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Sun H, Bai T, Zhang X, Fan X, Zhang K, Zhang J, Hu Q, Xu J, Tian Y, Wang K. Molecular mechanisms underlying structural plasticity of electroconvulsive therapy in major depressive disorder. Brain Imaging Behav 2024; 18:930-941. [PMID: 38664360 DOI: 10.1007/s11682-024-00884-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2024] [Indexed: 08/31/2024]
Abstract
Although previous studies reported structural changes associated with electroconvulsive therapy (ECT) in major depressive disorder (MDD), the underlying molecular basis of ECT remains largely unknown. Here, we combined two independent structural MRI datasets of MDD patients receiving ECT and transcriptomic gene expression data from Allen Human Brain Atlas to reveal the molecular basis of ECT for MDD. We performed partial least square regression to explore whether/how gray matter volume (GMV) alterations were associated with gene expression level. Functional enrichment analysis was conducted using Metascape to explore ontological pathways of the associated genes. Finally, these genes were further assigned to seven cell types to determine which cell types contribute most to the structural changes in MDD patients after ECT. We found significantly increased GMV in bilateral hippocampus in MDD patients after ECT. Transcriptome-neuroimaging association analyses showed that expression levels of 726 genes were positively correlated with the increased GMV in MDD after ECT. These genes were mainly involved in synaptic signaling, calcium ion binding and cell-cell signaling, and mostly belonged to excitatory and inhibitory neurons. Moreover, we found that the MDD risk genes of CNR1, HTR1A, MAOA, PDE1A, and SST as well as ECT related genes of BDNF, DRD2, APOE, P2RX7, and TBC1D14 showed significantly positive associations with increased GMV. Overall, our findings provide biological and molecular mechanisms underlying structural plasticity induced by ECT in MDD and the identified genes may facilitate future therapy for MDD.
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Affiliation(s)
- Hui Sun
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Tongjian Bai
- Department of Neurology, the First Hospital of Anhui Medical University, Hefei, 230022, China
| | - Xiaodong Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xinxin Fan
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Kai Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yanghua Tian
- Department of Neurology, the First Hospital of Anhui Medical University, Hefei, 230022, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230022, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China.
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230022, China.
- Department of Neurology, the Second Hospital of Anhui Medical University, Hefei, 230022, China.
| | - Kai Wang
- Department of Neurology, the First Hospital of Anhui Medical University, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230022, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230022, China
- Anhui Province clinical research center for neurological disease, Hefei, 230022, China
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2
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Lundin RM, Falcao VP, Kannangara S, Eakin CW, Abdar M, O'Neill J, Khosravi A, Eyre H, Nahavandi S, Loo C, Berk M. Machine Learning in Electroconvulsive Therapy: A Systematic Review. J ECT 2024:00124509-990000000-00167. [PMID: 38857315 DOI: 10.1097/yct.0000000000001009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
ABSTRACT Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate for machine learning approaches due to the large data sets with data captured through electroencephalography (EEG) and other objective measures. A systematic review of 6 databases led to the full-text examination of 26 articles using machine learning approaches in examining data predicting response to ECT treatment. The identified articles used a wide variety of data types covering structural and functional imaging data (n = 15), clinical data (n = 5), a combination of clinical and imaging data (n = 2), EEG (n = 3), and social media posts (n = 1). The clinical indications in which response prediction was assessed were depression (n = 21) and psychosis (n = 4). Changes in multiple anatomical regions in the brain were identified as holding a predictive value for response to ECT. These primarily centered on the limbic system and associated networks. Clinical features predicting good response to ECT in depression included shorter duration, lower severity, higher medication dose, psychotic features, low cortisol levels, and positive family history. It has also been possible to predict the likelihood of relapse of readmission with psychosis after ECT treatment, including a better response if higher transfer entropy was calculated from EEG signals. A transdisciplinary approach with an international consortium collecting a wide range of retrospective and prospective data may help to refine and extend these outcomes and translate them into clinical practice.
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Affiliation(s)
| | | | | | - Charles W Eakin
- From the Mental Health, Drug and Alcohol Services, Barwon Health
| | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia
| | - John O'Neill
- Waikato District Health Board, Hamilton, New Zealand
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia
| | | | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia
| | | | - Michael Berk
- IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Victoria, Australia
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Ma K, Wen X, Zhu Q, Zhang D. Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1526-1538. [PMID: 38090837 DOI: 10.1109/tmi.2023.3342047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Brain networks, describing the functional or structural interactions of brain with graph theory, have been widely used for brain imaging analysis. Currently, several network representation methods have been developed for describing and analyzing brain networks. However, most of these methods ignored the valuable weighted information of the edges in brain networks. In this paper, we propose a new representation method (i.e., ordinal pattern tree) for brain network analysis. Compared with the existing network representation methods, the proposed ordinal pattern tree (OPT) can not only leverage the weighted information of the edges but also express the hierarchical relationships of nodes in brain networks. On OPT, nodes are connected by ordinal edges which are constructed by using the ordinal pattern relationships of weighted edges. We represent brain networks as OPTs and further develop a new graph kernel called optimal transport (OT) based ordinal pattern tree (OT-OPT) kernel to measure the similarity between paired brain networks. In OT-OPT kernel, the OT distances are used to calculate the transport costs between the nodes on the OPTs. Based on these OT distances, we use exponential function to calculate OT-OPT kernel which is proved to be positive definite. To evaluate the effectiveness of the proposed method, we perform classification and regression experiments on ADHD-200, ABIDE and ADNI datasets. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph methods in the classification and regression tasks.
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4
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Bruin WB, Oltedal L, Bartsch H, Abbott C, Argyelan M, Barbour T, Camprodon J, Chowdhury S, Espinoza R, Mulders P, Narr K, Oudega M, Rhebergen D, Ten Doesschate F, Tendolkar I, van Eijndhoven P, van Exel E, van Verseveld M, Wade B, van Waarde J, Zhutovsky P, Dols A, van Wingen G. Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis. Psychol Med 2024; 54:495-506. [PMID: 37485692 DOI: 10.1017/s0033291723002040] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.
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Affiliation(s)
- Willem Benjamin Bruin
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Leif Oltedal
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hauke Bartsch
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Christopher Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Miklos Argyelan
- The Feinstein Institutes for Medical Research, Manhasset, NY, USA
- The Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Tracy Barbour
- Division of Neuropsychiatry and Neuromodulation, Massachusetts General Hospital, Harvard Medical School. Boston, MA, USA
| | - Joan Camprodon
- Division of Neuropsychiatry and Neuromodulation, Massachusetts General Hospital, Harvard Medical School. Boston, MA, USA
| | - Samadrita Chowdhury
- Division of Neuropsychiatry and Neuromodulation, Massachusetts General Hospital, Harvard Medical School. Boston, MA, USA
| | - Randall Espinoza
- Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, USA
| | - Peter Mulders
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, The Netherlands
| | - Katherine Narr
- Ahmanson-Lovelace Brain Mapping Center, Departments of Neurology, and Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, USA
| | - Mardien Oudega
- Department of Old Age Psychiatry, GGZinGeest, Department of Psychiatry, Amsterdam UMC, location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Didi Rhebergen
- Mental Health Institute GGZ Centraal, Amersfoort; Department of Psychiatry, Amsterdam UMC, location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Freek Ten Doesschate
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Rijnstate, Department of Psychiatry, Arnhem, The Netherlands
| | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, The Netherlands
| | - Philip van Eijndhoven
- Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, The Netherlands
| | - Eric van Exel
- Department of Old Age Psychiatry, GGZinGeest, Department of Psychiatry, Amsterdam UMC, location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | | | - Benjamin Wade
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, Los Angeles, USA
| | | | - Paul Zhutovsky
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Annemiek Dols
- Department of Old Age Psychiatry, GGZinGeest, Department of Psychiatry, Amsterdam UMC, location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Guido van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam Brain and Cognition, University of Amsterdam, The Netherlands
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5
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Yun JY, Kim YK. Electroconvulsive Therapy (ECT) in Major Depression: Oldies but Goodies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1456:187-196. [PMID: 39261430 DOI: 10.1007/978-981-97-4402-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Electroconvulsive therapy is one of the useful treatment methods for symptom improvement and remission in patients with treatment-resistant depression. Considering the various clinical characteristics of patients experiencing depression, key indicators are extracted from structural brain magnetic resonance imaging, functional brain magnetic resonance imaging, and electroencephalography (EEG) data taken before treatment, and applied as explanatory variables in machine learning and network analysis. Studies that attempt to make reliable predictions about the degree of response to electroconvulsive treatment and the possibility of remission in patients with treatment-resistant depression are continuously being published. In addition, studies are being conducted to identify the correlation with clinical improvement by taking structural-functional brain magnetic resonance imaging after electroconvulsive therapy in depressed patients. By reviewing and integrating the results of the latest studies on the above matters, we aim to present the usefulness of electroconvulsive therapy for improving the personalized prognosis of patients with treatment-resistant depression.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea.
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. PSYCHORADIOLOGY 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States
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7
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Frid LM, Kessler U, Ousdal OT, Hammar Å, Haavik J, Riemer F, Hirnstein M, Ersland L, Erchinger VJ, Ronold EH, Nygaard G, Jakobsen P, Craven AR, Osnes B, Alisauskiene R, Bartsch H, Le Hellard S, Stavrum AK, Oedegaard KJ, Oltedal L. Neurobiological mechanisms of ECT and TMS treatment in depression: study protocol of a multimodal magnetic resonance investigation. BMC Psychiatry 2023; 23:791. [PMID: 37904091 PMCID: PMC10617235 DOI: 10.1186/s12888-023-05239-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 09/30/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Noninvasive neurostimulation treatments are increasingly being used to treat major depression, which is a common cause of disability worldwide. While electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) are both effective in treating depressive episodes, their mechanisms of action are, however, not completely understood. ECT is given under general anesthesia, where an electrical pulse is administered through electrodes placed on the patient's head to trigger a seizure. ECT is used for the most severe cases of depression and is usually not prescribed before other options have failed. With TMS, brain stimulation is achieved through rapidly changing magnetic fields that induce electric currents underneath a ferromagnetic coil. Its efficacy in depressive episodes has been well documented. This project aims to identify the neurobiological underpinnings of both the effects and side effects of the neurostimulation techniques ECT and TMS. METHODS The study will utilize a pre-post case control longitudinal design. The sample will consist of 150 subjects: 100 patients (bipolar and major depressive disorder) who are treated with either ECT (N = 50) or TMS (N = 50) and matched healthy controls (N = 50) not receiving any treatment. All participants will undergo multimodal magnetic resonance imaging (MRI) as well as neuropsychological and clinical assessments at multiple time points before, during and after treatment. Arterial spin labeling MRI at baseline will be used to test whether brain perfusion can predict outcomes. Signs of brain disruption, potentiation and rewiring will be explored with resting-state functional MRI, magnetic resonance spectroscopy and multishell diffusion weighted imaging (DWI). Clinical outcome will be measured by clinician assessed and patient reported outcome measures. Memory-related side effects will be investigated, and specific tests of spatial navigation to test hippocampal function will be administered both before and after treatment. Blood samples will be stored in a biobank for future analyses. The observation time is 6 months. Data will be explored in light of the recently proposed disrupt, potentiate and rewire (DPR) hypothesis. DISCUSSION The study will contribute data and novel analyses important for our understanding of neurostimulation as well as for the development of enhanced and more personalized treatment. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT05135897.
