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Beliveau V, Hedeboe E, Fisher PM, Dam VH, Jørgensen MB, Frokjaer VG, Knudsen GM, Ganz M. Generalizability of treatment outcome prediction in major depressive disorder using structural MRI: A NeuroPharm study. Neuroimage Clin 2022; 36:103224. [PMID: 36252556 PMCID: PMC9668596 DOI: 10.1016/j.nicl.2022.103224] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 11/11/2022]
Abstract
Brain morphology has been suggested to be predictive of drug treatment outcome in major depressive disorders (MDD). The current study aims at evaluating the performance of pretreatment structural brain magnetic resonance imaging (MRI) measures in predicting the outcome of a drug treatment of MDD in a large single-site cohort, and, importantly, to assess the generalizability of these findings in an independent cohort. The random forest, boosted trees, support vector machines and elastic net classifiers were evaluated in predicting treatment response and remission following an eight week drug treatment of MDD using structural brain measures derived with FastSurfer (FreeSurfer). Models were trained and tested within a nested cross-validation framework using the NeuroPharm dataset (n = 79, treatment: escitalopram); their generalizability was assessed using an independent clinical dataset, EMBARC (n = 64, treatment: sertraline). Prediction of antidepressant treatment response in the Neuropharm cohort was statistically significant for the random forest (p = 0.048), whereas none of the models could significantly predict remission. Furthermore, none of the models trained using the entire NeuroPharm dataset could significantly predict treatment outcome in the EMBARC dataset. Although our primary findings in the NeuroPharm cohort support some, but limited value in using pretreatment structural brain MRI to predict drug treatment outcome in MDD, the models did not generalize to an independent cohort suggesting limited clinical applicability. This study emphasizes the importance of assessing model generalizability for establishing clinical utility.
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Affiliation(s)
- Vincent Beliveau
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria,Corresponding author at: Neurobiology Research Unit, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark.
| | - Ella Hedeboe
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Patrick M. Fisher
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibeke H. Dam
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin B. Jørgensen
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Vibe G. Frokjaer
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Gitte M. Knudsen
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit and NeuroPharm, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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52
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Karvelis P, Charlton CE, Allohverdi SG, Bedford P, Hauke DJ, Diaconescu AO. Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review. Netw Neurosci 2022; 6:1066-1103. [PMID: 38800454 PMCID: PMC11117101 DOI: 10.1162/netn_a_00233] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/14/2022] [Indexed: 05/29/2024] Open
Abstract
Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.
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Affiliation(s)
- Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Colleen E. Charlton
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Shona G. Allohverdi
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Peter Bedford
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Daniel J. Hauke
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O. Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- University of Toronto, Department of Psychiatry, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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53
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Kaiser RH, Chase HW, Phillips ML, Deckersbach T, Parsey RV, Fava M, McGrath PJ, Weissman M, Oquendo MA, McInnis MG, Carmody T, Cooper CM, Trivedi MH, Pizzagalli DA. Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response. Biol Psychiatry 2022; 92:533-542. [PMID: 35680431 PMCID: PMC10640874 DOI: 10.1016/j.biopsych.2022.03.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 12/26/2022]
Abstract
BACKGROUND Delivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. METHODS The Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response. RESULTS Dynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups. CONCLUSIONS Dynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.
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Affiliation(s)
- Roselinde H Kaiser
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado; Institute of Cognitive Science, University of Colorado Boulder, Boulder, Colorado; Renée Crown Wellness Institute, University of Colorado Boulder, Boulder, Colorado.
| | - Henry W Chase
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Thilo Deckersbach
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Ramin V Parsey
- Department of Psychiatry, Stony Brook University, Stony Brook, New York
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Patrick J McGrath
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Myrna Weissman
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, New York
| | - Maria A Oquendo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Thomas Carmody
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Crystal M Cooper
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Boston, Massachusetts
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Smith EE, Bel-Bahar TS, Kayser J. A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA. Psychophysiology 2022; 59:e14080. [PMID: 35478408 PMCID: PMC9427703 DOI: 10.1111/psyp.14080] [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: 08/10/2021] [Revised: 04/04/2022] [Accepted: 04/07/2022] [Indexed: 11/27/2022]
Abstract
Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.
