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Lin K, Sunko D, Wang J, Yang J, Parsey RV, DeLorenzo C. Investigating the relationship between hippocampus/dentate gyrus volume and hypothalamus metabolism in participants with major depressive disorder. Sci Rep 2024; 14:10622. [PMID: 38724691 PMCID: PMC11082185 DOI: 10.1038/s41598-024-61519-z] [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: 12/31/2023] [Accepted: 05/07/2024] [Indexed: 05/12/2024] Open
Abstract
Reduced hippocampal volume occurs in major depressive disorder (MDD), potentially due to elevated glucocorticoids from an overactivated hypothalamus-pituitary-adrenal (HPA) axis. To examine this in humans, hippocampal volume and hypothalamus (HPA axis) metabolism was quantified in participants with MDD before and after antidepressant treatment. 65 participants (n = 24 males, n = 41 females) with MDD were treated in a double-blind, randomized clinical trial of escitalopram. Participants received simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) before and after treatment. Linear mixed models examined the relationship between hippocampus/dentate gyrus volume and hypothalamus metabolism. Chi-squared tests and multivariable logistic regression examined the association between hippocampus/dentate gyrus volume change direction and hypothalamus activity change direction with treatment. Multiple linear regression compared these changes between remitter and non-remitter groups. Covariates included age, sex, and treatment type. No significant linear association was found between hippocampus/dentate gyrus volume and hypothalamus metabolism. 62% (38 of 61) of participants experienced a decrease in hypothalamus metabolism, 43% (27 of 63) of participants demonstrated an increase in hippocampus size (51% [32 of 63] for the dentate gyrus) following treatment. No significant association was found between change in hypothalamus activity and change in hippocampus/dentate gyrus volume, and this association did not vary by sex, medication, or remission status. As this multimodal study, in a cohort of participants on standardized treatment, did not find an association between hypothalamus metabolism and hippocampal volume, it supports a more complex pathway between hippocampus neurogenesis and hypothalamus metabolism changes in response to treatment.
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Affiliation(s)
| | | | - Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, NY, USA
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, New York, NY, USA
| | - Ramin V Parsey
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA.
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
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Wang J, Wu DD, DeLorenzo C, Yang J. Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features. PLoS One 2024; 19:e0299625. [PMID: 38547128 PMCID: PMC10977765 DOI: 10.1371/journal.pone.0299625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/13/2024] [Indexed: 04/02/2024] Open
Abstract
Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18-25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue.
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Affiliation(s)
- Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York, United states of America
| | - David D. Wu
- School of Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, New York, United States of America
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, United States of America
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, New York, United States of America
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Zimmer L. Recent applications of positron emission tomographic (PET) imaging in psychiatric drug discovery. Expert Opin Drug Discov 2024; 19:161-172. [PMID: 37948046 DOI: 10.1080/17460441.2023.2278635] [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] [Received: 08/31/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Psychiatry is one of the medical disciplines that suffers most from a lack of innovation in its therapeutic arsenal. Many failures in drug candidate trials can be explained by pharmacological properties that have been poorly assessed upstream, in terms of brain passage, brain target binding and clinical outcomes. Positron emission tomography can provide pharmacokinetic and pharmacodynamic data to help select candidate-molecules for further clinical trials. AREAS COVERED This review aims to explain and discuss the various methods using positron-emitting radiolabeled molecules to trace the cerebral distribution of the drug-candidate or indirectly measure binding to its therapeutic target. More than an exhaustive review of PET studies in psychopharmacology, this article highlights the contributions this technology can make in drug discovery applied to psychiatry. EXPERT OPINION PET neuroimaging is the only technological approach that can, in vivo in humans, measure cerebral delivery of a drug candidate, percentage and duration of target binding, and even the pharmacological effects. PET studies in a small number of subjects in the early stages of the development of a psychotropic drug can therefore provide the pharmacokinetic/pharmacodynamic data required for subsequent clinical evaluation. While PET technology is demanding in terms of radiochemical, radiopharmacological and nuclear medicine expertise, its integration into the development process of new drugs for psychiatry has great added value.
