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Zhong YL, Liu H, Huang X. Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma. Neuroscience 2024; 558:11-21. [PMID: 39154845 DOI: 10.1016/j.neuroscience.2024.08.013] [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: 05/12/2024] [Revised: 07/16/2024] [Accepted: 08/08/2024] [Indexed: 08/20/2024]
Abstract
Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.
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Affiliation(s)
- Yu-Lin Zhong
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi 330006, China
| | - Hao Liu
- School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi 330006, China.
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Copa D, Erritzoe D, Giribaldi B, Nutt D, Carhart-Harris R, Tagliazucchi E. Predicting the outcome of psilocybin treatment for depression from baseline fMRI functional connectivity. J Affect Disord 2024; 353:60-69. [PMID: 38423367 DOI: 10.1016/j.jad.2024.02.089] [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: 07/15/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Psilocybin is a serotonergic psychedelic drug under assessment as a potential therapy for treatment-resistant and major depression. Heterogeneous treatment responses raise interest in predicting the outcome from baseline data. METHODS A machine learning pipeline was implemented to investigate baseline resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI) as a predictor of symptom severity in psilocybin monotherapy for treatment-resistant depression (16 patients administered two 5 mg capsules followed by 25 mg, separated by one week). Generalizability was tested in a sample of 22 patients who participated in a psilocybin vs. escitalopram trial for moderate-to-severe major depression (two separate doses of 25 mg of psilocybin 3 weeks apart plus 6 weeks of daily placebo vs. two separate doses of 1 mg of psilocybin 3 weeks apart plus 6 weeks of daily oral escitalopram). The analysis was repeated using both samples combined. RESULTS Functional connectivity of visual, default mode and executive networks predicted early symptom improvement, while the salience network predicted responders up to 24 weeks after treatment (accuracy≈0.9). Generalization performance was borderline significant. Consistent results were obtained from the combined sample analysis. Fronto-occipital and fronto-temporal coupling predicted early and late symptom reduction, respectively. LIMITATIONS The number of participants and differences between the two datasets limit the generalizability of the findings, while the lack of a placebo arm limits their specificity. CONCLUSIONS Baseline neurophysiological measurements can predict the outcome of psilocybin treatment for depression. Future research based on larger datasets should strive to assess the generalizability of these predictions.
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Affiliation(s)
- Débora Copa
- Universidad de Buenos Aires, Facultad de Ingeniería, Instituto de Bioingeniería, Buenos Aires, Argentina.
| | - David Erritzoe
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom
| | - Bruna Giribaldi
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom
| | - David Nutt
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom
| | - Robin Carhart-Harris
- Centre for Psychedelic Research, Division of Academic Psychiatry, Imperial College London, London, United Kingdom; Psychedelics Division, Neuroscape, Department of Neurology, University of California, San Francisco, USA
| | - Enzo Tagliazucchi
- Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Ciudad Universitaria, Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Instituto de Física Interdisciplinaria y Aplicada (INFINA), Ciudad Universitaria, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
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Lohaus M, Maurer A, Upadhyay N, Daamen M, Bodensohn L, Werkhausen J, Manunzio C, Manunzio U, Radbruch A, Attenberger U, Boecker H. Differential modulation of resting-state functional connectivity between amygdala and precuneus after acute physical exertion of varying intensity: indications for a role in affective regulation. Front Hum Neurosci 2024; 18:1349477. [PMID: 38646163 PMCID: PMC11027744 DOI: 10.3389/fnhum.2024.1349477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/18/2024] [Indexed: 04/23/2024] Open
Abstract
Introduction Physical activity influences psychological well-being. This study aimed to determine the impact of exercise intensity on psychological well-being and alterations in emotion-related brain functional connectivity (FC). Methods Twenty young, healthy, trained athletes performed a low- and high-intensity interval exercise (LIIE and HIIE) as well as a control condition in a within-subject crossover design. Before and after each condition, Positive And Negative Affect Scale (PANAS) was assessed as well as resting-state functional MRI (rs-fMRI). Voxel-wise FC was examined for bilateral amygdala seed region to whole-brain and emotion-related anatomical regions (e.g., insula, temporal pole, precuneus). Data analyses were performed using linear mixed-effect models with fixed factors condition and time. Results The PANAS Positive Affect scale showed a significant increase after LIIE and HIIE and a significant reduction in Negative Affect after the control condition. In rs-fMRI, no significant condition-by-time interactions were observed between the amygdala and whole brain. Amygdala-precuneus FC analysis showed an interaction effect, suggesting reduced post-exercise anticorrelation after the control condition, but stable, or even slightly enhanced anticorrelation for the exercise conditions, especially HIIE. Discussion In conclusion, both LIIE and HIIE had positive effects on mood and concomitant effects on amygdala-precuneus FC, particularly after HIIE. Although no significant correlations were found between amygdala-precuneus FC and PANAS, results should be discussed in the context of affective disorders in whom abnormal amygdala-precuneus FC has been observed.
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Affiliation(s)
- Marvin Lohaus
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Angelika Maurer
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Neeraj Upadhyay
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Marcel Daamen
- Deutsche Zentrum für Neurodegenerative Erkrankungen Bonn, Bonn, Germany
| | - Luisa Bodensohn
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Judith Werkhausen
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Christian Manunzio
- Sportsmedicine, Department of Paediatric Cardiology, University Hospital Bonn, Bonn, Germany
| | - Ursula Manunzio
- Sportsmedicine, Department of Paediatric Cardiology, University Hospital Bonn, Bonn, Germany
| | | | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Henning Boecker
- Clinical Functional Imaging Group, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
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Strege MV, Siegle GJ, Richey JA, Krawczak RA, Young K. Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application. Brain Imaging Behav 2023; 17:450-460. [PMID: 36622532 PMCID: PMC10329727 DOI: 10.1007/s11682-022-00756-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/26/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023]
Abstract
We sought to identify baseline (pre-treatment) neural markers associated with treatment response in major depressive disorder (MDD), specific to treatment type, Cognitive Behavioral Therapy (CBT) or pharmacotherapy (selective serotonin reuptake inhibitors; SSRI). We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies to identify neural prognostic indicators of response to CBT or SSRI. To verify the regions derived from literature, the meta-analytic regions were used to predict clinical change in a verification sample of participants with MDD who received either CBT (n = 60) or an SSRI (n = 19) as part of prior clinical trials. The meta-analysis consisted of 21 fMRI studies that used emotion-related tasks. It yielded prognostic regions of the perigenual (meta pgACC) and subgenual anterior cingulate cortex (meta sgACC), associated with SSRI and CBT response, respectively. When applying the meta-analytic regions to predict treatment response in the verification sample, reactivity of the meta pgACC was prognostic for SSRI response, yet the effect direction was opposite of most prior studies. Meta sgACC reactivity failed to be prognostic for CBT response. Results confirm the prognostic potential of neural reactivity of ACC subregions in MDD but further research is necessary for clinical translation.
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Affiliation(s)
- Marlene V Strege
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States.
| | - Greg J Siegle
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States
| | - John A Richey
- Department of Psychology, Virginia Polytechnic Institute and State University, Blacksburg, United States
| | | | - Kymberly Young
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States
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Chen Y, Ma J, Zhu H, Peng H, Gan Y. The mediating role of default mode network during meaning-making aroused by mental simulation between stressful events and stress-related growth: a task fMRI study. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:12. [PMID: 37454095 DOI: 10.1186/s12993-023-00214-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Stressful events and meaning-making toward them play an important role in adolescents' life and growth. However, ignoring positive stressful events leads to negativity bias; further, the neural mechanisms of meaning-making are unclear. We aimed to verify the mediating role of meaning-making in stressful events and stress-related growth and the function of the default mode network (DMN) during meaning-making in this functional magnetic resonance imaging (fMRI) study. METHODS Participants comprised 59 university students. Stressful life events, meaning-making, and stress-related growth were assessed at baseline, followed by fMRI scanning during a meaning-making task aroused by mental simulation. General linear modeling and psychophysiological interaction (PPI) analyses were used to explore the activation and functional connectivity of DMN during meaning-making. RESULTS Mental simulation triggered meaning-making, and DMN activity decreased during meaning-making. Activation of the DMN was negatively correlated with coping flexibility, an indicator of stress-related growth. PPI analysis showed that meaning-making was accompanied by diminished connectivity in the DMN. DMN activation during meaning-making can mediate the relationship between positive stressful events and coping flexibility. CONCLUSIONS Decreased DMN activity and diminished functional connectivity in the DMN occurred during meaning-making. Activation of the DMN during meaning-making could mediate the relationship between positive stressful events and stress-related growth, which provides a cognitive neural basis for the mediating role of meaning-making in the relationship between stressful events and indicators of stress-related growth. IMPLICATIONS This study supports the idea that prosperity makes heroes, expands the meaning-making model, and suggests the inclusion of enhancing personal resources and meaning-making in education. This study was the first to validate the activation pattern and functional connectivity of the DMN during meaning-making aroused by mental simulation using an fMRI task-state examination, which can enhance our sense of meaning and provide knowledge that can be used in clinical psychology interventions. TRIAL REGISTRATION The study protocol was pre-registered in Open Science Framework (see osf.io/ahm6e for details).
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Affiliation(s)
- Yidi Chen
- School of Psychological Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China
| | - Jinjin Ma
- School of Psychological Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China
| | - Huanya Zhu
- School of Psychological Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China
| | - Huini Peng
- School of Psychological Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China
| | - Yiqun Gan
- School of Psychological Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China.
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Kotoula V, Evans JW, Punturieri CE, Zarate CA. Review: The use of functional magnetic resonance imaging (fMRI) in clinical trials and experimental research studies for depression. FRONTIERS IN NEUROIMAGING 2023; 2:1110258. [PMID: 37554642 PMCID: PMC10406217 DOI: 10.3389/fnimg.2023.1110258] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/12/2023] [Indexed: 08/10/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive technique that can be used to examine neural responses with and without the use of a functional task. Indeed, fMRI has been used in clinical trials and pharmacological research studies. In mental health, it has been used to identify brain areas linked to specific symptoms but also has the potential to help identify possible treatment targets. Despite fMRI's many advantages, such findings are rarely the primary outcome measure in clinical trials or research studies. This article reviews fMRI studies in depression that sought to assess the efficacy and mechanism of action of compounds with antidepressant effects. Our search results focused on selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed treatments for depression and ketamine, a fast-acting antidepressant treatment. Normalization of amygdala hyperactivity in response to negative emotional stimuli was found to underlie successful treatment response to SSRIs as well as ketamine, indicating a potential common pathway for both conventional and fast-acting antidepressants. Ketamine's rapid antidepressant effects make it a particularly useful compound for studying depression with fMRI; its effects on brain activity and connectivity trended toward normalizing the increases and decreases in brain activity and connectivity associated with depression. These findings highlight the considerable promise of fMRI as a tool for identifying treatment targets in depression. However, additional studies with improved methodology and study design are needed before fMRI findings can be translated into meaningful clinical trial outcomes.
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Ternovoy S, Ustyuzhanin D, Shariya M, Beliaevskaia A, Roldan-Valadez E, Shishorin R, Akhapkin R, Volel B. Recognition of Facial Emotion Expressions in Patients with Depressive Disorders: A Functional MRI Study. Tomography 2023; 9:529-540. [PMID: 36961002 PMCID: PMC10037615 DOI: 10.3390/tomography9020043] [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: 12/22/2022] [Revised: 02/09/2023] [Accepted: 02/16/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND The present study evaluated the cortical activation during emotional information recognition. METHODS The study group included 16 patients with depression, and 16 healthy subjects were enrolled as a control group. Patients received eight weeks of antidepressant therapy. Functional MRI evaluated the cortical activation twice in the patient group and once in the control group. The fMRI task processed the emotional information with face demonstration from the PennCNP test battery. RESULTS During the processing of emotional information, patients showed activation in the middle and the inferior frontal gyri, the fusiform gyrus, and the occipital cortex. After treatment, patients showed a significant decrease in the frontal cortex activation for negative face demonstration and no frontal activation for positive emotion recognition. The left superior temporal gyrus activation zone appeared in patients after treatment and in the control group. Healthy subjects showed more intense frontal cortex activation when processing neutral emotions and less when showing happy and sad faces. Activation zones in the amygdala and the insula and deactivation zones in the posterior cingulate cortex were revealed in the controls. CONCLUSION This study confirms the hypothesis that anomalies in the processing of emotional stimuli can be a sign of a depressive disorder.