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Affiliation(s)
- Ezra E. Smith
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Tarik S. Bel-Bahar
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Jürgen Kayser
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
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55
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Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI. Neuroinformatics 2022; 20:879-896. [PMID: 35291020 DOI: 10.1007/s12021-022-09565-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2022] [Indexed: 12/31/2022]
Abstract
In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson's Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.
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56
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Ang YS, Bruder GE, Keilp JG, Rutherford A, Alschuler DM, Pechtel P, Webb CA, Carmody T, Fava M, Cusin C, McGrath PJ, Weissman M, Parsey R, Oquendo MA, McInnis MG, Cooper CM, Deldin P, Trivedi MH, Pizzagalli DA. Exploration of baseline and early changes in neurocognitive characteristics as predictors of treatment response to bupropion, sertraline, and placebo in the EMBARC clinical trial. Psychol Med 2022; 52:2441-2449. [PMID: 33213541 PMCID: PMC7613805 DOI: 10.1017/s0033291720004286] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Treatment for major depressive disorder (MDD) is imprecise and often involves trial-and-error to determine the most effective approach. To facilitate optimal treatment selection and inform timely adjustment, the current study investigated whether neurocognitive variables could predict an antidepressant response in a treatment-specific manner. METHODS In the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial, outpatients with non-psychotic recurrent MDD were first randomized to an 8-week course of sertraline selective serotonin reuptake inhibitor or placebo. Behavioral measures of reward responsiveness, cognitive control, verbal fluency, psychomotor, and cognitive processing speeds were collected at baseline and week 1. Treatment responders then continued on another 8-week course of the same medication, whereas non-responders to sertraline or placebo were crossed-over under double-blinded conditions to bupropion noradrenaline/dopamine reuptake inhibitor or sertraline, respectively. Hamilton Rating for Depression scores were also assessed at baseline, weeks 8, and 16. RESULTS Greater improvements in psychomotor and cognitive processing speeds within the first week, as well as better pretreatment performance in these domains, were specifically associated with higher likelihood of response to placebo. Moreover, better reward responsiveness, poorer cognitive control and greater verbal fluency were associated with greater likelihood of response to bupropion in patients who previously failed to respond to sertraline. CONCLUSION These exploratory results warrant further scrutiny, but demonstrate that quick and non-invasive behavioral tests may have substantial clinical value in predicting antidepressant treatment response.
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Affiliation(s)
- Yuen-Siang Ang
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Gerard E. Bruder
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, USA
| | - John G. Keilp
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, USA
| | - Ashleigh Rutherford
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Daniel M. Alschuler
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, USA
| | - Pia Pechtel
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Christian A. Webb
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Thomas Carmody
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Cristina Cusin
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Patrick J. McGrath
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, USA
| | - Myrna Weissman
- Department of Psychiatry, New York State Psychiatric Institute and Columbia University Vagelos College of Physicians and Surgeons, New York, USA
| | - Ramin Parsey
- Department of Psychiatry, Stony Brook University, Stony Brook, New York, USA
| | - Maria A. Oquendo
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Melvin G. McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Crystal M. Cooper
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Patricia Deldin
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Diego A. Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts, USA
- McLean Imaging Center, McLean Hospital, Belmont, Massachusetts, USA
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Peng Y, Huang Y, Chen B, He M, Jiang L, Li Y, Huang X, Pei C, Zhang S, Li C, Zhang X, Zhang T, Zheng Y, Yao D, Li F, Xu P. Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients with Major Depressive Disorder. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2577-2588. [PMID: 36044502 DOI: 10.1109/tnsre.2022.3203073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.
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58
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Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders. J Pers Med 2022; 12:jpm12091403. [PMID: 36143188 PMCID: PMC9504356 DOI: 10.3390/jpm12091403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 01/10/2023] Open
Abstract
Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.
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59
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Xie S, Tarpey T, Petkova E, Ogden RT. Multiple domain and multiple kernel outcome-weighted learning for estimating individualized treatment regimes. J Comput Graph Stat 2022; 31:1375-1383. [PMID: 36970034 PMCID: PMC10035569 DOI: 10.1080/10618600.2022.2067552] [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: 03/23/2021] [Revised: 03/23/2022] [Accepted: 04/11/2022] [Indexed: 10/18/2022]
Abstract
Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient's own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple data domains (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this paper, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplemental materials for this article are available online.
