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Liang Q, Xu Z, Chen S, Lin S, Lin X, Li Y, Zhang Y, Peng B, Hou G, Qiu Y. Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder. J Affect Disord 2024; 365:134-143. [PMID: 39154985 DOI: 10.1016/j.jad.2024.08.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/03/2024] [Accepted: 08/10/2024] [Indexed: 08/20/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients. METHODS We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age. RESULTS SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001). LIMITATIONS Evaluation of brain dynamics was constrained by MRI temporal resolution. CONCLUSIONS Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.
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Affiliation(s)
- Qunjun Liang
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen 518060, People's Republic of China
| | - Ziyun Xu
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, People's Republic of China
| | - Shengli Chen
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Shiwei Lin
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Xiaoshan Lin
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Ying Li
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China
| | - Yingli Zhang
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Bo Peng
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Gangqiang Hou
- Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, People's Republic of China.
| | - Yingwei Qiu
- Department of Medical Imaging, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan District, Shenzhen 518000, People's Republic of China.
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2
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Gruzman R, Hempel M, Domke AK, Hartling C, Stippl A, Carstens L, Bajbouj M, Gärtner M, Grimm S. Investigating the impact of rumination and adverse childhood experiences on resting-state neural activity and connectivity in depression. J Affect Disord 2024; 358:283-291. [PMID: 38387672 DOI: 10.1016/j.jad.2024.02.068] [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: 10/18/2023] [Revised: 02/15/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Both ruminative thought processes and adverse childhood experiences (ACEs) are well-established risk factors for the emergence and maintenance of depression. However, the neurobiological mechanisms underlying these associations remain poorly understood. METHODS We examined resting-state functional magnetic resonance imaging data (3 T Tim Trio MR scanner; Siemens, Erlangen) of 44 individuals diagnosed with an acute depressive episode. Specifically, we focused on investigating functional brain activity and connectivity within and between three large-scale neural networks associated with processes affected in depression: the default mode network (DMN), the salience network (SN), and the central executive network (CEN). Correlational and regression-based analyses were performed. RESULTS Our regions of interest analyses revealed that region-specific spontaneous neural activity in the anterior DMN was associated with self-reported trait rumination, specifically, the pregenual anterior cingulate cortex (pgACC). Furthermore, using a liberal statistical threshold, we found that spontaneous neural activity of the ventromedial prefrontal cortex and the pgACC were associated with depression symptom severity. Neither spontaneous neural activity in the SN and CEN nor functional connectivity within and across the investigated networks was associated with depression severity or rumination. Furthermore, there was no association between ACEs and brain activity and connectivity. LIMITATIONS Lack of a formal control group or low-risk group for comparison. CONCLUSIONS Overall, our results indicate network-specific changes in spontaneous brain activity, that are linked to both depression severity and rumination. Findings underscore the crucial role of the pgACC in depression and contribute to a dimensional and symptom-based understanding of depression-related network imbalances.
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Affiliation(s)
- Rebecca Gruzman
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany.
| | - Moritz Hempel
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany
| | - Ann-Kathrin Domke
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, Berlin Institute of Health, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Corinna Hartling
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, Berlin Institute of Health, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Anna Stippl
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, Berlin Institute of Health, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Luisa Carstens
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany
| | - Malek Bajbouj
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, Berlin Institute of Health, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Matti Gärtner
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, Berlin Institute of Health, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Simone Grimm
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, Berlin Institute of Health, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Lenggstrasse 31, 8032 Zurich, Switzerland
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3
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Mitiureva D, Sysoeva O, Proshina E, Portnova G, Khayrullina G, Martynova O. Comparative analysis of resting-state EEG functional connectivity in depression and obsessive-compulsive disorder. Psychiatry Res Neuroimaging 2024; 342:111828. [PMID: 38833944 DOI: 10.1016/j.pscychresns.2024.111828] [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: 01/22/2024] [Revised: 05/09/2024] [Accepted: 05/19/2024] [Indexed: 06/06/2024]
Abstract
Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4-30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC.
