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365 results on '"Frokjaer, Vibe G."'

1. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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2. Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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3. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

Author

Fu, Cynthia HY, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen R, Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N, Frokjaer, Vibe G, Ganz, Melanie, Garcia, Jose, Godlewska, Beata R, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C, Tosun, Duygu, Victor, Teresa A, Wei, Dongtao, Wise, Toby, Woodham, Rachel D, Zahn, Roland, Anderson, Ian M, Deakin, JF William, Dunlop, Boadie W, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H, Harmer, Catherine J, Kennedy, Sidney H, Knudsen, Gitte M, Mayberg, Helen S, Paulus, Martin P, Qiu, Jiang, Trivedi, Madhukar H, Whalley, Heather C, Yan, Chao-Gan, Young, Allan H, and Davatzikos, Christos

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Biomedical Imaging, Neurosciences, Brain Disorders, Clinical Research, Depression, Bioengineering, Serious Mental Illness, Major Depressive Disorder, Mental Health, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Neurological, Mental health, Humans, Depressive Disorder, Major, Prospective Studies, Reproducibility of Results, Brain, Neuroimaging, Magnetic Resonance Imaging, Artificial Intelligence, Classification, Biomarkers, Deep learning, Harmonization, Predictors, MRI, Public Health and Health Services, Psychiatry, Clinical sciences, Epidemiology, Clinical and health psychology
Abstract

BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Published
2023

21. Molecular Imaging of Depressive Disorders

Author

Ruhé, Henricus G., Frokjaer, Vibe G., Haarman, Bartholomeus (Benno) C. M., Jacobs, Gabriël E., Booij, Jan, Dierckx, Rudi A.J.O., editor, Otte, Andreas, editor, de Vries, Erik F. J., editor, van Waarde, Aren, editor, and Sommer, Iris E., editor

Published
2021
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23. Effect of Antidepressant Treatment on Serotonin 4 Receptor Binding and Associations with Clinical Outcomes and Verbal Memory in Major Depressive Disorder

Author

Dam, Vibeke Høyrup, primary, Köhler-Forsberg, Kristin, additional, Ozenne, Brice, additional, Larsen, Søren Vinther, additional, Ip, Cheng Teng, additional, Jorgensen, Anders, additional, Stenbæk, Dea Siggaard, additional, Madsen, Jacob, additional, Svarer, Claus, additional, Jørgensen, Martin Balslev, additional, Knudsen, Gitte Moos, additional, and Frokjaer, Vibe G., additional

Published
2024
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28. Preprocessing, Prediction and Significance: Framework and Application to Brain Imaging

Author

Nørgaard, Martin, Ozenne, Brice, Svarer, Claus, Frokjaer, Vibe G., Schain, Martin, Strother, Stephen C., Ganz, Melanie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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29. The Center for Integrated Molecular Brain Imaging (Cimbi) database

Author

Knudsen, Gitte M, Jensen, Peter S, Erritzoe, David, Baaré, William FC, Ettrup, Anders, Fisher, Patrick M, Gillings, Nic, Hansen, Hanne D, Hansen, Lars Kai, Hasselbalch, Steen G, Henningsson, Susanne, Herth, Matthias M, Holst, Klaus K, Iversen, Pernille, Kessing, Lars V, Macoveanu, Julian, Madsen, Kathrine Skak, Mortensen, Erik L, Nielsen, Finn Årup, Paulson, Olaf B, Siebner, Hartwig R, Stenbæk, Dea S, Svarer, Claus, Jernigan, Terry L, Strother, Stephen C, and Frokjaer, Vibe G

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Clinical Research, Depression, Neurosciences, Brain Disorders, Mental Health, Biomedical Imaging, Neurological, Mental health, Good Health and Well Being, Biological Specimen Banks, Biomarkers, Computer Security, Databases, Factual, Healthy Volunteers, Humans, Information Dissemination, Magnetic Resonance Imaging, Mental Disorders, Molecular Imaging, Neuroimaging, Neuropsychological Tests, Quality Control, Receptors, Serotonin, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