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Affiliation(s)
- Shanghong Xie
- School of Statistics, Southwestern University of Finance and Economics
- Department of Biostatistics, Mailman School of Public Health, Columbia University
| | - Thaddeus Tarpey
- Division of Biostatistics, Department of Population Health, New York University
| | - Eva Petkova
- Division of Biostatistics, Department of Population Health, New York University
| | - R. Todd Ogden
- Department of Biostatistics, Mailman School of Public Health, Columbia University
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60
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Ma L, Cunningham KA, Anastasio NC, Bjork JM, Taylor BA, Arias AJ, Riley BP, Snyder AD, Moeller FG. A serotonergic biobehavioral signature differentiates cocaine use disorder participants administered mirtazapine. Transl Psychiatry 2022; 12:187. [PMID: 35523779 PMCID: PMC9076859 DOI: 10.1038/s41398-022-01934-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 11/18/2022] Open
Abstract
Cocaine use disorder (CUD) patients display heterogenous symptoms and unforeseeable responses to available treatment approaches, highlighting the need to identify objective, accessible biobehavioral signatures to predict clinical trial success in this population. In the present experiments, we employed a task-based behavioral and pharmacogenetic-fMRI approach to address this gap. Craving, an intense desire to take cocaine, can be evoked by exposure to cocaine-associated stimuli which can trigger relapse during attempted recovery. Attentional bias towards cocaine-associated words is linked to enhanced effective connectivity (EC) from the anterior cingulate cortex (ACC) to hippocampus in CUD participants, an observation which was replicated in a new cohort of participants in the present studies. Serotonin regulates attentional bias to cocaine and the serotonergic antagonist mirtazapine decreased activated EC associated with attentional bias, with greater effectiveness in those CUD participants carrying the wild-type 5-HT2CR gene relative to a 5-HT2CR single nucleotide polymorphism (rs6318). These data suggest that the wild-type 5-HT2CR is necessary for the efficacy of mirtazapine to decrease activated EC in CUD participants and that mirtazapine may serve as an abstinence enhancer to mitigate brain substrates of craving in response to cocaine-associated stimuli in participants with this pharmacogenetic descriptor. These results are distinctive in outlining a richer "fingerprint" of the complex neurocircuitry, behavior and pharmacogenetics profile of CUD participants which may provide insight into success of future medications development projects.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States.
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States.
| | - Kathryn A Cunningham
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States.
| | - Noelle C Anastasio
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - James M Bjork
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Brian A Taylor
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, United States
| | - Albert J Arias
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Brien P Riley
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Andrew D Snyder
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
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61
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Trombello JM, Cooper CM, Fatt CC, Grannemann BD, Carmody TJ, Jha MK, Mayes TL, Greer TL, Yezhuvath U, Aslan S, Pizzagalli DA, Weissman MM, Webb CA, Dillon DG, McGrath PJ, Fava M, Parsey RV, McInnis MG, Etkin A, Trivedi MH. Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial. J Psychiatr Res 2022; 149:243-251. [PMID: 35290819 PMCID: PMC9746288 DOI: 10.1016/j.jpsychires.2022.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 02/16/2022] [Accepted: 03/07/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND The brain circuitry of depression and anxiety/fear is well-established, involving regions such as the limbic system and prefrontal cortex. We expand prior literature by examining the extent to which four discrete factors of anxiety (immediate state anxiety, physiological/panic, neuroticism/worry, and agitation/restlessness) among depressed outpatients are associated with differential responses during reactivity to and regulation of emotional conflict. METHODS A total of 172 subjects diagnosed with major depressive disorder underwent functional magnetic resonance imaging while performing an Emotional Stroop Task. Two main contrasts were examined using whole brain voxel wise analyses: emotional reactivity and emotion regulation. We also evaluated the association of these contrasts with the four aforementioned anxiety factors. RESULTS During emotional reactivity, participants with higher immediate state anxiety showed potentiated activation in the rolandic operculum and insula, while individuals with higher levels of physiological/panic demonstrated decreased activation in the posterior cingulate. No significant results emerged for any of the four factors on emotion regulation. When re-analyzing these statistically-significant brain regions through analyses of a subsample with (n = 92) and without (n = 80) a current anxiety disorder, no significant associations occurred among those without an anxiety disorder. Among those with an anxiety disorder, results were similar to the full sample, except the posterior cingulate was associated with the neuroticism/worry factor. CONCLUSIONS Divergent patterns of task-related brain activation across four discrete anxiety factors could be used to inform treatment decisions and target specific aspects of anxiety that involve intrinsic processing to attenuate overactive responses to emotional stimuli.
