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Ma S, Li R, Gong Q, Lv H, Deng Z, Wang B, Yao L, Kang L, Xiang D, Yang J, Liu Z. Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression. J Proteome Res 2024; 23:4043-4054. [PMID: 39150755 DOI: 10.1021/acs.jproteome.4c00389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.
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Affiliation(s)
- Simeng Ma
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Ruiling Li
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Qian Gong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Honggang Lv
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zipeng Deng
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Beibei Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lihua Yao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lijun Kang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Dan Xiang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jun Yang
- School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430071, China
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Ramduny J, Kelly C. Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction. Neuropsychopharmacology 2024:10.1038/s41386-024-01962-8. [PMID: 39147868 DOI: 10.1038/s41386-024-01962-8] [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] [Received: 03/29/2024] [Revised: 08/02/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.
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Affiliation(s)
- Jivesh Ramduny
- Department of Psychology, Yale University, New Haven, CT, USA.
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
| | - Clare Kelly
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
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Kliamovich DAKOTA, Miranda-Dominguez OSCAR, Byington NORA, Espinoza ABIGAILV, Lopez Flores ARTURO, Fair DAMIENA, Nagel BONNIEJ. Leveraging distributed brain signal at rest to predict internalizing symptoms in youth. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00215-5. [PMID: 39127423 DOI: 10.1016/j.bpsc.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/30/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND The prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear. METHODS Youth from the Adolescent Brain and Cognitive DevelopmentSM Study (ages 9-10 years at baseline) with a resting-state fMRI scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor - Youth Form were combined across two years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (n = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity (RSFC) with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset. RESULTS The PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute β-weight) were retained. The RSFC networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1*10-6) as accounted for by permutation testing (n = 7000). CONCLUSIONS These results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between RSFC and behavior.
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Affiliation(s)
- D A K O T A Kliamovich
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR.
| | | | - N O R A Byington
- Department of Pediatrics, University of Minnesota, Minneapolis, MN
| | | | | | | | - B O N N I E J Nagel
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR; Department of Psychiatry, Oregon Health & Science University, Portland, OR
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Huang R, Gong M, Tan X, Shen J, Wu Y, Cai X, Wang S, Min L, Gong L, Liang W. Effects of Chaihu Shugan San on Brain Functional Network Connectivity in the Hippocampus of a Perimenopausal Depression Rat Model. Mol Neurobiol 2024; 61:1655-1672. [PMID: 37751044 DOI: 10.1007/s12035-023-03615-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/25/2023] [Indexed: 09/27/2023]
Abstract
In this study, we used Chaihu Shugan San (CSS), a traditional Chinese herbal formula, as a probe to investigate the involvement of brain functional network connectivity and hippocampus energy metabolism in perimenopausal depression. A network pharmacology approach was performed to discover the underlying mechanisms of CSS in improving perimenopausal depression, which were verified in perimenopausal depression rat models. Network pharmacology analysis indicated that complex mechanisms of energy metabolism, neurotransmitter metabolism, inflammation, and hormone metabolic processes were closely associated with the anti-depressive effects of CSS. Thus, the serum concentrations of estradiol (E2), glutamate (Glu), and 5-hydroxytryptamine (5-HT) were detected by ELISA. The brain functional network connectivity between the hippocampus and adjacent brain regions was evaluated using resting-state functional magnetic resonance imaging (fMRI). A targeted metabolomic analysis of the hippocampal tricarboxylic acid cycle was also performed to measure the changes in hippocampal energy metabolism using liquid chromatography-tandem mass spectrometry (LC-MS/MS). CSS treatment significantly improved the behavioral performance, decreased the serum Glu levels, and increased the serum 5-HT levels of PMS + CUMS rats. The brain functional connectivity between the hippocampus and other brain regions was significantly changed by PMS + CUMS processes but improved by CSS treatment. Moreover, among the metabolites in the hippocampal tricarboxylic acid cycle, the concentrations of citrate and the upregulation of isocitrate and downregulation of guanosine triphosphate (GTP) in PMS + CUMS rats could be significantly improved by CSS treatment. A brain functional network connectivity mechanism may be involved in perimenopausal depression, wherein the hippocampal tricarboxylic acid cycle plays a vital role.
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Affiliation(s)
- Ruiting Huang
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China
- Guangdong Engineering Research Center of Chinese Medicine & Disease Susceptibility, Jinan University, Guangzhou, 510632, People's Republic of China
- Faculty of Chinese Medicine, Macau University of Science and Technology, Taipa, Macau, 999078, People's Republic of China
| | - Min Gong
- College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, People's Republic of China
| | - Xue Tan
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, People's Republic of China
| | - Jianying Shen
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China
| | - You Wu
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China
| | - Xiaoshi Cai
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China
| | - Suying Wang
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China
| | - Li Min
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China
| | - Lin Gong
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China
| | - Wenna Liang
- School of Traditional Chinese Medicine, Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Fuzhou, 350122, People's Republic of China.
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5
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Squires M, Tao X, Elangovan S, Gururajan R, Zhou X, Li Y, Acharya UR. Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107771. [PMID: 37717523 DOI: 10.1016/j.cmpb.2023.107771] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/12/2023] [Accepted: 08/19/2023] [Indexed: 09/19/2023]
Abstract
Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries.
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Affiliation(s)
- Matthew Squires
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia.
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia.
| | | | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, Australia.
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, Australia.
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, Australia.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia.
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6
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Wang Q, He C, Wang Z, Fan D, Zhang Z, Xie C. Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder. Transl Psychiatry 2023; 13:365. [PMID: 38012129 PMCID: PMC10682490 DOI: 10.1038/s41398-023-02655-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 10/30/2023] [Accepted: 11/07/2023] [Indexed: 11/29/2023] Open
Abstract
Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a 'suicidality gradient'. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age (p < 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex (p < 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73-0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.
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Affiliation(s)
- Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China.
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, 210009, China.
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7
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Squires M, Tao X, Elangovan S, Gururajan R, Zhou X, Acharya UR, Li Y. Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment. Brain Inform 2023; 10:10. [PMID: 37093301 PMCID: PMC10123592 DOI: 10.1186/s40708-023-00188-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.
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Affiliation(s)
- Matthew Squires
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia.
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | | | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
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8
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Helman TJ, Headrick JP, Stapelberg NJC, Braidy N. The sex-dependent response to psychosocial stress and ischaemic heart disease. Front Cardiovasc Med 2023; 10:1072042. [PMID: 37153459 PMCID: PMC10160413 DOI: 10.3389/fcvm.2023.1072042] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Stress is an important risk factor for modern chronic diseases, with distinct influences in males and females. The sex specificity of the mammalian stress response contributes to the sex-dependent development and impacts of coronary artery disease (CAD). Compared to men, women appear to have greater susceptibility to chronic forms of psychosocial stress, extending beyond an increased incidence of mood disorders to include a 2- to 4-fold higher risk of stress-dependent myocardial infarction in women, and up to 10-fold higher risk of Takotsubo syndrome-a stress-dependent coronary-myocardial disorder most prevalent in post-menopausal women. Sex differences arise at all levels of the stress response: from initial perception of stress to behavioural, cognitive, and affective responses and longer-term disease outcomes. These fundamental differences involve interactions between chromosomal and gonadal determinants, (mal)adaptive epigenetic modulation across the lifespan (particularly in early life), and the extrinsic influences of socio-cultural, economic, and environmental factors. Pre-clinical investigations of biological mechanisms support distinct early life programming and a heightened corticolimbic-noradrenaline-neuroinflammatory reactivity in females vs. males, among implicated determinants of the chronic stress response. Unravelling the intrinsic molecular, cellular and systems biological basis of these differences, and their interactions with external lifestyle/socio-cultural determinants, can guide preventative and therapeutic strategies to better target coronary heart disease in a tailored sex-specific manner.