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Affiliation(s)
- Dina Mitiureva
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Centre for Cognition & Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Olga Sysoeva
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Sirius Center for Cognitive Sciences, Sirius University of Science and Technology, Sochi, Russia
| | - Ekaterina Proshina
- Centre for Cognition & Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia.
| | - Galina Portnova
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia
| | - Guzal Khayrullina
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Centre for Cognition & Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Olga Martynova
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia; Department of Biology and Biotechnology, National Research University Higher School of Economics, Moscow, Russia
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4
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Fu L, Cai M, Zhao Y, Zhang Z, Qian Q, Xue H, Chen Y, Sun Z, Zhao Q, Wang S, Wang C, Wang W, Jiang Y, Tian Y, Ma J, Guo W, Liu F. Twenty-five years of research on resting-state fMRI of major depressive disorder: A bibliometric analysis of hotspots, nodes, bursts, and trends. Heliyon 2024; 10:e33833. [PMID: 39050435 PMCID: PMC11266997 DOI: 10.1016/j.heliyon.2024.e33833] [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: 12/15/2023] [Revised: 06/15/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Major depressive disorder (MDD) is a debilitating mental health condition that poses significant risks and burdens. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a promising tool in investigating the neural mechanisms underlying MDD. However, a comprehensive bibliometric analysis of resting-state fMRI in MDD is currently lacking. Here, we aimed to thoroughly explore the trends and frontiers of resting-state fMRI in MDD research. The relevant publications were retrieved from the Web of Science database for the period between 1998 and 2022, and the CiteSpace software was employed to identify the influence of authors, institutions, countries/regions, and the latest research trends. A total of 1501 publications met the search criteria, revealing a gradual increase in the number of annual publications over the years. China contributed the largest publication output, accounting for the highest percentage among all countries. Particularly, the University of Electronic Science and Technology of China, Capital Medical University, and Harvard Medical School were identified as key institutions that have made substantial contributions to this growth. Neuroimage, Biological Psychiatry, Journal of Affective Disorders, and Proceedings of the National Academy of Sciences of the United States of America are among the influential journals in the field of resting-state fMRI research in MDD. Burst keywords analysis suggest the emerging research frontiers in this field are characterized by prominent keywords such as dynamic functional connectivity, cognitive control network, transcranial brain stimulation, and childhood trauma. Overall, our study provides a systematic overview into the historical development, current status, and future trends of resting-state fMRI in MDD, thus offering a useful guide for researchers to plan their future research.
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Affiliation(s)
- Linhan Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yao Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qian Qian
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Chunyang Wang
- Department of Scientific Research, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenqin Wang
- School of Mathematical Sciences, Tianjin Polytechnic University, Tianjin, 300387, China
| | - Yifan Jiang
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China
| | - Yuxuan Tian
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
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5
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Rao L, Verma V, Jain S, Pinapati KK, Bhagyawant SS, Sharma S, Srivastava N. Anti-Depressant Like Effects of Aethoscytus Foveolus Oil by Improving Stress-mediated Alterations of Monoamine Oxidase, Oxidative Stress, and Neuroinflammation In-vivo. Cell Biochem Biophys 2024:10.1007/s12013-024-01288-8. [PMID: 38904723 DOI: 10.1007/s12013-024-01288-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2024] [Indexed: 06/22/2024]
Abstract
Depression is a neuropsychological disorder with a complex pathophysiology and its pharmacotherapy is compromised by adverse side effects. Addressing the need for effective treatment for depression, the current study aims to characterize the antidepressant activity of oil extract derived from Aethoscytus foveolus, bugs that are widely available in India, in a mice model of stress-induced depression. Chemical moieties characterized by GC-MS of A. foveolus oil extract have shown good affinity for monoamine oxidase A (MAO-A) in-silico. In-vitro MAO-inhibitory assay using mouse brain homogenates also showed similar results at IC50 1.363 nM (R2 = 0.981, SD ± 0.05, n = 3) of it. These results encouraged us to investigate the antidepressant potential of this oil extract in vivo. Stress-exposed mice (Swiss Albino, either sex, 25-30 gm) were administered 5 and 10 mg/kg doses of oil extract and classified as separate groups (N = 6 per group). Behavioral tests like the forced-swim test, tail-suspension test, and open-field test demonstrated significant attenuation of stress-induced depressive-like behavior of mice by both doses (p < 0.0001 with positive control group i.e., stress group), while biochemical tests on mice brain tissues showed amelioration of stress-induced hyperactivation of MAO (p < 0.0001) and oxidative stress (by increasing Superoxide dismutase and catalase, while reducing lipid peroxidase and nitric oxide) (p < 0.0001). The altered mRNA expression of proinflammatory cytokines (NF-κB, IL-6, IL-12, and TNF-α) (p < 0.015) was also improved by this oil extract. In addition, histopathology of hippocampus tissues of mice supports that this oil recovers stress-mediated structural changes of the brain. In conclusion our findings suggest that oil derived from A. foveolus could be beneficial in the alleviation of stress-mediated depressive-like behavior of mice, and in our knowledge, this is the first report identifying anti-neurodegenerative potential of A. foveolus.
