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Kuai C, Pu J, Wang D, Tan Z, Wang Y, Xue SW. The association between gray matter volume in the hippocampal subfield and antidepressant efficacy mediated by abnormal dynamic functional connectivity. Sci Rep 2024; 14:8940. [PMID: 38637536 PMCID: PMC11026377 DOI: 10.1038/s41598-024-56866-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/12/2024] [Indexed: 04/20/2024] Open
Abstract
An abnormality of structures and functions in the hippocampus may have a key role in the pathophysiology of major depressive disorder (MDD). However, it is unclear whether structure factors of the hippocampus effectively impact antidepressant responses by hippocampal functional activity in MDD patients. We collected longitudinal data from 36 MDD patients before and after a 3-month course of antidepressant pharmacotherapy. Additionally, we obtained baseline data from 43 healthy controls matched for sex and age. Using resting-state functional magnetic resonance imaging (rs-fMRI), we estimated the dynamic functional connectivity (dFC) of the hippocampal subregions using a sliding-window method. The gray matter volume was calculated using voxel-based morphometry (VBM). The results indicated that patients with MDD exhibited significantly lower dFC of the left rostral hippocampus (rHipp.L) with the right precentral gyrus, left superior temporal gyrus and left postcentral gyrus compared to healthy controls at baseline. In MDD patients, the dFC of the rHipp.L with right precentral gyrus at baseline was correlated with both the rHipp.L volume and HAMD remission rate, and also mediated the effects of the rHipp.L volume on antidepressant performance. Our findings suggested that the interaction between hippocampal structure and functional activity might affect antidepressant performance, which provided a novel insight into the hippocampus-related neurobiological mechanism of MDD.
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Affiliation(s)
- Changxiao Kuai
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China
| | - Jiayong Pu
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China
| | - Donglin Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China.
| | - Zhonglin Tan
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People's Republic of China
| | - Yan Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
| | - Shao-Wei Xue
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China.
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China.
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Poirot MG, Ruhe HG, Mutsaerts HJMM, Maximov II, Groote IR, Bjørnerud A, Marquering HA, Reneman L, Caan MWA. Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial. Am J Psychiatry 2024; 181:223-233. [PMID: 38321916 DOI: 10.1176/appi.ajp.20230206] [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] [Indexed: 02/08/2024]
Abstract
OBJECTIVE Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment. METHODS This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores. RESULTS A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models. CONCLUSIONS The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.
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Affiliation(s)
- Maarten G Poirot
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henricus G Ruhe
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henk-Jan M M Mutsaerts
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Ivan I Maximov
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Inge R Groote
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Atle Bjørnerud
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henk A Marquering
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Matthan W A Caan
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
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Monereo-Sánchez J, Jansen JFA, van Boxtel MPJ, Backes WH, Köhler S, Stehouwer CDA, Linden DEJ, Schram MT. Association of hippocampal subfield volumes with prevalence, course and incidence of depressive symptoms: The Maastricht Study. Br J Psychiatry 2024; 224:66-73. [PMID: 37993980 PMCID: PMC10807974 DOI: 10.1192/bjp.2023.143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 08/09/2023] [Accepted: 09/26/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND Late-life depression has been associated with volume changes of the hippocampus. However, little is known about its association with specific hippocampal subfields over time. AIMS We investigated whether hippocampal subfield volumes were associated with prevalence, course and incidence of depressive symptoms. METHOD We extracted 12 hippocampal subfield volumes per hemisphere with FreeSurfer v6.0 using T1-weighted and fluid-attenuated inversion recovery 3T magnetic resonance images. Depressive symptoms were assessed at baseline and annually over 7 years of follow-up (9-item Patient Health Questionnaire). We used negative binominal, logistic, and Cox regression analyses, corrected for multiple comparisons, and adjusted for demographic, cardiovascular and lifestyle factors. RESULTS A total of n = 4174 participants were included (mean age 60.0 years, s.d. = 8.6, 51.8% female). Larger right hippocampal fissure volume was associated with prevalent depressive symptoms (odds ratio (OR) = 1.26, 95% CI 1.08-1.48). Larger bilateral hippocampal fissure (OR = 1.37-1.40, 95% CI 1.14-1.71), larger right molecular layer (OR = 1.51, 95% CI 1.14-2.00) and smaller right cornu ammonis (CA)3 volumes (OR = 0.61, 95% CI 0.48-0.79) were associated with prevalent depressive symptoms with a chronic course. No associations of hippocampal subfield volumes with incident depressive symptoms were found. Yet, lower left hippocampal amygdala transition area (HATA) volume was associated with incident depressive symptoms with chronic course (hazard ratio = 0.70, 95% CI 0.55-0.89). CONCLUSIONS Differences in hippocampal fissure, molecular layer and CA volumes might co-occur or follow the onset of depressive symptoms, in particular with a chronic course. Smaller HATA was associated with an increased risk of incident (chronic) depression. Our results could capture a biological foundation for the development of chronic depressive symptoms, and stresses the need to discriminate subtypes of depression to unravel its biological underpinnings.
