bibliographic research on the main databases (PubMed, Google Scholar and Web of Science) using the terms "microRNAs", "major depressive disorder", "synaptic plasticity", "biomarker", "antidepressant treatment", in order to find studies that propose the role of microRNAs in MDD pathogenesis and their potential as biomarkers for MDD diagnosis and antidepressant response. RESULTS microRNAs (miRNAs), a class of small noncoding RNAs, act as key regulators of synaptic plasticity in MDD pathogenesis. Growing researches provide the evidence for peripheral miRNAs as potential biomarkers for MDD diagnosis and antidepressant response. These results suggest that targeting miRNAs directly could be therapeutically beneficial for MDD and miRNAs are potential biomarkers of MDD and its treatment. LIMITATIONS The role of miRNAs in MDD pathogenesis needs further investigation. Whether miRNAs in peripheral tissues truly represent brain-derived miRNAs is still unclear at the present time. Moreover, only a few blood miRNAs alterations are consistent across studies. CONCLUSIONS Overall, miRNAs act key regulators of synaptic plasticity in MDD pathogenesis and hold significant promise as biomarkers or therapeutic targets for MDD, but further research is still needed.
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Jiang R, Calhoun VD, Cui Y, Qi S, Zhuo C, Li J, Jung R, Yang J, Du Y, Jiang T, Sui J. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imaging Behav 2021; 14:1979-1993. [PMID: 31278651 DOI: 10.1007/s11682-019-00146-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health Center, Nankai University Affiliated Anding Hospital, Tianjin, 300222, China
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Rex Jung
- Department of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,University of Electronic Science and Technology of China, Chengdu, 610054, China.,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China.
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Guo X, Liu L, Li T, Zhao Q, Li H, Huang F, Wang Y, Qian Q, Cao Q, Wang Y, Calhoun VD, Sui J, Sun L. Inhibition-directed multimodal imaging fusion patterns in adults with ADHD and its potential underlying "gene-brain-cognition" relationship. CNS Neurosci Ther 2021; 27:664-673. [PMID: 33724699 PMCID: PMC8111492 DOI: 10.1111/cns.13625] [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: 10/21/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 11/30/2022] Open
Abstract
Aims Inhibition deficits have been suggested to be a core cognitive impairment in attention‐deficit/hyperactivity disorder (ADHD). Exploring imaging patterns and the potential genetic components associated with inhibition deficits would definitely promote our understanding of the neuropathological mechanism of ADHD. This study aims to investigate the multimodal imaging fusion features related to inhibition deficits in adults with ADHD (aADHD) and to make an exploratory analysis of the role of inhibition‐related gene, NOS1, on those brain alterations. Methods Specifically, multisite canonical correlation analysis with reference plus joint independent component analysis (MCCAR + jICA) was conducted to identify the joint co‐varying gray matter volume (GMV) and the functional connectivity (FC) features related to inhibition in 69 aADHD and 44 healthy controls. Then, mediation analysis was employed to detect the relationship among inhibition‐related imaging features, NOS1 ex1f‐VNTR genotypes, and inhibition. Results Inhibition‐directed multimodal imaging fusion patterns of aADHD were reduced GMV and FC in inhibition network and increased GMV and FC in default mode network. The results showed a significant indirect effect of NOS1 ex1f‐VNTR on inhibition via FC component [effect size = −0.54 (SE = 0.29), 95% CI = −1.16 to −0.01]. In addition, the results indicated a significant indirect effect of GMV on the inhibition via FC component [effect size = 0.43 (SE = 0.23), 95% CI = 0.12 to 1.00]. Conclusion The findings suggested that reduced GMV and FC in inhibition network and increased GMV and FC in default mode network were jointly responsible for inhibition deficits in aADHD. Both the NOS1 ex1f‐VNTR genotypes and GMV might influence the inhibition through the mediation effect of the aforementioned FC (NOS1/GMV→FC→Inhibition).
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Affiliation(s)
- Xiaojie Guo
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Tiantian Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qihua Zhao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Hui Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Fang Huang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Yanfei Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Qiujin Qian
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Qingjiu Cao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Yufeng Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
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Miao C, Chang J. The important roles of microRNAs in depression: new research progress and future prospects. J Mol Med (Berl) 2021; 99:619-636. [PMID: 33641067 DOI: 10.1007/s00109-021-02052-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 01/04/2021] [Accepted: 02/16/2021] [Indexed: 12/11/2022]
Abstract
MicroRNAs (miRNAs) are non-encoding, single-stranded RNA molecules of about 22 nucleotides in length encoded by endogenous genes involved in posttranscriptional gene expression regulation. Studies have shown that miRNAs participate in a series of important pathophysiological processes, including the pathogenesis of depression. This article systematically summarized the research results published in the field of miRNAs and depression, which mainly involved three topics: circulating miRNAs as markers for diagnosis and prognosis of depression, the regulatory roles of miRNAs in the pathogenesis of depression, and the roles of miRNAs in the mechanisms of depression treatment. By summarizing and analyzing the research literature in recent years, we found that some circulating miRNAs can be potential biomarkers for the diagnosis and prognostic evaluation of depression. miRNAs that disorderly expressed during the disease play important roles in the depression pathogenesis, and miRNAs also play roles in the mechanisms of psychotherapy and drug therapy for depression. Elucidating the important roles of miRNAs in depression will bring people's understanding of the pathogenesis of depression to a new level. In addition, these miRNAs may be developed as new biomarkers for diagnosing depression, or as drug targets, or these molecules may be used as new drugs, which may provide new means for the treatment of depression. KEY MESSAGES: • The research results of miRNAs and depression are reviewed. • Circulating miRNAs can be potential biomarkers for depression. • MiRNAs play important roles in the depression pathogenesis. • MiRNAs play important roles in drug therapy for depression.