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Affiliation(s)
- Sergey Ternovoy
- National Medical Research Center of Cardiology, 121552 Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
| | | | - Merab Shariya
- National Medical Research Center of Cardiology, 121552 Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
| | - Alena Beliaevskaia
- National Medical Research Center of Cardiology, 121552 Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
| | - Ernesto Roldan-Valadez
- I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
- Directorate of Research, Hospital General de Mexico "Dr Eduardo Liceaga", Mexico City 06720, Mexico
| | - Rodion Shishorin
- I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
| | - Roman Akhapkin
- Serbsky National Medical Research Center of Psychiatry and Narcology, 119034 Moscow, Russia
| | - Beatrice Volel
- I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia
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Early Response to Antidepressant Medications in Adults With Major Depressive Disorder: A Naturalistic Study and Odds of Remission at 14 Weeks. J Clin Psychopharmacol 2023; 43:46-54. [PMID: 36584249 PMCID: PMC9803387 DOI: 10.1097/jcp.0000000000001638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
PURPOSE/BACKGROUND Early response after 2 to 4 weeks of antidepressant therapy has been shown to predict remission by 8 to 12 weeks. Most of the work to date on early response has been done using data from randomized controlled trials. METHODS/PROCEDURES This naturalistic study uses archival data from a national tele-mental health company. The positive and negative predictive values as well as sensitivity and specificity were calculated using different drops in baseline Patient Health Questionnaire 9 scores at various periods. Demographic and clinical characteristics were compared between early responders versus those lacking early response. Binary logistic regression analyses determined if early response was predictive of remission, response, and greater than minimal improvement at 14 weeks. For those who do not show early improvement, treatments were investigated using binary logistic regression to see if changes predicted later outcomes. FINDINGS/RESULTS Positive predictive values for all endpoints improved with the strength of early response but did not improve much with the time allowed for that response to occur. In contrast, negative predictive values increased substantially with time. Using a definition of 30% drop in Patient Health Questionnaire 9 score at week 4, 56.5% of patients were early responders. Early responders were ~3.2 times more likely to achieve remission than those lacking early response. Of nonresponders by week 4, those prescribed atypical antipsychotics (+SSRI) had significantly reduced odds of response at week 14, whereas those prescribed a norepinephrine and dopamine reuptake inhibitor had increased odds. IMPLICATIONS/CONCLUSIONS Early response may be associated with better outcomes at 14 weeks. In those with lack of response by week 4, patients prescribed a norepinephrine and dopamine reuptake inhibitor may achieve superior outcomes.
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Zhao Y, Dahmani L, Li M, Hu Y, Ren J, Lui S, Wang D, Kuang W, Gong Q, Liu H. Individualized Functional Connectome Identified Replicable Biomarkers for Dysphoric Symptoms in First-Episode Medication-Naïve Patients With Major Depressive Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:42-51. [PMID: 34995770 DOI: 10.1016/j.bpsc.2021.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/03/2021] [Accepted: 12/19/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a heterogeneous syndrome and can be conceptualized as a mixture of dimensional abnormalities across several specific brain circuits. The neural underpinnings of different symptom dimensions in MDD are not well understood. We aimed to identify robust, generalizable, functional connectivity (FC)-based biomarkers for different symptom dimensions in MDD using individualized functional connectomes. METHODS Patterns of FC associated with symptom severity were identified using a novel, individualized, functional network parcellation analysis in conjunction with hierarchical clustering. Dimension-specific prediction models were trained to estimate symptom severity in first-episode medication-naïve patients (discovery dataset, n = 95) and replicated in an independent validation dataset (n = 94). The correlation between FC changes and symptom changes was further explored in a treatment dataset (n = 55). RESULTS Two distinct symptom clusters previously identified in patients with MDD, namely dysphoric and anxiosomatic clusters, were robustly replicated in our data. A connectivity biomarker associated with dysphoric symptoms was identified, which mainly involved the default, dorsal attention, and limbic networks. Critically, this brain-symptom association was confirmed in the validation dataset. Moreover, the marker also tracked dysphoric symptom improvement following a 2-week antidepressant treatment. For comparison, we repeated our analyses using a nonindividualized approach and failed to identify replicable brain-symptom biomarkers. Further quantitative analysis indicated that the generalizability of the connectivity-symptom association was hampered when functional regions were not localized in individuals. CONCLUSIONS This work reveals robust, replicable FC biomarkers for dysphoric symptoms in MDD, demonstrates the advantage of individual-oriented approach, and emphasizes the importance of independent validation in psychiatric neuroimaging analysis.
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Affiliation(s)
- Youjin Zhao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Louisa Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Meiling Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Yongbo Hu
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Su Lui
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts; Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina.
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Chao ZC, Dillon DG, Liu YH, Barrick EM, Wu CT. Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder. J Psychiatry Neurosci 2022; 47:E367-E378. [PMID: 36318983 PMCID: PMC9633055 DOI: 10.1503/jpn.220046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 08/27/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND A hyperactive default mode network (DMN) has been observed in people with major depressive disorder (MDD), and weak DMN suppression has been linked to depressive symptoms. However, whether dysregulation of the DMN contributes to blunted positive emotional experience in people with MDD is unclear. METHODS We recorded 128-channel electroencephalograms (EEGs) from 24 participants with MDD and 31 healthy controls in a resting state (RS) and an emotion-induction state (ES), in which participants engaged with emotionally positive pictures. We combined Granger causality analysis and data-driven decomposition to extract latent brain networks shared among states and groups, and we further evaluated their interactions across individuals. RESULTS We extracted 2 subnetworks. Subnetwork 1 represented a delta (δ)-band (1~4 Hz) frontal network that was activated more in the ES than the RS (i.e., task-positive). Subnetwork 2 represented an alpha (α)-band (8~13 Hz) parietal network that was suppressed more in the ES than the RS (i.e., task-negative). These subnetworks were anticorrelated in both the healthy control and MDD groups, but with different sensitivities: for participants with MDD to achieve the same level of task-positive (subnetwork 1) activation as healthy controls, more suppression of task-negative (subnetwork 2) activation was necessary. Furthermore, the anticorrelation strength in participants with MDD correlated with the severity of 2 core MDD symptoms: anhedonia and rumination. LIMITATIONS The sample size was small. CONCLUSION Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative α-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future.
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Affiliation(s)
| | | | | | | | - Chien-Te Wu
- From the International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan (Chao, Wu); the Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Mass. (Dillon, Barrick); Harvard Medical School, Boston, Mass. (Dillon); the Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taiwan (Liu); the Yuan-Rung Medical System, Changhua, Taiwan (Liu)
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Ye Y, Wang C, Lan X, Li W, Fu L, Zhang F, Liu H, Wu K, Zhou Y, Ning Y. Baseline patterns of resting functional connectivity within posterior default-mode intranetwork associated with remission to antidepressants in major depressive disorder. Neuroimage Clin 2022; 36:103230. [PMID: 36274375 PMCID: PMC9668631 DOI: 10.1016/j.nicl.2022.103230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/08/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND The default mode network (DMN) is implicated in the pathophysiology of major depressive disorder (MDD), and functional connectivity (FC) involved in DMN is suggested to be associated with antidepressant remission. The goal of this study is to recognize relationships between FC within DMN and early amelioration in MDD patients and to further test the capacity of FC to predict early efficacy. METHODS In total 66 MDD patients and 57 healthy controls were recruited for resting-state functional magnetic resonance imaging scans at baseline. After four weeks of treatment with Escitalopram or Venlafaxine, patients were divided into subgroups with remitters (R, n = 31) and non-remitters (NR, n = 35). Independent component analysis (ICA) was used to compare intranetwork functional connectivity (intra-FC) in DMN between the three groups. RESULTS Relative to NR-MDD group and HCs, the R-MDD group showed significantly higher intra-FC in the right angular gyrus of DMN, and the intra-FC was positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that intra-FC exhibited a high diagnostic value for remission. CONCLUSION These findings indicated that intra-FC related to the DMN is a prognostic marker that can potentially predict early remission of symptoms after antidepressant treatment.
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Affiliation(s)
- Yanxiang Ye
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Chengyu Wang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Xiaofeng Lan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Weicheng Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Ling Fu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Fan Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Haiyan Liu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, China
| | - Yanling Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Metal Disorders, Guangzhou, China.
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12
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Sun J, Du Z, Ma Y, Chen L, Wang Z, Guo C, Luo Y, Gao D, Hong Y, Zhang L, Han M, Cao J, Hou X, Xiao X, Tian J, Yu X, Fang J, Zhao Y. Altered functional connectivity in first-episode and recurrent depression: A resting-state functional magnetic resonance imaging study. Front Neurol 2022; 13:922207. [PMID: 36119680 PMCID: PMC9475213 DOI: 10.3389/fneur.2022.922207] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 07/28/2022] [Indexed: 01/10/2023] Open
Abstract
Background Functional magnetic resonance imaging (fMRI) studies examining differences in the activity of brain networks between the first depressive episode (FDE) and recurrent depressive episode (RDE) are limited. The current study observed and compared the altered functional connectivity (FC) characteristics in the default mode network (DMN), cognitive control network (CCN), and affective network (AN) between the RDE and FDE. In addition, we further investigated the correlation between abnormal FC and clinical symptoms. Methods We recruited 32 patients with the RDE, 31 patients with the FDE, and 30 healthy controls (HCs). All subjects underwent resting-state fMRI. The seed-based FC method was used to analyze the abnormal brain networks in the DMN, CCN, and AN among the three groups and further explore the correlation between abnormal FC and clinical symptoms. Results One-way analysis of variance showed significant differences the FC in the DMN, CCN, and AN among the three groups in the frontal, parietal, temporal, and precuneus lobes and cerebellum. Compared with the RDE group, the FDE group generally showed reduced FC in the DMN, CCN, and AN. Compared with the HC group, the FDE group showed reduced FC in the DMN, CCN, and AN, while the RDE group showed reduced FC only in the DMN and AN. Moreover, the FC in the left posterior cingulate cortices and the right inferior temporal gyrus in the RDE group were positively correlated with the 17-item Hamilton Rating Scale for Depression (HAMD-17), and the FC in the left dorsolateral prefrontal cortices and the right precuneus in the FDE group were negatively correlated with the HAMD-17. Conclusions The RDE and FDE groups showed multiple abnormal brain networks. However, the alterations of abnormal FC were more extensive and intensive in the FDE group.
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Affiliation(s)
- Jifei Sun
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhongming Du
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yue Ma
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Limei Chen
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhi Wang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chunlei Guo
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yi Luo
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Deqiang Gao
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yang Hong
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Lei Zhang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ming Han
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jiudong Cao
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaobing Hou
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing, China
| | - Xue Xiao
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing, China
| | - Jing Tian
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing, China
| | - Xue Yu
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing, China
| | - Jiliang Fang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Jiliang Fang
| | - Yanping Zhao
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Yanping Zhao
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13
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Gerlach AR, Karim HT, Peciña M, Ajilore O, Taylor WD, Butters MA, Andreescu C. MRI predictors of pharmacotherapy response in major depressive disorder. Neuroimage Clin 2022; 36:103157. [PMID: 36027717 PMCID: PMC9420953 DOI: 10.1016/j.nicl.2022.103157] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/11/2022] [Accepted: 08/15/2022] [Indexed: 02/08/2023]
Abstract
Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.