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Affiliation(s)
- Leila Marie Frid
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Ute Kessler
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Olga Therese Ousdal
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Åsa Hammar
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Department of Clinical Sciences Lund, Psychiatry, Faculty of Medicine, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, , Psychiatry Research Skåne, Region Skåne, Sweden
| | - Jan Haavik
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marco Hirnstein
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Lars Ersland
- Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Vera Jane Erchinger
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Eivind Haga Ronold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Gyrid Nygaard
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Petter Jakobsen
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Alexander R Craven
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Berge Osnes
- Department of Clinical Psychology, University of Bergen, Bergen, Norway
| | | | - Hauke Bartsch
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Stephanie Le Hellard
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Anne-Kristin Stavrum
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Ketil J Oedegaard
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Leif Oltedal
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.
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Domke AK, Hempel M, Hartling C, Stippl A, Carstens L, Gruzman R, Herrera Melendez AL, Bajbouj M, Gärtner M, Grimm S. Functional connectivity changes between amygdala and prefrontal cortex after ECT are associated with improvement in distinct depressive symptoms. Eur Arch Psychiatry Clin Neurosci 2023; 273:1489-1499. [PMID: 36715751 PMCID: PMC10465635 DOI: 10.1007/s00406-023-01552-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/09/2023] [Indexed: 01/31/2023]
Abstract
Electroconvulsive therapy (ECT) is one of the most effective treatments for treatment-resistant depression. However, the underlying mechanisms of action are not yet fully understood. The investigation of depression-specific networks using resting-state fMRI and the relation to differential symptom improvement might be an innovative approach providing new insights into the underlying processes. In this naturalistic study, we investigated the relationship between changes in resting-state functional connectivity (rsFC) and symptom improvement after ECT in 21 patients with treatment-resistant depression. We investigated rsFC before and after ECT and focused our analyses on FC changes directly related to symptom reduction and on FC at baseline to identify neural targets that might predict individual clinical responses to ECT. Additional analyses were performed to identify the direct relationship between rsFC change and symptom dimensions such as sadness, negative thoughts, detachment, and neurovegetative symptoms. An increase in rsFC between the left amygdala and left dorsolateral prefrontal cortex (DLPFC) after ECT was related to overall symptom reduction (Bonferroni-corrected p = 0.033) as well as to a reduction in specific symptoms such as sadness (r = 0.524, uncorrected p = 0.014), negative thoughts (r = 0.700, Bonferroni-corrected p = 0.002) and detachment (r = 0.663, p = 0.004), but not in neurovegetative symptoms. Furthermore, high baseline rsFC between the left amygdala and the right frontal pole (FP) predicted treatment outcome (uncorrected p = 0.039). We conclude that changes in FC in regions of the limbic-prefrontal network are associated with symptom improvement, particularly in affective and cognitive dimensions. Frontal-limbic connectivity has the potential to predict symptom improvement after ECT. Further research combining functional imaging biomarkers and a symptom-based approach might be promising.
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Affiliation(s)
- Ann-Kathrin Domke
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Moritz Hempel
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Corinna Hartling
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Anna Stippl
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Luisa Carstens
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Rebecca Gruzman
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Ana Lucia Herrera Melendez
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Malek Bajbouj
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Matti Gärtner
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
| | - Simone Grimm
- Department of Psychiatry, Centre for Affective Neuroscience (CAN), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203, Berlin, Germany
- Department of Psychology, MSB Medical School Berlin, Rüdesheimer Straße 50, 14197, Berlin, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland
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9
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Xu J, Li W, Bai T, Li J, Zhang J, Hu Q, Wang J, Tian Y, Wang K. Volume of hippocampus-amygdala transition area predicts outcomes of electroconvulsive therapy in major depressive disorder: high accuracy validated in two independent cohorts. Psychol Med 2023; 53:4464-4473. [PMID: 35604047 DOI: 10.1017/s0033291722001337] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Although many previous studies reported structural plasticity of the hippocampus and amygdala induced by electroconvulsive therapy (ECT) in major depressive disorder (MDD), yet the exact roles of both areas for antidepressant effects are still controversial. METHODS In the current study, segmentation of amygdala and hippocampal sub-regions was used to investigate the longitudinal changes of volume, the relationship between volume and antidepressant effects, and prediction performances for ECT in MDD patients before and after ECT using two independent datasets. RESULTS As a result, MDD patients showed selectively and consistently increased volume in the left lateral nucleus, right accessory basal nucleus, bilateral basal nucleus, bilateral corticoamygdaloid transition (CAT), bilateral paralaminar nucleus of the amygdala, and bilateral hippocampus-amygdala transition area (HATA) after ECT in both datasets, whereas marginally significant increase of volume in bilateral granule cell molecular layer of the head of dentate gyrus, the bilateral head of cornu ammonis (CA) 4, and left head of CA 3. Correlation analyses revealed that increased volume of left HATA was significantly associated with antidepressant effects after ECT. Moreover, volumes of HATA in the MDD patients before ECT could be served as potential biomarkers to predict ECT remission with the highest accuracy of 86.95% and 82.92% in two datasets (The predictive models were trained on Dataset 2 and the sensitivity, specificity and accuracy of Dataset 2 were obtained from leave-one-out-cross-validation. Thus, they were not independent and very likely to be inflated). CONCLUSIONS These results not only suggested that ECT could selectively induce structural plasticity of the amygdala and hippocampal sub-regions associated with antidepressant effects of ECT in MDD patients, but also provided potential biomarkers (especially HATA) for effectively and timely interventions for ECT in clinical applications.
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Affiliation(s)
- Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wenfei Li
- Affiliated Psychological Hospital of Anhui Medical University, Hefei 230022 China
| | - Tongjian Bai
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei, 230022, China
| | - Jiaying Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jinhuan Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jiaojian Wang
- Key Laboratory of Biological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Yanghua Tian
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei, 230022, China
- Department of Neurology, the Second Hospital of Anhui Medical University, Hefei 230022, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230022, China
- Anhui Medical University, School of Mental Health and Psychological Sciences, Hefei 230022, China
| | - Kai Wang
- Department of Neurology, The First Hospital of Anhui Medical University, Hefei, 230022, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230022, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230022, China
- Anhui Medical University, School of Mental Health and Psychological Sciences, Hefei 230022, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230022, China
- Anhui Province clinical research center for neurological disease, Hefei 230022, China
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10
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Qi S, Calhoun VD, Zhang D, Miller J, Deng ZD, Narr KL, Sheline Y, McClintock SM, Jiang R, Yang X, Upston J, Jones T, Sui J, Abbott CC. Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder. BMC Med 2022; 20:477. [PMID: 36482369 PMCID: PMC9733153 DOI: 10.1186/s12916-022-02678-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Although electroconvulsive therapy (ECT) is an effective treatment for depression, ECT cognitive impairment remains a major concern. The neurobiological underpinnings and mechanisms underlying ECT antidepressant and cognitive impairment effects remain unknown. This investigation aims to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks and assesses whether they are associated with the ECT-induced electric field (E-field) with an optimal pulse amplitude estimation. METHODS A single site clinical trial focused on amplitude (600, 700, and 800 mA) included longitudinal multimodal imaging and clinical and cognitive assessments completed before and immediately after the ECT series (n = 54) for late-life depression. Another two independent validation cohorts (n = 84, n = 260) were included. Symptom and cognition were used as references to supervise fMRI and sMRI fusion to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks. Correlations between ECT-induced E-field within these two networks and clinical and cognitive outcomes were calculated. An optimal pulse amplitude was estimated based on E-field within antidepressant-response and cognitive-impairment networks. RESULTS Decreased function in the superior orbitofrontal cortex and caudate accompanied with increased volume in medial temporal cortex showed covarying functional and structural alterations in both antidepressant-response and cognitive-impairment networks. Volume increases in the hippocampal complex and thalamus were antidepressant-response specific, and functional decreases in the amygdala and hippocampal complex were cognitive-impairment specific, which were validated in two independent datasets. The E-field within these two networks showed an inverse relationship with HDRS reduction and cognitive impairment. The optimal E-filed range as [92.7-113.9] V/m was estimated to maximize antidepressant outcomes without compromising cognitive safety. CONCLUSIONS The large degree of overlap between antidepressant-response and cognitive-impairment networks challenges parameter development focused on precise E-field dosing with new electrode placements. The determination of the optimal individualized ECT amplitude within the antidepressant and cognitive networks may improve the treatment benefit-risk ratio. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02999269.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics & Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Yvette Sheline
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Shawn M McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rongtao Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Tom Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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11
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Yi S, Wang Z, Yang W, Huang C, Liu P, Chen Y, Zhang H, Zhao G, Li W, Fang J, Liu J. Neural activity changes in first-episode, drug-naïve patients with major depressive disorder after transcutaneous auricular vagus nerve stimulation treatment: A resting-state fMRI study. Front Neurosci 2022; 16:1018387. [PMID: 36312012 PMCID: PMC9597483 DOI: 10.3389/fnins.2022.1018387] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 09/26/2022] [Indexed: 11/14/2022] Open
Abstract
Introduction Major depressive disorder (MDD) is a disease with prominent individual, medical, and economic impacts. Drug therapy and other treatment methods (such as Electroconvulsive therapy) may induce treatment-resistance and have associated side effects including loss of memory, decrease of reaction time, and residual symptoms. Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel and non-invasive treatment approach which stimulates brain structures with no side-effects. However, it remains little understood whether and how the neural activation is modulated by taVNS in MDD patients. Herein, we used the regional homogeneity (ReHo) to investigate the brain activity in first-episode, drug-naïve MDD patients after taVNS treatment. Materials and methods Twenty-two first-episode, drug-naïve MDD patients were enrolled in the study. These patients received the first taVNS treatment at the baseline time, and underwent resting-state MRI scanning twice, before and after taVNS. All the patients then received taVNS treatments for 4 weeks. The severity of depression was assessed by the 17-item Hamilton Depression Rating Scale (HAMD) at the baseline time and after 4-week’s treatment. Pearson analysis was used to assess the correlation between alterations of ReHo and changes of the HAMD scores. Two patients were excluded due to excessive head movement, two patients lack clinical data in the fourth week, thus, imaging analysis was performed in 20 patients, while correlation analysis between clinical and imaging data was performed in only 18 patients. Results There were significant differences in the ReHo values in first-episode, drug-naïve MDD patients between pre- or post- taVNS. The primary finding is that the patients exhibited a significantly lower ReHo in the left/right median cingulate cortex, the left precentral gyrus, the left postcentral gyrus, the right calcarine cortex, the left supplementary motor area, the left paracentral lobule, and the right lingual gyrus. Pearson analysis revealed a positive correlation between changes of ReHo in the right median cingulate cortex/the left supplementary motor area and changes of HAMD scores after taVNS. Conclusion The decreased ReHo were found after taVNS. The sensorimotor, limbic and visual-related brain regions may play an important role in understanding the underlying neural mechanisms and be the target brain regions in the further therapy.