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Affiliation(s)
- Joseph M. Trombello
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Crystal M. Cooper
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Neuroscience Research, Cook Children’s Medical Center, Fort Worth, TX, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bruce D. Grannemann
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Thomas J. Carmody
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Manish K. Jha
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Taryn L. Mayes
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tracy L. Greer
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Department of Psychology, The University of Texas at Arlington, Arlington, TX, USA
| | | | - Sina Aslan
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Advance MRI LLC, Frisco, TX, USA
| | - Diego A. Pizzagalli
- Harvard Medical School, McLean Hospital, Department of Psychiatry, Boston, MA, USA
| | - Myrna M. Weissman
- Columbia University, Department of Psychiatry, New York, NY, USA,New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | - Christian A. Webb
- Harvard Medical School, McLean Hospital, Department of Psychiatry, Boston, MA, USA
| | - Daniel G. Dillon
- Harvard Medical School, McLean Hospital, Department of Psychiatry, Boston, MA, USA
| | - Patrick J. McGrath
- Columbia University, Department of Psychiatry, New York, NY, USA,New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | - Maurizio Fava
- Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
| | - Ramin V. Parsey
- Stony Brook University, Department of Psychiatry, Stony Brook, NY, USA
| | - Melvin G. McInnis
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, USA
| | - Amit Etkin
- Stanford University School of Medicine, Department of Psychiatry, Palo Alto, CA, USA
| | - Madhukar H. Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA,Corresponding author. Center for Depression Research and Clinical Care, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, USA. (M.H. Trivedi)
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Park H, Petkova E, Tarpey T, Ogden RT. A sparse additive model for treatment effect-modifier selection. Biostatistics 2022; 23:412-429. [PMID: 32808656 PMCID: PMC9308457 DOI: 10.1093/biostatistics/kxaa032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/05/2020] [Accepted: 07/10/2020] [Indexed: 11/26/2023] Open
Abstract
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.
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Affiliation(s)
- Hyung Park
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
| | - Eva Petkova
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
| | - Thaddeus Tarpey
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
| | - R Todd Ogden
- Division of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA
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Kennedy SH. Beyond Response: Aiming for Quality Remission in Depression. Adv Ther 2022; 39:20-28. [PMID: 35247185 PMCID: PMC9015986 DOI: 10.1007/s12325-021-02030-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/17/2021] [Indexed: 11/23/2022]
Abstract
To define treatment response in depression as at least a 50% reduction in total symptom severity is to accept that up to half of patients will continue to have residual symptoms, most commonly low mood/loss of interest, cognitive problems, lack of energy, and difficulty sleeping. In fact, patients' goals for treatment are to return to premorbid levels of functioning. This highlights the importance of assessing both functional outcomes and symptom improvement when evaluating the efficacy of antidepressant medication. Not all patients who achieve symptomatic response/remission will achieve a functional response/remission. In two studies (one with agomelatine and one with escitalopram), 54% of patients receiving agomelatine and 47% of those receiving escitalopram achieved a symptomatic response, and 53% of patients in each study achieved a functional response. However, 42% of patients receiving agomelatine and 35% of those receiving escitalopram had both a symptomatic and a functional response. The four symptoms of depression with the most marked effect on function are sad mood, impaired concentration, fatigue, and loss of interest. Low energy is particularly associated with poor occupational functioning, highlighting the importance of ongoing assessment of patients with depression, focusing particular attention on the symptoms that affect their ability to function, such as fatigue. Depending on the type of residual symptoms, some patients may benefit from combination therapy, such as adding dopamine modulator therapy. Antidepressant therapy is only effective if patients continue to take their medication, and high rates of early discontinuation have been reported. Therefore, when selecting treatment for depression, physicians can maximize the likelihood of adherence and persistence by taking into account both the antidepressant efficacy of treatment, its adverse effects and acceptability to patients.
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Affiliation(s)
- Sidney H Kennedy
- Centre for Depression and Suicide Studies, St Michael's Hospital, University of Toronto, 193 Yonge Street, Suite 6-001A, Toronto, ON, M5B 1M4, Canada.