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Affiliation(s)
- Andrew R Gerlach
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Peciña
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois-Chicago, Chicago, IL, USA
| | - Warren D Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carmen Andreescu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
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Afzali MH, Dagher A, Bourque J, Spinney S, Conrod P. Cross-lagged Relationships Between Depressive Symptoms and Altered Default Mode Network Connectivity Over the Course of Adolescence. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:774-781. [PMID: 34929346 DOI: 10.1016/j.bpsc.2021.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/05/2021] [Accepted: 10/28/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although the peak onset of depressive symptoms occurs during adolescence, very few studies have directly examined depression-related changes in resting-state (RS) default mode network activity during adolescence, controlling for potential neural markers of risk. METHODS This study used data from a longitudinal adolescent cohort to investigate age-specific, persistent (i.e., lagged), and dynamic associations between RS functional connectivity within the default mode network and depressive symptoms during adolescence using a random intercept cross-lagged panel framework. The Neuroventure sample consisted of 151 adolescents ages 12-14 at study entry without any neurological illness who were assessed three times during a 5-year follow-up with 97% follow-up across the three assessments. Depressive symptoms were measured using the depression subscale of the Brief Symptoms Inventory. RS functional magnetic resonance imaging data were collected using a 3T Siemens Magnetom Trio scanner in a single 6-minute sequence. RESULTS After controlling for relationships between random intercepts, future depression risk was predicted by RS couplings in the perigenual anterior cingulate cortex and anterior dorsomedial prefrontal cortex (β = -0.69, p = .014) and in the left inferior parietal lobule and anterior superior frontal gyrus (β = -0.43, p = .035). Increases in depressive symptoms at previous time points significantly predicted changes in functional connectivity between the posterior cingulate cortex and the precuneus and posterior middle temporal gyrus (β = 0.37, p = .039) and between the dorsal precuneus and posterior middle temporal gyrus (β = 0.47, p = .036). CONCLUSIONS This study was able to disassociate the RS brain markers of depression from those that appear to follow early-onset depression.
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Affiliation(s)
- Mohammad H Afzali
- Department of Psychiatry, University of Montréal, Montreal, Québec, Canada
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Josiane Bourque
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sean Spinney
- Department of Psychiatry, University of Montréal, Montreal, Québec, Canada; Department of Computer Science and Operations Research, University of Montréal, Montreal, Québec, Canada; Mila - Quebec AI Institute, Montreal, Québec, Canada
| | - Patricia Conrod
- Department of Psychiatry, University of Montréal, Montreal, Québec, Canada; Centre Hospitalier Universitaire Sainte-Justine, Research Centre, Montreal, Québec, Canada.
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15
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Zhong S, Shen J, Wang M, Mao Y, Du X, Ma J. Altered resting-state functional connectivity of insula in children with primary nocturnal enuresis. Front Neurosci 2022; 16:913489. [PMID: 35928018 PMCID: PMC9343997 DOI: 10.3389/fnins.2022.913489] [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: 04/05/2022] [Accepted: 06/24/2022] [Indexed: 11/17/2022] Open
Abstract
Objective Primary nocturnal enuresis (PNE) is a common developmental condition in school-aged children. The objective is to better understand the pathophysiology of PNE by using insula-centered resting-state functional connectivity (rsFC). Methods We recruited 66 right-handed participants in our analysis, 33 with PNE and 33 healthy control (HC) children without enuresis matched for gender and age. Functional and structural MRI data were obtained from all the children. Seed-based rsFC was used to examine differences in insular functional connectivity between the PNE and HC groups. Correlation analyses were carried out to explore the relationship between abnormal insula-centered functional connectivity and clinical characteristics in the PNE group. Results Compared with HC children, the children with PNE demonstrated decreased left and right insular rsFC with the right medial superior frontal gyrus (SFG). In addition, the bilateral dorsal anterior insula (dAI) seeds also indicated the reduced rsFC with right medial SFG. Furthermore, the right posterior insula (PI) seed showed the weaker rsFC with the right medial SFG, while the left PI seed displayed the weaker rsFC with the right SFG. No statistically significant correlations were detected between aberrant insular rsFC and clinical variables (e.g., micturition desire awakening, bed-wetting frequency, and bladder volume) in results without global signal regression (GSR) in the PNE group. However, before and after setting age as a covariate, significant and positive correlations between bladder volume and the rsFC of the left dAI with right medial SFG and the rsFC of the right PI with right medial SFG were found in results with GSR in the PNE group. Conclusion To the best of our knowledge, this study explored the rsFC patterns of the insula in children with PNE for the first time. These results uncovered the abnormal rsFC of the insula with the medial prefrontal cortex without and with GSR in the PNE group, suggesting that dysconnectivity of the salience network (SN)-default mode network (DMN) may involve in the underlying pathophysiology of children with PNE. However, the inconsistent associations between bladder volume and dysconnectivity of the SN-DMN in results without and with GSR need further studies.
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Affiliation(s)
- Shaogen Zhong
- Department of Developmental and Behavioral Pediatrics, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jiayao Shen
- Department of Nephrology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mengxing Wang
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yi Mao
- Department of Developmental and Behavioral Pediatrics, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoxia Du
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Jun Ma
- Department of Developmental and Behavioral Pediatrics, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Acute neurofunctional effects of escitalopram during emotional processing in pediatric anxiety: a double-blind, placebo-controlled trial. Neuropsychopharmacology 2022; 47:1081-1087. [PMID: 34580419 PMCID: PMC8938471 DOI: 10.1038/s41386-021-01186-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/19/2021] [Accepted: 09/07/2021] [Indexed: 02/08/2023]
Abstract
Anxiety disorders are the most common mental disorders in adolescents. However, only 50% of pediatric patients with anxiety disorders respond to the first-line pharmacologic treatments-selective serotonin reuptake inhibitors (SSRIs). Thus, identifying the neurofunctional targets of SSRIs and finding pretreatment or early-treatment neurofunctional markers of SSRI treatment response in this population is clinically important. We acquired pretreatment and early-treatment (2 weeks into treatment) functional magnetic resonance imaging during a continuous processing task with emotional and neutral distractors in adolescents with generalized anxiety disorder (GAD, N = 36) randomized to 8 weeks of double-blind escitalopram or placebo. Generalized psychophysiological interaction analysis was conducted to examine the functional connectivity of the amygdala while patients viewed emotional pictures. Full-factorial analysis was used to investigate the treatment effect of escitalopram on amygdala connectivity. Correlation analyses were performed to explore whether pretreatment and early (week 2) treatment-related connectivity were associated with treatment response (improvement in anxiety) at week 8. Compared to placebo, escitalopram enhanced emotional processing speed and enhanced negative right amygdala-bilateral ventromedial prefrontal cortex (vmPFC) and positive left amygdala-right angular gyrus connectivity during emotion processing. Baseline amygdala-vmPFC connectivity and escitalopram-induced increased amygdala-angular gyrus connectivity at week 2 predicted the magnitude of subsequent improvement in anxiety symptoms. These findings suggest that amygdala connectivity to hubs of the default mode network represents a target of acute SSRI treatment. Furthermore, pretreatment and early-treatment amygdala connectivity could serve as biomarkers of SSRI treatment response in adolescents with GAD. The trial registration for the study is ClinicalTrials.gov Identifier: NCT02818751.
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Escitalopram modulates learning content-specific neuroplasticity of functional brain networks. Neuroimage 2021; 247:118829. [PMID: 34923134 DOI: 10.1016/j.neuroimage.2021.118829] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 12/09/2021] [Accepted: 12/15/2021] [Indexed: 01/09/2023] Open
Abstract
Learning-induced neuroplastic changes, further modulated by content and setting, are mirrored in brain functional connectivity (FC). In animal models, selective serotonin reuptake inhibitors (SSRIs) have been shown to facilitate neuroplasticity. This is especially prominent during emotional relearning, such as fear extinction, which may translate to clinical improvements in patients. To investigate a comparable modulation of neuroplasticity in humans, 99 healthy subjects underwent three weeks of emotional (matching faces) or non-emotional learning (matching Chinese characters to unrelated German nouns). Shuffled pairings of the original content were subsequently relearned for the same time. During relearning, subjects received either a daily dose of the SSRI escitalopram or placebo. Resting-state functional magnetic resonance imaging was performed before and after the (re-)learning phases. FC changes in a network comprising Broca's area, the medial prefrontal cortex, the right inferior temporal and left lingual gyrus were modulated by escitalopram intake. More specifically, it increased the bidirectional connectivity between medial prefrontal cortex and lingual gyrus for non-emotional and the connectivity from medial prefrontal cortex to Broca's area for emotional relearning. The context dependence of these effects together with behavioral correlations supports the assumption that SSRIs in clinical practice improve neuroplasticity rather than psychiatric symptoms per se. Beyond expanding the complexities of learning, these findings emphasize the influence of external factors on human neuroplasticity.
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Li P, Zhou M, Yan W, Du J, Lu S, Xie S, Zhang R. Altered resting-state functional connectivity of the right precuneus and cognition between depressed and non-depressed schizophrenia. Psychiatry Res Neuroimaging 2021; 317:111387. [PMID: 34509807 DOI: 10.1016/j.pscychresns.2021.111387] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 08/31/2021] [Accepted: 08/31/2021] [Indexed: 01/27/2023]
Abstract
The study investigated the resting-state functional connectivity (FC) and cognitive changes in patients with depressed schizophrenia(DS) and non-depressed schizophrenia(NDS). Eighty patients with first-episode schizophrenia and 50 healthy controls (HC) were included to conduct resting-state fMRI. All participants completed MATRICS Consensus Cognitive Battery (MCCB). The right precuneus was selected as the seed in whole-brain FC analysis. Our results showed the cognitive function (All MCCB dimensions) of all schizophrenia patients were worse than HC, but no differences were found between DS and NDS. The DS had decreased FC than NDS between the right precuneus and left middle cingulate gyrus, left cerebellum, right cerebellum. The DS had increased FC than HC between the right precuneus and temporal lobe, occipital lobe, and decreased FC between the right precuneus and left cerebellum. However, the NDS had increased FC than HC between the right precuneus and left cerebellum, right cerebellum, temporal lobe, occipital lobe, left superior parietal lobule. Correlation analysis showed that FC between the right precuneus and occipital lobe was negatively correlated with visual learning in DS and with social cognition in NDS. Our results suggest DS and NDS patients have different patterns of FC, and their FC changes correlate with different domains of cognition.
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Affiliation(s)
- Pingping Li
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Min Zhou
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Wei Yan
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Jinglun Du
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Shuiping Lu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Shiping Xie
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China.
| | - Rongrong Zhang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
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Mothersill D, King S, Holleran L, Dauvermann M, Patlola S, Rokita K, McManus R, Keynon M, McDonald C, Hallahan B, Corvin A, Morris D, Kelly J, McKernan D, Donohoe G. Interleukin 6 predicts increased neural response during face processing in a sample of individuals with schizophrenia and healthy participants: A functional magnetic resonance imaging study. NEUROIMAGE-CLINICAL 2021; 32:102851. [PMID: 34634589 PMCID: PMC8515297 DOI: 10.1016/j.nicl.2021.102851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/12/2021] [Accepted: 10/04/2021] [Indexed: 01/19/2023]
Abstract
IL-6 has been associated with poorer facial emotion recognition. fMRI was performed during a faces task and IL-6 measured from blood samples. IL-6 predicted increased neural response during facial emotion recognition.
Background Deficits in facial emotion recognition are a core feature of schizophrenia and predictive of functional outcome. Higher plasma levels of the cytokine interleukin 6 (IL-6) have recently been associated with poorer facial emotion recognition in individuals with schizophrenia and healthy participants, but the neural mechanisms affected remain poorly understood. Methods Forty-nine individuals with schizophrenia or schizoaffective disorder and 158 healthy participants were imaged using functional magnetic resonance imaging during a dynamic facial emotion recognition task. Plasma IL-6 was measured from blood samples taken outside the scanner. Multiple regression was used in statistical parametric mapping software to test whether higher plasma IL-6 predicted increased neural response during task performance. Results Higher plasma IL-6 predicted increased bilateral medial prefrontal response during neutral face processing compared to angry face processing in the total sample (N = 207, tmax = 5.67) and increased left insula response during angry face processing compared to neutral face processing (N = 207, tmax = 4.40) (p < 0.05, family-wise error corrected across the whole brain at the cluster level). Conclusions These findings suggest that higher peripheral IL-6 levels predict altered neural response within brain regions involved in social cognition and emotion during facial emotion recognition. This is consistent with recent neuroimaging research on IL-6 and suggesting a possible neural mechanism by which this cytokine might affect facial emotion recognition accuracy.