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Affiliation(s)
- Chenggui Miao
- Department of Pharmacology, College of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, 1 Qianjiang Road, Xinzhan District, Hefei, 230012, Anhui Province, China. .,Institute of Integrated Chinese and Western Medicine, Anhui Academy of Chinese Medicine, Hefei, 230012, China. .,Anhui Provincial Key Laboratory of Chinese Medicine Compound, Anhui University of Chinese Medicine, Hefei, 230012, China. .,Institute of Life and Health Sciences, Anhui University of Science and Technology, Fengyang, 233100, China.
| | - Jun Chang
- Fourth Affiliated Hospital, Anhui Medical University, Hefei, 230032, China
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Zou T, Zhang J, Liu Y, Zhang Y, Sugimoto K, Mei C. Antidepressant-Like Effect of Geniposide in Mice Exposed to a Chronic Mild Stress Involves the microRNA-298-5p-Mediated Nox1. Front Mol Neurosci 2021; 13:131. [PMID: 33613190 PMCID: PMC7886707 DOI: 10.3389/fnmol.2020.00131] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 06/26/2020] [Indexed: 12/18/2022] Open
Abstract
Depression is a common mental disorder that presents a considerable challenge for public health. The natural product geniposide has neuroprotective effects on depression, but the underlying mechanism behind these effects had remained undefined. The present study was designed to investigate the role of microRNAs (miRs) in this mechanism. It studied mice with depression-like behavior established by exposure to chronic unpredictable mild stress (CUMS) for 2 months. The CUMS mice were intragastrically fed with geniposide at a dose of 10 ml/kg daily for two consecutive weeks. We monitored the depression-like behaviors of the CUMS mice by the forced swimming test (FST) and tail suspension test (TST). Then, we measured the cerebral expression of miR-298-5p and NADPH oxidase 1 (Nox1) mRNA in the CUMS mice by the RT-qPCR. The targeting relationship between miR-298-5p and Nox1 was evaluated by dual-luciferase reporter gene assay. The concentrations of adenosine triphosphate (ATP) and reactive oxygen species (ROS) were determined by the CellTiter-Glo® and flow cytometry, respectively. The mitochondrial membrane potential (MMP) was detected using JC-1 staining. Moreover, the expression of inflammatory cytokines (TNF-α, IL-1β, IL-6, and TGF-β) was determined by ELISA, RT-qPCR, and western blot analysis. We found that miR-298-5p was poorly-expressed while Nox1 was highly-expressed in the brain tissues of the CUMS-induced mice. Intriguingly, Geniposide treatment reversed the behavioral abnormalities of CUMS mice, including shortened immobility time. Geniposide inhibited the Nox1 expression by increasing miR-298-5p levels. There were increased ATP content and MMP and reduced contents of ROS and inflammatory cytokines in the CUMS mice receiving geniposide treatment. Hence, this study revealed an antidepressant effect of geniposide on CUMS-induced depression-like behavior in mice by down-regulating the miR-298-5p-targeted Nox1. This highlights a novel candidate target for the treatment of depression.
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Affiliation(s)
- Tianyu Zou
- Department of Encephalopathy, Heilongjiang Academy of Chinese Medical Sciences, Harbin, China
| | - Jielin Zhang
- Department of Dermatology, Heilongjiang Provincial Hospital Affiliated to Harbin Institute of Technology, Harbin, China
| | - Yongxiu Liu
- Department of Encephalopathy, Heilongjiang Academy of Chinese Medical Sciences, Harbin, China
| | - Yiming Zhang
- Department of Encephalopathy, Heilongjiang Academy of Chinese Medical Sciences, Harbin, China
| | - Kazuo Sugimoto
- Department of Neurology, Dongzhimen Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Cheng Mei
- Department of Encephalopathy, Heilongjiang Academy of Chinese Medical Sciences, Harbin, China
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Todeva-Radneva A, Paunova R, Kandilarova S, St Stoyanov D. The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review. Curr Top Med Chem 2021; 20:540-553. [PMID: 32003690 DOI: 10.2174/1568026620666200131095328] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 01/05/2023]
Abstract
Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.
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Affiliation(s)
- Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy St Stoyanov
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
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Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020; 88:818-828. [PMID: 32336400 PMCID: PMC7483317 DOI: 10.1016/j.biopsych.2020.02.016] [Citation(s) in RCA: 136] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 02/13/2020] [Accepted: 02/17/2020] [Indexed: 01/08/2023]
Abstract
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
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