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Affiliation(s)
- Jennifer Monereo-Sánchez
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands; and Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, the Netherlands
| | - Jacobus F. A. Jansen
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands; and Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, the Netherlands
| | - Martin P. J. van Boxtel
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands
| | - Walter H. Backes
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands; Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, the Netherlands; and School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands
| | - Sebastian Köhler
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands; and Department of Psychiatry and Neuropsychology, Maastricht University Medical Center, the Netherlands
| | - Coen D. A. Stehouwer
- School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands; Department of Psychiatry and Neuropsychology, Maastricht University Medical Center, the Netherlands; and Department of Internal Medicine, Maastricht University Medical Center, the Netherlands
| | - David E. J. Linden
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands
| | - Miranda T. Schram
- School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands; School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands; Department of Internal Medicine, Maastricht University Medical Center, the Netherlands; and Maastricht Heart + Vascular Center, Maastricht University Medical Center, the Netherlands
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Zavaliangos-Petropulu A, McClintock SM, Joshi SH, Taraku B, Al-Sharif NB, Espinoza RT, Narr KL. Hippocampal subfield volumes in treatment resistant depression and serial ketamine treatment. Front Psychiatry 2023; 14:1227879. [PMID: 37876623 PMCID: PMC10590913 DOI: 10.3389/fpsyt.2023.1227879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 10/26/2023] Open
Abstract
Introduction Subanesthetic ketamine is a rapidly acting antidepressant that has also been found to improve neurocognitive performance in adult patients with treatment resistant depression (TRD). Provisional evidence suggests that ketamine may induce change in hippocampal volume and that larger pre-treatment volumes might be related to positive clinical outcomes. Here, we examine the effects of serial ketamine treatment on hippocampal subfield volumes and relationships between pre-treatment subfield volumes and changes in depressive symptoms and neurocognitive performance. Methods Patients with TRD (N = 66; 31M/35F; age = 39.5 ± 11.1 years) received four ketamine infusions (0.5 mg/kg) over 2 weeks. Structural MRI scans, the National Institutes of Health Toolbox (NIHT) Cognition Battery, and Hamilton Depression Rating Scale (HDRS) were collected at baseline, 24 h after the first and fourth ketamine infusion, and 5 weeks post-treatment. The same data was collected for 32 age and sex matched healthy controls (HC; 17M/15F; age = 35.03 ± 12.2 years) at one timepoint. Subfield (CA1/CA3/CA4/subiculum/molecular layer/GC-ML-DG) volumes corrected for whole hippocampal volume were compared across time, between treatment remitters/non-remitters, and patients and HCs using linear regression models. Relationships between pre-treatment subfield volumes and clinical and cognitive outcomes were also tested. All analyses included Bonferroni correction. Results Patients had smaller pre-treatment left CA4 (p = 0.004) and GC.ML.DG (p = 0.004) volumes compared to HC, but subfield volumes remained stable following ketamine treatment (all p > 0.05). Pre-treatment or change in hippocampal subfield volumes over time showed no variation by remission status nor correlated with depressive symptoms (p > 0.05). Pre-treatment left CA4 was negatively correlated with improved processing speed after single (p = 0.0003) and serial ketamine infusion (p = 0.005). Left GC.ML.DG also negatively correlated with improved processing speed after single infusion (p = 0.001). Right pre-treatment CA3 positively correlated with changes in list sorting working memory at follow-up (p = 0.0007). Discussion These results provide new evidence to suggest that hippocampal subfield volumes at baseline may present a biomarker for neurocognitive improvement following ketamine treatment in TRD. In contrast, pre-treatment subfield volumes and changes in subfield volumes showed negligible relationships with ketamine-related improvements in depressive symptoms.