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Affiliation(s)
- Tessa J. Helman
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, NSW, Sydney, Australia
- Correspondence: Tessa J. Helman
| | - John P. Headrick
- Schoolof Pharmacy and Medical Sciences, Griffith University, Southport, QLD, Australia
| | | | - Nady Braidy
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, NSW, Sydney, Australia
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9
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Shirakawa Y, Yamazaki R, Kita Y, Kitamura Y, Okumura Y, Inoue Y, Matsuda Y, Kodaka F, Shigeta M, Kito S. Repetitive transcranial magnetic stimulation decreased effortful frontal activity for shifting in patients with major depressive disorder. Neuroreport 2022; 33:470-475. [PMID: 35775324 DOI: 10.1097/wnr.0000000000001806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Patients with major depressive disorder (MDD) exhibit several clinical symptoms including difficulties in flexible thinking. Flexible thinking mainly relies on a cognitive ability called shifting; however, the mechanisms underlying shifting in patients with MDD have not yet been clarified. Therefore, we conducted a preliminary intervention study to clarify the association between depression and shifting ability. We examined the hemodynamic responses in the frontal regions during the shifting task using functional near-infrared spectroscopy (fNIRS) in 21 patients with MDD who were treated using high-frequency repetitive transcranial magnetic stimulation (rTMS). Behavioral performance on the shifting task did not change between pre- and posttreatments, whereas patients who responded well to rTMS treatment showed a significant decrease in hemodynamic responses posttreatment. On the other hand, the poor responders did not show significant changes in the hemodynamic responses between pre- and posttreatments. These results suggest that the good responders were successfully remedied with rTMS treatment and did not need effortful activity in frontal regions for shifting, which made their brain activity more efficient.
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Affiliation(s)
- Yuka Shirakawa
- Department of Psychology, Faculty of Letters, Keio University
- Japan Society for the Promotion of Science
| | - Ryuichi Yamazaki
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Yosuke Kita
- Department of Psychology, Faculty of Letters, Keio University
- Cognitive Brain Research Unit (CBRU), Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yuzuki Kitamura
- Department of Design, Graduate School of Design, Kyushu University, Fukuoka
| | - Yasuko Okumura
- Department of Psychology, Faculty of Letters, Keio University
| | - Yuki Inoue
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Yuki Matsuda
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Fumitoshi Kodaka
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Masahiro Shigeta
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Shinsuke Kito
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
- Department of Psychiatry, National Center of Neurology and Psychiatry, Tokyo, Japan
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10
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Tejavibulya L, Rolison M, Gao S, Liang Q, Peterson H, Dadashkarimi J, Farruggia MC, Hahn CA, Noble S, Lichenstein SD, Pollatou A, Dufford AJ, Scheinost D. Predicting the future of neuroimaging predictive models in mental health. Mol Psychiatry 2022; 27:3129-3137. [PMID: 35697759 PMCID: PMC9708554 DOI: 10.1038/s41380-022-01635-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/09/2022] [Accepted: 05/18/2022] [Indexed: 12/11/2022]
Abstract
Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).
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Affiliation(s)
- Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Javid Dadashkarimi
- Department of Computer Science, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Michael C Farruggia
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - C Alice Hahn
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
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11
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Dai Z, Shao J, Zhou H, Chen Z, Zhang S, Wang H, Jiang H, Yao Z, Lu Q. Disrupted fronto-parietal network and default-mode network gamma interactions distinguishing suicidal ideation and suicide attempt in depression. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110475. [PMID: 34780814 DOI: 10.1016/j.pnpbp.2021.110475] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/03/2021] [Accepted: 11/08/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Precise suicide risk evaluation struggled in Major depressive disorder (MDD), especially for patients with only suicidal-ideation (SI) but without suicide attempt (SA). MDD patients have deficits in negative emotion processing, which is associated with the generation of SI and SA. Given the critical role of gamma oscillations in negative emotion processing, we hypothesize that the transition from SI to SA in MDD could be characterized by abnormal gamma interactions. METHODS We recruited 162 participants containing 106 MDD patients and 56 healthy controls (HCs). Participants performed facial recognition tasks while magnetoencephalography data were recorded. Time-frequency-representation (TFR) analysis was conducted to identify the dominant spectra differences between MDD and HCs, and then source analysis was applied to localize the region of interests. Furthermore, frequency-specific functional connectivity network were constructed and a semi-supervised clustering algorithm was utilized to predict potential suicide risk. RESULTS Gamma (50-70 Hz) power was found significantly increased in MDD, mainly residing in regions from fronto-parietal-control-network (FPN), visual-network (VN), default-mode-network (DMN) and salience-network (SN). Based on impaired gamma functional connectivity network between well-established SA group and non-SI group, semi-supervised algorithm clustered patients with only SI into two groups with different suicide risks. Moreover, Inter-network gamma connectivity between FPN and DMN significantly negatively correlated with suicide risk and not confounded by depression severity. CONCLUSION Inter-network gamma connectivity with FPN and DMN might be the key neuropathological interactions underling the progression from SI to SA. By applying semi-supervised clustering to electrophysiological data, it is possible to predict individual suicide risk.
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Affiliation(s)
- Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Hongliang Zhou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Zhilu Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Siqi Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Haiteng Jiang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Zhijian Yao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China.
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12
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Faller J, Doose J, Sun X, Mclntosh JR, Saber GT, Lin Y, Teves JB, Blankenship A, Huffman S, Goldman RI, George MS, Brown TR, Sajda P. Daily prefrontal closed-loop repetitive transcranial magnetic stimulation (rTMS) produces progressive EEG quasi-alpha phase entrainment in depressed adults. Brain Stimul 2022; 15:458-471. [PMID: 35231608 PMCID: PMC8979612 DOI: 10.1016/j.brs.2022.02.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/31/2022] [Accepted: 02/17/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation modality that can treat depression, obsessive-compulsive disorder, or help smoking cessation. Research suggests that timing the delivery of TMS relative to an endogenous brain state may affect efficacy and short-term brain dynamics. OBJECTIVE To investigate whether, for a multi-week daily treatment of repetitive TMS (rTMS), there is an effect on brain dynamics that depends on the timing of the TMS relative to individuals' prefrontal EEG quasi-alpha rhythm (between 6 and 13 Hz). METHOD We developed a novel closed-loop system that delivers personalized EEG-triggered rTMS to patients undergoing treatment for major depressive disorder. In a double blind study, patients received daily treatments of rTMS over a period of six weeks and were randomly assigned to either a synchronized or unsynchronized treatment group, where synchronization of rTMS was to their prefrontal EEG quasi-alpha rhythm. RESULTS When rTMS is applied over the dorsal lateral prefrontal cortex (DLPFC) and synchronized to the patient's prefrontal quasi-alpha rhythm, patients develop strong phase entrainment over a period of weeks, both over the stimulation site as well as in a subset of areas distal to the stimulation site. In addition, at the end of the course of treatment, this group's entrainment phase shifts to be closer to the phase that optimally engages the distal target, namely the anterior cingulate cortex (ACC). These entrainment effects are not observed in the group that is given rTMS without initial EEG synchronization of each TMS train. CONCLUSIONS The entrainment effects build over the course of days/weeks, suggesting that these effects engage neuroplastic changes which may have clinical consequences in depression or other diseases.
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Affiliation(s)
- Josef Faller
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Jayce Doose
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Xiaoxiao Sun
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, 20115, USA
| | - James R Mclntosh
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Golbarg T Saber
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Neurology, University of Chicago, Chicago, IL, 60637, USA
| | - Yida Lin
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Joshua B Teves
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Aidan Blankenship
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Sarah Huffman
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Robin I Goldman
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Mark S George
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, 29401, USA
| | - Truman R Brown
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Paul Sajda
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA; Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA; Data Science Institute, Columbia University, New York, NY, 10027, USA.