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Affiliation(s)
- Sijie Yi
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zhi Wang
- Department of Radiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Wenhan Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Chuxin Huang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ping Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yanjing Chen
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthineers Ltd., Wuhan, China
| | - Guangju Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Weihui Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Jun Liu,
| | - Jiliang Fang
- Department of Radiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Jiliang Fang,
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China
- Department of Radiology Quality Control Center, Changsha, China
- Weihui Li,
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12
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Jiang R, Woo CW, Qi S, Wu J, Sui J. Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging. IEEE SIGNAL PROCESSING MAGAZINE 2022; 39:107-118. [PMID: 36712588 PMCID: PMC9880880 DOI: 10.1109/msp.2022.3155951] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA, 06520
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea, 16419
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea, 16419
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, 16419
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 211106
| | - Jing Wu
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, China, 100069
| | - Jing Sui
- State Key Laboratory of Brain Cognition and Learning, Beijing Normal University, Beijing, China, 100875
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13
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Ousdal OT, Brancati GE, Kessler U, Erchinger V, Dale AM, Abbott C, Oltedal L. The Neurobiological Effects of Electroconvulsive Therapy Studied Through Magnetic Resonance: What Have We Learned, and Where Do We Go? Biol Psychiatry 2022; 91:540-549. [PMID: 34274106 PMCID: PMC8630079 DOI: 10.1016/j.biopsych.2021.05.023] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 05/12/2021] [Accepted: 05/12/2021] [Indexed: 12/14/2022]
Abstract
Electroconvulsive therapy (ECT) is an established treatment choice for severe, treatment-resistant depression, yet its mechanisms of action remain elusive. Magnetic resonance imaging (MRI) of the human brain before and after treatment has been crucial to aid our comprehension of the ECT neurobiological effects. However, to date, a majority of MRI studies have been underpowered and have used heterogeneous patient samples as well as different methodological approaches, altogether causing mixed results and poor clinical translation. Hence, an association between MRI markers and therapeutic response remains to be established. Recently, the availability of large datasets through a global collaboration has provided the statistical power needed to characterize whole-brain structural and functional brain changes after ECT. In addition, MRI technological developments allow new aspects of brain function and structure to be investigated. Finally, more recent studies have also investigated immediate and long-term effects of ECT, which may aid in the separation of the therapeutically relevant effects from epiphenomena. The goal of this review is to outline MRI studies (T1, diffusion-weighted imaging, proton magnetic resonance spectroscopy) of ECT in depression to advance our understanding of the ECT neurobiological effects. Based on the reviewed literature, we suggest a model whereby the neurobiological effects can be understood within a framework of disruption, neuroplasticity, and rewiring of neural circuits. An improved characterization of the neurobiological effects of ECT may increase our understanding of ECT's therapeutic effects, ultimately leading to improved patient care.
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Affiliation(s)
- Olga Therese Ousdal
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Centre for Crisis Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway.
| | - Giulio E Brancati
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Ute Kessler
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Vera Erchinger
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California; Department of Radiology, University of California San Diego, La Jolla, California; Department of Neurosciences, University of California San Diego, La Jolla, California
| | - Christopher Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Leif Oltedal
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
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14
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Fu Z, Sui J, Espinoza R, Narr K, Qi S, Sendi MSE, Abbot CC, Calhoun VD. Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:312-322. [PMID: 34303848 PMCID: PMC8783932 DOI: 10.1016/j.bpsc.2021.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy. METHODS In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis. RESULTS Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT. CONCLUSIONS These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Katherine Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mohammad S. E. Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Christopher C. Abbot
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States,Corresponding author: Dr. Christopher C. Abbott, Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States, , Phone: 505-272-0406
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States,Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut, United States,Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia, United States
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15
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Wu P, Zhang A, Sun N, Lei L, Liu P, Wang Y, Li H, Yang C, Zhang K. Cortical Thickness Predicts Response Following 2 Weeks of SSRI Regimen in First-Episode, Drug-Naive Major Depressive Disorder: An MRI Study. Front Psychiatry 2022; 12:751756. [PMID: 35273524 PMCID: PMC8902047 DOI: 10.3389/fpsyt.2021.751756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/23/2021] [Indexed: 12/14/2022] Open
Abstract
Objective Major depression disorder (MDD) is a harmful disorder, and the pathological mechanism remains unclear. The primary pharmacotherapy regimen for MDD is selective serotonin reuptake inhibitors (SSRIs), but fewer than 40% of patients with MDD are in remission following initial treatment. Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in MDD. This study aims to determine if cortical thickness can be used as a predictor for SSRIs. Methods A total of 126 first-episode, drug-naive MDD patients (MDDs) and 71 healthy controls (HCs) were enrolled in our study. Demographic data were collected according to the self-made case report form (CRF) at the baseline of all subjects. Magnetic resonance imaging (MRI) scanning was performed for all the participants at baseline, and all imaging was processed using the DPABISurf software. All MDDs were treated with SSRIs, and symptoms were assessed at both the baseline and 2 weeks using the 17-item Hamilton Rating Scale (HAMD-17). According to HAMD-17 total score improvement from baseline to the end of 2 weeks, the MDDs were divided into the non-responder group (defined as ≤ 20% HAMD-17 reduction) and responder group (defined as ≥50% HAMD-17 reduction). The receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of MDDs' and HCs' cortical thickness for MDD. Correlation analysis was performed for the responder group and the non-responder group separately to identify the relationship between cortical thickness and SSRI treatment efficacy. To analyze whether cortical thickness was sufficient to differentiate responders and non-responders at baseline, we used ROC curve analysis. Results Significant decreases were found in the cortical thickness of the right supplementary motor area (SMA) in MDDs at the baseline (corrected by the Monte Carlo permutation correction, cluster-wise significant threshold at p < 0.025 and vertex-wise threshold at p = 0.001), area under the curve (AUC) = 0.732 [95% confidence interval (CI) = 0.233-0.399]. In the responder group, the cortical thickness of the right SMA was significantly thinner than in the non-responder group at baseline. There was a negative correlation (r = -0.373, p = 0.044) between the cortical thickness of SMA (0 weeks) and HAMD-17 reductive rate (2 weeks) in the responder group. The results of ROC curve analyses of the responder and non-responder groups were AUC = 0.885 (95% CI = 0.803-0.968), sensitivity = 73.5%, and specificity = 96.6%, and the cutoff value was 0.701. Conclusion Lower cortical thickness of the right SMA in MDD patients at the baseline may be a neuroimaging biomarker for MDD diagnosis, and a greater extent of thinner cortical thickness in the right SMA at baseline may predict improved SSRI treatment response. Our study shows the potential of cortical thickness as a possible biomarker that predicts a patient's clinical treatment response to SSRIs in MDD.
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Affiliation(s)
- Peiyi Wu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
- Department of Psychiatry, Shanxi Medical University, Taiyuan, China
| | - Aixia Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ning Sun
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
- Department of Mental Health, Shanxi Medical University, Taiyuan, China
| | - Lei Lei
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
- Department of Psychiatry, Shanxi Medical University, Taiyuan, China
| | - Penghong Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yikun Wang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Hejun Li
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Chunxia Yang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
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16
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Li XK, Qiu HT. Current progress in neuroimaging research for the treatment of major depression with electroconvulsive therapy. World J Psychiatry 2022; 12:128-139. [PMID: 35111584 PMCID: PMC8783162 DOI: 10.5498/wjp.v12.i1.128] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/20/2021] [Accepted: 09/06/2021] [Indexed: 02/06/2023] Open
Abstract
Electroconvulsive therapy (ECT) uses a certain amount of electric current to pass through the head of the patient, causing convulsions throughout the body, to relieve the symptoms of the disease and achieve the purpose of treatment. ECT can effectively improve the clinical symptoms of patients with major depression, but its therapeutic mechanism is still unclear. With the rapid development of neuroimaging technology, it is necessary to explore the neurobiological mechanism of major depression from the aspects of brain structure, brain function and brain metabolism, and to find that ECT can improve the brain function, metabolism and even brain structure of patients to a certain extent. Currently, an increasing number of neuroimaging studies adopt various neuroimaging techniques including functional magnetic resonance imaging (MRI), positron emission tomography, magnetic resonance spectroscopy, structural MRI, and diffusion tensor imaging to reveal the neural effects of ECT. This article reviews the recent progress in neuroimaging research on ECT for major depression. The results suggest that the neurobiological mechanism of ECT may be to modulate the functional activity and connectivity or neural structural plasticity in specific brain regions to the normal level, to achieve the therapeutic effect.
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Affiliation(s)
- Xin-Ke Li
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Hai-Tang Qiu
- Mental Health Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022; 35:11497-11516. [PMID: 35039718 PMCID: PMC8754538 DOI: 10.1007/s00521-021-06710-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/27/2021] [Indexed: 01/10/2023]
Abstract
This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.
