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Long F, Chen Y, Zhang Q, Li Q, Wang Y, Wang Y, Li H, Zhao Y, McNamara RK, DelBello MP, Sweeney JA, Gong Q, Li F. Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis. Mol Psychiatry 2024:10.1038/s41380-024-02710-6. [PMID: 39187625 DOI: 10.1038/s41380-024-02710-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 08/18/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.
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Affiliation(s)
- Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yitian Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Haoran Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Youjin Zhao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - John A Sweeney
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China.
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Wade BSC, Loureiro J, Sahib A, Kubicki A, Joshi SH, Hellemann G, Espinoza RT, Woods RP, Congdon E, Narr KL. Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion. Psychol Med 2022; 52:2376-2386. [PMID: 35578581 PMCID: PMC9527672 DOI: 10.1017/s0033291722001313] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/20/2021] [Accepted: 04/19/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Ketamine is a rapidly-acting antidepressant treatment with robust response rates. Previous studies have reported that serial ketamine therapy modulates resting state functional connectivity in several large-scale networks, though it remains unknown whether variations in brain structure, function, and connectivity impact subsequent treatment success. We used a data-driven approach to determine whether pretreatment multimodal neuroimaging measures predict changes along symptom dimensions of depression following serial ketamine infusion. METHODS Patients with depression (n = 60) received structural, resting state functional, and diffusion MRI scans before treatment. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17), the Inventory of Depressive Symptomatology (IDS-C), and the Rumination Response Scale (RRS) before and 24 h after patients received four (0.5 mg/kg) infusions of racemic ketamine over 2 weeks. Nineteen unaffected controls were assessed at similar timepoints. Random forest regression models predicted symptom changes using pretreatment multimodal neuroimaging and demographic measures. RESULTS Two HDRS-17 subscales, the HDRS-6 and core mood and anhedonia (CMA) symptoms, and the RRS: reflection (RRSR) scale were predicted significantly with 19, 27, and 1% variance explained, respectively. Increased right medial prefrontal cortex/anterior cingulate and posterior insula (PoI) and lower kurtosis of the superior longitudinal fasciculus predicted reduced HDRS-6 and CMA symptoms following treatment. RRSR change was predicted by global connectivity of the left posterior cingulate, left insula, and right superior parietal lobule. CONCLUSIONS Our findings support that connectivity of the anterior default mode network and PoI may serve as potential biomarkers of antidepressant outcomes for core depressive symptoms.
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Affiliation(s)
- Benjamin S. C. Wade
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Joana Loureiro
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Ashish Sahib
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Antoni Kubicki
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Shantanu H. Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
| | - Gerhard Hellemann
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Randall T. Espinoza
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Roger P. Woods
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Eliza Congdon
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
| | - Katherine L. Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, UCLA, Los Angeles, USA
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Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 2021; 11:168. [PMID: 33723229 PMCID: PMC7960732 DOI: 10.1038/s41398-021-01286-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/05/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023] Open
Abstract
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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Wade BSC, Valcour VG, Puthanakit T, Saremi A, Gutman BA, Nir TM, Watson C, Aurpibul L, Kosalaraksa P, Ounchanum P, Kerr S, Dumrongpisutikul N, Visrutaratna P, Srinakarin J, Pothisri M, Narr KL, Thompson PM, Ananworanich J, Paul RH, Jahanshad N. Mapping abnormal subcortical neurodevelopment in a cohort of Thai children with HIV. NEUROIMAGE-CLINICAL 2019; 23:101810. [PMID: 31029050 PMCID: PMC6482384 DOI: 10.1016/j.nicl.2019.101810] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 03/25/2019] [Accepted: 04/01/2019] [Indexed: 10/27/2022]
Abstract
Alterations in subcortical brain structures have been reported in adults with HIV and, to a lesser extent, pediatric cohorts. The extent of longitudinal structural abnormalities in children with perinatal HIV infection (PaHIV) remains unclear. We modeled subcortical morphometry from whole brain structural magnetic resonance imaging (1.5 T) scans of 43 Thai children with PaHIV (baseline age = 11.09±2.36 years) and 50 HIV- children (11.26±2.80 years) using volumetric and surface-based shape analyses. The PaHIV sample were randomized to initiate combination antiretroviral treatment (cART) when CD4 counts were 15-24% (immediate: n = 22) or when CD4 < 15% (deferred: n = 21). Follow-up scans were acquired approximately 52 weeks after baseline. Volumetric and shape descriptors capturing local thickness and surface area dilation were defined for the bilateral accumbens, amygdala, putamen, pallidum, thalamus, caudate, and hippocampus. Regression models adjusting for clinical and demographic variables examined between and within group differences in morphometry associated with HIV. We assessed whether baseline CD4 count and cART status or timing associated with brain maturation within the PaHIV group. All models were adjusted for multiple comparisons using the false discovery rate. A pallidal subregion was significantly thinner in children with PaHIV. Regional thickness, surface area, and volume of the pallidum was associated with CD4 count in children with PaHIV. Longitudinal morphometry was not associated with HIV or cART status or timing, however, the trajectory of the left pallidum volume was positively associated with baseline CD4 count. Our findings corroborate reports in adult cohorts demonstrating a high predilection for HIV-mediated abnormalities in the basal ganglia, but suggest the effect of stable PaHIV infection on morphological aspects of brain development may be subtle.
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Affiliation(s)
- Benjamin S C Wade
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA; Ahmanson-Lovelace Brain Mapping Center University of California, Los Angeles, Los Angeles, CA, USA; Missouri Institute of Mental Health, University of Missouri St. Louis, St. Louis, USA
| | - Victor G Valcour
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Arvin Saremi
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Boris A Gutman
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Talia M Nir
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Christa Watson
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Pope Kosalaraksa
- Department of Pediatrics, Khon Kaen University, Khon Kaen, Thailand
| | | | - Stephen Kerr
- HIV-NAT, the Thai Red Cross AIDS Research Centre, Bangkok, Thailand
| | | | | | - Jiraporn Srinakarin
- Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Monthana Pothisri
- Department of Radiology, Chulalongkorn University Medical Center, Bangkok, Thailand
| | - Katherine L Narr
- Ahmanson-Lovelace Brain Mapping Center University of California, Los Angeles, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Jintanat Ananworanich
- HIV-NAT, the Thai Red Cross AIDS Research Centre, Bangkok, Thailand; U.S. Military HIV Research Program, Walter Reed Army Institute of Research, MD, USA; Department of Global Health, University of Amsterdam, Amsterdam, the Netherlands; Henry M. Jackson Foundation for the Advancement of Military Medicine, MD, USA
| | - Robert H Paul
- Missouri Institute of Mental Health, University of Missouri St. Louis, St. Louis, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
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