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Affiliation(s)
- David Mothersill
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland; Center for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Ireland; Department of Psychiatry, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Sinead King
- Center for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Ireland
| | - Laurena Holleran
- Center for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Ireland
| | - Maria Dauvermann
- Center for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Ireland; Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England, UK
| | - Saahithh Patlola
- Center for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Ireland
| | - Karolina Rokita
- Center for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Ireland
| | - Ross McManus
- Department of Psychiatry, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Marcus Keynon
- Department of Psychiatry, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Colm McDonald
- Department of Psychiatry, Clinical Science Institute, National University of Ireland Galway, Ireland
| | - Brian Hallahan
- Department of Psychiatry, Clinical Science Institute, National University of Ireland Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Derek Morris
- School of Natural Sciences, National University of Ireland Galway, Ireland
| | - John Kelly
- Pharmacology & Therapeutics, National University of Ireland Galway, Ireland
| | - Declan McKernan
- Pharmacology & Therapeutics, National University of Ireland Galway, Ireland
| | - Gary Donohoe
- Center for Neuroimaging and Cognitive Genomics (NICOG), School of Psychology, National University of Ireland Galway, Ireland.
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Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder. J Affect Disord 2021; 291:315-321. [PMID: 34077821 DOI: 10.1016/j.jad.2021.05.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 05/01/2021] [Accepted: 05/05/2021] [Indexed: 02/08/2023]
Abstract
BAKGROUD The hippocampus is involved in the pathophysiology of major depressive disorder (MDD), and its structure and function have been reported to be related to the antidepressant response. This study aimed to identify relationships between hippocampal functional connectivity (FC) and early improvement in patients with MDD and to further explore the ability of hippocampal FC to predict early efficacy. METHODS Thirty-six patients with nonpsychotic MDD were recruited and underwent resting-state functional magnetic resonance imaging scans at baseline. After two weeks of treatment with escitalopram, patients were divided into subgroups with early improved depression (EID, n= 19) and nonimproved depression (NID, n=17) . A voxelwise FC analysis was performed with the bilateral hippocampus as seeds, two-sample t-tests were used to compare hippocampal FC between groups. Receiver operating characteristic (ROC) curves were constructed to determine the best FC measures and optimal threshold for differentiating EID from END. RESULTS The EID group showed significantly higher FC between the left hippocampus and left inferior frontal gyrus and precuneus than the END group. And the left hippocampal FC of these two regions were positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that summed FC scores for these two regions exhibited the highest area under the curve, with a sensitivity of 0.947 and specificity of 0.882 at a summed score of 0.14. LIMITATIONS The sample used in this study was relatively small. CONCLUSIONS These findings demonstrated that FC of the left hippocampus can predict early efficacy of antidepressant.
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21
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Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, Gordon J, Jeong J, Astolfi L, Shpigel E, Longwell P, Sarhadi K, El-Said D, Li Y, Cooper C, Chin-Fatt C, Arns M, Goodkind MS, Trivedi MH, Marmar CR, Etkin A. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng 2021; 5:309-323. [PMID: 33077939 PMCID: PMC8053667 DOI: 10.1038/s41551-020-00614-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 08/24/2020] [Indexed: 12/21/2022]
Abstract
The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.
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Affiliation(s)
- Yu Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Russell T Toll
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sharon Naparstek
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Mallissa Watts
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Joseph Gordon
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Jisoo Jeong
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy
- IRCCF Fondazione Santa Lucia, Rome, Italy
| | - Emmanuel Shpigel
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Parker Longwell
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Kamron Sarhadi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Dawlat El-Said
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Pazhou Lab, Guangzhou, China
| | - Crystal Cooper
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cherise Chin-Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
- neuroCare Group, Munich, Germany
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Location AMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Charles R Marmar
- Steven and Alexandra Cohen Veterans Center for Post-traumatic Stress and Traumatic Brain Injury, New York University Langone School of Medicine, New York, NY, USA
- Center for Alcohol Use Disorder and PTSD, New York University Langone School of Medicine, New York, NY, USA
- Department of Psychiatry, New York University Langone School of Medicine, New York, NY, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Alto Neuroscience, Inc., Los Altos, CA, USA.
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22
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Bednarik P, Spurny B, Silberbauer LR, Svatkova A, Handschuh PA, Reiter B, Konadu ME, Stimpfl T, Spies M, Bogner W, Lanzenberger R. Effect of Ketamine on Human Neurochemistry in Posterior Cingulate Cortex: A Pilot Magnetic Resonance Spectroscopy Study at 3 Tesla. Front Neurosci 2021; 15:609485. [PMID: 33841073 PMCID: PMC8024494 DOI: 10.3389/fnins.2021.609485] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/23/2021] [Indexed: 12/28/2022] Open
Abstract
Ketamine is a powerful glutamatergic long-lasting antidepressant, efficient in intractable major depression. Whereas ketamine's immediate psychomimetic side-effects were linked to glutamate changes, proton MRS (1H-MRS) showed an association between the ratio of glutamate and glutamine and delayed antidepressant effect emerging ∼2 h after ketamine administration. While most 1H-MRS studies focused on anterior cingulate, recent functional MRI connectivity studies revealed an association between ketamine's antidepressant effect and disturbed connectivity patterns to the posterior cingulate cortex (PCC), and related PCC dysfunction to rumination and memory impairment involved in depressive pathophysiology. The current study utilized the state-of-the-art single-voxel 3T sLASER 1H-MRS methodology optimized for reproducible measurements. Ketamine's effects on neurochemicals were assessed before and ∼3 h after intravenous ketamine challenge in PCC. Concentrations of 11 neurochemicals, including glutamate (CRLB ∼ 4%) and glutamine (CRLB ∼ 13%), were reliably quantified with the LCModel in 12 healthy young men with between-session coefficients of variation (SD/mean) <8%. Also, ratios of glutamate/glutamine and glutamate/aspartate were assessed as markers of synaptic function and activated glucose metabolism, respectively. Pairwise comparison of metabolite profiles at baseline and 193 ± 4 min after ketamine challenge yielded no differences. Minimal detectable concentration differences estimated with post hoc power analysis (power = 80%, alpha = 0.05) were below 0.5 μmol/g, namely 0.39 μmol/g (∼4%) for glutamate, 0.28 μmol/g (∼10%) for Gln, ∼14% for glutamate/glutamine and ∼8% for glutamate/aspartate. Despite the high sensitivity to detect between-session differences in glutamate and glutamine concentrations, our study did not detect delayed glutamatergic responses to subanesthetic ketamine doses in PCC.
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Affiliation(s)
- Petr Bednarik
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Institute for Clinical Molecular MRI in Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria
| | - Benjamin Spurny
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Leo R. Silberbauer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Alena Svatkova
- Department of Medicine III, Clinical Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria
| | - Patricia A. Handschuh
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Birgit Reiter
- Clinical Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Melisande E. Konadu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Stimpfl
- Clinical Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Marie Spies
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Institute for Clinical Molecular MRI in Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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23
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Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response. J Affect Disord 2021; 283:130-138. [PMID: 33548906 DOI: 10.1016/j.jad.2021.01.047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Serotonin 2A receptors (HTR2A) play a crucial role in the therapeutic response to antidepressant. The activity of serotonergic system could modulate the connectivity of the default mode network (DMN) in human brain. Our research investigated the influence of the single nucleotide polymorphism (SNP) of HTR2A on the early treatment response of antidepressant and their relation to dynamic changes of DMN for the first time. METHODS A total of 134 major depressive disorder patients and 95 healthy controls from two independent datasets were enrolled. All subjects have genotyped candidate HTR2A polymorphisms, dynamic brain parameters flexibility and integration were calculated according to the resting-state functional magnetic resonance imaging (rs-fMRI) at baseline. Patients received selective serotonin reuptake inhibitors (SSRIs) treatment with conventional dose in the next two weeks. RESULTS We found the correlation of the risk-associated variant belonged to HTR2A polymorphism rs3803189 with the achievements of antidepressant early response, and also with the stronger dynamic changes of DMN. Further mediation analysis indicated that the bond between rs3803189 and antidepressant early response was mediated by the integration between the right angular gyrus (AG.R) and the subcortical network (SCN), which were validated over both the main and replication datasets. LIMITATIONS Except the AG.R-SCN circuit, other factors which influence the relationship between rs3803189 and antidepressant therapy deserve to be explored further. Besides, heterogeneity of samples limited the power of the current result. CONCLUSIONS Our findings provided a potential biomarker for individual treatment sensitivity and produced positive effects on revealing the complicated gene-brain-disorder relationship.
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24
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Hou Z, Kong Y, Yin Y, Zhang Y, Yuan Y. Identification of first-episode unmedicated major depressive disorder using pretreatment features of dominant coactivation patterns. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:110038. [PMID: 32682877 DOI: 10.1016/j.pnpbp.2020.110038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 07/05/2020] [Accepted: 07/12/2020] [Indexed: 01/17/2023]
Abstract
Identifying neuroimaging features to diagnose major depressive disorder (MDD) and predict treatment response remains challenging. Using the pretreatment dominant coactivation pattern (dCAP) analysis approach, we aimed to identify patients with MDD and predict antidepressant efficacy. Seventy-seven first-episode unmedicated MDD patients and forty-two age- and sex-matched healthy controls (HCs) were recruited in the study. The dCAP analysis was performed for the reward and default mode network (DMN) to identify the MDD patients from the HCs. The dCAP1 of the left posterior DMN and bilateral anterior DMN were significantly higher in the MDD group than in the HC group (P < .001), and the dCAP1 in the left posterior DMN was positively correlated with the baseline severity of depression (rho = 0.248, P = .030). Besides, the MDD group exhibited significantly higher dCAP1 in the right reward network than the HC group. Further correlation analyses revealed that the transfer probability in the right reward network was positively correlated with the treatment responsivity (r = 0.247, P = .030). Importantly, integrating the dCAPs of the above four subnetworks can effectively identify the patients with MDD (AUC = 0.920, P < .001). The distinct pretreatment features of the dCAP in the subnetwork of the DMN and reward network may serve as potential indicators for individual diagnosis and prediction of antidepressant response in the early stage.
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Affiliation(s)
- Zhenghua Hou
- Department of Psychosomatics & Psychiatry, Institute of Psychosomatic Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing 210009, China; Department of Psychiatry, Columbia University College of Physicians and Surgeons, The New York State Psychiatric Institute, New York, NY 10032, United States
| | - Youyong Kong
- Lab of Image Science and Technology, School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210009, China
| | - Yingying Yin
- Department of Psychosomatics & Psychiatry, Institute of Psychosomatic Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing 210009, China
| | - Yuqun Zhang
- Department of Psychosomatics & Psychiatry, Institute of Psychosomatic Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing 210009, China
| | - Yonggui Yuan
- Department of Psychosomatics & Psychiatry, Institute of Psychosomatic Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing 210009, China.
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25
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Lai CH. Task MRI-Based Functional Brain Network of Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:19-33. [PMID: 33834392 DOI: 10.1007/978-981-33-6044-0_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This chapter will focus on task magnetic resonance imaging (MRI) to understand the biological mechanisms and pathophysiology of brain in major depressive disorder (MDD), which would have minor alterations in the brain function. Therefore, the functional study, such as task MRI functional connectivity, would play a crucial role to explore the brain function in MDD. Different kinds of tasks would determine the alterations in functional connectivity in task MRI studies of MDD. The emotion-related tasks are linked with alterations in anterior cingulate cortex, insula, and default mode network. The emotional memory task is linked with amygdala-hippocampus alterations. The reward-related task would be related to the reward circuit alterations, such as fronto-straital. The cognitive-related tasks would be associated with frontal-related functional connectivity alterations, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, and other frontal regions. The visuo-sensory characteristics of tasks might be associated with the parieto-occipital alterations. The frontolimbic regions might be major components of task MRI-based functional connectivity in MDD. However, different scenarios and tasks would influence the representations of results.
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Affiliation(s)
- Chien-Han Lai
- Psychiatry & Neuroscience Clinic, Taoyuan, Taiwan. .,Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan.