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13
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Poologaindran A, Profyris C, Young IM, Dadario NB, Ahsan SA, Chendeb K, Briggs RG, Teo C, Romero-Garcia R, Suckling J, Sughrue ME. Interventional neurorehabilitation for promoting functional recovery post-craniotomy: a proof-of-concept. Sci Rep 2022; 12:3039. [PMID: 35197490 PMCID: PMC8866464 DOI: 10.1038/s41598-022-06766-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 02/02/2022] [Indexed: 02/06/2023] Open
Abstract
The human brain is a highly plastic ‘complex’ network—it is highly resilient to damage and capable of self-reorganisation after a large perturbation. Clinically, neurological deficits secondary to iatrogenic injury have very few active treatments. New imaging and stimulation technologies, though, offer promising therapeutic avenues to accelerate post-operative recovery trajectories. In this study, we sought to establish the safety profile for ‘interventional neurorehabilitation’: connectome-based therapeutic brain stimulation to drive cortical reorganisation and promote functional recovery post-craniotomy. In n = 34 glioma patients who experienced post-operative motor or language deficits, we used connectomics to construct single-subject cortical networks. Based on their clinical and connectivity deficit, patients underwent network-specific transcranial magnetic stimulation (TMS) sessions daily over five consecutive days. Patients were then assessed for TMS-related side effects and improvements. 31/34 (91%) patients were successfully recruited and enrolled for TMS treatment within two weeks of glioma surgery. No seizures or serious complications occurred during TMS rehabilitation and 1-week post-stimulation. Transient headaches were reported in 4/31 patients but improved after a single session. No neurological worsening was observed while a clinically and statistically significant benefit was noted in 28/31 patients post-TMS. We present two clinical vignettes and a video demonstration of interventional neurorehabilitation. For the first time, we demonstrate the safety profile and ability to recruit, enroll, and complete TMS acutely post-craniotomy in a high seizure risk population. Given the lack of randomisation and controls in this study, prospective randomised sham-controlled stimulation trials are now warranted to establish the efficacy of interventional neurorehabilitation following craniotomy.
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Affiliation(s)
- Anujan Poologaindran
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,The Alan Turing Institute, British Library, London, UK
| | - Christos Profyris
- Netcare Linksfield Hospital, Johannesburg, South Africa.,Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | - Isabella M Young
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | - Nicholas B Dadario
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia.,Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Syed A Ahsan
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | - Kassem Chendeb
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | - Robert G Briggs
- Department of Neurosurgery, University of Southern California, Los Angeles, CA, USA
| | - Charles Teo
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,The Alan Turing Institute, British Library, London, UK
| | - Michael E Sughrue
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK. .,Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia.
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14
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Liang S, Wu Y, Hanxiaoran L, Greenshaw AJ, Li T. Anhedonia in Depression and Schizophrenia: Brain Reward and Aversion Circuits. Neuropsychiatr Dis Treat 2022; 18:1385-1396. [PMID: 35836582 PMCID: PMC9273831 DOI: 10.2147/ndt.s367839] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/28/2022] [Indexed: 11/23/2022] Open
Abstract
Anhedonia, which is defined as markedly diminished interest or pleasure, is a prominent symptom of psychiatric disorders, most notably major depressive disorder (MDD) and schizophrenia. Anhedonia is considered a transdiagnostic symptom that is associated with deficits in neural reward and aversion functions. Here, we review the characteristics of anhedonia in depression and schizophrenia as well as shared or disorder-specific anhedonia-related alterations in reward and aversion pathways of the brain. In particular, we highlight that anhedonia is characterized by impairments in anticipatory pleasure and integration of reward-related information in MDD, whereas anhedonia in schizophrenia is associated with neurocognitive deficits in representing the value of rewards. Dysregulation of the frontostriatal circuit and mesocortical and mesolimbic circuit systems may be the transdiagnostic neurobiological basis of reward and aversion impairments underlying anhedonia in these two disorders. Blunted aversion processing in depression and relatively strong aversion in schizophrenia are primarily attributed to the dysfunction of the habenula, insula, amygdala, and anterior cingulate cortex. Furthermore, patients with schizophrenia appear to exhibit greater abnormal activation and extended functional coupling than those with depression. From a transdiagnostic perspective, understanding the neural mechanisms underlying anhedonia in patients with psychiatric disorders may help in the development of more targeted and efficacious treatment and intervention strategies.
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Affiliation(s)
- Sugai Liang
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
| | - Yue Wu
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
| | - Li Hanxiaoran
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton, AB, T6G 2B7, Canada
| | - Tao Li
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
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15
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Ramos-da-Silva L, Carlson PT, Silva-Costa LC, Martins-de-Souza D, de Almeida V. Molecular Mechanisms Associated with Antidepressant Treatment on Major Depression. Complex Psychiatry 2021; 7:49-59. [PMID: 35813936 PMCID: PMC8739385 DOI: 10.1159/000518098] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 06/23/2021] [Indexed: 11/25/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and multifactorial psychiatric disorder that causes serious health, social, and economic concerns worldwide. The main treatment of the symptoms is through antidepressant (AD) drugs. However, not all patients respond properly to these drugs. Omic sciences are widely used to analyze not only biomarkers for the AD response but also their molecular mechanism. In this review, we aimed to focus on omics data to better understand the molecular mechanisms involving AD effects on MDD. We consistently found, from preclinical to clinical data, that glutamatergic transmission, immune/inflammatory processes, energy metabolism, oxidative stress, and lipid metabolism were associated with traditional and potential new ADs. Despite efforts of studies investigating biomarkers of response to ADs, which could contribute to personalized treatment, there is no biomarker panel available for clinical application. From clinical genomic studies, we found that the main findings contribute to the development of pharmacogenomic tests for AD efficacy for each patient. Several studies pointed at DRD2, PXDNL, CACNA1E, and CACNA2D1 genes as potential targets for MDD treatment and the efficacy and rapid-antidepressant effect of ketamine. Finally, more in-depth studies of the molecular targets pointed here are needed to determine the clinical relevance and provide further evidence for precision MDD treatment.
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Affiliation(s)
- Lívia Ramos-da-Silva
- Department of Biochemistry and Tissue Biology, Laboratory of Neuroproteomics, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Pamela T. Carlson
- Department of Biochemistry and Tissue Biology, Laboratory of Neuroproteomics, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Licia C. Silva-Costa
- Department of Biochemistry and Tissue Biology, Laboratory of Neuroproteomics, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Daniel Martins-de-Souza
- Department of Biochemistry and Tissue Biology, Laboratory of Neuroproteomics, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
- Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, Brazil
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria, Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo, Brazil
| | - Valéria de Almeida
- Department of Biochemistry and Tissue Biology, Laboratory of Neuroproteomics, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
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16
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Miranda L, Paul R, Pütz B, Koutsouleris N, Müller-Myhsok B. Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Front Psychiatry 2021; 12:665536. [PMID: 34744805 PMCID: PMC8569315 DOI: 10.3389/fpsyt.2021.665536] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/07/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Riya Paul
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Benno Pütz
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Bertram Müller-Myhsok
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Health Data Science, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
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17
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Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:856-864. [PMID: 33571718 PMCID: PMC8349367 DOI: 10.1016/j.bpsc.2021.02.001] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/01/2021] [Accepted: 02/02/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.