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Brush CJ, Hajcak G, Bocchine AJ, Ude AA, Muniz KM, Foti D, Alderman BL. A randomized trial of aerobic exercise for major depression: examining neural indicators of reward and cognitive control as predictors and treatment targets. Psychol Med 2022; 52:893-903. [PMID: 32838817 DOI: 10.1017/s0033291720002573] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Aerobic exercise has demonstrated antidepressant efficacy among adults with major depression. There is a poor understanding of the neural mechanisms associated with these effects. Deficits in reward processing and cognitive control may be two candidate targets and predictors of treatment outcome to exercise in depression. METHODS Sixty-six young adults aged 20.23 years (s.d. = 2.39) with major depression were randomized to 8 weeks of moderate-intensity aerobic exercise (n = 35) or light stretching (n = 31). Depressive symptoms were assessed across the intervention to track symptom reduction. Reward processing [reward positivity (RewP)] and cognitive control [error-related negativity (ERN)] were assessed before and after the intervention using event-related brain potentials. RESULTS Compared to stretching, aerobic exercise resulted in greater symptom reduction (gs = 0.66). Aerobic exercise had no impact on the RewP (gav = 0.08) or ERN (gav = 0.21). In the aerobic exercise group, individuals with a larger pre-treatment RewP [odds ratio (OR) = 1.45] and increased baseline depressive symptom severity (OR = 1.18) were more likely to respond to an aerobic exercise program. Pre-treatment ERN did not predict response (OR = 0.74). CONCLUSIONS Aerobic exercise is effective in alleviating depressive symptoms in adults with major depression, particularly for those with increased depressive symptom severity and a larger RewP at baseline. Although aerobic exercise did not modify the RewP or ERN, there is preliminary support for the utility of the RewP in predicting who is most likely to respond to exercise as a treatment for depression.
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Affiliation(s)
- C J Brush
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Kinesiology and Health and Center of Alcohol & Substance Use Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Greg Hajcak
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Anthony J Bocchine
- Department of Kinesiology and Health and Center of Alcohol & Substance Use Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Andrew A Ude
- Department of Kinesiology and Health and Center of Alcohol & Substance Use Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Kristina M Muniz
- Department of Kinesiology and Health and Center of Alcohol & Substance Use Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Psychiatry and Neurobehavioral Sciences, Division of Child and Family Psychiatry, University of Virginia Health System, Charlottesville, VA, USA
| | - Dan Foti
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Brandon L Alderman
- Department of Kinesiology and Health and Center of Alcohol & Substance Use Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
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Nguyen KP, Chin Fatt C, Treacher A, Mellema C, Cooper C, Jha MK, Kurian B, Fava M, McGrath PJ, Weissman M, Phillips ML, Trivedi MH, Montillo AA. Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial. Biol Psychiatry 2022; 91:550-560. [PMID: 34916068 PMCID: PMC8857018 DOI: 10.1016/j.biopsych.2021.09.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 08/31/2021] [Accepted: 09/14/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND The lack of biomarkers to inform antidepressant selection is a key challenge in personalized depression treatment. This work identifies candidate biomarkers by building deep learning predictors of individual treatment outcomes using reward processing measures from functional magnetic resonance imaging, clinical assessments, and demographics. METHODS Participants in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study (n = 222) underwent reward processing task-based functional magnetic resonance imaging at baseline and were randomized to 8 weeks of sertraline (n = 106) or placebo (n = 116). Subsequently, sertraline nonresponders (n = 37) switched to 8 weeks of bupropion. The change in Hamilton Depression Rating Scale was measured after treatment. Reward processing, clinical measurements, and demographics were used to train treatment-specific deep learning models. RESULTS The predictive model for sertraline achieved R2 of 48% (95% CI, 33%-61%; p < 10-3) in predicting the change in Hamilton Depression Rating Scale and number-needed-to-treat (NNT) of 4.86 participants in predicting response. The placebo model achieved R2 of 28% (95% CI, 15%-42%; p < 10-3) and NNT of 2.95 in predicting response. The bupropion model achieved R2 of 34% (95% CI, 10%-59%, p < 10-3) and NNT of 1.68 in predicting response. Brain regions where reward processing activity was predictive included the prefrontal cortex and cerebellar crus 1 for sertraline and the cingulate cortex, caudate, orbitofrontal cortex, and crus 1 for bupropion. CONCLUSIONS These findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.