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26
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Hou Z, Li T, He X, Zhang Y, Chen H, Jiang W, Yin Y, Yuan Y. Distinct Features of Cerebral Blood Flow and Spontaneous Neural Activity as Integrated Predictors of Early Response to Antidepressants. Front Psychiatry 2021; 12:788398. [PMID: 35115965 PMCID: PMC8804095 DOI: 10.3389/fpsyt.2021.788398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
AIMS The purpose of this study is to explore whether pre-treatment features of brain function can discriminate non-responders to antidepressant medication in the early phase. METHODS Forty-four treatment-responsive depressed (RD) patients, 36 non-responsive depressed (NRD) patients, and 42 healthy controls (HCs) were recruited. Regional cerebral blood flow (CBF) and amplitude of low-frequency fluctuation (ALFF) values were calculated for all subjects. Correlation analyses were used to explore the relationship between symptom improvement and CBF/ALFF. Receiver operating characteristics (ROC) and the 10-fold cross-validation support vector machine (SVM) classifier were applied for the discrimination of treatment response. RESULTS Compared with the HCs, the RD and NRD groups exhibited lower CBF and ALFF in the right posterior lobe of the cerebellum. Compared with the NRD group, the RD group showed distinct CBF patterns in the left frontal striatal regions and right frontal cerebellar regions, as well as distinct ALFF features in the left frontoparietal striatum and right frontotemporal striatal cerebellar regions. The ROC and SVM classifier revealed the optimal power to distinguish the RD and NRD groups based on the combined measures (i.e., CBF and ALFF). CONCLUSION Distinct features of CBF and ALFF in the frontal striatal network may serve as promising neuroimaging predictors for identifying patients with blunted responsiveness, which may facilitate personalized antidepressant treatment.
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Affiliation(s)
- Zhenghua Hou
- Department of Psychosomatics and Psychiatry, Institute of Psychosomatics, School of Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing, China
| | - Tong Li
- Department of Psychiatry, The New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, United States.,Department of Information Engineering, Harbin Institute of Technology, Harbin, China
| | - Xiaofu He
- Department of Psychiatry, The New York State Psychiatric Institute, Columbia University Medical Center, New York, NY, United States
| | - Yuqun Zhang
- Department of Psychosomatics and Psychiatry, Institute of Psychosomatics, School of Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing, China
| | - Huanxin Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, School of Psychology, Southwest University, Chongqing, China
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Institute of Psychosomatics, School of Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, Institute of Psychosomatics, School of Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Institute of Psychosomatics, School of Medicine, Affiliated Zhongda Hospital, Southeast University, Nanjing, China
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27
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Lu L, Li H, Mills JA, Schroeder H, Mossman SA, Varney ST, Cecil KM, Huang X, Gong Q, Levine A, DelBello MP, Sweeny JA, Strawn JR. Greater Dynamic and Lower Static Functional Brain Connectivity Prospectively Predict Placebo Response in Pediatric Generalized Anxiety Disorder. J Child Adolesc Psychopharmacol 2020; 30:606-616. [PMID: 32721213 PMCID: PMC7864114 DOI: 10.1089/cap.2020.0024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Objectives: Placebo response is one of the most significant barriers to detecting treatment effects in pediatric (and adult) clinical trials focusing on affective and anxiety disorders. We sought to identify neurofunctional predictors of placebo response in adolescents with generalized anxiety disorder (GAD) by examining dynamic and static functional brain connectivity. Methods: Before randomization to blinded placebo, adolescents, aged 12-17 years, with GAD (N = 25) underwent resting state functional magnetic resonance imaging. Whole brain voxelwise correlation analyses were used to determine the relationship between change in anxiety symptoms from baseline to week 8 and seed-based dynamic and static functional connectivity maps of regions in the salience and ventral attention networks (amygdala, dorsal anterior cingulate cortex [dACC], and ventrolateral prefrontal cortex [VLPFC]). Results: Greater dynamic functional connectivity variability in amygdala, dACC, VLPFC, and regions within salience, default mode, and frontoparietal networks was associated with greater placebo response. Lower static functional connectivity between amygdala and dorsolateral prefrontal cortex, amygdala and medial prefrontal cortex, dACC and posterior cingulate cortex and greater static functional connectivity between VLPFC and inferior parietal lobule were associated with greater placebo response. Conclusion: Placebo response is associated with a distinct dynamic and static connectivity fingerprint characterized by "variable" dynamic but "weak" static connectivity in the salience, default mode, frontoparietal, and ventral attention networks. These data provide granular evidence of how circuit-based biotypes mechanistically relate to placebo response. Finding biosignatures that predict placebo response is critically important in clinical psychopharmacology and to improve our ability to detect medication-placebo differences in clinical trials.
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Affiliation(s)
- Lu Lu
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Hailong Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Jeffrey A. Mills
- Department of Economics, Lindner College of Business, University of Cincinnati, Cincinnati, Ohio, USA
| | - Heidi Schroeder
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Sarah A. Mossman
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Sara T. Varney
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Kim M. Cecil
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA,Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Xiaoqi Huang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, China.,Address correspondence to: Qiyong Gong, MD, PhD, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan 610041, China
| | - Amir Levine
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York City, New York, USA
| | - Melissa P. DelBello
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - John A. Sweeny
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
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28
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Kwak S, Kim M, Kim T, Kwak Y, Oh S, Lho SK, Moon SY, Lee TY, Kwon JS. Defining data-driven subgroups of obsessive-compulsive disorder with different treatment responses based on resting-state functional connectivity. Transl Psychiatry 2020; 10:359. [PMID: 33106472 PMCID: PMC7589530 DOI: 10.1038/s41398-020-01045-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 09/07/2020] [Accepted: 09/09/2020] [Indexed: 12/18/2022] Open
Abstract
Characterization of obsessive-compulsive disorder (OCD), like other psychiatric disorders, suffers from heterogeneities in its symptoms and therapeutic responses, and identification of more homogeneous subgroups may help to resolve the heterogeneity. We aimed to identify the OCD subgroups based on resting-state functional connectivity (rsFC) and to explore their differences in treatment responses via a multivariate approach. From the resting-state functional MRI data of 107 medication-free OCD patients and 110 healthy controls (HCs), we selected rsFC features, which discriminated OCD patients from HCs via support vector machine (SVM) analyses. With the selected brain features, we subdivided OCD patients into subgroups using hierarchical clustering analyses. We identified 35 rsFC features that achieved a high sensitivity (82.74%) and specificity (76.29%) in SVM analyses. The OCD patients were subdivided into two subgroups, which did not show significant differences in their demographic and clinical backgrounds. However, one of the OCD subgroups demonstrated more impaired rsFC that was involved either within the default mode network (DMN) or between DMN brain regions and other network regions. This subgroup also showed both lower improvements in symptom severity in the 16-week follow-up visit and lower responder percentage than the other subgroup. Our results highlight that not only abnormalities within the DMN but also aberrant rsFC between the DMN and other networks may contribute to the treatment response and support the importance of these neurobiological alterations in OCD patients. We suggest that abnormalities in these connectivity may play predictive biomarkers of treatment response, and aid to build more optimal treatment strategies.
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Affiliation(s)
- Seoyeon Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Taekwan Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Yoobin Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Sanghoon Oh
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Silvia Kyungjin Lho
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sun-Young Moon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Tae Young Lee
- Department of Neuropsychiatry, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea.
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29
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Nan D, Yuqi C, Zonglin S, Chenglong D, Na L, Fang L, Cong Z, Xiufeng X. Association of a SIRT1 polymorphism with changes of gray matter volume in patients with first-episode medication-naïve major depression. Psychiatry Res Neuroimaging 2020; 301:111101. [PMID: 32447184 DOI: 10.1016/j.pscychresns.2020.111101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/18/2020] [Accepted: 04/23/2020] [Indexed: 01/08/2023]
Abstract
A single nucleotide polymorphism (SNP) rs12415800 of the silent mating type information regulation 2 homolog 1 gene (SIRT1) has shown a genome-wide significant association with major depression disorder (MDD) in a recent GWAS using a large sample. Subsequent studies of SIRT1's biological functions were supportive of a possible role in the pathophysiology of MDD. However, SIRT1-mediated physiopathology of MDD may be brain region specific. In the present study, we investigated the impact of SIRT1 rs12415800 genotypes on gray matter volumes (GMV) among different brain regions in both MDD patients and healthy controls. The rs12415800 was genotyped in 170 patients with first-episode medication-naïve MDD (cases) and 170 healthy controls. Magnetic resonance imaging was conducted and the voxel-based morphometry (VBM) approach was employed to analyze obtained images. When compared with the cases carrying GG genotype, the cases carrying GA or AA genotypes (A for risk allele) showed decreased GMV in right precuneus, left cuneus/precuneus, and right frontal superior. In contrast, the rs12415800-associated GMV abnormalities were not observed in controls. The SIRT1-rs12415800 polymorphism may be associated with the changes of GMV in MDD patients.
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Affiliation(s)
- Dai Nan
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China
| | - Cheng Yuqi
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China
| | - Shen Zonglin
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China
| | - Dong Chenglong
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China
| | - Li Na
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China
| | - Liu Fang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China
| | - Zhou Cong
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China
| | - Xu Xiufeng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, No. 295 Xichang RD, Kunming 650032, Kunming, Yunnan, China.
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30
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Kraus C, Kadriu B, Lanzenberger R, Zarate CA, Kasper S. Prognosis and Improved Outcomes in Major Depression: A Review. FOCUS: JOURNAL OF LIFE LONG LEARNING IN PSYCHIATRY 2020; 18:220-235. [PMID: 33343240 DOI: 10.1176/appi.focus.18205] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
(Reprinted from Transl Psychiatry. 2019 Apr 3; 9(1):127. Open access; is licensed under a Creative Commons Attribution 4.0 International License).
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Preuss A, Bolliger B, Schicho W, Hättenschwiler J, Seifritz E, Brühl AB, Herwig U. SSRI Treatment Response Prediction in Depression Based on Brain Activation by Emotional Stimuli. Front Psychiatry 2020; 11:538393. [PMID: 33281635 PMCID: PMC7691246 DOI: 10.3389/fpsyt.2020.538393] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 09/17/2020] [Indexed: 12/16/2022] Open
Abstract
Introduction: The prediction of antidepressant treatment response may improve outcome. Functional magnetic resonance imaging (fMRI) of emotion processing in major depressive disorder (MDD) may reveal regional brain function serving as predictors of response to treatment with selective serotonin reuptake inhibitor (SSRI). Methods: We examined the association between pre-treatment neural activity by means of fMRI during the perception of emotional stimuli in 22 patients with MDD and the treatment outcome after 6 weeks' medication with an SSRI. A whole brain correlation analysis with Beck Depression Inventory (BDI) change between pre- to post-treatment was conducted to identify neural regions associated with treatment response. Results: During the perception of positive stimuli, responders were characterized by more activation in posterior cingulate cortex (PCC), medial prefrontal cortex, and thalamus as well as middle temporal gyrus. During perception of negative stimuli, PCC, and pregenual anterior cingulate cortex showed the highest correlation with treatment response. Furthermore, responders exhibited higher activation to emotional stimuli than to neutral stimuli in all the above-mentioned regions, while non-responders demonstrated an attenuated neural response to emotional compared to neutral stimuli. Conclusion: Our data suggest that the activity of distinct brain regions is correlated with SSRI treatment outcome and may serve as treatment response predictor. While some regions, in which activity was correlated with treatment response, can be assigned to networks that have been implied in the pathophysiology of depression, most of our regions of interest could also be matched to the default mode network (DMN). Higher DMN activity has been associated with increased rumination as well as negative self-referential processing in previous studies. This may suggest our responders to SSRI to be characterized by such dysregulations and that SSRIs might modify the function associated with this network.