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Affiliation(s)
- Lavisha Rao
- Department of Bioscience and Biotechnology, Banasthali Vidyapith, Rajasthan, 304022, India
| | - Vartika Verma
- Department of Bioscience and Biotechnology, Banasthali Vidyapith, Rajasthan, 304022, India
| | - Smita Jain
- Department of Pharmacy, Banasthali Vidyapith, Rajasthan, 304022, India
| | - Kishore Kumar Pinapati
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research (NIPER) Raebareli, Lucknow, Uttar Pradesh, 226002, India
| | - Sameer S Bhagyawant
- School of Studies in Biotechnology, Jiwaji university, Gwalior, 44011, India
| | - Swapnil Sharma
- Department of Pharmacy, Banasthali Vidyapith, Rajasthan, 304022, India.
| | - Nidhi Srivastava
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research (NIPER) Raebareli, Lucknow, Uttar Pradesh, 226002, India.
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6
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Stolfi F, Abreu H, Sinella R, Nembrini S, Centonze S, Landra V, Brasso C, Cappellano G, Rocca P, Chiocchetti A. Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine. Front Psychiatry 2024; 15:1422939. [PMID: 38938457 PMCID: PMC11210496 DOI: 10.3389/fpsyt.2024.1422939] [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: 04/24/2024] [Accepted: 05/28/2024] [Indexed: 06/29/2024] Open
Abstract
Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.
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Affiliation(s)
- Fabiola Stolfi
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Hugo Abreu
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Riccardo Sinella
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Nembrini
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Centonze
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Virginia Landra
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Giuseppe Cappellano
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Paola Rocca
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Annalisa Chiocchetti
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
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7
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Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, Gong G, Wang Q, Pang C, Yu Q, Bi Y, Chen P, Chen R, Chen Y, Chen T, Cheng J, Cheng Y, Cui Z, Dai Z, Deng Y, Ding Y, Dong Q, Duan D, Gao JH, Gong Q, Han Y, Han Z, Huang CC, Huang R, Huo R, Li L, Lin CP, Lin Q, Liu B, Liu C, Liu N, Liu Y, Liu Y, Lu J, Ma L, Men W, Qin S, Qiu J, Qiu S, Si T, Tan S, Tang Y, Tao S, Wang D, Wang F, Wang J, Wang P, Wang X, Wang Y, Wei D, Wu Y, Xie P, Xu X, Xu Y, Xu Z, Yang L, Yuan H, Zeng Z, Zhang H, Zhang X, Zhao G, Zheng Y, Zhong S, He Y. Functional connectome through the human life span. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.12.557193. [PMID: 37745373 PMCID: PMC10515818 DOI: 10.1101/2023.09.12.557193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Qian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxuan Pang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ruiwang Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, China
- Department of Education and Research, Taipei City Hospital, Taipei, China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji’nan, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | | | | | | | | | | | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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8
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Elorette C, Fujimoto A, Stoll FM, Fujimoto SH, Bienkowska N, London L, Fleysher L, Russ BE, Rudebeck PH. The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation. Nat Commun 2024; 15:4669. [PMID: 38821963 PMCID: PMC11143237 DOI: 10.1038/s41467-024-49140-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/23/2024] [Indexed: 06/02/2024] Open
Abstract
Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and the underlying patterns of neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the amygdala of two male macaques. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5 Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-modal approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.