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Affiliation(s)
- Luc Zimmer
- Lyon Neuroscience Research Center, Université Claude Bernard, Lyon, France
- CERMEP, Hospices Civils de Lyon, Lyon, France
- Institut National des Sciences et Technologies Nucléaire, Saclay, France
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Perna G, Spiti A, Torti T, Daccò S, Caldirola D. Biomarker-Guided Tailored Therapy in Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1456:379-400. [PMID: 39261439 DOI: 10.1007/978-981-97-4402-2_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.
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Affiliation(s)
- Giampaolo Perna
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, Como, Italy.
- Humanitas SanpioX, Milan, Italy.
| | - Alessandro Spiti
- IRCCS Humanitas Research Hospital, Milan, Italy
- Psicocare, Humanitas Medical Care, Monza, Italy
| | - Tatiana Torti
- ASIPSE School of Cognitive-Behavioral-Therapy, Milan, Italy
| | - Silvia Daccò
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Humanitas SanpioX, Milan, Italy
- Psicocare, Humanitas Medical Care, Monza, Italy
| | - Daniela Caldirola
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, Como, Italy
- Humanitas SanpioX, Milan, Italy
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Donnelly BM, Hsu DT, Gardus J, Wang J, Yang J, Parsey RV, DeLorenzo C. Orbitofrontal and striatal metabolism, volume, thickness and structural connectivity in relation to social anhedonia in depression: A multimodal study. Neuroimage Clin 2023; 41:103553. [PMID: 38134743 PMCID: PMC10777107 DOI: 10.1016/j.nicl.2023.103553] [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: 08/19/2023] [Revised: 11/10/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Social anhedonia is common within major depressive disorder (MDD) and associated with worse treatment outcomes. The orbitofrontal cortex (OFC) is implicated in both reward (medial OFC) and punishment (lateral OFC) in social decision making. Therefore, to understand the biology of social anhedonia in MDD, medial/lateral OFC metabolism, volume, and thickness, as well as structural connectivity to the striatum, amygdala, and ventral tegmental area/nucleus accumbens were examined. A positive relationship between social anhedonia and these neurobiological outcomes in the lateral OFC was hypothesized, whereas an inverse relationship was hypothesized for the medial OFC. The association between treatment-induced changes in OFC neurobiology and depression improvement were also examined. METHODS 85 medication-free participants diagnosed with MDD were assessed with Wisconsin Schizotypy Scales to assess social anhedonia and received pretreatment simultaneous fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI), including structural and diffusion. Participants were then treated in an 8-week randomized placebo-controlled double-blind course of escitalopram. PET/MRI were repeated following treatment. Metabolic rate of glucose uptake was quantified from dynamic FDG-PET frames using Patlak graphical analysis. Structure (volume and cortical thickness) was quantified from structural MRI using Freesurfer. To assess structural connectivity, probabilistic tractography was performed on diffusion MRI and average FA was calculated within the derived tracts. Linear mixed models with Bonferroni correction were used to examine the relationships between variables. RESULTS A significantly negative linear relationship between pretreatment social anhedonia score and structural connectivity between the medial OFC and the amygdala (estimated coefficient: -0.006, 95 % CI: -0.0108 - -0.0012, p-value = 0.0154) was observed. However, this finding would not survive multiple comparisons correction. No strong evidence existed to show a significant linear relationship between pretreatment social anhedonia score and metabolism, volume, thickness, or structural connectivity to any of the regions examined. There was also no strong evidence to suggest significant linear relationships between improvement in depression and percent change in these variables. CONCLUSIONS Based on these multimodal findings, the OFC likely does not underlie social anhedonia in isolation and therefore should not be the sole target of treatment for social anhedonia. This is consistent with previous reports that other areas of the brain such as the amygdala and the striatum are highly involved in this behavior. Relatedly, amygdala-medial OFC structural connectivity could be a future target. The results of this study are crucial as, to our knowledge, they are the first to relate structure/function of the OFC with social anhedonia severity in MDD. Future work may need to involve a whole brain approach in order to develop therapeutics for social anhedonia.
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Affiliation(s)
| | - David T Hsu
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - John Gardus
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Ramin V Parsey
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA.