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Xiao Y, Zhao L, Wang D, Xue SW, Tan Z, Lan Z, Kuai C, Wang Y, Li H, Pan C, Fu S, Hu X. Effective Connectivity of Right Amygdala Subregions Predicts Symptom Improvement Following 12-Week Pharmacological Therapy in Major Depressive Disorder. Front Neurosci 2021; 15:742102. [PMID: 34588954 PMCID: PMC8473745 DOI: 10.3389/fnins.2021.742102] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 08/13/2021] [Indexed: 12/28/2022] Open
Abstract
The low rates of treatment response still exist in the pharmacological therapy of major depressive disorder (MDD). Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. The amygdala is closely related to the pathological mechanism of MDD and is expected to be a predictor of the treatment. However, previous studies ignored the heterogeneousness and lateralization of amygdala. Therefore, this study mainly aimed to explore whether the right amygdala subregion function at baseline can predict symptom improvement after 12-week pharmacotherapy in MDD patients. We performed granger causality analysis (GCA) to identify abnormal effective connectivity (EC) of right amygdala subregions in MDD and compared the EC strength before and after 12-week pharmacological therapy. The results show that the abnormal EC mainly concentrated on the frontolimbic circuitry and default mode network (DMN). With relief of the clinical symptom, these abnormal ECs also change toward normalization. In addition, the EC strength of right amygdala subregions at baseline showed significant predictive ability for symptom improvement using a regularized least-squares regression predict model. These findings indicated that the EC of right amygdala subregions may be functionally related in symptom improvement of MDD. It may aid us to understand the neurological mechanism of pharmacotherapy and can be used as a promising predictor for symptom improvement in MDD.
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Affiliation(s)
- Yang Xiao
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Lei Zhao
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Donglin Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Shao-Wei Xue
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Zhonglin Tan
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhihui Lan
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,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 of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Yan Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Hanxiaoran Li
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Chenyuan Pan
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Sufen Fu
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Xiwen Hu
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Increased Homotopic Connectivity in the Prefrontal Cortex Modulated by Olanzapine Predicts Therapeutic Efficacy in Patients with Schizophrenia. Neural Plast 2021; 2021:9954547. [PMID: 34512748 PMCID: PMC8429031 DOI: 10.1155/2021/9954547] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/08/2021] [Accepted: 08/18/2021] [Indexed: 11/18/2022] Open
Abstract
Background Previous studies have revealed the abnormalities in homotopic connectivity in schizophrenia. However, the relationship of these deficits to antipsychotic treatment in schizophrenia remains unclear. This study explored the effects of antipsychotic therapy on brain homotopic connectivity and whether the homotopic connectivity of these regions might predict individual treatment response in schizophrenic patients. Methods A total of 21 schizophrenic patients and 20 healthy controls were scanned by the resting-state functional magnetic resonance imaging. The patients received olanzapine treatment and were scanned at two time points. Voxel-mirrored homotopic connectivity (VMHC) and pattern classification techniques were applied to analyze the imaging data. Results Schizophrenic patients presented significantly decreased VMHC in the temporal and inferior frontal gyri, medial prefrontal cortex (MPFC), and motor and low-level sensory processing regions (including the fusiform gyrus and cerebellum lobule VI) relative to healthy controls. The VMHC in the superior/middle MPFC was significantly increased in the patients after eight weeks of treatment. Support vector regression (SVR) analyses revealed that VMHC in the superior/middle MPFC at baseline can predict the symptomatic improvement of the positive and negative syndrome scale after eight weeks of treatment. Conclusions This study demonstrated that olanzapine treatment may normalize decreased homotopic connectivity in the superior/middle MPFC in schizophrenic patients. The VMHC in the superior/middle MPFC may predict individual response for antipsychotic therapy. The findings of this study conduce to the comprehension of the therapy effects of antipsychotic medications on homotopic connectivity in schizophrenia.
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Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome. Diagnostics (Basel) 2021; 11:diagnostics11091631. [PMID: 34573974 PMCID: PMC8468112 DOI: 10.3390/diagnostics11091631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/03/2021] [Accepted: 09/03/2021] [Indexed: 12/30/2022] Open
Abstract
The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.
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21
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Takamiya A, Kishimoto T, Hirano J, Kikuchi T, Yamagata B, Mimura M. Association of electroconvulsive therapy-induced structural plasticity with clinical remission. Prog Neuropsychopharmacol Biol Psychiatry 2021; 110:110286. [PMID: 33621611 DOI: 10.1016/j.pnpbp.2021.110286] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 02/10/2021] [Accepted: 02/12/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is the most effective treatment for severe depression. Recent neuroimaging studies have consistently reported that ECT induces volume increases in widely distributed brain regions. However, it still remains unclear about ECT-induced volume changes associated with clinical improvement. METHODS Longitudinal assessments of structural magnetic resonance imaging were conducted in 48 participants. Twenty-seven elderly melancholic depressed individuals (mean 67.5 ± 8.1 years old; 19 female) were scanned before (TP1) and after (TP2) ECT. Twenty-one healthy controls were also scanned twice. Whole-brain gray matter volume (GMV) was analyzed via group (remitters, nonremitters, and controls) by time (TP1 and TP2) analysis of covariance to identify ECT-related GMV changes and GMV changes specific to remitters. Within-subject and between-subjects correlation analyses were conducted to investigate the associations between clinical improvement and GMV changes. Depressive symptoms were evaluated using the 17-item Hamilton Depression Rating Scale (HAM-D), and remission was defined as HAM-D total score ≤ 7. RESULTS Bilateral ECT increased GMV in multiple brain regions bilaterally regardless of clinical improvement. Remitters showed a larger GMV increase in the right-lateralized frontolimbic brain regions compared to nonremitters and healthy controls. GMV changes in the right hippocampus/amygdala and right middle frontal gyrus showed correlations with clinical improvement in within-/between-subjects correlation analyses. CONCLUSIONS ECT-induced GMV increase in the right frontolimbic regions was associated with clinical remission.
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Affiliation(s)
- Akihiro Takamiya
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Taishiro Kishimoto
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Toshiaki Kikuchi
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Bun Yamagata
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
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22
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Wade BSC, Hellemann G, Espinoza RT, Woods RP, Joshi SH, Redlich R, Dannlowski U, Jorgensen A, Abbott CC, Oltedal L, Narr KL. Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy. Hum Brain Mapp 2021; 42:5322-5333. [PMID: 34390089 PMCID: PMC8519875 DOI: 10.1002/hbm.25620] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/21/2021] [Accepted: 07/29/2021] [Indexed: 12/19/2022] Open
Abstract
Depression symptom heterogeneity limits the identifiability of treatment‐response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17‐item Hamilton Depression Rating Scale (HDRS) and use data‐driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms. We included 110 ECT patients from Global ECT‐MRI Research Collaboration (GEMRIC) sites who underwent structural MRI and HDRS assessments before and after treatment. Cross validated random forest regression models predicted change along symptom dimensions. HDRS symptoms clustered into dimensions of somatic disturbances (SoD), core mood and anhedonia (CMA), and insomnia. The coefficient of determination between predicted and actual changes were 22%, 39%, and 39% (all p < .01) for SoD, CMA, and insomnia, respectively. CMA and insomnia change were predicted more accurately than HDRS‐6 and HDRS‐17 changes (p < .05). Pretreatment symptoms, body‐mass index, and age were important predictors. Important imaging predictors included the right transverse temporal gyrus and left frontal pole for the SoD dimension; right transverse temporal gyrus and right rostral middle frontal gyrus for the CMA dimension; and right superior parietal lobule and left accumbens for the insomnia dimension. Our findings support that recovery along depressive symptom dimensions is predicted more accurately than HDRS total scores and are related to unique and overlapping patterns of clinical and demographic data and volumetric changes in brain regions related to depression and near ECT electrodes.
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Affiliation(s)
- Benjamin S C Wade
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, Los Angeles, California, USA
| | - Gerhard Hellemann
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, California, USA
| | - Randall T Espinoza
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, California, USA
| | - Roger P Woods
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, Los Angeles, California, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, California, USA
| | - Shantanu H Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, Los Angeles, California, USA
| | - Ronny Redlich
- Institute of Translational Psychiatry, Department of Mental Health, University of Münster, Münster, Germany.,Department of Clinical Psychology, University of Halle, Halle, Germany
| | - Udo Dannlowski
- Institute of Translational Psychiatry, Department of Mental Health, University of Münster, Münster, Germany
| | | | - Christopher C Abbott
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
| | - Leif Oltedal
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Katherine L Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, Los Angeles, California, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, California, USA
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23
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Gyger L, Regen F, Ramponi C, Marquis R, Mall JF, Swierkosz-Lenart K, von Gunten A, Toni N, Kherif F, Heuser I, Draganski B. Gradient of electro-convulsive therapy's antidepressant effects along the longitudinal hippocampal axis. Transl Psychiatry 2021; 11:191. [PMID: 33782387 PMCID: PMC8007583 DOI: 10.1038/s41398-021-01310-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 02/12/2021] [Accepted: 03/02/2021] [Indexed: 12/17/2022] Open
Abstract
Despite decades of successful treatment of therapy-resistant depression and major scientific advances in the field, our knowledge about electro-convulsive therapy's (ECT) mechanisms of action is still scarce. Building on strong empirical evidence for ECT-induced hippocampus anatomy changes, we sought to test the hypothesis that ECT has a differential impact along the hippocampus longitudinal axis. We acquired behavioural and brain anatomy magnetic resonance imaging (MRI) data in patients with depressive episode undergoing ECT (n = 9) or pharmacotherapy (n = 24) and healthy controls (n = 30) at two time points 3 months apart. Using whole-brain voxel-based statistical parametric mapping and topographic analysis focused on the hippocampus, we observed ECT-induced gradient of grey matter volume increase along the hippocampal longitudinal axis with predominant impact on its anterior portion. Clinical outcome measures showed strong correlations with both baseline volume and rate of ECT-induced change exclusively for the anterior, but not posterior hippocampus. We interpret our findings confined to the anterior hippocampus and amygdala as additional evidence of the regional specific impact of ECT that unfolds its beneficial effect on depression via the "limbic" system. Main limitations of the study are patients' polypharmacy, heterogeneity of psychiatric diagnosis, and long-time interval between scans.