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Affiliation(s)
- Antonia Preuss
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland.,Clinic for Psychiatry and Psychotherapy Clienia, Oetwil am See, Switzerland
| | - Bianca Bolliger
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland
| | - Wenzel Schicho
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland
| | - Josef Hättenschwiler
- Center for Treatment of Anxiety and Affection Disorder Zentrum für Angst- und Depressionsbehandlung Zürich (ZADZ), Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland
| | - Annette Beatrix Brühl
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland
| | - Uwe Herwig
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland.,Center for Psychiatry Reichenau, Academic Hospital University of Konstanz, Konstanz, Germany
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32
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Wu GR, Wang X, Baeken C. Baseline functional connectivity may predict placebo responses to accelerated rTMS treatment in major depression. Hum Brain Mapp 2019; 41:632-639. [PMID: 31633261 PMCID: PMC7267925 DOI: 10.1002/hbm.24828] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 10/03/2019] [Indexed: 01/04/2023] Open
Abstract
Although in theory sham repetitive transcranial magnetic stimulation (rTMS) has no inherent therapeutic value, nonetheless, such placebo stimulations may have relevant therapeutic effects in clinically depressed patients. On the other hand, antidepressant responses to sham rTMS are quite heterogeneous across individuals and its neural underpinnings have not been explored yet. The current brain imaging study aims to detect baseline neural fingerprints resulting in clinically beneficial placebo rTMS treatment responses. We collected resting‐state functional magnetic resonance imaging data prior to a registered randomized clinical trial of accelerated placebo stimulation protocol in patients documented with treatment‐resistant depression (http://clinicaltrials.gov/show/NCT01832805). In addition to global brain connectivity and rostral anterior cingulate cortex (rACC) seed‐based functional connectivity (FC), elastic‐net regression and cross‐validation procedures were used to identify baseline intrinsic brain connectivity biomarkers for sham‐rTMS responses. Placebo responses to accelerated sham rTMS were correlated with baseline global brain connectivity in the rACC/ventral medial prefrontal cortex (vmPFC). Concerning the rACC seed‐based FC analysis, the placebo response was associated positively with the precuneus/posterior cingulate (PCun/PCC) cortex and negatively with the middle frontal gyrus. Our findings provide first brain imaging evidence for placebo responses to sham stimulation being predictable from rACC rsFC profiles, especially in brain areas implicated in (re)appraisal and self‐focus processes.
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Affiliation(s)
- Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaowan Wang
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Chris Baeken
- Department of Psychiatry and Medical Psychology, Ghent University, Ghent, Belgium.,Department of Psychiatry, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZBrussel), Brussels, Belgium.,Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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33
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Alves PN, Foulon C, Karolis V, Bzdok D, Margulies DS, Volle E, Thiebaut de Schotten M. An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings. Commun Biol 2019; 2:370. [PMID: 31633061 PMCID: PMC6787009 DOI: 10.1038/s42003-019-0611-3] [Citation(s) in RCA: 184] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 09/16/2019] [Indexed: 12/16/2022] Open
Abstract
The brain is constituted of multiple networks of functionally correlated brain areas, out of which the default-mode network (DMN) is the largest. Most existing research into the DMN has taken a corticocentric approach. Despite its resemblance with the unitary model of the limbic system, the contribution of subcortical structures to the DMN may be underappreciated. Here, we propose a more comprehensive neuroanatomical model of the DMN including subcortical structures such as the basal forebrain, cholinergic nuclei, anterior and mediodorsal thalamic nuclei. Additionally, tractography of diffusion-weighted imaging was employed to explore the structural connectivity, which revealed that the thalamus and basal forebrain are of central importance for the functioning of the DMN. The contribution of these neurochemically diverse brain nuclei reconciles previous neuroimaging with neuropathological findings in diseased brains and offers the potential for identifying a conserved homologue of the DMN in other mammalian species.
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Affiliation(s)
- Pedro Nascimento Alves
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Department of Neurosciences and Mental Health, Neurology, Hospital de Santa Maria, CHULN, Lisbon, Portugal
- Language Research Laboratory, Faculty of Medicine, Universidade de Lisboa, Lisbon, Portugal
| | - Chris Foulon
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Computational Neuroimaging Laboratory, Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX USA
| | - Vyacheslav Karolis
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- FMRIB centre, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Danilo Bzdok
- INRIA, Parietal Team, Saclay, France
- Neurospin, CEA, Gif-sur-Yvette, France
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
| | - Daniel S. Margulies
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
| | - Emmanuelle Volle
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
- Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
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Kraus C, Seiger R, Pfabigan DM, Sladky R, Tik M, Paul K, Woletz M, Gryglewski G, Vanicek T, Komorowski A, Kasper S, Lamm C, Windischberger C, Lanzenberger R. Hippocampal Subfields in Acute and Remitted Depression-an Ultra-High Field Magnetic Resonance Imaging Study. Int J Neuropsychopharmacol 2019; 22:513-522. [PMID: 31175352 PMCID: PMC6672627 DOI: 10.1093/ijnp/pyz030] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 04/29/2019] [Accepted: 06/05/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Studies investigating hippocampal volume changes after treatment with serotonergic antidepressants in patients with major depressive disorder yielded inconsistent results, and effects on hippocampal subfields are unclear. METHODS To detail treatment effects on total hippocampal and subfield volumes, we conducted an open-label study with escitalopram followed by venlafaxine upon nonresponse in 20 unmedicated patients with major depressive disorder. Before and after 12 weeks treatment, we measured total hippocampal formation volumes and subfield volumes with ultra-high field (7 Tesla), T1-weighted, structural magnetic resonance imaging, and FreeSurfer. Twenty-eight remitted patients and 22 healthy subjects were included as controls. We hypothesized to detect increased volumes after treatment in major depressive disorder. RESULTS We did not detect treatment-related changes of total hippocampal or subfield volumes in patients with major depressive disorder. Secondary results indicated that the control group of untreated, stable remitted patients, compared with healthy controls, had larger volumes of the right hippocampal-amygdaloid transition area and right fissure at both measurement time points. Depressed patients exhibited larger volumes of the right subiculum compared with healthy controls at MRI-2. Exploratory data analyses indicated lower baseline volumes in the subgroup of remitting (n = 10) vs nonremitting (n = 10) acute patients. CONCLUSIONS The results demonstrate that monoaminergic antidepressant treatment in major depressive disorder patients was not associated with volume changes in hippocampal subfields. Studies with larger sample sizes to detect smaller effects as well as other imaging modalities are needed to further assess the impact of antidepressant treatment on hippocampal subfields.
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Affiliation(s)
- Christoph Kraus
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Rene Seiger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Daniela M Pfabigan
- Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Ronald Sladky
- Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Martin Tik
- MR Centre of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria
| | - Katharina Paul
- Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Michael Woletz
- MR Centre of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Thomas Vanicek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Claus Lamm
- Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Christian Windischberger
- MR Centre of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
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35
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Malhi GS, Das P, Outhred T, Gessler D, John Mann J, Bryant R. Cognitive and emotional impairments underpinning suicidal activity in patients with mood disorders: an fMRI study. Acta Psychiatr Scand 2019; 139:454-463. [PMID: 30865285 DOI: 10.1111/acps.13022] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/07/2019] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Mood disorders are strongly associated with suicide, the prevention of which is predicated on timely detection of suicidal activity (ideation, behaviour). Building on our previous work, we sought to determine the nature of neural responses to an emotional-cognitive task in patients with varying degrees of suicidal activity. METHOD Seventy-nine patients with mood disorders were assessed clinically and scanned using fMRI. Neural responses to an Emotional Face-Word Stroop task were compared with 66 healthy controls. We identified regions of interest from seven key networks and examined responses to incongruent stimuli (Happy face-'Sad' word; Sad face-'Happy' word). RESULTS In comparison with healthy controls, patients had differential activity during both incongruent conditions. When examining for associations with suicidal activity within the patient group, those with higher scores had decreased default mode network activity for Happy face-'Sad' word manipulation, and increased basal ganglia network activity for Sad face-'Happy' word manipulation, after controlling for patient characteristics. CONCLUSION The fMRI findings suggest that suicidal activity in patients with mood disorders may be underpinned by cognitive-emotional deficits. These findings have implications for future suicide research and for achieving a deeper understanding of suicidal activity that may ultimately inform clinical detection and management.
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Affiliation(s)
- G S Malhi
- Department of Academic Psychiatry, Northern Sydney Local Health District, St Leonards, NSW, Australia.,ARCHI, Sydney Medical School Northern, The University of Sydney, Sydney, NSW, Australia.,Discipline of Psychiatry, Northern Clinical School, University of Sydney, Sydney, NSW, Australia.,CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - P Das
- Department of Academic Psychiatry, Northern Sydney Local Health District, St Leonards, NSW, Australia.,ARCHI, Sydney Medical School Northern, The University of Sydney, Sydney, NSW, Australia.,Discipline of Psychiatry, Northern Clinical School, University of Sydney, Sydney, NSW, Australia.,CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - T Outhred
- Department of Academic Psychiatry, Northern Sydney Local Health District, St Leonards, NSW, Australia.,ARCHI, Sydney Medical School Northern, The University of Sydney, Sydney, NSW, Australia.,Discipline of Psychiatry, Northern Clinical School, University of Sydney, Sydney, NSW, Australia.,CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - D Gessler
- Department of Academic Psychiatry, Northern Sydney Local Health District, St Leonards, NSW, Australia.,ARCHI, Sydney Medical School Northern, The University of Sydney, Sydney, NSW, Australia.,Discipline of Psychiatry, Northern Clinical School, University of Sydney, Sydney, NSW, Australia.,CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - J John Mann
- Department of Psychiatry, Columbia University, New York, NY, USA.,Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, USA
| | - R Bryant
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
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36
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Kraus C, Klöbl M, Tik M, Auer B, Vanicek T, Geissberger N, Pfabigan DM, Hahn A, Woletz M, Paul K, Komorowski A, Kasper S, Windischberger C, Lamm C, Lanzenberger R. The pulvinar nucleus and antidepressant treatment: dynamic modeling of antidepressant response and remission with ultra-high field functional MRI. Mol Psychiatry 2019; 24:746-756. [PMID: 29422521 PMCID: PMC6756007 DOI: 10.1038/s41380-017-0009-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 10/05/2017] [Accepted: 10/27/2017] [Indexed: 11/21/2022]
Abstract
Functional magnetic resonance imaging (fMRI) successfully disentangled neuronal pathophysiology of major depression (MD), but only a few fMRI studies have investigated correlates and predictors of remission. Moreover, most studies have used clinical outcome parameters from two time points, which do not optimally depict differential response times. Therefore, we aimed to detect neuronal correlates of response and remission in an antidepressant treatment study with 7 T fMRI, potentially harnessing advances in detection power and spatial specificity. Moreover, we modeled outcome parameters from multiple study visits during a 12-week antidepressant fMRI study in 26 acute (aMD) patients compared to 36 stable remitted (rMD) patients and 33 healthy control subjects (HC). During an electrical painful stimulation task, significantly higher baseline activity in aMD compared to HC and rMD in the medial thalamic nuclei of the pulvinar was detected (p = 0.004, FWE-corrected), which was reduced by treatment. Moreover, clinical response followed a sigmoid function with a plateau phase in the beginning, a rapid decline and a further plateau at treatment end. By modeling the dynamic speed of response with fMRI-data, perigenual anterior cingulate activity after treatment was significantly associated with antidepressant response (p < 0.001, FWE-corrected). Temporoparietal junction (TPJ) baseline activity significantly predicted non-remission after 2 antidepressant trials (p = 0.005, FWE-corrected). The results underline the importance of the medial thalamus, attention networks in MD and antidepressant treatment. Moreover, by using a sigmoid model, this study provides a novel method to analyze the dynamic nature of response and remission for future trials.
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Affiliation(s)
- Christoph Kraus
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Manfred Klöbl
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Martin Tik
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Bastian Auer
- Social, Cognitive and Affective Neuroscience Unit, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Thomas Vanicek
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Nicole Geissberger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Daniela M Pfabigan
- Social, Cognitive and Affective Neuroscience Unit, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Michael Woletz
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Katharina Paul
- Social, Cognitive and Affective Neuroscience Unit, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Arkadiusz Komorowski
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Christian Windischberger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Claus Lamm
- Social, Cognitive and Affective Neuroscience Unit, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
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37
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Kraus C, Kadriu B, Lanzenberger R, Zarate Jr. CA, Kasper S. Prognosis and improved outcomes in major depression: a review. Transl Psychiatry 2019; 9:127. [PMID: 30944309 PMCID: PMC6447556 DOI: 10.1038/s41398-019-0460-3] [Citation(s) in RCA: 227] [Impact Index Per Article: 45.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/10/2019] [Accepted: 02/11/2019] [Indexed: 02/07/2023] Open
Abstract
Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD.