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Oh SJ, Lee N, Nam KR, Kang KJ, Han SJ, Choi JY. Effects of Escitalopram on the Functional Neural Circuits in an Animal Model of Adolescent Depression. Mol Imaging Biol 2023:10.1007/s11307-023-01825-6. [PMID: 37193806 DOI: 10.1007/s11307-023-01825-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/18/2023]
Abstract
PURPOSE Although escitalopram is known to be an effective drug for adult depression, its disease-modifying efficacy on adolescents remains controversial. The present study aimed to evaluate the therapeutic effect of escitalopram on behavioral aspects as well as functional neural circuits by means of positron emission tomography. PROCEDURES To generate the animal models of depression, restraint stress was used during the peri-adolescent period (RS group). Thereafter, escitalopram was administered after the end of stress exposure (Tx group). We performed NeuroPET studies of glutamate, glutamate, GABA, and serotonin systems. RESULTS The Tx group showed no body weight change compared to the RS group. In the behavioral tests, the Tx group also displayed the similar time spent in open arms and immobility time to those for RS. In the PET studies, brain uptake values for the Tx group revealed no significant differences in terms of glucose, GABAA, and 5-HT1A receptor densities, but lower mGluR5 PET uptake compared to the RS group. In the immunohistochemistry, the Tx group showed the significant loss of neuronal cells in the hippocampus compared to the RS group. CONCLUSION The administration of escitalopram had no therapeutic effect on the adolescent depression.
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Affiliation(s)
- Se Jong Oh
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea, 01812
| | - Namhun Lee
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea, 01812
| | - Kyung Rok Nam
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea, 01812
| | - Kyung Jun Kang
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea, 01812
| | - Sang Jin Han
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea, 01812
| | - Jae Yong Choi
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea, 01812.
- Radiological and Medico-Oncological Sciences, University of Science and Technology (UST), Seoul, South Korea, 01812.
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Ali FZ, Parsey RV, Lin S, Schwartz J, DeLorenzo C. Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response. NPJ Digit Med 2023; 6:81. [PMID: 37120493 PMCID: PMC10148831 DOI: 10.1038/s41746-023-00827-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/11/2023] [Indexed: 05/01/2023] Open
Abstract
Major depressive disorder (MDD) is associated with circadian rhythm disruption. Yet, no circadian rhythm biomarkers have been clinically validated for assessing antidepressant response. In this study, 40 participants with MDD provided actigraphy data using wearable devices for one week after initiating antidepressant treatment in a randomized, double-blind, placebo-controlled trial. Their depression severity was calculated pretreatment, after one week and eight weeks of treatment. This study assesses the relationship between parametric and nonparametric measures of circadian rhythm and change in depression. Results show significant association between a lower circadian quotient (reflecting less robust rhythmicity) and improvement in depression from baseline following first week of treatment (estimate = 0.11, F = 7.01, P = 0.01). There is insufficient evidence of an association between circadian rhythm measures acquired during the first week of treatment and outcomes after eight weeks of treatment. Despite this lack of association with future treatment outcome, this scalable, cost-effective biomarker may be useful for timely mental health care through remote monitoring of real-time changes in current depression.
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Affiliation(s)
- Farzana Z Ali
- Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA.
| | - Ramin V Parsey
- Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychology, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Radiology, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Shan Lin
- Department of Electrical and Computer Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Joseph Schwartz
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Christine DeLorenzo
- Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Road, Stony Brook, NY, 11794, USA
- Department of Psychiatry, Columbia University, 1051 Riverside Drive, New York, NY, 10032, USA
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8
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Sheu YH, Magdamo C, Miller M, Das S, Blacker D, Smoller JW. AI-assisted prediction of differential response to antidepressant classes using electronic health records. NPJ Digit Med 2023; 6:73. [PMID: 37100858 PMCID: PMC10133261 DOI: 10.1038/s41746-023-00817-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 04/04/2023] [Indexed: 04/28/2023] Open
Abstract
Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.