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Affiliation(s)
- Lucien Gyger
- LREN, Dept. of clinical neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Francesca Regen
- Department of Psychiatry, Charité-Campus Benjamin Franklin, Berlin, Germany
| | - Cristina Ramponi
- LREN, Dept. of clinical neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Renaud Marquis
- EEG and Epilepsy Unit, Department of Clinical Neuroscience, University Hospital of Geneva and Faculty of Medicine, Geneva, Switzerland
| | - Jean-Frederic Mall
- Old Age Psychiatry service, Department of Psychiatry, Lausanne University Hospital (CHUV), and University of Lausanne, Lausanne, Switzerland
| | - Kevin Swierkosz-Lenart
- Old Age Psychiatry service, Department of Psychiatry, Lausanne University Hospital (CHUV), and University of Lausanne, Lausanne, Switzerland
| | - Armin von Gunten
- Old Age Psychiatry service, Department of Psychiatry, Lausanne University Hospital (CHUV), and University of Lausanne, Lausanne, Switzerland
| | - Nicolas Toni
- Centre for Psychiatric Neurosciences, Department of Psychiatry, Lausanne University Hospital (CHUV) and Lausanne University, Lausanne, Switzerland
| | - Ferath Kherif
- LREN, Dept. of clinical neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Isabella Heuser
- Department of Psychiatry, Charité-Campus Benjamin Franklin, Berlin, Germany
| | - Bogdan Draganski
- LREN, Dept. of clinical neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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24
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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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25
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Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021; 339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 12/13/2022]
Abstract
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.
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Affiliation(s)
- Fabian Eitel
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Marc-André Schulz
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Moritz Seiler
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany.
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26
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Gbyl K, Rostrup E, Raghava JM, Andersen C, Rosenberg R, Larsson HBW, Videbech P. Volume of hippocampal subregions and clinical improvement following electroconvulsive therapy in patients with depression. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:110048. [PMID: 32730916 DOI: 10.1016/j.pnpbp.2020.110048] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 07/08/2020] [Accepted: 07/21/2020] [Indexed: 12/14/2022]
Abstract
It is thought that the hippocampal neurogenesis is an important mediator of the antidepressant effect of electroconvulsive therapy (ECT). However, most previous studies failed to demonstrate the relationship between the increase in the hippocampal volume and the antidepressant effect. We reinvestigated this relationship by looking at distinct hippocampal subregions and applying repeated measures correlation. Using a 3 Tesla MRI-scanner, we scanned 22 severely depressed in-patients at three time points: before the ECT series, after the series, and at six-month follow-up. The depression severity was assessed by the 17-item Hamilton Rating Scale for Depression (HAMD-17). The hippocampus was segmented into subregions using Freesurfer software. The dentate gyrus (DG) was the primary region of interest (ROI), due to the role of this region in neurogenesis. The other major hippocampal subregions were the secondary ROIs (n = 20). The general linear mixed model and the repeated measures correlation were used for statistical analyses. Immediately after the ECT series, a significant volume increase was present in the right DG (Cohen's d = 1.7) and the left DG (Cohen's d = 1.5), as well as 15 out of 20 secondary ROIs. The clinical improvement, i.e., the decrease in HAMD-17 score, was correlated to the increase in the right DG volume (rrm = -0.77, df = 20, p < .001), and the left DG volume (rrm = -0.75, df = 20, p < .001). Similar correlations were observed in 14 out of 20 secondary ROIs. Thus, ECT induces an increase not only in the volume of the DG, but also in the volume of other major hippocampal subregions. The volumetric increases may reflect a neurobiological process that may be related to the ECT's antidepressant effect. Further investigation of the relationship between hippocampal subregions and the antidepressant effect is warranted. A statistical approach taking the repeated measurements into account should be preferred in the analyses.
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Affiliation(s)
- Krzysztof Gbyl
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Glostrup, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Science, The University of Copenhagen, Copenhagen, Denmark.
| | - Egill Rostrup
- Center for Neuropsychiatric Schizophrenia Research, and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
| | - Jayachandra Mitta Raghava
- Center for Neuropsychiatric Schizophrenia Research, and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark; Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Glostrup, Glostrup, Denmark
| | | | | | - Henrik Bo Wiberg Larsson
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Glostrup, Glostrup, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Science, The University of Copenhagen, Copenhagen, Denmark
| | - Poul Videbech
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Glostrup, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Science, The University of Copenhagen, Copenhagen, Denmark
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27
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Shan X, Liao R, Ou Y, Pan P, Ding Y, Liu F, Chen J, Zhao J, Guo W, He Y. Increased regional homogeneity modulated by metacognitive training predicts therapeutic efficacy in patients with schizophrenia. Eur Arch Psychiatry Clin Neurosci 2021; 271:783-798. [PMID: 32215727 PMCID: PMC8119286 DOI: 10.1007/s00406-020-01119-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 03/11/2020] [Indexed: 02/07/2023]
Abstract
Previous studies have demonstrated the efficacy of metacognitive training (MCT) in schizophrenia. However, the underlying mechanisms related to therapeutic effect of MCT remain unknown. The present study explored the treatment effects of MCT on brain regional neural activity using regional homogeneity (ReHo) and whether these regions' activities could predict individual treatment response in schizophrenia. Forty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly divided into drug therapy (DT) and drug plus psychotherapy (DPP) groups. The DT group received only olanzapine treatment, whereas the DPP group received olanzapine and MCT for 8 weeks. The results revealed that ReHo in the right precuneus, left superior medial prefrontal cortex (MPFC), right parahippocampal gyrus and left rectus was significantly increased in the DPP group after 8 weeks of treatment. Patients in the DT group showed significantly increased ReHo in the left ventral MPFC/anterior cingulate cortex (ACC), left superior MPFC/middle frontal gyrus (MFG), left precuneus, right rectus and left MFG, and significantly decreased ReHo in the bilateral cerebellum VIII and left inferior occipital gyrus (IOG) after treatment. Support vector regression analyses showed that high ReHo levels at baseline in the right precuneus and left superior MPFC could predict symptomatic improvement of Positive and Negative Syndrome Scale (PANSS) after 8 weeks of DPP treatment. Moreover, high ReHo levels at baseline and alterations of ReHo in the left ventral MPFC/ACC could predict symptomatic improvement of PANSS after 8 weeks of DT treatment. This study suggests that MCT is associated with the modulation of ReHo in schizophrenia. ReHo in the right precuneus and left superior MPFC may predict individual therapeutic response for MCT in patients with schizophrenia.
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Affiliation(s)
- Xiaoxiao Shan
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Rongyuan Liao
- grid.412990.70000 0004 1808 322XThe Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan China
| | - Yangpan Ou
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Pan Pan
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Yudan Ding
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Feng Liu
- grid.412645.00000 0004 1757 9434Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300000 China
| | - Jindong Chen
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Jingping Zhao
- grid.452708.c0000 0004 1803 0208Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011 Hunan China ,National Clinical Research Center on Mental Disorders, Changsha, 410011 Hunan China
| | - Wenbin Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China. .,National Clinical Research Center on Mental Disorders, Changsha, 410011, Hunan, China.
| | - Yiqun He
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China.
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Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach. J ECT 2020; 36:205-210. [PMID: 32118692 DOI: 10.1097/yct.0000000000000669] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach. METHODS Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation. RESULTS Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder. CONCLUSIONS Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.
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Yang X, Xu Z, Xi Y, Sun J, Liu P, Liu P, Li P, Jia J, Yin H, Qin W. Predicting responses to electroconvulsive therapy in schizophrenia patients undergoing antipsychotic treatment: Baseline functional connectivity among regions with strong electric field distributions. Psychiatry Res Neuroimaging 2020; 299:111059. [PMID: 32135406 DOI: 10.1016/j.pscychresns.2020.111059] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 02/16/2020] [Accepted: 02/21/2020] [Indexed: 01/15/2023]
Abstract
This study explored imaging predictors of electroconvulsive therapy (ECT) outcome in schizophrenia patients based on pre-treatment functional connectivity (FC) within regions with strong ECT electric fields distribution. Forty-seven patients received standard antipsychotic drugs combined with ECT as well as two brain imaging sessions. Regions of interest (ROI) with strong electric field distribution were determined by ECT simulation. Using baseline functional connectivity between ROIs, a model was constructed to predict the percentage reduction of Positive and Negative Syndrome Scale (PANSS) scores. The strong electric fields were distributed in the orbital prefrontal lobe, medial temporal lobe, and other parts of the temporal lobe. Ten functional connectivity features within the electric field distribution areas showed a predictive ability for ECT outcome. The correlation coefficient between the predictive and real values of cross-validation was 0.7165. Among the predictive features, ECT induced a significant decrease in functional connectivity between the right amygdala and the left hippocampus. These results suggest that pretreatment functional connectivity patterns in brain regions with strong electric field distributions during ECT could be potential predictors of the efficacy of ECT augmentation in schizophrenia. These findings may help to improve individualized clinical treatment in the future.
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Affiliation(s)
- Xuejuan Yang
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Ziliang Xu
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, Shaanxi 710032, China
| | - Jinbo Sun
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Peng Liu
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Peng Liu
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Ping Li
- Department of Medical Imaging, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, China
| | - Jie Jia
- Department of early intervention, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, Shaanxi 710032, China.
| | - Wei Qin
- Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China.
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30
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Qi S, Abbott CC, Narr KL, Jiang R, Upston J, McClintock SM, Espinoza R, Jones T, Zhi D, Sun H, Yang X, Sui J, Calhoun VD. Electroconvulsive therapy treatment responsive multimodal brain networks. Hum Brain Mapp 2020; 41:1775-1785. [PMID: 31904902 PMCID: PMC7267951 DOI: 10.1002/hbm.24910] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/02/2019] [Accepted: 12/16/2019] [Indexed: 02/05/2023] Open
Abstract
Electroconvulsive therapy is regarded as the most effective antidepressant treatment for severe and treatment-resistant depressive episodes. Despite the efficacy of electroconvulsive therapy, the neurobiological underpinnings and mechanisms underlying electroconvulsive therapy induced antidepressant effects remain unclear. The objective of this investigation was to identify electroconvulsive therapy treatment responsive multimodal biomarkers with the 17-item Hamilton Depression Rating Scale guided brain structure-function fusion in 118 patients with depressive episodes and 60 healthy controls. Results show that reduced fractional amplitude of low frequency fluctuations in the prefrontal cortex, insula and hippocampus, linked with increased gray matter volume in anterior cingulate, medial temporal cortex, insula, thalamus, caudate and hippocampus represent electroconvulsive therapy responsive covarying functional and structural brain networks. In addition, relative to nonresponders, responder-specific electroconvulsive therapy related brain networks occur in frontal-limbic network and are associated with successful therapeutic outcomes. Finally, electroconvulsive therapy responsive brain networks were unrelated to verbal declarative memory. Using a data-driven, supervised-learning method, we demonstrated that electroconvulsive therapy produces a remodeling of brain functional and structural covariance that was unique to antidepressant symptom response, but not linked to memory impairment.