We here describe a multimodality neuroimaging containing data from healthy volunteers and patients, acquired within the Lundbeck Foundation Center for Integrated Molecular Brain Imaging (Cimbi) in Copenhagen, Denmark. The data is of particular relevance for neurobiological research questions related to the serotonergic transmitter system with its normative data on the serotonergic subtype receptors 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4 and the 5-HT transporter (5-HTT), but can easily serve other purposes. The Cimbi database and Cimbi biobank were formally established in 2008 with the purpose to store the wealth of Cimbi-acquired data in a highly structured and standardized manner in accordance with the regulations issued by the Danish Data Protection Agency as well as to provide a quality-controlled resource for future hypothesis-generating and hypothesis-driven studies. The Cimbi database currently comprises a total of 1100 PET and 1000 structural and functional MRI scans and it holds a multitude of additional data, such as genetic and biochemical data, and scores from 17 self-reported questionnaires and from 11 neuropsychological paper/computer tests. The database associated Cimbi biobank currently contains blood and in some instances saliva samples from about 500 healthy volunteers and 300 patients with e.g., major depression, dementia, substance abuse, obesity, and impulsive aggression. Data continue to be added to the Cimbi database and biobank.

Published
2016

37. Stress Hormone Dynamics Are Coupled to Brain Serotonin 4 Receptor Availability in Unmedicated Patients With Major Depressive Disorder: A NeuroPharm Study

Author

Vulpius, Gunild M, primary, Köhler-Forsberg, Kristin, additional, Ozenne, Brice, additional, Larsen, Søren V, additional, Nasser, Arafat, additional, Svarer, Claus, additional, Gillings, Nic, additional, Keller, Sune H, additional, Jørgensen, Martin B, additional, Knudsen, Gitte M, additional, and Frokjaer, Vibe G, additional

Published
2023
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39. In Vivo Imaging of Cerebral Serotonin Transporter and Serotonin2A Receptor Binding in 3,4-Methylenedioxymethamphetamine (MDMA or “Ecstasy”) and Hallucinogen Users

Author

Erritzoe, David, Frokjaer, Vibe G, Holst, Klaus K, Christoffersen, Maria, Johansen, Sys S, Svarer, Claus, Madsen, Jacob, Rasmussen, Peter M, Ramsøy, Thomas, Jernigan, Terry L, and Knudsen, Gitte M

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Substance Misuse, Neurosciences, Drug Abuse (NIDA only), Brain Disorders, Adult, Amphetamine-Related Disorders, Aniline Compounds, Brain, Case-Control Studies, Cross-Sectional Studies, Female, Fluorine Radioisotopes, Hallucinogens, Humans, Ketanserin, Male, N-Methyl-3, 4-methylenedioxyamphetamine, Positron-Emission Tomography, Protein Binding, Radioligand Assay, Receptor, Serotonin, 5-HT2A, Serotonin Plasma Membrane Transport Proteins, Sulfides, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

ContextBoth hallucinogens and 3,4-methylenedioxymethamphetamine (MDMA or "ecstasy") have direct agonistic effects on postsynaptic serotonin(2A) receptors, the key site for hallucinogenic actions. In addition, MDMA is a potent releaser and reuptake inhibitor of presynaptic serotonin.ObjectiveTo assess the differential effects of MDMA and hallucinogen use on cerebral serotonin transporter (SERT) and serotonin(2A) receptor binding.DesignA positron emission tomography study of 24 young adult drug users and 21 nonusing control participants performed with carbon 11 ((11)C)-labeled 3-amino-4-[2-[(di(methyl)amino)methyl]phenyl]sulfanylbenzonitrile (DASB) and fluorine 18 ((18)F)-labeled altanserin, respectively. Scans were performed in the user group after a minimum drug abstinence period of 11 days, and the group was subdivided into hallucinogen-preferring users (n = 10) and MDMA-preferring users (n = 14).ParticipantsTwenty-four young adult users of MDMA and/or hallucinogenic drugs and 21 nonusing controls.Main outcome measuresIn vivo cerebral SERT and serotonin(2A) receptor binding.ResultsCompared with nonusers, MDMA-preferring users showed significant decreases in SERT nondisplaceable binding potential (neocortex, -56%; pallidostriatum, -19%; and amygdala, -32%); no significant changes were seen in hallucinogen-preferring users. Both cortical and pallidostriatal SERT nondisplaceable binding potential was negatively correlated with the number of lifetime MDMA exposures, and the time of abstinence from MDMA was positively correlated with subcortical, but not cortical, SERT binding. A small decrease in neocortical serotonin(2A) receptor binding in the serotonin(2A) receptor agonist users (both user groups) was also detected.ConclusionsWe found evidence that MDMA but not hallucinogen use is associated with changes in the cerebral presynaptic serotonergic transmitter system. Because hallucinogenic drugs primarily have serotonin(2A) receptor agonistic actions, we conclude that the negative association between MDMA use and cerebral SERT binding is mediated through a direct presynaptic MDMA effect rather than by the serotonin(2A) agonistic effects of MDMA. Our cross-sectional data suggest that subcortical, but not cortical, recovery of SERT binding might take place after several months of MDMA abstinence.