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Affiliation(s)
- Yi-Han Sheu
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Matthew Miller
- Harvard Injury Control Research Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Yao J, Chen C, Guo Y, Yang Y, Liu X, Chu S, Ai Q, Zhang Z, Lin M, Yang S, Chen N. A Review of Research on the Association between Neuron-Astrocyte Signaling Processes and Depressive Symptoms. Int J Mol Sci 2023; 24:ijms24086985. [PMID: 37108148 PMCID: PMC10139177 DOI: 10.3390/ijms24086985] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/02/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
Depression is a mental illness that has a serious negative impact on physical and mental health. The pathophysiology of depression is still unknown, and therapeutic medications have drawbacks, such as poor effectiveness, strong dependence, adverse drug withdrawal symptoms, and harmful side effects. Therefore, the primary purpose of contemporary research is to understand the exact pathophysiology of depression. The connection between astrocytes, neurons, and their interactions with depression has recently become the focus of great research interest. This review summarizes the pathological changes of neurons and astrocytes, and their interactions in depression, including the alterations of mid-spiny neurons and pyramidal neurons, the alterations of astrocyte-related biomarkers, and the alterations of gliotransmitters between astrocytes and neurons. In addition to providing the subjects of this research and suggestions for the pathogenesis and treatment techniques of depression, the intention of this article is to more clearly identify links between neuronal-astrocyte signaling processes and depressive symptoms.
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Affiliation(s)
- Jiao Yao
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
- Key Laboratory of Modern Research of TCM, Education Department of Hunan Province, Changsha 410208, China
| | - Cong Chen
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha 410208, China
| | - Yi Guo
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
- School of Acupuncture & Tuina and Rehabilitation, Hunan University of Chinese Medicine, Changsha 410208, China
| | - Yantao Yang
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
| | - Xinya Liu
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
| | - Shifeng Chu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica & Neuroscience Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Qidi Ai
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
- Key Laboratory of Modern Research of TCM, Education Department of Hunan Province, Changsha 410208, China
| | - Zhao Zhang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica & Neuroscience Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Meiyu Lin
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
| | - Songwei Yang
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
- Key Laboratory of Modern Research of TCM, Education Department of Hunan Province, Changsha 410208, China
| | - Naihong Chen
- Hunan Engineering Technology Center of Standardization and Function of Chinese Herbal Decoction Pieces, College of Pharmacy, Hunan University of Chinese Medicine, Changsha 410208, China
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica & Neuroscience Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
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Lin K, Sunko D, Wang J, Yang J, Parsey R, DeLorenzo C. Investigating The Relationship Between Hippocampus:Dentate Gyrus Volume and Hypothalamus Metabolism in Participants with Major Depressive Disorder. RESEARCH SQUARE 2023:rs.3.rs-2729363. [PMID: 37066238 PMCID: PMC10104266 DOI: 10.21203/rs.3.rs-2729363/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Reduced hippocampal volume occurs in major depressive disorder (MDD), theoretically due to elevated glucocorticoids from an overactivated hypothalamus-pituitary-adrenal (HPA) axis. To examine this in humans, hippocampal volume, and hypothalamus (HPA axis) metabolism was quantified in participants with MDD before and after antidepressant treatment. 65 participants (n = 24 males, n = 41 females) with MDD were treated in a double-blind, randomized clinical trial of escitalopram. Participants received simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) before and after treatment. Linear mixed models examined the relationship between hippocampus/dentate gyrus volume and hypothalamus metabolism. Chi-squared tests and multivariable logistic regression examined the association between hippocampus/dentate gyrus volume change direction and hypothalamus activity change direction with treatment. Multiple linear regression compared these changes between remitter and non-remitter groups. Covariates included age, sex, and treatment type. No significant linear association was found between hippocampus/dentate gyrus volume and hypothalamus metabolism. 62% (38 of 61) of participants experienced a decrease in hypothalamus metabolism, 43% (27 of 63) of participants demonstrated an increase in hippocampus size (51% [32 of 63] for the dentate gyrus) following treatment. No significant association was found between change in hypothalamus activity and change in hippocampus/dentate gyrus volume, and this association did not vary by sex, medication, or remission status. As this multimodal study, in a cohort of participants on standardized treatment, did not find an association between hypothalamus metabolism and hippocampal volume, it supports a more complex pathway between hippocampus neurogenesis and treatment response.
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11
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Frohman DFT, Nnah K, Tsirka SE. Intersection of Sex and Depression: Pathogenesis, Presentation, and Treatments. Handb Exp Pharmacol 2023; 282:163-180. [PMID: 37439845 DOI: 10.1007/164_2023_670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Major Depressive Disorder (MDD) is a highly prevalent, debilitating disorder. According to the World Health Organization, approximately 5% of adults suffer from depression worldwide and more women than men are affected. Yet, we have a very limited understanding of the pathogenesis of the disease, how sex and genetics influence the pathophenotype of MDD, and how they contribute to the responses to pharmacological treatment. This chapter addresses key theories about the etiology of depression, the variations in epidemiology and presentation, and the treatment options with respect to sex and gender. Additionally, we discuss the emerging wave of treatment modalities, diagnosis, and research focusing on MDD.