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Affiliation(s)
- Shile Qi
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University]AtlantaGeorgia
| | | | - Katherine L. Narr
- Department of Neurology, Psychiatry and Biobehavioral SciencesUniversity of CaliforniaLos Angeles (UCLA)California
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Joel Upston
- Department of PsychiatryUniversity of New MexicoAlbuquerqueNew Mexico
| | - Shawn M. McClintock
- Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTexas
| | - Randall Espinoza
- Department of Neurology, Psychiatry and Biobehavioral SciencesUniversity of CaliforniaLos Angeles (UCLA)California
| | - Tom Jones
- Department of PsychiatryUniversity of New MexicoAlbuquerqueNew Mexico
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Hailun Sun
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xiao Yang
- Huaxi Brain Research CenterWest China Hospital of Sichuan UniversityChengduChina
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of AutomationBeijingChina
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University]AtlantaGeorgia
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31
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Du Y, Sui J, Lin D. Editorial: Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders. Front Neurosci 2020; 14:327. [PMID: 32322189 PMCID: PMC7156887 DOI: 10.3389/fnins.2020.00327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 03/19/2020] [Indexed: 12/04/2022] Open
Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA, United States.,Chinese Academy of Sciences (CAS) Centre for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Beijing, China.,Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA, United States
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32
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Jiang R, Calhoun VD, Fan L, Zuo N, Jung R, Qi S, Lin D, Li J, Zhuo C, Song M, Fu Z, Jiang T, Sui J. Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores. Cereb Cortex 2020; 30:888-900. [PMID: 31364696 PMCID: PMC7132922 DOI: 10.1093/cercor/bhz134] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 05/08/2019] [Accepted: 05/28/2019] [Indexed: 12/15/2022] Open
Abstract
Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions-particularly prefrontal-parietal and basal ganglia-whereas the network pattern differs between genders.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Rex Jung
- Department of Neurosurgery, University of New Mexico, Albuquerque, NM 87131, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Dongdong Lin
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Anding Hospital, Tianjin Mental Health Center, Tianjin, 300222, China
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- University of Electronic Science and Technology of China, Chengdu, 610054, China
- Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
- Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China
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Lisanby SH, McClintock SM, Alexopoulos G, Bailine SH, Bernhardt E, Briggs MC, Cullum CM, Deng ZD, Dooley M, Geduldig ET, Greenberg RM, Husain MM, Kaliora S, Knapp RG, Latoussakis V, Liebman LS, McCall WV, Mueller M, Petrides G, Prudic J, Rosenquist PB, Rudorfer MV, Sampson S, Teklehaimanot AA, Tobias KG, Weiner RD, Young RC, Kellner CH. Neurocognitive Effects of Combined Electroconvulsive Therapy (ECT) and Venlafaxine in Geriatric Depression: Phase 1 of the PRIDE Study. Am J Geriatr Psychiatry 2020; 28:304-316. [PMID: 31706638 PMCID: PMC7050408 DOI: 10.1016/j.jagp.2019.10.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/04/2019] [Accepted: 10/04/2019] [Indexed: 11/16/2022]
Abstract
OBJECTIVE There is limited information regarding the tolerability of electroconvulsive therapy (ECT) combined with pharmacotherapy in elderly adults with major depressive disorder (MDD). Addressing this gap, we report acute neurocognitive outcomes from Phase 1 of the Prolonging Remission in Depressed Elderly (PRIDE) study. METHODS Elderly adults (age ≥60) with MDD received an acute course of 6 times seizure threshold right unilateral ultrabrief pulse (RUL-UB) ECT. Venlafaxine was initiated during the first treatment week and continued throughout the study. A comprehensive neurocognitive battery was administered at baseline and 72 hours following the last ECT session. Statistical significance was defined as a two-sided p-value of less than 0.05. RESULTS A total of 240 elderly adults were enrolled. Neurocognitive performance acutely declined post ECT on measures of psychomotor and verbal processing speed, autobiographical memory consistency, short-term verbal recall and recognition of learned words, phonemic fluency, and complex visual scanning/cognitive flexibility. The magnitude of change from baseline to end for most neurocognitive measures was modest. CONCLUSION This is the first study to characterize the neurocognitive effects of combined RUL-UB ECT and venlafaxine in elderly adults with MDD and provides new evidence for the tolerability of RUL-UB ECT in an elderly sample. Of the cognitive domains assessed, only phonemic fluency, complex visual scanning, and cognitive flexibility qualitatively declined from low average to mildly impaired. While some acute changes in neurocognitive performance were statistically significant, the majority of the indices as based on the effect sizes remained relatively stable.
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Affiliation(s)
- Sarah H. Lisanby
- Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC (Now at the National Institute of Mental Health),Noninvasive Neuromodulation Unit, Experimental Therapeutics Branch, Intramural Research Program, National Institute of Mental Health
| | - Shawn M. McClintock
- Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC (Now at the National Institute of Mental Health),Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX
| | - George Alexopoulos
- Department of Psychiatry and Behavioral Sciences, New York Presbyterian/Weill Cornell Medical Center, White Plains, NY
| | - Samuel H. Bailine
- Department of Psychiatry, Zucker Hillside Hospital/North Shore-LIJ Health System, New York, NY
| | | | - Mimi C. Briggs
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - C. Munro Cullum
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics Branch, Intramural Research Program, National Institute of Mental Health
| | - Mary Dooley
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC
| | - Emma T. Geduldig
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Mustafa M. Husain
- Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC (Now at the National Institute of Mental Health),Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX
| | - Styliani Kaliora
- Department of Psychiatry, Zucker Hillside Hospital/North Shore-LIJ Health System, New York, NY
| | - Rebecca G. Knapp
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC
| | - Vassilios Latoussakis
- Department of Psychiatry and Behavioral Sciences, New York Presbyterian/Weill Cornell Medical Center, White Plains, NY
| | - Lauren S. Liebman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - William V. McCall
- Department of Psychiatry and Health Behavior, Medical College of Georgia, Augusta University, Augusta, GA
| | - Martina Mueller
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC
| | - Georgios Petrides
- Department of Psychiatry, Zucker Hillside Hospital/North Shore-LIJ Health System, New York, NY
| | - Joan Prudic
- Department of Psychiatry, Columbia University/New York State Psychiatric Institute, New York, NY
| | - Peter B. Rosenquist
- Department of Psychiatry and Health Behavior, Medical College of Georgia, Augusta University, Augusta, GA
| | - Matthew V. Rudorfer
- Division of Services and Intervention Research, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Shirlene Sampson
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN
| | - Abeba A. Teklehaimanot
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC
| | - Kristen G. Tobias
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Richard D. Weiner
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
| | - Robert C. Young
- Department of Psychiatry and Behavioral Sciences, New York Presbyterian/Weill Cornell Medical Center, White Plains, NY
| | - Charles H. Kellner
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
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Shan X, Liao R, Ou Y, Ding Y, Liu F, Chen J, Zhao J, Guo W, He Y. Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia. Front Psychiatry 2020; 11:234. [PMID: 32292360 PMCID: PMC7118222 DOI: 10.3389/fpsyt.2020.00234] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 03/10/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Previous studies have revealed the efficacy of metacognitive training for schizophrenia. However, the underlying mechanisms of metacognitive training on brain function alterations, including the default-mode network (DMN), remain unknown. The present study explored treatment effects of metacognitive training on functional connectivity of the brain regions in the DMN. METHODS Forty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly assigned to drug plus psychotherapy (DPP) and drug therapy (DT) groups. The DPP group received olanzapine and metacognitive training, and the DT group received only olanzapine for 8 weeks. Network homogeneity (NH) was applied to analyze the imaging data, and pattern classification techniques were applied to test whether abnormal NH deficits at baseline might be used to discriminate patients from healthy controls. Abnormal NH in predicting treatment response was also examined in each patient group. RESULTS Compared with healthy controls, patients at baseline showed decreased NH in the bilateral ventral medial prefrontal cortex (MPFC), right posterior cingulate cortex (PCC)/precuneus, and bilateral precuneus and increased NH in the right cerebellum Crus II and bilateral superior MPFC. NH values in the right PCC/precuneus increased in the DPP group after 8 weeks of treatment, whereas no substantial difference in NH value was observed in the DT group. Support vector machine analyses showed that the accuracy, sensitivity, and specificity for distinguishing patients from healthy controls were more than 0.7 in the NH values of the right PCC/precuneus, bilateral ventral MPFC, bilateral superior MPFC, and bilateral precuneus regions. Support vector regression analyses showed that high NH levels at baseline in the bilateral superior MPFC could predict symptomatic improvement of positive and negative syndrome scale (PANSS) after 8 weeks of DPP treatment. No correlations were found between alterations in the NH values and changes in the PANSS scores/cognition parameters in the patients. CONCLUSION This study provides evidence that metacognitive training is related to the modulation of DMN homogeneity in schizophrenia.
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Affiliation(s)
- Xiaoxiao Shan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Rongyuan Liao
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Yangpan Ou
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Yudan Ding
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jindong Chen
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Jingping Zhao
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Wenbin Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Yiqun He
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
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35
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Enneking V, Leehr EJ, Dannlowski U, Redlich R. Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies. Psychol Med 2020; 50:187-209. [PMID: 31858931 DOI: 10.1017/s0033291719003660] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Antidepressive pharmacotherapy (AD), electroconvulsive therapy (ECT) and cognitive behavioural therapy (CBT) are effective treatments for major depressive disorder. With our review, we aim to provide a systematic overview of neuroimaging studies that investigate the effects of AD, ECT and CBT on brain grey matter volume (GMV) and biomarkers associated with response. After a systematic database research on PubMed, we included 50 studies using magnetic resonance imaging and investigating (1) changes in GMV, (2) pre-treatment GMV biomarkers associated with response, or (3) the accuracy of predictions of response to AD, ECT or CBT based on baseline GMV data. The strongest evidence for brain structural changes was found for ECT, showing volume increases within the temporal lobe and subcortical structures - such as the hippocampus-amygdala complex, anterior cingulate cortex (ACC) and striatum. For AD, the evidence is heterogeneous as only 4 of 11 studies reported significant changes in GMV. The results are not sufficient in order to draw conclusions about the structural brain effects of CBT. The findings show consistently that higher pre-treatment ACC volume is associated with response to AD, ECT and CBT. An association of higher pre-treatment hippocampal volume and response has only been reported for AD. Machine learning approaches based on pre-treatment whole brain patterns reach accuracies of 64-90% for predictions of AD or ECT response on the individual patient level. The findings underline the potential of brain biomarkers for the implementation in clinical practice as an additive feature within the process of treatment selection.