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Affiliation(s)
- Artemis Zavaliangos-Petropulu
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Shawn M. McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States
| | - Shantanu H. Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Brandon Taraku
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Noor B. Al-Sharif
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Randall T. Espinoza
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L. Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, United States
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Twait EL, Blom K, Koek HL, Zwartbol MHT, Ghaznawi R, Hendrikse J, Gerritsen L, Geerlings MI. Psychosocial factors and hippocampal subfields: The Medea-7T study. Hum Brain Mapp 2022; 44:1964-1984. [PMID: 36583397 PMCID: PMC9980899 DOI: 10.1002/hbm.26185] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/21/2022] [Accepted: 12/05/2022] [Indexed: 12/31/2022] Open
Abstract
Specific subfields within the hippocampus have shown vulnerability to chronic stress, highlighting the importance of looking regionally within the hippocampus to understand the role of psychosocial factors in the development of neurodegenerative diseases. A systematic review on psychosocial factors and hippocampal subfield volumes was performed and showed inconsistent results, highlighting the need for future studies to explore this relationship. The current study aimed to explore the association of psychosocial factors with hippocampal (subfield) volumes, using high-field 7T MRI. Data were from the Memory Depression and Aging (Medea)-7T study, which included 333 participants without dementia. Hippocampal subfields were automatically segmented from T2-weighted images using ASHS software. Generalized linear models accounting for correlated outcomes were used to assess the association between subfields (i.e., entorhinal cortex, subiculum, Cornu Ammonis [CA]1, CA2, CA3, dentate gyrus, and tail) and each psychosocial factor (i.e., depressive symptoms, anxiety symptoms, childhood maltreatment, recent stressful life events, and social support), adjusted for age, sex, and intracranial volume. Neither depression nor anxiety was associated with specific hippocampal (subfield) volumes. A trend for lower total hippocampal volume was found in those reporting childhood maltreatment, and a trend for higher total hippocampal volume was found in those who experienced a recent stressful life event. Among subfields, low social support was associated with lower volume in the CA3 (B = -0.43, 95% CI: -0.72; -0.15). This study suggests possible differential effects among hippocampal (subfield) volumes and psychosocial factors.
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Affiliation(s)
- Emma L. Twait
- Department of Epidemiology, Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht and Utrecht UniversityUtrechtThe Netherlands
| | - Kim Blom
- Department of Epidemiology, Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht and Utrecht UniversityUtrechtThe Netherlands
| | - Huiberdina L. Koek
- Department of GeriatricsUniversity Medical Center Utrecht and Utrecht UniversityUtrechtThe Netherlands
| | - Maarten H. T. Zwartbol
- Department of RadiologyUniversity Medical Center Utrecht and Utrecht UniversityUtrechtThe Netherlands
| | - Rashid Ghaznawi
- Department of RadiologyUniversity Medical Center Utrecht and Utrecht UniversityUtrechtThe Netherlands
| | - Jeroen Hendrikse
- Department of RadiologyUniversity Medical Center Utrecht and Utrecht UniversityUtrechtThe Netherlands
| | - Lotte Gerritsen
- Department of PsychologyUtrecht UniversityUtrechtThe Netherlands
| | - Mirjam I. Geerlings
- Department of Epidemiology, Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht and Utrecht UniversityUtrechtThe Netherlands,Department of General PracticeAmsterdam UMC, Location University of AmsterdamAmsterdamThe Netherlands,Amsterdam Public Health, Aging & Later life, and Personalized MedicineAmsterdamThe Netherlands,Amsterdam Neuroscience, Neurodegeneration, and Mood, Anxiety, Psychosis, Stress, and SleepAmsterdamThe Netherlands
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Zhang L, Hu X, Hu Y, Tang M, Qiu H, Zhu Z, Gao Y, Li H, Kuang W, Ji W. Structural covariance network of the hippocampus-amygdala complex in medication-naïve patients with first-episode major depressive disorder. PSYCHORADIOLOGY 2022; 2:190-198. [PMID: 38665275 PMCID: PMC10917195 DOI: 10.1093/psyrad/kkac023] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/05/2022] [Accepted: 12/14/2022] [Indexed: 04/28/2024]
Abstract