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Affiliation(s)
- Dafni F T Frohman
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Kimberly Nnah
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
- Program in Neuroscience, Stony Brook, NY, USA
- Department of Pharmacological Sciences, Stony Brook, NY, USA
| | - Stella E Tsirka
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
- Program in Neuroscience, Stony Brook, NY, USA.
- Department of Pharmacological Sciences, Stony Brook, NY, USA.
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Ali FZ, Wengler K, He X, Nguyen MH, Parsey RV, DeLorenzo C. Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression. NEUROSCIENCE INFORMATICS 2022; 2:100110. [PMID: 36699194 PMCID: PMC9873411 DOI: 10.1016/j.neuri.2022.100110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Introduction Pretreatment positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials. Methods This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤ 7 on 17-item Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training. Results In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission. Conclusions The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance.
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Affiliation(s)
- Farzana Z. Ali
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Kenneth Wengler
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Xiang He
- Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA
- Department of Radiology, Northshore University Hospital, Manhasset, NY, USA
| | - Minh Hoai Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Ramin V. Parsey
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
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Jones JS, Goldstein SJ, Wang J, Gardus J, Yang J, Parsey RV, DeLorenzo C. Evaluation of brain structure and metabolism in currently depressed adults with a history of childhood trauma. Transl Psychiatry 2022; 12:392. [PMID: 36115855 PMCID: PMC9482635 DOI: 10.1038/s41398-022-02153-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 08/26/2022] [Accepted: 09/05/2022] [Indexed: 11/22/2022] Open
Abstract
Structural differences in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), hippocampus, and amygdala were reported in adults who experienced childhood trauma; however, it is unknown whether metabolic differences accompany these structural differences. This multimodal imaging study examined structural and metabolic correlates of childhood trauma in adults with major depressive disorder (MDD). Participants with MDD completed the Childhood Trauma Questionnaire (CTQ, n = 83, n = 54 female (65.1%), age: 30.4 ± 14.1) and simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI). Structure (volume, n = 80, and cortical thickness, n = 81) was quantified from MRI using Freesurfer. Metabolism (metabolic rate of glucose uptake) was quantified from dynamic 18F-fluorodeoxyglucose (FDG)-PET images (n = 70) using Patlak graphical analysis. A linear mixed model was utilized to examine the association between structural/metabolic variables and continuous childhood trauma measures while controlling for confounding factors. Bonferroni correction was applied. Amygdala volumes were significantly inversely correlated with continuous CTQ scores. Specifically, volumes were lower by 7.44 mm3 (95% confidence interval [CI]: -12.19, -2.68) per point increase in CTQ. No significant relationship was found between thickness/metabolism and CTQ score. While longitudinal studies are required to establish causation, this study provides insight into potential consequences of, and therefore potential therapeutic targets for, childhood trauma in the prevention of MDD. This work aims to reduce heterogeneity in MDD studies by quantifying neurobiological correlates of trauma within MDD. It further provides biological targets for future interventions aimed at preventing MDD following trauma. To our knowledge, this is the first simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) study to assess both structure and metabolism associated with childhood trauma in adults with MDD.