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Affiliation(s)
- Verena Enneking
- Department of Psychiatry, University of Münster, Münster, Germany
| | | | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Ronny Redlich
- Department of Psychiatry, University of Münster, Münster, Germany
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Sun H, Jiang R, Qi S, Narr KL, Wade BS, Upston J, Espinoza R, Jones T, Calhoun VD, Abbott CC, Sui J. Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data. NEUROIMAGE-CLINICAL 2019; 26:102080. [PMID: 31735637 PMCID: PMC7229344 DOI: 10.1016/j.nicl.2019.102080] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 11/03/2019] [Accepted: 11/05/2019] [Indexed: 12/12/2022]
Abstract
The negative FC networks achieve predictive accuracy of 76.23% for ECT response. The consensus FCs represent predominately frontal, temporal and subcortical regions. FCs that changed significantly were concentrated in frontal and limbic networks. Longitudinal change overlapped with two FCs compared with FC with predictive power.
Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies.
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Affiliation(s)
- Hailun Sun
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
| | - Benjamin Sc Wade
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA
| | - Tom Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | | | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
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Takamiya A, Kishimoto T, Liang KC, Terasawa Y, Nishikata S, Tarumi R, Sawada K, Kurokawa S, Hirano J, Yamagata B, Mimura M. Thalamic volume, resting-state activity, and their association with the efficacy of electroconvulsive therapy. J Psychiatr Res 2019; 117:135-141. [PMID: 31419618 DOI: 10.1016/j.jpsychires.2019.08.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 07/26/2019] [Accepted: 08/05/2019] [Indexed: 12/28/2022]
Abstract
Electroconvulsive therapy (ECT) is the most effective antidepressant treatment. Biological predictors of clinical outcome to ECT are valuable. We aimed to examine multimodal magnetic resonance imaging (MRI) data that correlates to the efficacy of ECT. Structural and resting-state functional MRI data were acquired from 46 individuals (25 depressed individuals who received ECT, and 21 healthy controls). Whole-brain grey matter volume (GMV) and fractional amplitude of low frequency fluctuations (fALFF) were investigated to identify brain regions associated with post-ECT Hamilton Depression Rating Scale (HAM-D) total scores. GMV and fALFF values were compared with those in healthy controls using analysis of covariance (ANCOVA). Remission was defined by HAM-D ≤7. A multiple regression analysis revealed that pretreatment smaller GMV in the left thalamus was associated with worse response to ECT (i.e. higher post-ECT HAM-D). Pretreatment higher fALFF in the right anterior insula, and lower fALFF in the left thalamus and the cerebellum were associated with worse outcomes. The left thalamus was identified in both GMV and fALFF analyses. Nonremitters showed significantly smaller thalamic GMV compared to remitters and controls. We found that pretreatment thalamic volume and resting-state activity were associated with the efficacy of ECT. Our results highlight the importance of the thalamus as a possible biological predictor and its role in the underlying mechanisms of ECT action.
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Affiliation(s)
- Akihiro Takamiya
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; Center for Psychiatry and Behavioral Science, Tokyo, Japan
| | - Taishiro Kishimoto
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
| | - Kuo-Ching Liang
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yuri Terasawa
- Center for Psychiatry and Behavioral Science, Tokyo, Japan
| | | | - Ryosuke Tarumi
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; Center for Psychiatry and Behavioral Science, Tokyo, Japan
| | - Kyosuke Sawada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Shunya Kurokawa
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Bun Yamagata
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
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Sinha P, Reddy RV, Srivastava P, Mehta UM, Bharath RD. Network neurobiology of electroconvulsive therapy in patients with depression. Psychiatry Res Neuroimaging 2019; 287:31-40. [PMID: 30952030 DOI: 10.1016/j.pscychresns.2019.03.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 03/16/2019] [Accepted: 03/19/2019] [Indexed: 12/22/2022]
Abstract
Graph theory, a popular analytic tool for resting state fMRI (rsfMRI) has provided important insights in the neurobiology of depression. We aimed to analyze the changes in the network measures of segregation and integration associated with the administration of ECT in patients with depression and to correlate with both clinical response and cognitive deficits. Changes in normalised clustering coefficient (γ), path length (λ) and small-world (σ) index were explored in 17 patients with depressive episode before 1st and after 6th brief-pulse bifrontal ECT (BFECT) sessions. Significant brain regions were then correlated with differences in clinical and cognitive scales. There was significantly increased γ and σ despite significant increase in λ in several brain regions after ECT in patients with depression. The brain areas revealing significant differences in γ before and after ECT were medial left superior frontal gyrus, left paracentral lobule, right pallidum and left inferior frontal operculum; correlating with changes in verbal fluency, HAM-D scores and delayed verbal memory (last two regions) respectively. BFECT reorganized the brain network topology in patients with depression and made it more segregated and less integrated; these correlated with clinical improvement and associated cognitive deficits.
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Affiliation(s)
- Preeti Sinha
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - R Venkateswara Reddy
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India; Cognitive Neuroscience Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Prerna Srivastava
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Urvakhsh M Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India; Cognitive Neuroscience Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.
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Chen J, Liu J, Calhoun VD. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2019; 107:912-927. [PMID: 32051642 PMCID: PMC7015534 DOI: 10.1109/jproc.2019.2913145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
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Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM 87106 USA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
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Wilcox CE, Abbott CC, Calhoun VD. Alterations in resting-state functional connectivity in substance use disorders and treatment implications. Prog Neuropsychopharmacol Biol Psychiatry 2019; 91:79-93. [PMID: 29953936 PMCID: PMC6309756 DOI: 10.1016/j.pnpbp.2018.06.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 06/18/2018] [Accepted: 06/23/2018] [Indexed: 02/06/2023]
Abstract
Substance use disorders (SUD) are diseases of the brain, characterized by aberrant functioning in the neural circuitry of the brain. Resting state functional connectivity (rsFC) can illuminate these functional changes by measuring the temporal coherence of low-frequency fluctuations of the blood oxygenation level-dependent magnetic resonance imaging signal in contiguous or non-contiguous regions of the brain. Because this data is easy to obtain and analyze, and therefore fairly inexpensive, it holds promise for defining biological treatment targets in SUD, which could help maximize the efficacy of existing clinical interventions and develop new ones. In an effort to identify the most likely "treatment targets" obtainable with rsFC we summarize existing research in SUD focused on 1) the relationships between rsFC and functionality within important psychological domains which are believed to underlie relapse vulnerability 2) changes in rsFC from satiety to deprived or abstinent states 3) baseline rsFC correlates of treatment outcome and 4) changes in rsFC induced by treatment interventions which improve clinical outcomes and reduce relapse risk. Converging evidence indicates that likely "treatment target" candidates, emerging consistently in all four sections, are reduced connectivity within executive control network (ECN) and between ECN and salience network (SN). Other potential treatment targets also show promise, but the literature is sparse and more research is needed. Future research directions include data-driven prediction analyses and rsFC analyses with longitudinal datasets that incorporate time since last use into analysis to account for drug withdrawal. Once the most reliable biological markers are identified, they can be used for treatment matching, during preliminary testing of new pharmacological compounds to establish clinical potential ("target engagement") prior to carrying out costly clinical trials, and for generating hypotheses for medication repurposing.
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Leaver AM, Vasavada M, Joshi SH, Wade B, Woods RP, Espinoza R, Narr KL. Mechanisms of Antidepressant Response to Electroconvulsive Therapy Studied With Perfusion Magnetic Resonance Imaging. Biol Psychiatry 2019; 85:466-476. [PMID: 30424864 PMCID: PMC6380917 DOI: 10.1016/j.biopsych.2018.09.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 09/10/2018] [Accepted: 09/23/2018] [Indexed: 12/19/2022]
Abstract
BACKGROUND Converging evidence suggests that electroconvulsive therapy (ECT) induces neuroplasticity in patients with severe depression, though how this relates to antidepressant response is less clear. Arterial spin-labeled functional magnetic resonance imaging tracks absolute changes in cerebral blood flow (CBF) linked with brain function and offers a potentially powerful tool when observing neurofunctional plasticity with functional magnetic resonance imaging. METHODS Using arterial spin-labeled functional magnetic resonance imaging, we measured global and regional CBF associated with clinically prescribed ECT and therapeutic response in patients (n = 57, 30 female) before ECT, after two treatments, after completing an ECT treatment "index" (∼4 weeks), and after long-term follow-up (6 months). Age- and sex-matched control subjects were also scanned twice (n = 36, 19 female), ∼4 weeks apart. RESULTS Patients with lower baseline global CBF were more likely to respond to ECT. Regional CBF increased in the right anterior hippocampus in all patients irrespective of clinical outcome, both after 2 treatments and after ECT index. However, hippocampal CBF increases postindex were more pronounced in nonresponders. ECT responders exhibited CBF increases in the dorsomedial thalamus and motor cortex near the vertex ECT electrode, as well as decreased CBF within lateral frontoparietal regions. CONCLUSIONS ECT induces functional neuroplasticity in the hippocampus, which could represent functional precursors of ECT-induced increases in hippocampal volume reported previously. However, excessive functional neuroplasticity within the hippocampus may not be conducive to positive clinical outcome. Instead, our results suggest that although hippocampal plasticity may contribute to antidepressant response in ECT, balanced plasticity in regions relevant to seizure physiology including thalamocortical networks may also play a critical role.