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Affiliation(s)
- Ellen E Lee
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard University, Boston, Massachusetts
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia
| | - Colin A Depp
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - Sarah A Graham
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California
| | - Ho-Cheol Kim
- AI and Cognitive Software, IBM Research-Almaden, San Jose, California
| | | | - John H Krystal
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Dilip V Jeste
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Neurosciences, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California.
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18
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Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 1:135-145. [PMID: 36324992 PMCID: PMC9616319 DOI: 10.1016/j.bpsgos.2021.04.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 01/05/2023] Open
Abstract
Background Identifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD. Methods This study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes. Results Source-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2. Conclusions Overall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.
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19
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Chen D, Jia T, Zhang Y, Cao M, Loth E, Lo CYZ, Cheng W, Liu Z, Gong W, Sahakian BJ, Feng J. Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals. Front Hum Neurosci 2021; 15:657857. [PMID: 34025376 PMCID: PMC8134539 DOI: 10.3389/fnhum.2021.657857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/11/2021] [Indexed: 01/01/2023] Open
Abstract
Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments.
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Affiliation(s)
- Di Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine, MRC SGDP Centre, IoPPN, King’s College London, London, United Kingdom
| | - Yuning Zhang
- Centre for Population Neuroscience and Precision Medicine, MRC SGDP Centre, IoPPN, King’s College London, London, United Kingdom
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- School of Psychology, University of Southampton, Southampton, United Kingdom
| | - Miao Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Eva Loth
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, IoPPN, King’s College London, London, United Kingdom
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Zhaowen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Weikang Gong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Barbara Jacquelyn Sahakian
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
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20
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Liang S, Deng W, Li X, Greenshaw AJ, Wang Q, Li M, Ma X, Bai TJ, Bo QJ, Cao J, Chen GM, Chen W, Cheng C, Cheng YQ, Cui XL, Duan J, Fang YR, Gong QY, Guo WB, Hou ZH, Hu L, Kuang L, Li F, Li KM, Liu YS, Liu ZN, Long YC, Luo QH, Meng HQ, Peng DH, Qiu HT, Qiu J, Shen YD, Shi YS, Si TM, Wang CY, Wang F, Wang K, Wang L, Wang X, Wang Y, Wu XP, Wu XR, Xie CM, Xie GR, Xie HY, Xie P, Xu XF, Yang H, Yang J, Yu H, Yao JS, Yao SQ, Yin YY, Yuan YG, Zang YF, Zhang AX, Zhang H, Zhang KR, Zhang ZJ, Zhao JP, Zhou RB, Zhou YT, Zou CJ, Zuo XN, Yan CG, Li T. Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns. NEUROIMAGE-CLINICAL 2020; 28:102514. [PMID: 33396001 PMCID: PMC7724374 DOI: 10.1016/j.nicl.2020.102514] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/20/2020] [Accepted: 11/22/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. METHODS The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. RESULTS Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. CONCLUSIONS Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.
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Affiliation(s)
- Sugai Liang
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Wei Deng
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaojing Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton T6G 2B7, AB, Canada
| | - Qiang Wang
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Mingli Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiaohong Ma
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Tong-Jian Bai
- Anhui Medical University, Hefei 230032, Anhui, China
| | - Qi-Jing Bo
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Jun Cao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Guan-Mao Chen
- The First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong, China
| | - Wei Chen
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chang Cheng
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Yu-Qi Cheng
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Xi-Long Cui
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Jia Duan
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning, China
| | - Yi-Ru Fang
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Qi-Yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu 610040, Sichuan, China
| | - Wen-Bin Guo
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Zheng-Hua Hou
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Lan Hu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Li Kuang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Feng Li
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Kai-Ming Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Yan-Song Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215031, Jiangsu, China
| | - Zhe-Ning Liu
- The Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Yi-Cheng Long
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Qing-Hua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Hua-Qing Meng
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215031, Jiangsu, China
| | - Dai-Hui Peng
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Hai-Tang Qiu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Yue-Di Shen
- Department of Diagnostics, Affiliated Hospital, Hangzhou Normal University Medical School, Hangzhou 311121, Zhejiang, China
| | - Yu-Shu Shi
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tian-Mei Si
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Chuan-Yue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Fei Wang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning, China
| | - Kai Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100069, China
| | - Li Wang
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Xiang Wang
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong, China
| | - Xiao-Ping Wu
- Xi'an Central Hospital, Xi'an 710032, Shaanxi, China
| | - Xin-Ran Wu
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Chun-Ming Xie
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210096, Jiangsu, China
| | - Guang-Rong Xie
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Hai-Yan Xie
- Department of Psychiatry, The Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing 400016, China; Chongqing Key Laboratory of Neurobiology, Chongqing 400016, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiu-Feng Xu
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jian Yang
- The First Affiliated Hospital of Xi'an Jiaotong University, 710049 Shaanxi, China
| | - Hua Yu
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jia-Shu Yao
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Shu-Qiao Yao
- The Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Ying-Ying Yin
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Yong-Gui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210096, Jiangsu, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou 311121, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, Zhejiang, China
| | - Ai-Xia Zhang
- The First Affiliated Hospital of Xi'an Jiaotong University, 710049 Shaanxi, China
| | - Hong Zhang
- Xi'an Central Hospital, Xi'an 710032, Shaanxi, China
| | - Ke-Rang Zhang
- First Hospital of Shanxi Medical University, Taiyuan 030607, Shanxi, China
| | - Zhi-Jun Zhang
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210096, Jiangsu, China
| | - Jing-Ping Zhao
- The Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha 410083, Hunan, China
| | - Ru-Bai Zhou
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Yi-Ting Zhou
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Chao-Jie Zou
- First Affiliated Hospital of Kunming Medical University, Kunming 650211, Yunnan, China
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain (CLIMB), Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain (CLIMB), Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Tao Li
- Mental Health Center & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; West China Brain Research Centre, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
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21
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Davidson B, Suresh H, Goubran M, Rabin JS, Meng Y, Mithani K, Pople CB, Giacobbe P, Hamani C, Lipsman N. Predicting response to psychiatric surgery: a systematic review of neuroimaging findings. J Psychiatry Neurosci 2020; 45:387-394. [PMID: 32293838 PMCID: PMC7595737 DOI: 10.1503/jpn.190208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Psychiatric surgery, including deep brain stimulation and stereotactic ablation, is an important treatment option in severe refractory psychiatric illness. Several large trials have demonstrated response rates of approximately 50%, underscoring the need to identify and select responders preoperatively. Recent advances in neuroimaging have brought this possibility into focus. We systematically reviewed the psychiatric surgery neuroimaging literature to assess the current state of evidence for preoperative imaging predictors of response. METHODS We performed this study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) frameworks, and preregistered it using PROSPERO. We systematically searched the Medline, Embase and Cochrane databases for studies reporting preoperative neuroimaging analyses correlated with clinical outcomes in patients who underwent psychiatric surgery. We recorded and synthesized the methodological details, imaging results and clinical correlations from these studies. RESULTS After removing duplicates, the search yielded 8388 unique articles, of which 7 met the inclusion criteria. The included articles were published between 2001 and 2018 and reported on the outcomes of 101 unique patients. Of the 6 studies that reported significant findings, all identified clusters of hypermetabolism, hyperconnectivity or increased size in the frontostriatal limbic circuitry. LIMITATIONS The included studies were few and highly varied, spanning 2 decades. CONCLUSION Although few studies have analyzed preoperative imaging for predictors of response to psychiatric surgery, we found consistency among the reported results: most studies implicated overactivity in the frontostriatal limbic network as being correlated with clinical response. Larger prospective studies are needed. REGISTRATION www.crd.york.ac.uk/prospero/display_record.php?RecordID=131151.