Background The hippocampus and amygdala are densely interconnected structures that work together in multiple affective and cognitive processes that are important to the etiology of major depressive disorder (MDD). Each of these structures consists of several heterogeneous subfields. We aim to explore the topologic properties of the volume-based intrinsic network within the hippocampus-amygdala complex in medication-naïve patients with first-episode MDD. Methods High-resolution T1-weighted magnetic resonance imaging scans were acquired from 123 first-episode, medication-naïve, and noncomorbid MDD patients and 81 age-, sex-, and education level-matched healthy control participants (HCs). The structural covariance network (SCN) was constructed for each group using the volumes of the hippocampal subfields and amygdala subregions; the weights of the edges were defined by the partial correlation coefficients between each pair of subfields/subregions, controlled for age, sex, education level, and intracranial volume. The global and nodal graph metrics were calculated and compared between groups. Results Compared with HCs, the SCN within the hippocampus-amygdala complex in patients with MDD showed a shortened mean characteristic path length, reduced modularity, and reduced small-worldness index. At the nodal level, the left hippocampal tail showed increased measures of centrality, segregation, and integration, while nodes in the left amygdala showed decreased measures of centrality, segregation, and integration in patients with MDD compared with HCs. Conclusion Our results provide the first evidence of atypical topologic characteristics within the hippocampus-amygdala complex in patients with MDD using structure network analysis. It provides more delineate mechanism of those two structures that underlying neuropathologic process in MDD.
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Affiliation(s)
- Lianqing Zhang
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Xinyue Hu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Yongbo Hu
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Mengyue Tang
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Hui Qiu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Ziyu Zhu
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Yingxue Gao
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Hailong Li
- Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Weidong Ji
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science and Affiliated Mental Health Center, East China Normal University, Shanghai 200335, China
- Child Psychiatry, Shanghai Changning Mental Health Center, Shanghai 200335, China
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7
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Zavaliangos-Petropulu A, Al-Sharif NB, Taraku B, Leaver AM, Sahib AK, Espinoza RT, Narr KL. Neuroimaging-Derived Biomarkers of the Antidepressant Effects of Ketamine. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 8:361-386. [PMID: 36775711 DOI: 10.1016/j.bpsc.2022.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2022]
Abstract
Major depressive disorder is a highly prevalent psychiatric disorder. Despite an extensive range of treatment options, about a third of patients still struggle to respond to available therapies. In the last 20 years, ketamine has gained considerable attention in the psychiatric field as a promising treatment of depression, particularly in patients who are treatment resistant or at high risk for suicide. At a subanesthetic dose, ketamine produces a rapid and pronounced reduction in depressive symptoms and suicidal ideation, and serial treatment appears to produce a greater and more sustained therapeutic response. However, the mechanism driving ketamine's antidepressant effects is not yet well understood. Biomarker discovery may advance knowledge of ketamine's antidepressant action, which could in turn translate to more personalized and effective treatment strategies. At the brain systems level, neuroimaging can be used to identify functional pathways and networks contributing to ketamine's therapeutic effects by studying how it alters brain structure, function, connectivity, and metabolism. In this review, we summarize and appraise recent work in this area, including 51 articles that use resting-state and task-based functional magnetic resonance imaging, arterial spin labeling, positron emission tomography, structural magnetic resonance imaging, diffusion magnetic resonance imaging, or magnetic resonance spectroscopy to study brain and clinical changes 24 hours or longer after ketamine treatment in populations with unipolar or bipolar depression. Though individual studies have included relatively small samples, used different methodological approaches, and reported disparate regional findings, converging evidence supports that ketamine leads to neuroplasticity in structural and functional brain networks that contribute to or are relevant to its antidepressant effects.