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Affiliation(s)
- Kevin P Nguyen
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Cherise Chin Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Alex Treacher
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Cooper Mellema
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas; Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, Texas
| | - Manish K Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Benji Kurian
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Patrick J McGrath
- New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, New York
| | - Myrna Weissman
- New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, New York
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas.
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas.
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Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, Furukawa TA, Kessler RC, Kohrt BA, Maj M, McGorry P, Reynolds CF, Weissman MM, Chibanda D, Dowrick C, Howard LM, Hoven CW, Knapp M, Mayberg HS, Penninx BWJH, Xiao S, Trivedi M, Uher R, Vijayakumar L, Wolpert M. Time for united action on depression: a Lancet-World Psychiatric Association Commission. Lancet 2022; 399:957-1022. [PMID: 35180424 DOI: 10.1016/s0140-6736(21)02141-3] [Citation(s) in RCA: 301] [Impact Index Per Article: 150.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Helen Herrman
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia.
| | - Vikram Patel
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA; Sangath, Goa, India; Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Christian Kieling
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Child & Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Michael Berk
- Deakin University, IMPACT Institute, Geelong, VIC, Australia
| | - Claudia Buchweitz
- Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Toshiaki A Furukawa
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Brandon A Kohrt
- Department of Psychiatry and Behavioral Sciences, George Washington University, Washington, DC, USA
| | - Mario Maj
- Department of Psychiatry, University of Campania L Vanvitelli, Naples, Italy
| | - Patrick McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Charles F Reynolds
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Myrna M Weissman
- Columbia University Mailman School of Public Health, New York, NY, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA
| | - Dixon Chibanda
- Department of Psychiatry, University of Zimbabwe, Harare, Zimbabwe; Centre for Global Mental Health, The London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher Dowrick
- Department of Primary Care and Mental Health, University of Liverpool, Liverpool, UK
| | - Louise M Howard
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Christina W Hoven
- Columbia University Mailman School of Public Health, New York, NY, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA
| | - Martin Knapp
- Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
| | - Helen S Mayberg
- Departments of Neurology, Neurosurgery, Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Shuiyuan Xiao
- Central South University Xiangya School of Public Health, Changsha, China
| | - Madhukar Trivedi
- Peter O'Donnell Jr Brain Institute and the Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Lakshmi Vijayakumar
- Sneha, Suicide Prevention Centre and Voluntary Health Services, Chennai, India
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Rush AJ, Sackeim HA, Conway CR, Bunker MT, Hollon SD, Demyttenaere K, Young AH, Aaronson ST, Dibué M, Thase ME, McAllister-Williams RH. Clinical research challenges posed by difficult-to-treat depression. Psychol Med 2022; 52:419-432. [PMID: 34991768 PMCID: PMC8883824 DOI: 10.1017/s0033291721004943] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 11/02/2021] [Accepted: 11/09/2021] [Indexed: 12/13/2022]
Abstract
Approximately one-third of individuals in a major depressive episode will not achieve sustained remission despite multiple, well-delivered treatments. These patients experience prolonged suffering and disproportionately utilize mental and general health care resources. The recently proposed clinical heuristic of 'difficult-to-treat depression' (DTD) aims to broaden our understanding and focus attention on the identification, clinical management, treatment selection, and outcomes of such individuals. Clinical trial methodologies developed to detect short-term therapeutic effects in treatment-responsive populations may not be appropriate in DTD. This report reviews three essential challenges for clinical intervention research in DTD: (1) how to define and subtype this heterogeneous group of patients; (2) how, when, and by what methods to select, acquire, compile, and interpret clinically meaningful outcome metrics; and (3) how to choose among alternative clinical trial design options to promote causal inference and generalizability. The boundaries of DTD are uncertain, and an evidence-based taxonomy and reliable assessment tools are preconditions for clinical research and subtyping. Traditional outcome metrics in treatment-responsive depression may not apply to DTD, as they largely reflect the only short-term symptomatic change and do not incorporate durability of benefit, side effect burden, or sustained impact on quality of life or daily function. The trial methodology will also require modification as trials will likely be of longer duration to examine the sustained impact, raising complex issues regarding control group selection, blinding and its integrity, and concomitant treatments.