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Affiliation(s)
- Benjamin Davidson
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Hrishikesh Suresh
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Maged Goubran
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Jennifer S Rabin
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Ying Meng
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Karim Mithani
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Christopher B Pople
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Peter Giacobbe
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Clement Hamani
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Nir Lipsman
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
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22
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Torres-Berrío A, Hernandez G, Nestler EJ, Flores C. The Netrin-1/DCC Guidance Cue Pathway as a Molecular Target in Depression: Translational Evidence. Biol Psychiatry 2020; 88:611-624. [PMID: 32593422 PMCID: PMC7529861 DOI: 10.1016/j.biopsych.2020.04.025] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 04/14/2020] [Accepted: 04/28/2020] [Indexed: 12/15/2022]
Abstract
The Netrin-1/DCC guidance cue pathway plays a critical role in guiding growing axons toward the prefrontal cortex during adolescence and in the maturational organization and adult plasticity of prefrontal cortex connectivity. In this review, we put forward the idea that alterations in prefrontal cortex architecture and function, which are intrinsically linked to the development of major depressive disorder, originate in part from the dysregulation of the Netrin-1/DCC pathway by a mechanism that involves microRNA-218. We discuss evidence derived from mouse models of stress and from human postmortem brain and genome-wide association studies indicating an association between the Netrin-1/DCC pathway and major depressive disorder. We propose a potential role of circulating microRNA-218 as a biomarker of stress vulnerability and major depressive disorder.
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Affiliation(s)
- Angélica Torres-Berrío
- Integrated Program in Neuroscience, Montreal, Quebec, Canada; Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Eric J Nestler
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Cecilia Flores
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada.
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23
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Goldsmith DR, Bekhbat M, Le NA, Chen X, Woolwine BJ, Li Z, Haroon E, Felger JC. Protein and gene markers of metabolic dysfunction and inflammation together associate with functional connectivity in reward and motor circuits in depression. Brain Behav Immun 2020; 88:193-202. [PMID: 32387344 PMCID: PMC7415617 DOI: 10.1016/j.bbi.2020.05.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 12/14/2022] Open
Abstract
Bidirectional relationships between inflammation and metabolic dysfunction may contribute to the pathophysiology of psychiatric illnesses like depression. Metabolic disturbances drive inflammation, which in turn exacerbate metabolic outcomes including insulin resistance. Both inflammatory (e.g. endotoxin, vaccination) and metabolic challenges (e.g. glucose ingestion) have been shown to affect activity and functional connectivity (FC) in brain regions that subserve reward and motor processing. We previously reported relationships between elevated concentrations of endogenous inflammatory markers including C-reactive protein (CRP) and low corticostriatal FC, which correlated with symptoms of anhedonia and motor slowing in major depression (MD). Herein, we examined whether similar relationships were observed between plasma markers related to glucose metabolism (non-fasting concentrations of glucose, insulin, leptin, adiponectin and resistin) in 42 medically-stable, unmedicated MD outpatients who underwent fMRI. A targeted, hypothesis-driven approach was used to assess FC between seeds in subdivisions of the ventral and dorsal striatum and a region in ventromedial prefrontal cortex (VS-vmPFC), which was previously found to correlate with both inflammation and symptoms of anhedonia and motor slowing. Associations between FC and gene expression signatures were also explored. A composite score of all 5 glucose-related markers (with increasing values reflecting higher concentrations) was negatively correlated with both ventral striatum (VS)-vmPFC (r = -0.33, p < 0.05) and dorsal caudal putamen (dcP)-vmPFC (r = -0.51, p < 0.01) FC, and remained significant after adjusting for covariates including body mass index (p < 0.05). Moreover, an interaction between the glucose-related composite score and CRP was observed for these relationships (F[2,33] = 4.3, p < 0.05) whereby significant correlations between the glucose-related metabolic markers and FC was found only in patients with high plasma CRP (>3 mg/L; r = -0.61 to -0.81, p < 0.05). Insulin and resistin were the individual markers most predictive of VS-vmPFC and dcP-mPFC FC, respectively, and insulin, resistin and CRP clustered together and in association with both LV-vmPFC and dcP-vmPFC in principal component analyses. Exploratory whole blood gene expression analyses also confirmed that gene probes negatively associated with FC were enriched for both inflammatory and metabolic pathways (FDR p < 0.05). These results provide preliminary evidence that inflammation and metabolic dysfunction contribute jointly to deficits in reward and motor circuits in MD. Future studies using fasting samples and longitudinal and interventional approaches are required to further elucidate the respective contributions of inflammation and metabolic dysfunction to circuits and symptoms relevant to motivation and motor activity, which may have treatment implications for patients with psychiatric illnesses like depression.
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Affiliation(s)
- David R Goldsmith
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322, United States
| | - Mandakh Bekhbat
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322, United States
| | - Ngoc-Anh Le
- Biomarker Core Laboratory, Foundation for Atlanta Veterans Education and Research, Atlanta VAHSC, Decatur, GA 30033, United States
| | - Xiangchuan Chen
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322, United States
| | - Bobbi J Woolwine
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322, United States
| | - Zhihao Li
- School of Psychology, Shenzhen University, Shenzhen, Guangdong 518060, China; Center for Brain Disorders and Cognitive Neuroscience, Shenzhen University, Shenzhen, Guangdong 518060, China.
| | - Ebrahim Haroon
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322, United States; The Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States.
| | - Jennifer C Felger
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA 30322, United States; The Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States.
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Predicting second-generation antidepressant effectiveness in treating sadness using demographic and clinical information: A machine learning approach. J Affect Disord 2020; 272:295-304. [PMID: 32553371 DOI: 10.1016/j.jad.2020.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 03/24/2020] [Accepted: 04/17/2020] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Current guidelines for choosing antidepressant medications involve a trial-and-error process. Most patients try multiple antidepressants before finding an effective antidepressant. This study uses demographic and clinical information to create models predicting effectiveness of different antidepressants in treating sadness in a nationally representative sample of US adults. METHODS A secondary analysis of the Collaborative Psychiatric Epidemiology Survey (CPES) was performed. Participants with or without a mental health diagnosis who reported sadness as a symptom, and were taking fluoxetine (n=156), sertraline (n=224), citalopram (n=91), paroxetine (n=156), venlafaxine (n=69), bupropion (n=92), or trazadone (n=26) within the past year were included. Two sets of principal component analyses (PCAs) and logistic regressions were performed: one determined associations between symptom clusters and antidepressant effectiveness for sadness, and the other created models to predict effectiveness. Both PCAs controlled for psychiatric and medical diagnoses, substance use, psychiatric medications, alternative treatments, and demographics. RESULTS Anxiety was associated with ineffectiveness of fluoxetine in treating sadness. Low mood scores were associated with ineffectiveness of paroxetine and venlafaxine, and fatigue was associated with ineffectiveness of sertraline. The models for predicting drug effectiveness had a mean accuracy of 83% and internal validity of 72%. LIMITATIONS CPES data were collected from 2001-2003, so newer drugs were not included. Effectiveness was for sadness, so results are not directly comparable to studies using overall depressive symptom reductions as outcomes. CONCLUSION Since fewer than 50% of patients currently respond to their first antidepressant, this model could provide modest improvement to choosing starting antidepressants in treating sadness.