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Affiliation(s)
- Joshua S. Jones
- grid.16416.340000 0004 1936 9174University of Rochester, Rochester, NY USA
| | - Samantha J. Goldstein
- grid.36425.360000 0001 2216 9681Department of Psychiatry and Behavioral Science, Stony Brook University, New York, NY USA
| | - Junying Wang
- grid.36425.360000 0001 2216 9681Department of Applied Mathematics and Statistics, Stony Brook University, New York, NY USA
| | - John Gardus
- grid.36425.360000 0001 2216 9681Department of Psychiatry and Behavioral Science, Stony Brook University, New York, NY USA
| | - Jie Yang
- grid.36425.360000 0001 2216 9681Department of Family, Population & Preventive Medicine, Stony Brook University, New York, NY USA
| | - Ramin V. Parsey
- grid.36425.360000 0001 2216 9681Department of Psychiatry and Behavioral Science, Stony Brook University, New York, NY USA
| | - Christine DeLorenzo
- grid.36425.360000 0001 2216 9681Department of Psychiatry and Behavioral Science, Stony Brook University, New York, NY USA ,grid.36425.360000 0001 2216 9681Department of Biomedical Engineering, Stony Brook University, New York, NY USA
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Does the change in glutamate to GABA ratio correlate with change in depression severity? A randomized, double-blind clinical trial. Mol Psychiatry 2022; 27:3833-3841. [PMID: 35982258 PMCID: PMC9712215 DOI: 10.1038/s41380-022-01730-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/09/2022] [Accepted: 07/29/2022] [Indexed: 02/08/2023]
Abstract
Previous proton magnetic resonance spectroscopy (1H-MRS) studies suggest a perturbation in glutamate and/or GABA in Major Depressive Disorder (MDD). However, no studies examine the ratio of glutamate and glutamine (Glx) to GABA (Glx/GABA) as it relates to depressive symptoms, which may be more sensitive than either single metabolite. Using a within-subject design, we hypothesized that reduction in depressive symptoms correlates with reduction in Glx/GABA in the anterior cingulate cortex (ACC). The present trial is a randomized clinical trial that utilized 1H-MRS to examine Glx/GABA before and after 8 weeks of escitalopram or placebo. Participants completed the 17-item Hamilton Depression Rating Scale (HDRS17) and underwent magnetic resonance spectroscopy before and after treatment. Two GABA-edited MEGA-PRESS acquisitions were interleaved with a water unsuppressed reference scan. GABA and Glx were quantified from the average difference spectrum, with preprocessing using Gannet and spectral fitting using TARQUIN. Linear mixed models were utilized to evaluate relationships between change in HDRS17 and change in Glx/GABA using a univariate linear regression model, multiple linear regression incorporating treatment type as a covariate, and Bayes Factor (BF) hypothesis testing to examine strength of evidence. No significant relationship was detected between percent change in Glx, GABA, or Glx/GABA and percent change in HDRS17, regardless of treatment type. Further, MDD severity before/after treatment did not correlate with ACC Glx/GABA. In light of variable findings in the literature and lack of association in our investigation, future directions should include evaluating glutamate and glutamine individually to shed light on the underpinnings of MDD severity. Advancing Personalized Antidepressant Treatment Using PET/MRI, ClinicalTrials.gov, NCT02623205.
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Cervenka S, Frick A, Bodén R, Lubberink M. Application of positron emission tomography in psychiatry-methodological developments and future directions. Transl Psychiatry 2022; 12:248. [PMID: 35701411 PMCID: PMC9198063 DOI: 10.1038/s41398-022-01990-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/20/2022] [Accepted: 05/25/2022] [Indexed: 11/09/2022] Open
Abstract
Mental disorders represent an increasing source of disability and high costs for societies globally. Molecular imaging techniques such as positron emission tomography (PET) represent powerful tools with the potential to advance knowledge regarding disease mechanisms, allowing the development of new treatment approaches. Thus far, most PET research on pathophysiology in psychiatric disorders has focused on the monoaminergic neurotransmission systems, and although a series of discoveries have been made, the results have not led to any material changes in clinical practice. We outline areas of methodological development that can address some of the important obstacles to fruitful progress. First, we point towards new radioligands and targets that can lead to the identification of processes upstream, or parallel to disturbances in monoaminergic systems. Second, we describe the development of new methods of PET data quantification and PET systems that may facilitate research in psychiatric populations. Third, we review the application of multimodal imaging that can link molecular imaging data to other aspects of brain function, thus deepening our understanding of disease processes. Fourth, we highlight the need to develop imaging study protocols to include longitudinal and interventional paradigms, as well as frameworks to assess dimensional symptoms such that the field can move beyond cross-sectional studies within current diagnostic boundaries. Particular effort should be paid to include also the most severely ill patients. Finally, we discuss the importance of harmonizing data collection and promoting data sharing to reach the desired sample sizes needed to fully capture the phenotype of psychiatric conditions.
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Affiliation(s)
- Simon Cervenka
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden. .,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
| | - Andreas Frick
- grid.8993.b0000 0004 1936 9457Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Robert Bodén
- grid.8993.b0000 0004 1936 9457Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Mark Lubberink
- grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
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