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Affiliation(s)
- Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Frederic M Stoll
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Satoka H Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Niranjana Bienkowska
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Liza London
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Brian E Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY, 10962, USA.
- Department of Psychiatry, New York University at Langone, 550 1st Avenue, New York, NY, 10016, USA.
| | - Peter H Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
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9
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biol Psychiatry 2024:S0006-3223(24)01286-1. [PMID: 38718880 DOI: 10.1016/j.biopsych.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, University of Southern California, Los Angeles, California.
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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10
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Tian B, Chen Q, Zou M, Xu X, Liang Y, Liu Y, Hou M, Zhao J, Liu Z, Jiang L. Decreased resting-state functional connectivity and brain network abnormalities in the prefrontal cortex of elderly patients with Parkinson's disease accompanied by depressive symptoms. Glob Health Med 2024; 6:132-140. [PMID: 38690130 PMCID: PMC11043130 DOI: 10.35772/ghm.2023.01043] [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: 04/01/2023] [Revised: 12/07/2023] [Accepted: 12/25/2023] [Indexed: 05/02/2024]
Abstract
This study aimed to explore the brain network characteristics in elderly patients with Parkinson's disease (PD) with depressive symptoms. Thirty elderly PD patients with depressive symptoms (PD-D) and 26 matched PD patients without depressive symptoms (PD-NOD) were recruited based on HAMD-24 with a cut-off of 7. The resting-state functional connectivity (RSFC) was conducted by 53-channel functional near-infrared spectroscopy (fNIRS). There were no statistically significant differences in MMSE scores, disease duration, Hoehn-Yahr stage, daily levodopa equivalent dose, and MDS-UPDRS III between the two groups. However, compared to the PD-NOD group, the PD-D group showed significantly higher MDS-UPDRS II, HAMA-14, and HAMD-24. The interhemispheric FC strength and the FC strength between the left dorsolateral prefrontal cortex (DLPFC-L) and the left frontal polar area (FPA-L) was significantly lower in the PD-D group (FDR p < 0.05). As for graph theoretic metrics, the PD-D group had significantly lower degree centrality (aDc) and node efficiency (aNe) in the DLPFC-L and the FPA-L (FDR, p < 0.05), as well as decreased global efficiency (aEg). Pearson correlation analysis indicated moderate negative correlations between HAMD-24 scores and the interhemispheric FC strength, FC between DLPFC-L and FPA-L, aEg, aDc in FPA-L, aNe in DLPFC-L and FPA-L. In conclusion, PD-D patients show decreased integration and efficiency in their brain networks. Furthermore, RSFC between DLPFC-L and FPA-L regions is negatively correlated with depressive symptoms. These findings propose that targeting DLPFC-L and FPA-L regions via non-invasive brain stimulation may be a potential intervention for alleviating depressive symptoms in elderly PD patients.
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Affiliation(s)
- Bingjie Tian
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Qing Chen
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Zou
- Emergency Department, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Xu
- Department of Nursing, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuqi Liang
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Yiyan Liu
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Miaomiao Hou
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiahao Zhao
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhenguo Liu
- Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liping Jiang
- Department of Nursing, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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11
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Guo Y, Xia M, Ye R, Bai T, Wu Y, Ji Y, Yu Y, Ji GJ, Wang K, He Y, Tian Y. Electroconvulsive Therapy Regulates Brain Connectome Dynamics in Patients With Major Depressive Disorder. Biol Psychiatry 2024:S0006-3223(24)01171-5. [PMID: 38521158 DOI: 10.1016/j.biopsych.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 02/22/2024] [Accepted: 03/06/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome. METHODS We collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity. RESULTS At baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group. CONCLUSIONS We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.
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Affiliation(s)
- Yuanyuan Guo
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rong Ye
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China; Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Tongjian Bai
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
| | - Yue Wu
- Department of Psychology and Sleep Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yang Ji
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yue Yu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Gong-Jun Ji
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Anhui Institute of Translational Medicine, Hefei, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China.
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China; Department of Psychology and Sleep Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Neurology, the Second Affiliated Hospital of Anhui Medical University, Hefei, China.