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Affiliation(s)
- A. John Rush
- Duke-NUS Medical School, Singapore
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Department of Psychiatry, Texas Tech University, Permian Basin, TX, USA
| | - Harold A. Sackeim
- Departments of Psychiatry and Radiology, Columbia University, New York, NY, USA
| | - Charles R. Conway
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Steven D. Hollon
- Departments of Psychology and Psychiatry, Vanderbilt University, Nashville, TN, USA
| | - Koen Demyttenaere
- University Psychiatric Center, KU Leuven, Leuven, Belgium
- Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Allan H. Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Scott T. Aaronson
- Department of Clinical Research, Sheppard Pratt Health System, Baltimore, MD, USA
| | - Maxine Dibué
- Department of Neurosurgery, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Medical Affairs Europe, LivaNova Deutschland GmbH, Munich, Germany
| | - Michael E. Thase
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - R. Hamish McAllister-Williams
- Northern Centre for Mood Disorders, Newcastle University, Newcastle upon Tyne, UK
- Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, UK
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Prado P, Birba A, Cruzat J, Santamaría-García H, Parra M, Moguilner S, Tagliazucchi E, Ibáñez A. Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration. Int J Psychophysiol 2022; 172:24-38. [PMID: 34968581 PMCID: PMC9887537 DOI: 10.1016/j.ijpsycho.2021.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/26/2021] [Accepted: 12/19/2021] [Indexed: 02/02/2023]
Abstract
The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Josefina Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Hernando Santamaría-García
- Pontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Departamento de Física, Universidad de Buenos Aires and Instituto de Fisica de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland,Corresponding author at: Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile., (A. Ibáñez)
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Zsido RG, Molloy EN, Cesnaite E, Zheleva G, Beinhölzl N, Scharrer U, Piecha FA, Regenthal R, Villringer A, Nikulin VV, Sacher J. One‐week escitalopram intake alters the excitation–inhibition balance in the healthy female brain. Hum Brain Mapp 2022; 43:1868-1881. [PMID: 35064716 PMCID: PMC8933318 DOI: 10.1002/hbm.25760] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 11/11/2022] Open
Affiliation(s)
- Rachel G. Zsido
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- International Max Planck Research School NeuroCom Leipzig Germany
- Max Planck School of Cognition Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Eóin N. Molloy
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- International Max Planck Research School NeuroCom Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- University Clinic for Radiology and Nuclear Medicine, Otto von Guericke University Magdeburg Magdeburg Germany
| | - Elena Cesnaite
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Gergana Zheleva
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Nathalie Beinhölzl
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Ulrike Scharrer
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Clinic for Cognitive Neurology Leipzig University Leipzig Germany
| | - Fabian A. Piecha
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Ralf Regenthal
- Division of Clinical Pharmacology, Rudolf Boehm Institute of Pharmacology and Toxicology Leipzig University Leipzig Germany
| | - Arno Villringer
- International Max Planck Research School NeuroCom Leipzig Germany
- Max Planck School of Cognition Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Clinic for Cognitive Neurology Leipzig University Leipzig Germany
- Berlin School of Mind and Brain Berlin Germany
| | - Vadim V. Nikulin
- International Max Planck Research School NeuroCom Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Julia Sacher
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- International Max Planck Research School NeuroCom Leipzig Germany
- Max Planck School of Cognition Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Clinic for Cognitive Neurology Leipzig University Leipzig Germany
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Mason BL, Minhajuddin A, Czysz AH, Jha MK, Gadad BS, Mayes TL, Trivedi MH. Fibroblast growth factor 21 (FGF21) is increased in MDD and interacts with body mass index (BMI) to affect depression trajectory. Transl Psychiatry 2022; 12:16. [PMID: 35017468 PMCID: PMC8752780 DOI: 10.1038/s41398-021-01679-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 02/06/2023] Open
Abstract
Fibroblast growth factor 21 (FGF21) is a key regulator of metabolic function and nutrient preference. It also affects biological pathways associated with major depressive disorder (MDD), including corticotrophin-releasing hormone (CRH), leptin, and sympathetic activity. Lower levels of cerebrospinal fluid FGF21 have been associated with higher Beck Depression Inventory scores. FGF21 was examined as a metabolic marker that could be associated with MDD and evaluated as a biomarker of antidepressant treatment response in a large, randomized placebo-controlled trial in chronic, early-onset MDD participants. FGF21 levels at baseline and during treatment were determined for participants in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) study. FGF21 was analyzed by ELISA in individuals with chronic, early-onset MDD (first major depressive episode before 30 years) compared to healthy control participants. Participants with MDD had higher levels of FGF21 compared to healthy controls (HCs), even after controlling for baseline age, sex, race, Hispanic ethnicity, BMI, and site (β-coefficient = 1.20, p < 0.0001, Cohen's d = 0.60). FGF21 did not change over time nor differ between treatment groups. Interestingly though, those with normal BMI and lower FGF21 levels showed a reduction in depression severity over time compared to all other groups. In conclusion, depression is associated with higher levels of FGF21 compared to healthy controls and those with lower levels of FGF21 (25th percentile of the sample) in the context of normal-weight BMI seem to have improved depression severity over time.