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Affiliation(s)
- Artemis Zavaliangos-Petropulu
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
| | - Noor B Al-Sharif
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Brandon Taraku
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Amber M Leaver
- Department of Radiology, Northwestern University, Chicago, Illinois
| | - Ashish K Sahib
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Randall T Espinoza
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Katherine L Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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8
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Zhang L, Meng J, Li H, Tang M, Zhou Z, Zhou X, Feng L, Li X, Guo Y, He Y, He W, Huang X. Hippocampal adaptation to high altitude: a neuroanatomic profile of hippocampal subfields in Tibetans and acclimatized Han Chinese residents. Front Neuroanat 2022; 16:999033. [DOI: 10.3389/fnana.2022.999033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/25/2022] [Indexed: 11/19/2022] Open
Abstract
The hippocampus is highly plastic and vulnerable to hypoxia. However, it is unknown whether and how it adapts to chronic hypobaric hypoxia in humans. With a unique sample of Tibetans and acclimatized Han Chinese individuals residing on the Tibetan plateau, we aimed to build a neuroanatomic profile of the altitude-adapted hippocampus by measuring the volumetric differences in the whole hippocampus and its subfields. High-resolution T1-weighted magnetic resonance imaging was performed in healthy Tibetans (TH, n = 72) and healthy Han Chinese individuals living at an altitude of more than 3,500 m (HH, n = 27). In addition, healthy Han Chinese individuals living on a plain (HP, n = 72) were recruited as a sea-level reference group. Whereas the total hippocampal volume did not show a significant difference across groups when corrected for age, sex, and total intracranial volume, subfield-level differences within the hippocampus were found. Post hoc analyses revealed that Tibetans had larger core hippocampal subfields (bilateral CA3, right CA4, right dentate gyrus); a larger right hippocampus–amygdala transition area; and smaller bilateral presubiculum, right subiculum, and bilateral fimbria, than Han Chinese subjects (HH and/or HP). The hippocampus and all its subfields were found to be slightly and non-significantly smaller in HH subjects than in HP subjects. As a primary explorational study, our data suggested that while the overall hippocampal volume did not change, the core hippocampus of Tibetans may have an effect of adaptation to chronic hypobaric hypoxia. However, this adaptation may have required generations rather than mere decades to accumulate in the population.
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9
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Xue SW, Kuai C, Xiao Y, Zhao L, Lan Z. Abnormal Dynamic Functional Connectivity of the Left Rostral Hippocampus in Predicting Antidepressant Efficacy in Major Depressive Disorder. Psychiatry Investig 2022; 19:562-569. [PMID: 35903058 PMCID: PMC9334807 DOI: 10.30773/pi.2021.0386] [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: 12/22/2021] [Accepted: 06/06/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Some pharmacological treatments are ineffective in parts of patients with major depressive disorder (MDD), hence this needs prediction of effective treatment responses. The study aims to examine the relationship between dynamic functional connectivity (dFC) of the hippocampal subregion and antidepressant improvement of MDD patients and to estimate the capability of dFC to predict antidepressant efficacy. METHODS The data were from 70 MDD patients and 43 healthy controls (HC); the dFC of hippocampal subregions was estimated by sliding-window approach based on resting-state functional magnetic resonance imaging (R-fMRI). After 3 months treatment, 36 patients underwent second R-fMRI scan and were then divided into the response group and non-response group according to clinical responses. RESULTS The result manifested that MDD patients exhibited lower mean dFC of the left rostral hippocampus (rHipp.l) compared with HC. After 3 months therapy, the response group showed lower dFC of rHipp.l compared with the non-response group. The dFC of rHipp.l was also negatively correlated with the reduction rate of Hamilton Depression Rating Scale. CONCLUSION These findings highlighted the importance of rHipp in MDD from the dFC perspective. Detection and estimation of these changes might demonstrate helpful for comprehending the pathophysiological mechanism and for assessment of treatment reaction of MDD.