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12
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Wang Y, Tian Y, Long Z, Dong D, He Q, Qiu J, Feng T, Chen H, Tahmasian M, Lei X. Volume of the Dentate Gyrus/CA4 Hippocampal subfield mediates the interplay between sleep quality and depressive symptoms. Int J Clin Health Psychol 2024; 24:100432. [PMID: 38269356 PMCID: PMC10806754 DOI: 10.1016/j.ijchp.2023.100432] [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: 09/12/2023] [Revised: 12/18/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024] Open
Abstract
Background Emerging evidence increasingly suggests that poor sleep quality is associated with depressive symptoms. The hippocampus might play a crucial role in the interplay between sleep disturbance and depressive symptomatology, e.g., hippocampal atrophy is typically seen in both insomnia disorder and depression. Thus, examining the role of hippocampal volume in the interplay between poor sleep quality and depressive symptoms in large healthy populations is vital. Methods We investigated the association between self-reported sleep quality, depressive symptoms, and hippocampal total and subfields' volumes in 1603 healthy young adults from the Behavioral Brain Research Project. Mediation analysis explored the mediating role of hippocampal volumes between sleep quality and depressive symptoms. Results Self-reported sleep quality and depressive symptoms were positively correlated. In addition, it negatively related to three hippocampal subfields but not total hippocampal volume. In particular, hippocampal subfield DG and CA4 volumes mediated the interrelationship between poor sleep quality and depressive symptoms. Conclusions Our findings improved the current understanding of the relationship between sleep disturbance, depressive symptomatology, and hippocampal subfields in healthy populations. Considering the crucial role of DG in hippocampal neurogenesis, our results suggest that poor sleep quality may contribute to depression through a reduction of DG volume leading to impaired neurogenesis which is crucial for the regulation of mood.
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Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yun Tian
- Sleep and NeuroImaging Center, Faculty of psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Zhiliang Long
- Sleep and NeuroImaging Center, Faculty of psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Masoud Tahmasian
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
- Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
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13
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Rodrigues-Ribeiro L, Resende BL, Pinto Dias ML, Lopes MR, de Barros LLM, Moraes MA, Verano-Braga T, Souza BR. Neuroproteomics: Unveiling the Molecular Insights of Psychiatric Disorders with a Focus on Anxiety Disorder and Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1443:103-128. [PMID: 38409418 DOI: 10.1007/978-3-031-50624-6_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Anxiety and depression are two of the most common mental disorders worldwide, with a lifetime prevalence of up to 30%. These disorders are complex and have a variety of overlapping factors, including genetic, environmental, and behavioral factors. Current pharmacological treatments for anxiety and depression are not perfect. Many patients do not respond to treatment, and those who do often experience side effects. Animal models are crucial for understanding the complex pathophysiology of both disorders. These models have been used to identify potential targets for new treatments, and they have also been used to study the effects of environmental factors on these disorders. Recent proteomic methods and technologies are providing new insights into the molecular mechanisms of anxiety disorder and depression. These methods have been used to identify proteins that are altered in these disorders, and they have also been used to study the effects of pharmacological treatments on protein expression. Together, behavioral and proteomic research will help elucidate the factors involved in anxiety disorder and depression. This knowledge will improve preventive strategies and lead to the development of novel treatments.
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Affiliation(s)
- Lucas Rodrigues-Ribeiro
- Department of Physiology and Biophysics, National Institute of Science and Technology in Nanobiopharmaceutics (INCT-Nanobiofar), Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Physiology and Biophysics, Proteomics Group (NPF), Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Bruna Lopes Resende
- Department of Physiology and Biophysics, Laboratory of Neurodevelopment and Evolution (NeuroDEv), Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Maria Luiza Pinto Dias
- Department of Physiology and Biophysics, National Institute of Science and Technology in Nanobiopharmaceutics (INCT-Nanobiofar), Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Physiology and Biophysics, Proteomics Group (NPF), Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Megan Rodrigues Lopes
- Department of Physiology and Biophysics, National Institute of Science and Technology in Nanobiopharmaceutics (INCT-Nanobiofar), Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Physiology and Biophysics, Proteomics Group (NPF), Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Physiology and Biophysics, Laboratory of Neurodevelopment and Evolution (NeuroDEv), Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Larissa Luppi Monteiro de Barros
- Department of Physiology and Biophysics, National Institute of Science and Technology in Nanobiopharmaceutics (INCT-Nanobiofar), Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Physiology and Biophysics, Proteomics Group (NPF), Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Muiara Aparecida Moraes
- Department of Physiology and Biophysics, Laboratory of Neurodevelopment and Evolution (NeuroDEv), Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Thiago Verano-Braga
- Department of Physiology and Biophysics, National Institute of Science and Technology in Nanobiopharmaceutics (INCT-Nanobiofar), Federal University of Minas Gerais, Belo Horizonte, Brazil.