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25
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Zhang T, Tang X, Li H, Woodberry KA, Kline ER, Xu L, Cui H, Tang Y, Wei Y, Li C, Hui L, Niznikiewicz MA, Shenton ME, Keshavan MS, Stone WS, Wang J. Clinical subtypes that predict conversion to psychosis: A canonical correlation analysis study from the ShangHai At Risk for Psychosis program. Aust N Z J Psychiatry 2020; 54:482-495. [PMID: 31486343 DOI: 10.1177/0004867419872248] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Since only 30% or fewer of individuals at clinical high risk convert to psychosis within 2 years, efforts are underway to refine risk identification strategies to increase their predictive power. The clinical high risk is a heterogeneous syndrome presenting with highly variable clinical symptoms and cognitive dysfunctions. This study investigated whether subtypes defined by baseline clinical and cognitive features improve the prediction of psychosis. METHOD Four hundred clinical high-risk subjects from the ongoing ShangHai At Risk for Psychosis program were enrolled in a prospective cohort study. Canonical correlation analysis was applied to 289 clinical high-risk subjects with completed Structured Interview for Prodromal Syndromes and cognitive battery tests at baseline, and at least 1-year follow-up. Canonical variates were generated by canonical correlation analysis and then used for hierarchical cluster analysis to produce subtypes. Kaplan-Meier survival curves were constructed from the three subtypes to test their utility further in predicting psychosis. RESULTS Canonical correlation analysis determined two linear combinations: (1) negative symptom and functional deterioration-related cognitive features, and (2) Positive symptoms and emotional disorganization-related cognitive features. Cluster analysis revealed three subtypes defined by distinct and relatively homogeneous patterns along two dimensions, comprising 14.2% (subtype 1, n = 41), 37.4% (subtype 2, n = 108) and 48.4% (subtype 3, n = 140) of the sample, and each with distinctive features of clinical and cognitive performance. Those with subtype 1, which is characterized by extensive negative symptoms and cognitive deficits, appear to have the highest risk for psychosis. The conversion risk for subtypes 1-3 are 39.0%, 11.1% and 18.6%, respectively. CONCLUSION Our results define important subtypes within clinical high-risk syndromes that highlight clinical symptoms and cognitive features that transcend current diagnostic boundaries. The three different subtypes reflect significant differences in clinical and cognitive characteristics as well as in the risk of conversion to psychosis.
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Affiliation(s)
- TianHong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - HuiJun Li
- Department of Psychology, Florida A&M University, Tallahassee, FL, USA
| | - Kristen A Woodberry
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Emily R Kline
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - HuiRu Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Li Hui
- Institute of Mental Health, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Margaret A Niznikiewicz
- Veterans Administration Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Brigham and Women's Hospital, Departments of Psychiatry and Radiology, Harvard Medical School, Boston, MA, USA
- Research and Development, Veterans Affairs Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - Matcheri S Keshavan
- Veterans Administration Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - William S Stone
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai, P.R. China
- CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, P.R. China
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26
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Tian S, Sun Y, Shao J, Zhang S, Mo Z, Liu X, Wang Q, Wang L, Zhao P, Chattun MR, Yao Z, Si T, Lu Q. Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex. Hum Brain Mapp 2019; 41:1249-1260. [PMID: 31758634 PMCID: PMC7268019 DOI: 10.1002/hbm.24872] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 11/04/2019] [Accepted: 11/12/2019] [Indexed: 12/22/2022] Open
Abstract
Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first-line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first-episode, drug-naïve patients and 109 healthy controls from three different multicenters underwent resting-state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft-threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.
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Affiliation(s)
- Shui Tian
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Siqi Zhang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhaoqi Mo
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Xiaoxue Liu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Qiang Wang
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Li Wang
- Peking University Institute of Mental Health & Sixth Hospital, Beijing, China.,National Clinical Research Center for Mental Disorder & The Key Laboratory of Mental Health, Ministry of Health, Ministry of Health (Peking University), Beijing, China
| | - Peng Zhao
- Department of Medical Psychology, Affiliated Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Tianmei Si
- Peking University Institute of Mental Health & Sixth Hospital, Beijing, China.,National Clinical Research Center for Mental Disorder & The Key Laboratory of Mental Health, Ministry of Health, Ministry of Health (Peking University), Beijing, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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27
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McCutcheon RA, Bloomfield MAP, Dahoun T, Mehta M, Howes OD. Chronic psychosocial stressors are associated with alterations in salience processing and corticostriatal connectivity. Schizophr Res 2019; 213:56-64. [PMID: 30573409 PMCID: PMC6817361 DOI: 10.1016/j.schres.2018.12.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 12/05/2018] [Accepted: 12/07/2018] [Indexed: 12/23/2022]
Abstract
Psychosocial stressors including childhood adversity, migration, and living in an urban environment, have been associated with several psychiatric disorders, including psychotic disorders. The neural and psychological mechanisms mediating this relationship remain unclear. In parallel, alterations in corticostriatal connectivity and abnormalities in the processing of salience, are seen in psychotic disorders. Aberrant functioning of these mechanisms secondary to chronic stress exposure, could help explain how common environmental exposures are associated with a diverse range of symptoms. In the current study, we recruited two groups of adults, one with a high degree of exposure to chronic psychosocial stressors (the exposed group, n = 20), and one with minimal exposure (the unexposed group, n = 22). All participants underwent a resting state MRI scan, completed the Aberrant Salience Inventory, and performed a behavioural task - the Salience Attribution Test (SAT). The exposed group showed reduced explicit adaptive salience scores (cohen's d = 0.69, p = 0.03) and increased aberrant salience inventory scores (d = 0.65, p = 0.04). The exposed group also showed increased corticostriatal connectivity between the ventral striatum and brain regions previously implicated in salience processing. Corticostriatal connectivity in these regions negatively correlated with SAT explicit adaptive salience (r = -0.48, p = 0.001), and positively correlated with aberrant salience inventory scores (r = 0.42, p = 0.006). Furthermore, in a mediation analysis there was tentative evidence that differences in striato-cortical connectivity mediated the group differences in salience scores.
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Affiliation(s)
- Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, De Crespigny Park, London SE5 8AF, UK; Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London W12 0NN, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK
| | - Michael A P Bloomfield
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London W12 0NN, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK; Translational Psychiatry Research Group, Research Department of Mental Health Neuroscience, Division of Psychiatry, University College London, 6th Floor, Maple House, 149 Tottenham Court Road, London WC1T 7NF, UK; Clinical Psychopharmacology Unit, Research Department of Clinical, Educational and Health Psychology, University College London, 1-19 Torrington Place, London WC1E 6BT, UK; National Institute of Health Research University College London Hospitals Biomedical Research Centre, University College Hospital, Euston Road, London W1T 7DN, UK; The Traumatic Stress Clinic, St Pancras Hospital, 4 St Pancras Way, London NW1 0PE, UK
| | - Tarik Dahoun
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London W12 0NN, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX37 JX, UK
| | - Mitul Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, De Crespigny Park, London SE5 8AF, UK
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, De Crespigny Park, London SE5 8AF, UK; Psychiatric Imaging Group, MRC London Institute of Medical Sciences, Hammersmith Hospital, London W12 0NN, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.