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Affiliation(s)
- Amber M. Leaver
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095,Department of Radiology, Northwestern University, Chicago, IL, 60611,Corresponding Author: Amber M. Leaver Ph.D., Address: 737 N Michigan Ave, Suite 1600,Chicago, IL 60611, Phone 312 694 2966, Fax 310 926 5991,
| | - Megha Vasavada
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095
| | - Shantanu H. Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095
| | - Benjamin Wade
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095
| | - Roger P. Woods
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095
| | - Randall Espinoza
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095
| | - Katherine L. Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095
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Gryglewski G, Baldinger-Melich P, Seiger R, Godbersen GM, Michenthaler P, Klöbl M, Spurny B, Kautzky A, Vanicek T, Kasper S, Frey R, Lanzenberger R. Structural changes in amygdala nuclei, hippocampal subfields and cortical thickness following electroconvulsive therapy in treatment-resistant depression: longitudinal analysis. Br J Psychiatry 2019; 214:159-167. [PMID: 30442205 PMCID: PMC6383756 DOI: 10.1192/bjp.2018.224] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is the treatment of choice for severe mental illness including treatment-resistant depression (TRD). Increases in volume of the hippocampus and amygdala following ECT have consistently been reported.AimsTo investigate neuroplastic changes after ECT in specific hippocampal subfields and amygdala nuclei using high-resolution structural magnetic resonance imaging (MRI) (trial registration: clinicaltrials.gov - NCT02379767). METHOD MRI scans were carried out in 14 patients (11 women, 46.9 years (s.d. = 8.1)) with unipolar TRD twice before and once after a series of right unilateral ECT in a pre-post study design. Volumes of subcortical structures, including subfields of the hippocampus and amygdala, and cortical thickness were extracted using FreeSurfer. The effect of ECT was tested using repeated-measures ANOVA. Correlations of imaging and clinical parameters were explored. RESULTS Increases in volume of the right hippocampus by 139.4 mm3 (s.d. = 34.9), right amygdala by 82.3 mm3 (s.d. = 43.9) and right putamen by 73.9 mm3 (s.d. = 77.0) were observed. These changes were localised in the basal and lateral nuclei, and the corticoamygdaloid transition area of the amygdala, the hippocampal-amygdaloid transition area and the granule cell and molecular layer of the dentate gyrus. Cortical thickness increased in the temporal, parietal and insular cortices of the right hemisphere. CONCLUSIONS Following ECT structural changes were observed in hippocampal subfields and amygdala nuclei that are specifically implicated in the pathophysiology of depression and stress-related disorders and retain a high potential for neuroplasticity in adulthood.Declaration of interestS.K. has received grants/research support, consulting fees and/or honoraria within the past 3 years from Angelini, AOP Orphan Pharmaceuticals AG, AstraZeneca, Celegne GmbH, Eli Lilly, Janssen-Cilag Pharma GmbH, KRKA-Pharma, Lundbeck A/S, Neuraxpharm, Pfizer, Pierre Fabre, Schwabe and Servier. R.L. received travel grants and/or conference speaker honoraria from Shire, AstraZeneca, Lundbeck A/S, Dr. Willmar Schwabe GmbH, Orphan Pharmaceuticals AG, Janssen-Cilag Pharma GmbH, and Roche Austria GmbH.
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Affiliation(s)
- Gregor Gryglewski
- Resident, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Pia Baldinger-Melich
- Consultant Psychiatrist, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - René Seiger
- Research Associate, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | | | - Paul Michenthaler
- Resident, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Manfred Klöbl
- Research Assistant, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Benjamin Spurny
- Research Assistant, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Alexander Kautzky
- Resident, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Thomas Vanicek
- Resident, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Siegfried Kasper
- Chair, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Richard Frey
- Vice Chair, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Associate Professor and Head of the Neuroimaging Labs, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria,Correspondence: Professor Rupert Lanzenberger, Neuroimaging labs (NIL) – PET, MRI, EEG, TMS & Chemical Lab, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
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McClintock SM, Kallioniemi E, Martin DM, Kim JU, Weisenbach SL, Abbott CC. A Critical Review and Synthesis of Clinical and Neurocognitive Effects of Noninvasive Neuromodulation Antidepressant Therapies. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2019; 17:18-29. [PMID: 31975955 PMCID: PMC6493152 DOI: 10.1176/appi.focus.20180031] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
There is a plethora of current and emerging antidepressant therapies in the psychiatric armamentarium for the treatment of major depressive disorder. Noninvasive neuromodulation therapies are one such therapeutic category; they typically involve the transcranial application of electrical or magnetic stimulation to modulate cortical and subcortical brain activity. Although electroconvulsive therapy (ECT) has been used since the 1930s, with the prevalence of major depressive disorder and treatment-resistant depression (TRD), the past three decades have seen a proliferation of noninvasive neuromodulation antidepressant therapeutic development. The purpose of this critical review was to synthesize information regarding the clinical effects, neurocognitive effects, and possible mechanisms of action of noninvasive neuromodulation therapies, including ECT, transcranial magnetic stimulation, magnetic seizure therapy, and transcranial direct current stimulation. Considerable research has provided substantial information regarding their antidepressant and neurocognitive effects, but their mechanisms of action remain unknown. Although the four therapies vary in how they modulate neurocircuitry and their resultant antidepressant and neurocognitive effects, they are nonetheless useful for patients with acute and chronic major depressive disorder and TRD. Continued research is warranted to inform dosimetry, algorithm for administration, and integration among the noninvasive neuromodulation therapies and with other antidepressant strategies to continue to maximize their safety and antidepressant benefit.
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Affiliation(s)
- Shawn M McClintock
- Neurocognitive Research Laboratory, Department of Psychiatry, University of Texas (UT) Southwestern Medical Center, Dallas, Texas (McClintock, Kallioniemi, Martin); Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina (McClintock); Black Dog Institute, Sydney, Australia, and School of Psychiatry, University of New South Wales, Sydney (Martin); Department of Psychiatry, University of Utah School of Medicine, Salt Lake City (Kim, Weisenbach); VA Salt Lake City, Mental Health Program (Weisenbach); Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque (Abbott)
| | - Elisa Kallioniemi
- Neurocognitive Research Laboratory, Department of Psychiatry, University of Texas (UT) Southwestern Medical Center, Dallas, Texas (McClintock, Kallioniemi, Martin); Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina (McClintock); Black Dog Institute, Sydney, Australia, and School of Psychiatry, University of New South Wales, Sydney (Martin); Department of Psychiatry, University of Utah School of Medicine, Salt Lake City (Kim, Weisenbach); VA Salt Lake City, Mental Health Program (Weisenbach); Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque (Abbott)
| | - Donel M Martin
- Neurocognitive Research Laboratory, Department of Psychiatry, University of Texas (UT) Southwestern Medical Center, Dallas, Texas (McClintock, Kallioniemi, Martin); Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina (McClintock); Black Dog Institute, Sydney, Australia, and School of Psychiatry, University of New South Wales, Sydney (Martin); Department of Psychiatry, University of Utah School of Medicine, Salt Lake City (Kim, Weisenbach); VA Salt Lake City, Mental Health Program (Weisenbach); Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque (Abbott)
| | - Joseph U Kim
- Neurocognitive Research Laboratory, Department of Psychiatry, University of Texas (UT) Southwestern Medical Center, Dallas, Texas (McClintock, Kallioniemi, Martin); Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina (McClintock); Black Dog Institute, Sydney, Australia, and School of Psychiatry, University of New South Wales, Sydney (Martin); Department of Psychiatry, University of Utah School of Medicine, Salt Lake City (Kim, Weisenbach); VA Salt Lake City, Mental Health Program (Weisenbach); Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque (Abbott)
| | - Sara L Weisenbach
- Neurocognitive Research Laboratory, Department of Psychiatry, University of Texas (UT) Southwestern Medical Center, Dallas, Texas (McClintock, Kallioniemi, Martin); Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina (McClintock); Black Dog Institute, Sydney, Australia, and School of Psychiatry, University of New South Wales, Sydney (Martin); Department of Psychiatry, University of Utah School of Medicine, Salt Lake City (Kim, Weisenbach); VA Salt Lake City, Mental Health Program (Weisenbach); Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque (Abbott)
| | - Christopher C Abbott
- Neurocognitive Research Laboratory, Department of Psychiatry, University of Texas (UT) Southwestern Medical Center, Dallas, Texas (McClintock, Kallioniemi, Martin); Division of Brain Stimulation and Neurophysiology, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina (McClintock); Black Dog Institute, Sydney, Australia, and School of Psychiatry, University of New South Wales, Sydney (Martin); Department of Psychiatry, University of Utah School of Medicine, Salt Lake City (Kim, Weisenbach); VA Salt Lake City, Mental Health Program (Weisenbach); Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque (Abbott)
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Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther 2018; 24:1037-1052. [PMID: 30136381 DOI: 10.1111/cns.13048] [Citation(s) in RCA: 160] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/19/2018] [Accepted: 07/21/2018] [Indexed: 01/10/2023] Open
Abstract
AIMS Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. DISCUSSIONS In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. CONCLUSIONS We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
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Affiliation(s)
- Shuang Gao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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45
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Sui J, Qi S, van Erp TGM, Bustillo J, Jiang R, Lin D, Turner JA, Damaraju E, Mayer AR, Cui Y, Fu Z, Du Y, Chen J, Potkin SG, Preda A, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, McEwen SC, O'Leary DS, McMahon A, Jiang T, Calhoun VD. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat Commun 2018; 9:3028. [PMID: 30072715 PMCID: PMC6072778 DOI: 10.1038/s41467-018-05432-w] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 07/04/2018] [Indexed: 01/06/2023] Open
Abstract
Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia. Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive performance in individuals with schizophrenia and healthy controls. A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia using multiple cognitive domain scores. Results highlight the salience network (gray matter, GM), corpus callosum (fractional anisotropy, FA), central executive and default-mode networks (fractional amplitude of low-frequency fluctuation, fALFF) as modality-specific biomarkers of generalized cognition. FALFF features are found to be more sensitive to cognitive domain differences, while the salience network in GM and corpus callosum in FA are highly consistent and predictive of multiple cognitive domains. These modality-specific brain regions define-in three separate cohorts-promising co-varying multimodal signatures that can be used as predictors of multi-domain cognition.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- The Mind Research Network, Albuquerque, NM, 87106, USA.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Shile Qi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Dongdong Lin
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM, 87106, USA
- Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, 30302, USA
| | | | - Andrew R Mayer
- The Mind Research Network, Albuquerque, NM, 87106, USA
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA
- San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA
- San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - James Voyvodic
- Department of Radiology, Brain Imaging and Analysis Center, Duke University, Durham, NC, 27710, USA
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Sarah C McEwen
- Department of Psychiatry, University of California, San Diego, CA, 92093, USA
| | - Daniel S O'Leary
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa, IA, 52242, USA
| | - Agnes McMahon
- USC Stevens Neuroimaging and Informatics Institute, University of Southern California, San Diego, CA, 90033, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, 87106, USA.
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA.
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA.
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46
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Leaver AM, Wade B, Vasavada M, Hellemann G, Joshi SH, Espinoza R, Narr KL. Fronto-Temporal Connectivity Predicts ECT Outcome in Major Depression. Front Psychiatry 2018; 9:92. [PMID: 29618992 PMCID: PMC5871748 DOI: 10.3389/fpsyt.2018.00092] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 03/06/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. METHODS A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n = 46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. RESULTS The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). CONCLUSION Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.
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Affiliation(s)
- Amber M Leaver
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, United States
| | - Benjamin Wade
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, United States
| | - Megha Vasavada
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, United States
| | - Gerhard Hellemann
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Shantanu H Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, United States
| | - Randall Espinoza
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Katherine L Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
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