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Affiliation(s)
- Shao-Wei Xue
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Changxiao Kuai
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Yang Xiao
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Lei Zhao
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Zhihui Lan
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
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10
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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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11
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Lai CH. Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 107:110234. [PMID: 33370569 DOI: 10.1016/j.pnpbp.2020.110234] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 12/02/2020] [Accepted: 12/21/2020] [Indexed: 12/23/2022]
Abstract
The neuroimaging is an important tool for understanding the biomarkers and predicting treatment responses in major depressive disorder (MDD). The potential biomarkers and prediction of treatment response in MDD will be addressed in the review article. The brain regions of cognitive control and emotion regulation, such as the frontal and limbic regions, might represent the potential targets for MDD biomarkers. The potential targets of frontal lobes might include anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). For the limbic system, hippocampus and amygdala might be the potentially promising targets for MDD. The potential targets of fronto-limbic regions have been found in the studies of several major neuroimaging modalities, such as the magnetic resonance imaging, near-infrared spectroscopy, electroencephalography, positron emission tomography, and single-photon emission computed tomography. Additional regions, such as brainstem and midbrain, might also play a part in the MDD biomarkers. For the prediction of treatment response, the gray matter volumes, white matter tracts, functional representations and receptor bindings of ACC, DLPFC, OFC, amygdala, and hippocampus might play a role in the prediction of antidepressant responses in MDD. For the response prediction of psychotherapies, the fronto-limbic, reward regions, and insula will be the potential targets. For the repetitive transcranial magnetic stimulation, the DLPFC, ACC, limbic, and visuospatial regions might represent the predictive targets for treatment. The neuroimaging targets of MDD might be focused in the fronto-limbic regions. However, the neuroimaging targets for the prediction of treatment responses might be inconclusive and beyond the fronto-limbic regions.
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Affiliation(s)
- Chien-Han Lai
- Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan; PhD Psychiatry & Neuroscience Clinic, Taoyuan, Taiwan.
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12
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Zhang L, Lu L, Bu X, Li H, Tang S, Gao Y, Liang K, Zhang S, Hu X, Wang Y, Li L, Hu X, Lim KO, Gong Q, Huang X. Alterations in hippocampal subfield and amygdala subregion volumes in posttraumatic subjects with and without posttraumatic stress disorder. Hum Brain Mapp 2021; 42:2147-2158. [PMID: 33566375 PMCID: PMC8046112 DOI: 10.1002/hbm.25356] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/02/2020] [Accepted: 01/20/2021] [Indexed: 02/05/2023] Open
Abstract
The hippocampus and amygdala are important structures in the posttraumatic stress disorder (PTSD); however, the exact relationship between these structures and stress or PTSD remains unclear. Moreover, they consist of several functionally distinct subfields/subregions that may serve different roles in the neuropathophysiology of PTSD. Here we present a subregional profile of the hippocampus and amygdala in 145 survivors of a major earthquake and 56 non‐traumatized healthy controls (HCs). We found that the bilateral hippocampus and left amygdala were significantly smaller in survivors than in HCs, and there was no difference between survivors with (n = 69) and without PTSD (trauma‐exposed controls [TCs], n = 76). Analyses revealed similar results in most subfields/subregions, except that the right hippocampal body (in a head‐body‐tail segmentation scheme), right presubiculum, and left amygdala medial nuclei (Me) were significantly larger in PTSD patients than in TCs but smaller than in HCs. Larger hippocampal body were associated with the time since trauma in PTSD patients. The volume of the right cortical nucleus (Co) was negatively correlated with the severity of symptoms in the PTSD group but positively correlated with the same measurement in the TC group. This correlation between symptom severity and Co volume was significantly different between the PTSD and TCs. Together, we demonstrated that generalized smaller volumes in the hippocampus and amygdala were more likely to be trauma‐related than PTSD‐specific, and their subfields/subregions were distinctively affected. Notably, larger left Me, right hippocampal body and presubiculum were PTSD‐specific; these could be preexisting factors for PTSD or reflect rapid posttraumatic reshaping.
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Affiliation(s)
- Lianqing Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Xuan Bu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Hailong Li
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Shi Tang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Yingxue Gao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Kaili Liang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Suming Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Xinyue Hu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Yanlin Wang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Lei Li
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Xinyu Hu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, and Minneapolis VA Medical Center, Minneapolis, Minnesota, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
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13
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Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies. Front Med 2021; 15:528-540. [PMID: 33511554 DOI: 10.1007/s11684-020-0798-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 04/25/2020] [Indexed: 02/05/2023]
Abstract
Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.