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Affiliation(s)
- Christoph Kraus
- 0000 0000 9259 8492grid.22937.3dDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria ,0000 0001 2297 5165grid.94365.3dSection on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Bashkim Kadriu
- 0000 0001 2297 5165grid.94365.3dSection on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Rupert Lanzenberger
- 0000 0000 9259 8492grid.22937.3dDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Carlos A. Zarate Jr.
- 0000 0001 2297 5165grid.94365.3dSection on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
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38
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Meyer BM, Rabl U, Huemer J, Bartova L, Kalcher K, Provenzano J, Brandner C, Sezen P, Kasper S, Schatzberg AF, Moser E, Chen G, Pezawas L. Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study. Transl Psychiatry 2019; 9:64. [PMID: 30718459 PMCID: PMC6362173 DOI: 10.1038/s41398-019-0395-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 01/03/2019] [Accepted: 01/10/2019] [Indexed: 12/28/2022] Open
Abstract
Due to lacking predictors of depression recovery, successful treatment of major depressive disorder (MDD) is frequently only achieved after therapeutic optimization leading to a prolonged suffering of patients. This study aimed to determine neural prognostic predictors identifying non-remitters prior or early after treatment initiation. Moreover, it intended to detect time-sensitive neural mediators indicating depression recovery. This longitudinal, interventional, single-arm, open-label, phase IV, pharmacological functional magnetic resonance imaging (fMRI) study comprised four scans at important stages prior (day 0) and after escitalopram treatment initiation (day 1, 28, and 56). Totally, 22 treatment-free MDD patients (age mean ± SD: 31.5 ± 7.7; females: 50%) suffering from a concurrent major depressive episode without any comorbid DSM-IV axis I diagnosis completed the study protocol. Primary outcome were neural prognostic predictors of depression recovery. Enhanced de-activation of anterior medial prefrontal cortex (amPFC, single neural mediator) indicated depression recovery correlating with MADRS score and working memory improvements. Strong dorsolateral PFC (dlPFC) activation and weak dlPFC-amPFC, dlPFC-posterior cingulate cortex (PCC), dlPFC-parietal lobe (PL) coupling (three prognostic predictors) hinted at depression recovery at day 0 and 1. Preresponse prediction of continuous (dlPFC-PL: R2day1 = 55.9%, 95% CI: 22.6-79%, P < 0.005) and dichotomous (specificity/sensitivity: SP/SNday1 = 0.91/0.82) recovery definitions remained significant after leave-one-out cross-validation. Identified prefrontal neural predictors might propel the future development of fMRI markers for clinical decision making, which could lead to increased response rates and adherence during acute phase treatment periods. Moreover, this study underscores the importance of the amPFC in depression recovery.
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Affiliation(s)
- Bernhard M. Meyer
- 0000 0000 9259 8492grid.22937.3dDivision of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Ulrich Rabl
- 0000 0000 9259 8492grid.22937.3dDivision of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Julia Huemer
- 0000 0000 9259 8492grid.22937.3dDepartment of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria
| | - Lucie Bartova
- 0000 0000 9259 8492grid.22937.3dDivision of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Klaudius Kalcher
- 0000 0000 9259 8492grid.22937.3dMR Centre of Excellence, Medical University of Vienna, Vienna, Austria ,0000 0000 9259 8492grid.22937.3dCenter for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Julian Provenzano
- 0000 0000 9259 8492grid.22937.3dDivision of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Christoph Brandner
- 0000 0000 9259 8492grid.22937.3dDivision of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Patrick Sezen
- 0000 0000 9259 8492grid.22937.3dDivision of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- 0000 0000 9259 8492grid.22937.3dDivision of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Alan F. Schatzberg
- 0000000419368956grid.168010.eDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA USA
| | - Ewald Moser
- 0000 0000 9259 8492grid.22937.3dMR Centre of Excellence, Medical University of Vienna, Vienna, Austria ,0000 0000 9259 8492grid.22937.3dCenter for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Gang Chen
- 0000 0004 0464 0574grid.416868.5Scientific and Statistical Computational Core, National Institute of Mental Health, Bethesda, MA USA
| | - Lukas Pezawas
- Division of General Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
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Reed JL, Nugent AC, Furey ML, Szczepanik JE, Evans JW, Zarate CA. Effects of Ketamine on Brain Activity During Emotional Processing: Differential Findings in Depressed Versus Healthy Control Participants. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:610-618. [PMID: 30826253 DOI: 10.1016/j.bpsc.2019.01.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/04/2019] [Accepted: 01/07/2019] [Indexed: 12/24/2022]
Abstract
BACKGROUND In the search for novel treatments for depression, ketamine has emerged as a unique agent with rapid antidepressant effects. Experimental tasks involving emotional processing can be used during functional magnetic resonance imaging scanning to investigate ketamine's effects on brain function in major depressive disorder (MDD). This study examined ketamine's effects on functional magnetic resonance imaging activity during an emotional processing task. METHODS A total of 33 individuals with treatment-resistant MDD and 24 healthy control participants (HCs) took part in this double-blind, placebo-controlled crossover study. Participants received ketamine and placebo infusions 2 weeks apart, and functional magnetic resonance imaging scans were conducted at baseline and 2 days after each infusion. Blood oxygen level-dependent signal was measured during an emotional processing task, and a linear mixed-effects model was used to analyze differences in activation among group, drug, and task-specific factors. RESULTS A group-by-drug interaction was observed in several brain regions, including a right frontal cluster extending into the anterior cingulate cortex and insula. Participants with MDD had greater activity than HCs after placebo infusion but showed lower activity after ketamine infusion, which was similar to the activity in HCs after placebo. A group-by-drug-by-task condition interaction was also found, which showed further differences that varied between implicit and explicit emotional conditions. CONCLUSIONS The main results indicate that ketamine had differential effects on brain activity in participants with MDD versus HCs. The pattern of activation in participants with MDD after ketamine infusion resembled the activation in HCs after placebo infusion, suggesting a normalization of function during emotional processing. The findings contribute to a better understanding of ketamine's actions in the brain.
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Affiliation(s)
- Jessica L Reed
- Section on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland.
| | - Allison C Nugent
- Section on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Maura L Furey
- Section on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland; Janssen Pharmaceuticals of Johnson & Johnson, San Diego, California
| | - Joanna E Szczepanik
- Section on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Jennifer W Evans
- Section on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Carlos A Zarate
- Section on Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
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Abstract
PURPOSE OF REVIEW Poor treatment response is a hallmark of major depressive disorder. To tackle this problem, recent neuroimaging studies have sought to characterize antidepressant response in terms of pretreatment differences in intrinsic functional brain networks. Our aim is to review recent studies that predict antidepressant response using intrinsic network connectivity. We discuss current methodological limitations and directions for future antidepressant biomarker studies. RECENT FINDINGS Functional connectivity stemming from the subgenual and rostral anterior cingulate has shown particular consistency in predicting antidepressant response. Differences in this connectivity may prove fruitful in differentiating treatment responders to many antidepressant interventions. Future biomarker studies should integrate biological MDD subtypes to address the disorder's inherent clinical heterogeneity. These clinical and scientific advancements have the potential to address this population marked by limited treatment response. Methodological considerations, including patient selection, response criteria, and model overfitting, will require future investigation to ensure that biomarkers generalize for prospective prediction of treatment response.
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Affiliation(s)
- Katharine Dunlop
- Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA.
| | - Aleksandr Talishinsky
- 000000041936877Xgrid.5386.8Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY 10021 USA
| | - Conor Liston
- 000000041936877Xgrid.5386.8Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY 10021 USA ,000000041936877Xgrid.5386.8Department of Psychiatry, Weill Cornell Medicine, New York, NY USA
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41
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Huang X, Tong Y, Qi CX, Xu YT, Dan HD, Shen Y. Disrupted topological organization of human brain connectome in diabetic retinopathy patients. Neuropsychiatr Dis Treat 2019; 15:2487-2502. [PMID: 31695385 PMCID: PMC6717727 DOI: 10.2147/ndt.s214325] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 08/03/2019] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE There is increasing neuroimaging evidence that type 2 diabetes patients with retinal microvascular complications show abnormal brain functional and structural architecture and are at an increased risk of cognitive decline and dementia. However, changes in the topological properties of the functional brain connectome in diabetic retinopathy (DR) patients remain unknown. The aim of this study was to explore the topological organization of the brain connectome in DR patients using graph theory approaches. METHODS Thirty-five DR patients (18 males and 17 females) and 38 healthy controls (HCs) (18 males and 20 females), matched for age, sex, and education, underwent resting-state magnetic resonance imaging scans. Graph theory analysis was performed to investigate the topological properties of brain functional connectome at both global and nodal levels. RESULTS Both DR and HC groups showed high-efficiency small-world network in their brain functional networks. Notably, the DR group showed reduction in the clustering coefficient (P=0.0572) and local efficiency (P=0.0151). Furthermore, the DR group showed reduced nodal centralities in the default-mode network (DMN) and increased nodal centralities in the visual network (VN) (P<0.01, Bonferroni-corrected). The DR group also showed abnormal functional connections among the VN, DMN, salience network (SN), and sensorimotor network (SMN). Altered network metrics and nodal centralities were significantly correlated with visual acuity and fasting blood glucose level in DR patients. CONCLUSION DR patients showed abnormal topological organization of the human brain connectome. Specifically, the DR group showed reduction in the clustering coefficient and local efficiency, relative to HC group. Abnormal nodal centralities and functional disconnections were mainly located in the DMN, VN, SN, and SMN in DR patients. Furthermore, the disrupted topological attributes showed correlations with clinical variables. These findings offer important insight into the neural mechanism of visual loss and cognitive deficits in DR patients.
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Affiliation(s)
- Xin Huang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, People's Republic of China
| | - Yan Tong
- Eye Center, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, People's Republic of China
| | - Chen-Xing Qi
- Eye Center, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, People's Republic of China
| | - Yang-Tao Xu
- Eye Center, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, People's Republic of China
| | - Han-Dong Dan
- Eye Center, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, People's Republic of China
| | - Yin Shen
- Eye Center, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, People's Republic of China
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42
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Development of Neuroimaging-Based Biomarkers in Psychiatry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1192:159-195. [PMID: 31705495 DOI: 10.1007/978-981-32-9721-0_9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.
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43
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Simões M, Monteiro R, Andrade J, Mouga S, França F, Oliveira G, Carvalho P, Castelo-Branco M. A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder. Front Neurosci 2018; 12:791. [PMID: 30443204 PMCID: PMC6221955 DOI: 10.3389/fnins.2018.00791] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 10/12/2018] [Indexed: 11/25/2022] Open
Abstract
Imagery of facial expressions in Autism Spectrum Disorder (ASD) is likely impaired but has been very difficult to capture at a neurophysiological level. We developed an approach that allowed to directly link observation of emotional expressions and imagery in ASD, and to derive biomarkers that are able to classify abnormal imagery in ASD. To provide a handle between perception and action imagery cycles it is important to use visual stimuli exploring the dynamical nature of emotion representation. We conducted a case-control study providing a link between both visualization and mental imagery of dynamic facial expressions and investigated source responses to pure face-expression contrasts. We were able to replicate the same highly group discriminative neural signatures during action observation (dynamical face expressions) and imagery, in the precuneus. Larger activation in regions involved in imagery for the ASD group suggests that this effect is compensatory. We conducted a machine learning procedure to automatically identify these group differences, based on the EEG activity during mental imagery of facial expressions. We compared two classifiers and achieved an accuracy of 81% using 15 features (both linear and non-linear) of the signal from theta, high-beta and gamma bands extracted from right-parietal locations (matching the precuneus region), further confirming the findings regarding standard statistical analysis. This robust classification of signals resulting from imagery of dynamical expressions in ASD is surprising because it far and significantly exceeds the good classification already achieved with observation of neutral face expressions (74%). This novel neural correlate of emotional imagery in autism could potentially serve as a clinical interventional target for studies designed to improve facial expression recognition, or at least as an intervention biomarker.