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28
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Borrione L, Brunoni AR. Precision noninvasive brain stimulation: is it precise? Is it needed? BRAZILIAN JOURNAL OF PSYCHIATRY 2019; 41:376-377. [PMID: 31644777 PMCID: PMC6796823 DOI: 10.1590/1516-4446-2019-4110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Lucas Borrione
- Departamento e Instituto de Psiquiatria, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Laboratório de Neurociências (LIM27), Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBioN), USP, São Paulo, SP, Brazil
| | - Andre R Brunoni
- Departamento e Instituto de Psiquiatria, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.,Laboratório de Neurociências (LIM27), Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBioN), USP, São Paulo, SP, Brazil.,Departamento de Clínica Médica, Faculdade de Medicina, USP, São Paulo, SP, Brazil.,Hospital Universitário, USP, São Paulo, SP, Brazil
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29
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Liang S, Wang Q, Kong X, Deng W, Yang X, Li X, Zhang Z, Zhang J, Zhang C, Li XM, Ma X, Shao J, Greenshaw AJ, Li T. White Matter Abnormalities in Major Depression Biotypes Identified by Diffusion Tensor Imaging. Neurosci Bull 2019; 35:867-876. [PMID: 31062333 PMCID: PMC6754492 DOI: 10.1007/s12264-019-00381-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 02/25/2019] [Indexed: 02/05/2023] Open
Abstract
Identifying data-driven biotypes of major depressive disorder (MDD) has promise for the clarification of diagnostic heterogeneity. However, few studies have focused on white-matter abnormalities for MDD subtyping. This study included 116 patients with MDD and 118 demographically-matched healthy controls assessed by diffusion tensor imaging and neurocognitive evaluation. Hierarchical clustering was applied to the major fiber tracts, in conjunction with tract-based spatial statistics, to reveal white-matter alterations associated with MDD. Clinical and neurocognitive differences were compared between identified subgroups and healthy controls. With fractional anisotropy extracted from 20 fiber tracts, cluster analysis revealed 3 subgroups based on the patterns of abnormalities. Patients in each subgroup versus healthy controls showed a stepwise pattern of white-matter alterations as follows: subgroup 1 (25.9% of patient sample), widespread white-matter disruption; subgroup 2 (43.1% of patient sample), intermediate and more localized abnormalities in aspects of the corpus callosum and left cingulate; and subgroup 3 (31.0% of patient sample), possible mild alterations, but no statistically significant tract disruption after controlling for family-wise error. The neurocognitive impairment in each subgroup accompanied the white-matter alterations: subgroup 1, deficits in sustained attention and delayed memory; subgroup 2, dysfunction in delayed memory; and subgroup 3, no significant deficits. Three subtypes of white-matter abnormality exist in individuals with major depression, those having widespread abnormalities suffering more neurocognitive impairments, which may provide evidence for parsing the heterogeneity of the disorder and help optimize type-specific treatment approaches.
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Affiliation(s)
- Sugai Liang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiang Wang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiangzhen Kong
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6525 XD, Nijmegen, Netherlands
| | - Wei Deng
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiao Yang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaojing Li
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhong Zhang
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jian Zhang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Chengcheng Zhang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xin-Min Li
- Department of Psychiatry, University of Alberta, Edmonton, T6G 2B7, Canada
| | - Xiaohong Ma
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Junming Shao
- Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton, T6G 2B7, Canada
| | - Tao Li
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Chengdu, 610041, China.
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30
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Wolfers T, Floris DL, Dinga R, van Rooij D, Isakoglou C, Kia SM, Zabihi M, Llera A, Chowdanayaka R, Kumar VJ, Peng H, Laidi C, Batalle D, Dimitrova R, Charman T, Loth E, Lai MC, Jones E, Baumeister S, Moessnang C, Banaschewski T, Ecker C, Dumas G, O’Muircheartaigh J, Murphy D, Buitelaar JK, Marquand AF, Beckmann CF. From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. Neurosci Biobehav Rev 2019; 104:240-254. [DOI: 10.1016/j.neubiorev.2019.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/10/2019] [Accepted: 07/15/2019] [Indexed: 11/17/2022]
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31
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Yu M, Linn KA, Shinohara RT, Oathes DJ, Cook PA, Duprat R, Moore TM, Oquendo MA, Phillips ML, McInnis M, Fava M, Trivedi MH, McGrath P, Parsey R, Weissman MM, Sheline YI. Childhood trauma history is linked to abnormal brain connectivity in major depression. Proc Natl Acad Sci U S A 2019; 116:8582-8590. [PMID: 30962366 PMCID: PMC6486762 DOI: 10.1073/pnas.1900801116] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.
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Affiliation(s)
- Meichen Yu
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Kristin A Linn
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Russell T Shinohara
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Philip A Cook
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Romain Duprat
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Tyler M Moore
- Brain and Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Maria A Oquendo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor, MI 48109
| | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114
| | - Madhukar H Trivedi
- Center for Depression Research and Clinical Care, Peter O'Donnell Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390
| | - Patrick McGrath
- Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, NY 10032
| | - Ramin Parsey
- Department of Psychiatry, Stony Brook University, Stony Brook, NY 11794
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, NY 10032
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104;
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
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32
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The Role of the Amygdala and the Ventromedial Prefrontal Cortex in Emotional Regulation: Implications for Post-traumatic Stress Disorder. Neuropsychol Rev 2019; 29:220-243. [DOI: 10.1007/s11065-019-09398-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 02/14/2019] [Indexed: 10/27/2022]
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33
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Luo L, Wu K, Lu Y, Gao S, Kong X, Lu F, Wu F, Wu H, Wang J. Increased Functional Connectivity Between Medulla and Inferior Parietal Cortex in Medication-Free Major Depressive Disorder. Front Neurosci 2019; 12:926. [PMID: 30618555 PMCID: PMC6295569 DOI: 10.3389/fnins.2018.00926] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Accepted: 11/26/2018] [Indexed: 11/13/2022] Open
Abstract
Emerging evidence has documented the abnormalities of primary brain functions in major depressive disorder (MDD). The brainstem has shown to play an important role in regulating basic functions of the human brain, but little is known about its role in MDD, especially the roles of its subregions. To uncover this, the present study adopted resting-state functional magnetic resonance imaging with fine-grained brainstem atlas in 23 medication-free MDD patients and 34 matched healthy controls (HC). The analysis revealed significantly increased functional connectivity of the medulla, one of the brainstem subregions, with the inferior parietal cortex (IPC) in MDD patients. A positive correlation was further identified between the increased medulla-IPC functional connectivity and Hamilton anxiety scores. Functional characterization of the medulla and IPC using a meta-analysis revealed that both regions primarily participated in action execution and inhibition. Our findings suggest that increased medulla-IPC functional connectivity may be related to over-activity or abnormal control of negative emotions in MDD, which provides a new insight for the neurobiology of MDD.
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Affiliation(s)
- Lizhu Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Kunhua Wu
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Yi Lu
- The Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shan Gao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangchao Kong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Jiaojian Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
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34
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Kopetzky SJ, Butz-Ostendorf M. From Matrices to Knowledge: Using Semantic Networks to Annotate the Connectome. Front Neuroanat 2018; 12:111. [PMID: 30581382 PMCID: PMC6292998 DOI: 10.3389/fnana.2018.00111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 11/23/2018] [Indexed: 11/18/2022] Open
Abstract
The connectome is regarded as the key to brain function in health and disease. Structural and functional neuroimaging enables us to measure brain connectivity in the living human brain. The field of connectomics describes the connectome as a mathematical graph with its connection strengths being represented by connectivity matrices. Graph theory algorithms are used to assess the integrity of the graph as a whole and to reveal brain network biomarkers for brain diseases; however, the faulty wiring of single connections or subnetworks as the structural correlate for neurological or mental diseases remains elusive. We describe a novel approach to represent the knowledge of human brain connectivity by a semantic network – a formalism frequently used in knowledge management to describe the semantic relations between objects. In our novel approach, objects are brain areas and connectivity is modeled as semantic relations among them. The semantic network turns the graph of the connectome into an explicit knowledge base about which brain areas are interconnected. Moreover, this approach can semantically enrich the measured connectivity of an individual subject by the semantic context from ontologies, brain atlases and molecular biological databases. Integrating all measurements and facts into one unified feature space enables cross-modal comparisons and analyses. We used a query mechanism for semantic networks to extract functional, structural and transcriptome networks. We found that in general higher structural and functional connectivity go along with a lower differential gene expression among connected brain areas; however, subcortical motor areas and limbic structures turned out to have a localized high differential gene expression while being strongly connected. In an additional explorative use case, we could show a localized high availability of fkbp5, gmeb1, and gmeb2 genes at a connection hub of temporo-limbic brain networks. Fkbp5 is known for having a role in stress-related psychiatric disorders, while gmeb1 and gmeb2 encode for modulator proteins of the glucocorticoid receptor, a key receptor in the hormonal stress system. Semantic networks tremendously ease working with multimodal neuroimaging and neurogenetics data and may reveal relevant coincidences between transcriptome and connectome networks.