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14
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Cheng B, Meng Y, Zuo Y, Guo Y, Wang X, Wang S, Zhang R, Deng W, Guo Y, Ning G. Functional connectivity patterns of the subgenual anterior cingulate cortex in first-episode refractory major depressive disorder. Brain Imaging Behav 2021; 15:2397-2405. [PMID: 33432537 DOI: 10.1007/s11682-020-00436-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/18/2020] [Indexed: 02/05/2023]
Abstract
Although accumulating evidence has been elucidating the neuronal basis of refractory/nonrefractory major depressive disorder (rMDD/nrMDD), the results are inconsistent, and little is known about the distinct neural mechanisms underlying rMDD. Here, we explored the convergent/divergent brain networks between first-episode MDD subtypes using the resting-state functional connectivity (RSFC) approach. In total, 33 healthy controls (HCs), 31 first-episode rMDD patients and 33 first-episode nrMDD patients were enrolled and underwent MRI scanning. The left subgenual anterior cingulate cortex (sgACC) was selected as the seed region, and RSFC was employed to evaluate associations between the seed and other regions in the whole brain. Both MDD subtypes exhibited convergent left sgACC-based neural networks, including increased RSFC with the dorsal prefrontal cortex (DPFC) and decreased RSFC with the bilateral orbitofrontal cortex (OFC) and right parahippocampus. rMDD patients exhibited increased left sgACC-OFC RSFC relative to nrMDD patients, and RSFC with the bilateral OFC in rMDD patients was negatively correlated with HAMD scores. These findings confirmed our speculation that convergent and divergent neural networks exist between rMDD and nrMDD. Cortical-limbic circuits, especially the prefrontal-limbic circuit, may serve as the convergent dysfunctional neural circuitry in MDD subtypes. As an important biomarker, a unique OFC-sgACC circuit abnormality was identified in rMDD patients, which might help elucidate the underlying mechanism regarding treatment responses in rMDD patients.
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Affiliation(s)
- Bochao Cheng
- Department of Radiology, West China Second University Hospital, Sichuan University, South Renmin Road, Chengdu, Sichuan province, People's Republic of China, 610041
| | - Yajing Meng
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China
| | - Yan Zuo
- Maternity clinic, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yi Guo
- Department of Radiology, West China Second University Hospital, Sichuan University, South Renmin Road, Chengdu, Sichuan province, People's Republic of China, 610041
| | - Xiuli Wang
- Department of Psychiatry, the Fourth People's Hospital of Chengdu, University of Electronic Science and Technology of China, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ran Zhang
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Deng
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China
| | - Yingkun Guo
- Department of Radiology, West China Second University Hospital, Sichuan University, South Renmin Road, Chengdu, Sichuan province, People's Republic of China, 610041. .,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, South Renmin Road, Chengdu, Sichuan province, People's Republic of China, 610041.
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15
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Sämann PG, Iglesias JE, Gutman B, Grotegerd D, Leenings R, Flint C, Dannlowski U, Clarke‐Rubright EK, Morey RA, Erp TG, Whelan CD, Han LKM, Velzen LS, Cao B, Augustinack JC, Thompson PM, Jahanshad N, Schmaal L. FreeSurfer
‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for
ENIGMA
studies and other collaborative efforts. Hum Brain Mapp 2020; 43:207-233. [PMID: 33368865 PMCID: PMC8805696 DOI: 10.1002/hbm.25326] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 11/26/2020] [Accepted: 12/13/2020] [Indexed: 12/11/2022] Open
Abstract
Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized.