Published
2011

40. Familial Risk for Mood Disorder and the Personality Risk Factor, Neuroticism, Interact in Their Association with Frontolimbic Serotonin 2A Receptor Binding

Author

Frokjaer, Vibe G, Vinberg, Maj, Erritzoe, David, Baaré, William, Holst, Klaus Kähler, Mortensen, Erik Lykke, Arfan, Haroon, Madsen, Jacob, Jernigan, Terry L, Kessing, Lars Vedel, and Knudsen, Gitte Moos

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Social and Personality Psychology, Psychology, Pharmacology and Pharmaceutical Sciences, Clinical Research, Depression, Behavioral and Social Science, Prevention, Brain Disorders, Mental Health, Neurosciences, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adult, Family, Female, Frontal Lobe, Genetic Predisposition to Disease, Humans, Ketanserin, Limbic System, Male, Middle Aged, Mood Disorders, Personality, Personality Tests, Positron-Emission Tomography, Receptor, Serotonin, 5-HT2A, Risk Factors, Sex Factors, Surveys and Questionnaires, Twins, Young Adult, 5-HT2A, depression, personality, neuroticism, stress, PET, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract

Life stress is a robust risk factor for later development of mood disorders, particularly for individuals at familial risk. Likewise, scoring high on the personality trait neuroticism is associated with an increased risk for mood disorders. Neuroticism partly reflects stress vulnerability and is positively correlated to frontolimbic serotonin 2A (5-HT(2A)) receptor binding. Here, we investigate whether neuroticism interacts with familial risk in relation to frontolimbic 5-HT(2A) receptor binding. Twenty-one healthy twins with a co-twin history of mood disorder and 16 healthy twins without a co-twin history of mood disorder were included. They answered self-report personality questionnaires and underwent [(18)F]altanserin positron emission tomography. We found a significant interaction between neuroticism and familial risk in predicting the frontolimbic 5-HT(2A) receptor binding (p=0.026) in an analysis adjusting for age and body mass index. Within the high-risk group only, neuroticism and frontolimbic 5-HT(2A) receptor binding was positively associated (p=0.0037). In conclusion, our data indicate that familial risk and neuroticism interact in their relation to frontolimbic 5-HT(2A) receptor binding. These findings point at a plausible neurobiological link between genetic and personality risk factors and vulnerability to developing mood disorders. It contributes to our understanding of why some people at high risk develop mood disorders while others do not. We speculate that an increased stress reactivity in individuals at high familial risk for mood disorders might enhance the effect of neuroticism in shaping the impact of potential environmental stress and thereby influence serotonergic neurotransmission.