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Arena R, Ozemek C, Laddu D, Campbell T, Rouleau CR, Standley R, Bond S, Abril EP, Hills AP, Lavie CJ. Applying Precision Medicine to Healthy Living for the Prevention and Treatment of Cardiovascular Disease. Curr Probl Cardiol 2018; 43:448-483. [DOI: 10.1016/j.cpcardiol.2018.06.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Viviano JD, Buchanan RW, Calarco N, Gold JM, Foussias G, Bhagwat N, Stefanik L, Hawco C, DeRosse P, Argyelan M, Turner J, Chavez S, Kochunov P, Kingsley P, Zhou X, Malhotra AK, Voineskos AN. Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects. Biol Psychiatry 2018; 84:665-674. [PMID: 29779671 PMCID: PMC6177285 DOI: 10.1016/j.biopsych.2018.03.013] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/12/2018] [Accepted: 03/31/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome). METHODS We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance. RESULTS Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75). CONCLUSIONS A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function.
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Affiliation(s)
- Joseph D Viviano
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario
| | - Robert W Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, Catonsville, Maryland
| | - Navona Calarco
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario
| | - James M Gold
- Department of Psychiatry, Maryland Psychiatric Research Center, Catonsville, Maryland
| | - George Foussias
- Department of Psychiatry, University of Toronto, Toronto, Ontario
| | - Nikhil Bhagwat
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario; Computational Brain Anatomy Laboratory, Brain Imaging Center, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Laura Stefanik
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario
| | - Colin Hawco
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario; Department of Psychiatry, University of Toronto, Toronto, Ontario
| | - Pamela DeRosse
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Miklos Argyelan
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Jessica Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Sofia Chavez
- Department of Psychiatry, University of Toronto, Toronto, Ontario; MRI Unit, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, Catonsville, Maryland
| | - Peter Kingsley
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Xiangzhi Zhou
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Anil K Malhotra
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, Manhasset; Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, New York; Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario; Department of Psychiatry, University of Toronto, Toronto, Ontario.
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Luo L, Becker B, Zheng X, Zhao Z, Xu X, Zhou F, Wang J, Kou J, Dai J, Kendrick KM. A dimensional approach to determine common and specific neurofunctional markers for depression and social anxiety during emotional face processing. Hum Brain Mapp 2018; 39:758-771. [PMID: 29105895 PMCID: PMC6866417 DOI: 10.1002/hbm.23880] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 09/23/2017] [Accepted: 10/26/2017] [Indexed: 02/03/2023] Open
Abstract
Major depression disorder (MDD) and anxiety disorder are both prevalent and debilitating. High rates of comorbidity between MDD and social anxiety disorder (SAD) suggest common pathological pathways, including aberrant neural processing of interpersonal signals. In patient populations, the determination of common and distinct neurofunctional markers of MDD and SAD is often hampered by confounding factors, such as generally elevated anxiety levels and disorder-specific brain structural alterations. This study employed a dimensional disorder approach to map neurofunctional markers associated with levels of depression and social anxiety symptoms in a cohort of 91 healthy subjects using an emotional face processing paradigm. Examining linear associations between levels of depression and social anxiety, while controlling for trait anxiety revealed that both were associated with exaggerated dorsal striatal reactivity to fearful and sad expression faces respectively. Exploratory analysis revealed that depression scores were positively correlated with dorsal striatal functional connectivity during processing of fearful faces, whereas those of social anxiety showed a negative association during processing of sad faces. No linear relationships between levels of depression and social anxiety were observed during a facial-identity matching task or with brain structure. Together, the present findings indicate that dorsal striatal neurofunctional alterations might underlie aberrant interpersonal processing associated with both increased levels of depression and social anxiety.
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Affiliation(s)
- Lizhu Luo
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Xiaoxiao Zheng
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Zhiying Zhao
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Xiaolei Xu
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Jiaojian Wang
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Juan Kou
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Jing Dai
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
| | - Keith M. Kendrick
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of ChinaChengdu611731PR China
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Berglund J. Imaging Depression: New Biological Markers May Mean More Targeted Treatments are Just over the Horizon. IEEE Pulse 2017; 8:19-22. [PMID: 29155373 DOI: 10.1109/mpul.2017.2751119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
On a balmy evening in mid-May 2017, Chris Cornell, the legendary head of the internationally renowned rock band Soundgarden, strummed his last chord at the Fox Theater in Detroit and headed to the MGM Grand Hotel. According to the police report, he swallowed a few tablets of the antidepression drug Ativan in his room and called his wife. "I'm just tired," he said and hung up the phone. Later that night, at the request of his concerned wife, his bodyguard forced open Cornell's door to discover him on the bathroom floor, an exercise band tied around his neck. His death was ruled a suicide by hanging, the conclusion of a life riddled with drug abuse and a depression he'd never managed to shake.
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Esterman M, Thai M, Okabe H, DeGutis J, Saad E, Laganiere SE, Halko MA. Network-targeted cerebellar transcranial magnetic stimulation improves attentional control. Neuroimage 2017; 156:190-198. [PMID: 28495634 PMCID: PMC5973536 DOI: 10.1016/j.neuroimage.2017.05.011] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 05/04/2017] [Accepted: 05/07/2017] [Indexed: 11/22/2022] Open
Abstract
Developing non-invasive brain stimulation interventions to improve attentional control is extremely relevant to a variety of neurological and psychiatric populations, yet few studies have identified reliable biomarkers that can be readily modified to improve attentional control. One potential biomarker of attention is functional connectivity in the core cortical network supporting attention - the dorsal attention network (DAN). We used a network-targeted cerebellar transcranial magnetic stimulation (TMS) procedure, intended to enhance cortical functional connectivity in the DAN. Specifically, in healthy young adults we administered intermittent theta burst TMS (iTBS) to the midline cerebellar node of the DAN and, as a control, the right cerebellar node of the default mode network (DMN). These cerebellar targets were localized using individual resting-state fMRI scans. Participants completed assessments of both sustained (gradual onset continuous performance task, gradCPT) and transient attentional control (attentional blink) immediately before and after stimulation, in two sessions (cerebellar DAN and DMN). Following cerebellar DAN stimulation, participants had significantly fewer attentional lapses (lower commission error rates) on the gradCPT. In contrast, stimulation to the cerebellar DMN did not affect gradCPT performance. Further, in the DAN condition, individuals with worse baseline gradCPT performance showed the greatest enhancement in gradCPT performance. These results suggest that temporarily increasing functional connectivity in the DAN via network-targeted cerebellar stimulation can enhance sustained attention, particularly in those with poor baseline performance. With regard to transient attention, TMS stimulation improved attentional blink performance across both stimulation sites, suggesting increasing functional connectivity in both networks can enhance this aspect of attention. These findings have important implications for intervention applications of TMS and theoretical models of functional connectivity.
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Affiliation(s)
- Michael Esterman
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, United States; Boston Attention and Learning Laboratory, VA Boston Healthcare System, United States; Geriatric Research Education and Clinical Center (GRECC), Boston Division VA Healthcare System, United States; Department of Psychiatry, Boston University School of Medicine, United States.
| | - Michelle Thai
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, United States
| | - Hidefusa Okabe
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, United States
| | - Joseph DeGutis
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, United States; Department of Psychiatry, Harvard Medical School, United States
| | - Elyana Saad
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, United States; Department of Psychiatry, Harvard Medical School, United States
| | - Simon E Laganiere
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, United States
| | - Mark A Halko
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, United States
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