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Affiliation(s)
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing University College London London UK
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology Massachusetts General Hospital/Harvard Medical School Boston Massachusetts US
- Computer Science and AI Laboratory (CSAIL), Massachusetts Institute of Technology (MIT) Cambridge Massachusetts US
| | - Boris Gutman
- Department of Biomedical Engineering Illinois Institute of Technology Chicago USA
| | | | - Ramona Leenings
- Department of Psychiatry University of Münster Münster Germany
| | - Claas Flint
- Department of Psychiatry University of Münster Münster Germany
- Department of Mathematics and Computer Science University of Münster Germany
| | - Udo Dannlowski
- Department of Psychiatry University of Münster Münster Germany
| | - Emily K. Clarke‐Rubright
- Brain Imaging and Analysis Center, Duke University Durham North Carolina USA
- VISN 6 MIRECC, Durham VA Durham North Carolina USA
| | - Rajendra A. Morey
- Brain Imaging and Analysis Center, Duke University Durham North Carolina USA
- VISN 6 MIRECC, Durham VA Durham North Carolina USA
| | - Theo G.M. Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior University of California Irvine California USA
- Center for the Neurobiology of Learning and Memory University of California Irvine Irvine California USA
| | - Christopher D. Whelan
- Imaging Genetics Center Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Los Angeles California USA
| | - Laura K. M. Han
- Department of Psychiatry Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience Amsterdam The Netherlands
| | - Laura S. Velzen
- Orygen Parkville Australia
- Centre for Youth Mental Health The University of Melbourne Melbourne Australia
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry University of Alberta Edmonton Canada
| | - Jean C. Augustinack
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology Massachusetts General Hospital/Harvard Medical School Boston Massachusetts US
| | - Paul M. Thompson
- Imaging Genetics Center Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Los Angeles California USA
| | - Neda Jahanshad
- Imaging Genetics Center Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California Los Angeles California USA
| | - Lianne Schmaal
- Orygen Parkville Australia
- Centre for Youth Mental Health The University of Melbourne Melbourne Australia
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Cui X, Deng Q, Lang B, Su Q, Liu F, Zhang Z, Chen J, Zhao J, Guo W. Less reduced gray matter volume in the subregions of superior temporal gyrus predicts better treatment efficacy in drug-naive, first-episode schizophrenia. Brain Imaging Behav 2020; 15:1997-2004. [PMID: 33033986 DOI: 10.1007/s11682-020-00393-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2020] [Indexed: 11/26/2022]
Abstract
Decreased gray matter volume (GMV) in the superior temporal gyrus (STG) has been implicated in the neurophysiology of schizophrenia. However, it remains unclear whether volumetric reduction in the subregions of the STG can predict treatment efficacy for schizophrenia. Our cohort included 44 drug-naive, first-episode patients, 42 unaffected siblings and 44 healthy controls. Voxel-based morphometry and pattern classification were utilized to analyze the acquired imaging data as per the anatomical subdivision by a well-defined brainnetome atlas. The patients presented lower GMV values in left TE1.0/1.2 (TE, anterior temporal visual association area) than the siblings, and lower GMV values in the left/right TE1.0/1.2 and left A22r (rostral area 22) than the controls. A positive correlation is observed between the GMV values in the right A38l (lateral area 38) and baseline Positive and Negative Syndrome Scale (PANSS) total scores in the patients. Support vector regression (SVR) results exhibited a significant association between predicted (based on the GMV values in the right A38l) and actual symptomatic improvement based on the reduction ratio of the PANSS total scores (r = 0.498, p = 0.001). Our results suggest that normal structure in the right A38l of the STG may be an important factor indicative of the effects of antipsychotic drugs, which can be potentially used to monitor drug effects for first-episode patients at an early stage in clinical practice.
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Affiliation(s)
- Xilong Cui
- Department of Psychaitry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Qijian Deng
- Department of Psychaitry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Bing Lang
- Department of Psychaitry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Qinji Su
- Mental Health Center, the Second Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, 530007, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhikun Zhang
- Mental Health Center, the Second Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, 530007, China
| | - Jindong Chen
- Department of Psychaitry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Jingping Zhao
- Department of Psychaitry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Wenbin Guo
- Department of Psychaitry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China.
- The Third People's Hospital of Foshan, Foshan, Guangdong, 528000, China.
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Abstract
The application of personalized medicine to psychiatry is challenging. Psychoradiology could provide biomarkers based on objective tests in support of the diagnostic classifications and treatment planning. We review potential psychoradiological biomarkers for psychopharmaceutical effects. Although none of the biomarkers reviewed are yet of sufficient clinical utility to inform the selection of a specific pharmacologic compound for an individual patient, there is strong consensus that advanced multimodal approaches will contribute to discovery of novel treatment predictors in psychiatric disorders. Progress has been sufficient to warrant enthusiasm, in which application of neuroimaging-based biomarkers would represent a paradigm shift and modernization of psychiatric practice.
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Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92:20181000. [PMID: 31170803 PMCID: PMC6732936 DOI: 10.1259/bjr.20181000] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 02/05/2023] Open
Abstract
Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.
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Affiliation(s)
| | | | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, USA
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