- Department of Physiology and Biophysics, Proteomics Group (NPF), Federal University of Minas Gerais, Belo Horizonte, Brazil.
| | - Bruno Rezende Souza
- Department of Physiology and Biophysics, Laboratory of Neurodevelopment and Evolution (NeuroDEv), Federal University of Minas Gerais, Belo Horizonte, Brazil.
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Li J, Leng Y, Ma H, Yang F, Liu B, Fan W. Functional reorganization of intranetwork and internetwork connectivity in patients with Ménière's disease. Sci Rep 2023; 13:16775. [PMID: 37798378 PMCID: PMC10556034 DOI: 10.1038/s41598-023-44090-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/03/2023] [Indexed: 10/07/2023] Open
Abstract
Ménière's disease (MD) is associated with functional reorganization not only in the auditory or sensory cortex but also in other control and cognitive areas. In this study, we examined intranetwork and internetwork connectivity differences between 55 MD patients and 70 healthy controls (HC) in 9 well-defined resting-state networks. Functional connectivity degree was lower in MD compared to HC in 19 brain areas involved in the somatomotor, auditory, ventral attention, default mode, limbic, and deep gray matter networks. In addition, we observed lower intranetwork connectivity in the auditory, ventral attention, and limbic networks, as well as lower internetwork connectivity between the somatomotor and limbic networks, and between the auditory and somatomotor, deep gray matter, and ventral attention networks, and between the deep gray matter and default mode network. Furthermore, we identified 81 pairs of brain areas with significant differences in functional connectivity between MD patients and HC at the edge level. Notably, the left amygdala's functional connectivity degree was positively correlated with MD's disease stage, and the ventral attention network's intranetwork connectivity was positively correlated with the healthy side vestibular ratio. Our findings suggest that these functional network reorganization alterations may serve as potential biomarkers for predicting clinical progression, evaluating disease severity, and gaining a better understanding of MD's pathophysiology. Large-scale network studies using neuroimaging techniques can provide additional insights into the underlying mechanisms of MD.
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Affiliation(s)
- Jing Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Yangming Leng
- Department of Otorhinolaryngology Head and Neck Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Hui Ma
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Bo Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
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15
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Elorette C, Fujimoto A, Stoll FM, Fujimoto SH, Fleysher L, Bienkowska N, Russ BE, Rudebeck PH. The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.21.545778. [PMID: 37745436 PMCID: PMC10515745 DOI: 10.1101/2023.06.21.545778] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the macaque amygdala and activated them with a highly selective and potent DREADD agonist, deschloroclozapine. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Interestingly, activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-disciplinary approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.
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Affiliation(s)
- Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Frederic M. Stoll
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Satoka H. Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Niranjana Bienkowska
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Brian E. Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY 10962
- Department of Psychiatry, New York University at Langone, One, 8, Park Ave, New York, NY 10016
| | - Peter H. Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
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16
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Srivastava S, Arenkiel BR, Salas R. Habenular molecular targets for depression, impulsivity, and addiction. Expert Opin Ther Targets 2023; 27:757-761. [PMID: 37705488 PMCID: PMC10591939 DOI: 10.1080/14728222.2023.2257390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/06/2023] [Indexed: 09/15/2023]
Affiliation(s)
- Snigdha Srivastava
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Jan and Dan Duncan Neurological Institute, Texas Children’s Hospital, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA
| | - Benjamin R Arenkiel
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Jan and Dan Duncan Neurological Institute, Texas Children’s Hospital, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- Center for Translational Research on Inflammatory Diseases, Michael E DeBakey VA Medical Center, Houston TX, USA
- The Menninger Clinic, Houston TX, USA
- Department of Neurosciences, Baylor College of Medicine, Houston, TX, USA
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