Published
2010

41. Serotonin 4 Receptor Brain Binding in Major Depressive Disorder and Association With Memory Dysfunction

Author

Köhler-Forsberg, Kristin, primary, Dam, Vibeke H., additional, Ozenne, Brice, additional, Sankar, Anjali, additional, Beliveau, Vincent, additional, Landman, Elizabeth B., additional, Larsen, Søren V., additional, Poulsen, Asbjørn S., additional, Ip, Cheng-Teng, additional, Jørgensen, Anders, additional, Meyer, Michal, additional, Stenbæk, Dea S., additional, Eiberg, Hans R. L., additional, Madsen, Jacob, additional, Svarer, Claus, additional, Jørgensen, Martin B., additional, Frokjaer, Vibe G., additional, and Knudsen, Gitte M., additional

Published
2023
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42. Whole-brain turbulent dynamics predict responsiveness to pharmacological treatment in major depressive disorder

Author

Escrichs, Anira, Sanz Perl, Yonatan, Fisher, Patrick M., Martínez-Molina, Noelia, G-Guzman, Elvira, Frokjaer, Vibe G., Kringelbach, Morten L., Knudsen, Gitte M., and Deco, Gustavo

Abstract

Depression is a multifactorial clinical syndrome with a low pharmacological treatment response rate. Therefore, identifying predictors of treatment response capable of providing the basis for future developments of individualized therapies is crucial. Here, we applied model-free and model-based measures of whole-brain turbulent dynamics in resting-state functional magnetic resonance imaging (fMRI) in healthy controls and unmedicated depressed patients. After eight weeks of treatment with selective serotonin reuptake inhibitors (SSRIs), patients were classified as responders and non-responders according to the Hamilton Depression Rating Scale 6 (HAMD6). Using the model-free approach, we found that compared to healthy controls and responder patients, non-responder patients presented disruption of the information transmission across spacetime scales. Furthermore, our results revealed that baseline turbulence level is positively correlated with beneficial pharmacological treatment outcomes. Importantly, our model-free approach enabled prediction of which patients would turn out to be non-responders. Finally, our model-based approach provides mechanistic evidence that non-responder patients are less sensitive to stimulation and, consequently, less prone to respond to treatment. Overall, we demonstrated that different levels of turbulent dynamics are suitable for predicting response to SSRIs treatment in depression.

Published
2024
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46. Molecular Imaging of Depressive Disorders

Author

Ruhé, Henricus G., Visser, Anniek K. D., Frokjaer, Vibe G., Haarman, Bartholomeus (Benno) C. M., Klein, Hans C., Booij, Jan, Dierckx, Rudi A.J.O., editor, Otte, Andreas, editor, de Vries, Erik F. J., editor, van Waarde, Aren, editor, and den Boer, Johan A., editor

Published
2014
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49. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

Author

Fu, Cynthia H. Y., primary, Erus, Guray, additional, Fan, Yong, additional, Antoniades, Mathilde, additional, Arnone, Danilo, additional, Arnott, Stephen, additional, Chen, Taolin, additional, Choi, Ki Sueng, additional, Fatt, Cherise Chin, additional, Frey, Benicio N., additional, Frokjaer, Vibe G., additional, Ganz, Melanie, additional, Garcia, Jose, additional, Godlewska, Beata R., additional, Hassel, Stefanie, additional, Ho, Keith, additional, McIntosh, Andrew M., additional, Qin, Kun, additional, Rotzinger, Susan, additional, Sacchet, Matthew D., additional, Savitz, Johnathan, additional, Shou, Haochang, additional, Singh, Ashish, additional, Stolicyn, Aleks, additional, Strigo, Irina, additional, Strother, Stephen C., additional, Tosun, Duygu, additional, Victor, Teresa A., additional, Wei, Dongtao, additional, Wise, Toby, additional, Woodham, Rachel D., additional, Zahn, Roland, additional, Anderson, Ian M., additional, Deakin, J.F. William, additional, Dunlop, Boadie W., additional, Elliott, Rebecca, additional, Gong, Qiyong, additional, Gotlib, Ian H., additional, Harmer, Catherine J., additional, Kennedy, Sidney H., additional, Knudsen, Gitte M., additional, Mayberg, Helen S., additional, Paulus, Martin P., additional, Qiu, Jiang, additional, Trivedi, Madhukar H., additional, Whalley, Heather C., additional, Yan, Chao-Gan, additional, Young, Allan H., additional, and Davatzikos, Christos, additional

Published
2022
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