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Affiliation(s)
- Marco Simões
- Coimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Center for Informatics and Systems, University of Coimbra, Coimbra, Portugal
| | - Raquel Monteiro
- Coimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Andrade
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Susana Mouga
- Coimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurodevelopmental and Autism Unit from Child Developmental Center, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Felipe França
- PESC-COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Guiomar Oliveira
- Coimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurodevelopmental and Autism Unit from Child Developmental Center, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,University Clinic of Pediatrics, Faculty of Medicine of the University of Coimbra, Coimbra, Portugal.,Centro de Investigação e Formação Clínica, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Paulo Carvalho
- Center for Informatics and Systems, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research, Instituto de Ciências Nucleares Aplicadas à Saúde, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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44
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Rischka L, Gryglewski G, Pfaff S, Vanicek T, Hienert M, Klöbl M, Hartenbach M, Haug A, Wadsak W, Mitterhauser M, Hacker M, Kasper S, Lanzenberger R, Hahn A. Reduced task durations in functional PET imaging with [18F]FDG approaching that of functional MRI. Neuroimage 2018; 181:323-330. [DOI: 10.1016/j.neuroimage.2018.06.079] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 06/08/2018] [Accepted: 06/28/2018] [Indexed: 01/01/2023] Open
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45
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Analysis of voxel-mirrored homotopic connectivity in medication-free, current major depressive disorder. J Affect Disord 2018; 240:171-176. [PMID: 30075387 DOI: 10.1016/j.jad.2018.07.037] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 06/01/2018] [Accepted: 07/14/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND Recent neuroimaging studies suggest that abnormal function connectivity exists in patients with major depressive disorder (MDD). The aim of this study was to further analyze the underlying neural mechanism of MDD and explore whether clinical characteristics are correlated with the alerted homotopic connectivity in patients with MDD. METHODS Using voxel-mirrored homotopic connectivity (VMHC) during resting state, we compared 80 medication-free patients having current episodes of MDD and 124 never-depressed healthy controls (HCs) matched for age and gender. RESULTS We found decreased VMHC in patients with MDD in bilateral posterior cingulate cortex (PCC) extending to precuneus (Pre) compared with the HCs, which provided strong support for the potential role of PCC/Pre in recognizing interhemispheric connectivity deficits of MDD. Negative correlation between illness course and VMHC in PCC was observed as well. LIMITATIONS First, we just compared the functional connectivity at a rest state but not under a specific task. Second, we did not mitigate the delayed effect on the measurable alterations in homotopic brain activity. Third, we did not make a longitudinal comparison after patients receiving therapeutic drugs. CONCLUSIONS These findings that linking illness course with functional brain changes in depression help us understand the neural architecture of MDD.
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46
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Höflich A, Michenthaler P, Kasper S, Lanzenberger R. Circuit Mechanisms of Reward, Anhedonia, and Depression. Int J Neuropsychopharmacol 2018; 22:105-118. [PMID: 30239748 PMCID: PMC6368373 DOI: 10.1093/ijnp/pyy081] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 09/12/2018] [Indexed: 12/23/2022] Open
Abstract
Pleasure and motivation are important factors for goal-directed behavior and well-being in both animals and humans. Intact hedonic capacity requires an undisturbed interplay between a number of different brain regions and transmitter systems. Concordantly, dysfunction of networks encoding for reward have been shown in depression and other psychiatric disorders. The development of technological possibilities to investigate connectivity on a functional level in humans and to directly influence networks in animals using optogenetics among other techniques has provided new important insights in this field of research.In this review, we aim to provide an overview on the neurobiological substrates of anhedonia on a network level. For this purpose, definition of anhedonia and the involved reward components are described first, then current data on reward networks in healthy individuals and in depressed patients are summarized, and the roles of different neurotransmitter systems involved in reward processing are specified. Based on this information, the impact of different therapeutic approaches on reward processing is described with a particular focus on deep brain stimulation (DBS) as a possibility for a direct modulation of human brain structures in vivo.Overall, results of current studies emphasize the importance of anhedonia in psychiatric disorders and the relevance of targeting this phenotype for a successful psychiatric treatment. However, more data incorporating these results for the refinement of methodological approaches are needed to be able to develop individually tailored therapeutic concepts based on both clinical and neurobiological profiles of patients.
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Affiliation(s)
- Anna Höflich
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria,Correspondence: Rupert Lanzenberger, MD, PD, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18–20, 1090 Vienna, Austria ()
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47
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Webb CA, Olson EA, Killgore WDS, Pizzagalli DA, Rauch SL, Rosso IM. Rostral Anterior Cingulate Cortex Morphology Predicts Treatment Response to Internet-Based Cognitive Behavioral Therapy for Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:255-262. [PMID: 29486867 PMCID: PMC6005352 DOI: 10.1016/j.bpsc.2017.08.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 07/21/2017] [Accepted: 08/15/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND Rostral and subgenual anterior cingulate cortex (rACC and sgACC) activity and, to a lesser extent, volume have been shown to predict depressive symptom improvement across different antidepressant treatments. This study extends prior work by examining whether rACC and/or sgACC morphology predicts treatment response to Internet-based cognitive behavioral therapy (iCBT) for major depressive disorder. This is the first study to examine neural predictors of response to iCBT. METHODS Hierarchical linear modeling tested whether pretreatment rACC and sgACC volumes predicted depressive symptom improvement during a six-session (10-week) randomized clinical trial of iCBT (n = 35) versus a monitored attention control condition (n = 38). Analyses also tested whether pretreatment rACC and sgACC volumes differed between patients who achieved depression remission versus patients who did not remit. RESULTS Larger pretreatment right rACC volume was a significant predictor of greater depressive symptom improvement in iCBT even when controlling for demographic (age, gender, race) and clinical (baseline depression, anhedonia, and anxiety) variables previously linked to treatment response. In addition, pretreatment right rACC volume was larger among patients receiving iCBT whose depression eventually remitted relative to patients who did not remit. Corresponding analyses in the monitored attention control group and for the sgACC were not significant. CONCLUSIONS rACC volume before iCBT demonstrated incremental predictive validity beyond clinical and demographic variables previously found to predict symptom improvement. Such findings may help inform our understanding of the mediating anatomy of iCBT and, if replicated, may suggest neural targets to augment treatment response (e.g., via modulation of rACC function).
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Affiliation(s)
- Christian A Webb
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Elizabeth A Olson
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | | | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Scott L Rauch
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Isabelle M Rosso
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
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48
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Pillai RLI, Malhotra A, Rupert DD, Weschler B, Williams JC, Zhang M, Yang J, Mann JJ, Oquendo MA, Parsey RV, DeLorenzo C. Relations between cortical thickness, serotonin 1A receptor binding, and structural connectivity: A multimodal imaging study. Hum Brain Mapp 2017; 39:1043-1055. [PMID: 29323797 DOI: 10.1002/hbm.23903] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 11/19/2017] [Accepted: 11/22/2017] [Indexed: 01/03/2023] Open
Abstract
Serotonin 1A (5-HT1A ) receptors play a direct role in neuronal development, cell proliferation, and dendritic branching. We hypothesized that variability in 5-HT1A binding can affect cortical thickness, and may account for a subtype of major depressive disorder (MDD) in which both are altered. To evaluate this, we measured cortical thickness from structural magnetic resonance imaging (MRI) and 5-HT1A binding by positron emission tomography (PET) in an exploratory study. To examine a range of 5-HT1A binding and cortical thickness values, we recruited 25 healthy controls and 19 patients with MDD. We hypothesized increased 5-HT1A binding in the raphe nucleus (RN) would be negatively associated with cortical thickness due to reduced serotonergic transmission. Contrary to our hypothesis, raphe 5-HT1A binding was positively correlated with cortical thickness in right posterior cingulate cortex (PCC), a region implicated in the default mode network. Cortical thickness was also positively correlated with 5-HT1A in each cortical region. We further hypothesized that the strength of 5-HT1A -cortical thickness correlation depends on the number of axons between the raphe nucleus and each region. To explore this we related 5-HT1A -cortical thickness correlation coefficients to the number of tracts connecting that region and the raphe, as measured by diffusion tensor imaging (DTI) in an independent sample. The 5-HT1A -cortical thickness association correlated significantly with the number of tracts to each region, supporting our hypothesis. We posit a defect in the raphe may affect the PCC within the default mode network in MDD through serotonergic fibers, resulting in increased ruminative processing.
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Affiliation(s)
- Rajapillai L I Pillai
- Stony Brook University SOM, Stony Brook, New York.,Department of Psychiatry, Stony Brook University, Stony Brook, New York.,Center for Understanding Biology using Imaging Technology, Stony Brook University, Stony Brook, New York
| | - Ashwin Malhotra
- Department of Neurology, New York-Presbyterian Weill Cornell Medical Center, New York, New York
| | | | | | | | - Mengru Zhang
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York
| | - Jie Yang
- Department of Family, Population, and Preventive Medicine, Stony Brook University, Stony Brook, New York
| | - J John Mann
- Department of Biomedical Engineering, Columbia University, New York, New York
| | - Maria A Oquendo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philidelphia, Pennsylvania
| | - Ramin V Parsey
- Department of Psychiatry, Stony Brook University, Stony Brook, New York.,Center for Understanding Biology using Imaging Technology, Stony Brook University, Stony Brook, New York
| | - Christine DeLorenzo
- Department of Psychiatry, Stony Brook University, Stony Brook, New York.,Center for Understanding Biology using Imaging Technology, Stony Brook University, Stony Brook, New York.,Department of Biomedical Engineering, Columbia University, New York, New York
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49
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Hahn A, Gryglewski G, Nics L, Rischka L, Ganger S, Sigurdardottir H, Vraka C, Silberbauer L, Vanicek T, Kautzky A, Wadsak W, Mitterhauser M, Hartenbach M, Hacker M, Kasper S, Lanzenberger R. Task-relevant brain networks identified with simultaneous PET/MR imaging of metabolism and connectivity. Brain Struct Funct 2017; 223:1369-1378. [PMID: 29134288 PMCID: PMC5869947 DOI: 10.1007/s00429-017-1558-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 10/30/2017] [Indexed: 10/24/2022]
Abstract
Except for task-specific functional MRI, the vast majority of imaging studies assessed human brain function at resting conditions. However, tracking task-specific neuronal activity yields important insight how the brain responds to stimulation. We specifically investigated changes in glucose metabolism, functional connectivity and white matter microstructure during task performance using several recent methodological advancements. Opening the eyes and right finger tapping had elicited an increased glucose metabolism in primary visual and motor cortices, respectively. Furthermore, a decreased metabolism was observed in the regions of the default mode network, which allowed absolute quantification of commonly described deactivations during cognitive tasks. These brain regions showed widespread task-specific changes in functional connectivity, which stretched beyond their primary resting-state networks and presumably reflected the level of recruitment of certain brain regions for each task. Finally, the corresponding white matter fiber pathways exhibited changes in axial and radial diffusivity during the tasks, which were regionally distinctive for certain tract groups. These results highlight that even simple task performance leads to substantial changes of entire brain networks. Exploiting the complementary nature of the different imaging modalities may reveal novel insights how the brain processes external stimuli and which networks are involved in certain tasks.
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Affiliation(s)
- Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Lukas Nics
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Sebastian Ganger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Helen Sigurdardottir
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Chrysoula Vraka
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Leo Silberbauer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Thomas Vanicek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Alexander Kautzky
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Wolfgang Wadsak
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Markus Mitterhauser
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Ludwig Bolzmann Institute Applied Diagnostics, Vienna, Austria
| | - Markus Hartenbach
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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50
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Distinctive pretreatment features of bilateral nucleus accumbens networks predict early response to antidepressants in major depressive disorder. Brain Imaging Behav 2017; 12:1042-1052. [DOI: 10.1007/s11682-017-9773-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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