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Affiliation(s)
- Brittany L. Mason
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX USA
| | - Abu Minhajuddin
- grid.267313.20000 0000 9482 7121Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX USA
| | - Andrew H. Czysz
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX USA
| | - Manish K. Jha
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX USA
| | - Bharathi S. Gadad
- grid.416992.10000 0001 2179 3554Department of Psychiatry, Texas Tech University Health Sciences Center El Paso, Dallas, TX USA
| | - Taryn L. Mayes
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX USA
| | - Madhukar H. Trivedi
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX USA
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71
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Wendt K, Denison T, Foster G, Krinke L, Thomson A, Wilson S, Widge AS. Physiologically informed neuromodulation. J Neurol Sci 2021; 434:120121. [PMID: 34998239 PMCID: PMC8976285 DOI: 10.1016/j.jns.2021.120121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 01/09/2023]
Abstract
The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.
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Affiliation(s)
- Karen Wendt
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
| | - Timothy Denison
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Gaynor Foster
- Welcony Inc., Plymouth, MN, United States of America
| | - Lothar Krinke
- Welcony Inc., Plymouth, MN, United States of America; Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Alix Thomson
- Welcony Inc., Plymouth, MN, United States of America
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America; Medical Discovery Team on Additions, University of Minnesota, Minneapolis, MN, United States of America
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72
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Kelly JR, Gillan CM, Prenderville J, Kelly C, Harkin A, Clarke G, O'Keane V. Psychedelic Therapy's Transdiagnostic Effects: A Research Domain Criteria (RDoC) Perspective. Front Psychiatry 2021; 12:800072. [PMID: 34975593 PMCID: PMC8718877 DOI: 10.3389/fpsyt.2021.800072] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/19/2021] [Indexed: 12/12/2022] Open
Abstract
Accumulating clinical evidence shows that psychedelic therapy, by synergistically combining psychopharmacology and psychological support, offers a promising transdiagnostic treatment strategy for a range of disorders with restricted and/or maladaptive habitual patterns of emotion, cognition and behavior, notably, depression (MDD), treatment resistant depression (TRD) and addiction disorders, but perhaps also anxiety disorders, obsessive-compulsive disorder (OCD), Post-Traumatic Stress Disorder (PTSD) and eating disorders. Despite the emergent transdiagnostic evidence, the specific clinical dimensions that psychedelics are efficacious for, and associated underlying neurobiological pathways, remain to be well-characterized. To this end, this review focuses on pre-clinical and clinical evidence of the acute and sustained therapeutic potential of psychedelic therapy in the context of a transdiagnostic dimensional systems framework. Focusing on the Research Domain Criteria (RDoC) as a template, we will describe the multimodal mechanisms underlying the transdiagnostic therapeutic effects of psychedelic therapy, traversing molecular, cellular and network levels. These levels will be mapped to the RDoC constructs of negative and positive valence systems, arousal regulation, social processing, cognitive and sensorimotor systems. In summarizing this literature and framing it transdiagnostically, we hope we can assist the field in moving toward a mechanistic understanding of how psychedelics work for patients and eventually toward a precise-personalized psychedelic therapy paradigm.
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Affiliation(s)
- John R. Kelly
- Department of Psychiatry, Trinity College, Dublin, Ireland
- Department of Psychiatry, Tallaght University Hospital, Dublin, Ireland
| | - Claire M. Gillan
- Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
- School of Psychology, Trinity College, Dublin, Ireland
- Global Brain Health Institute, Trinity College, Dublin, Ireland
| | - Jack Prenderville
- Transpharmation Ireland Ltd, Institute of Neuroscience, Trinity College, Dublin, Ireland
- Discipline of Physiology, School of Medicine, Trinity College, Dublin, Ireland
| | - Clare Kelly
- Department of Psychiatry, Trinity College, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
- School of Psychology, Trinity College, Dublin, Ireland
| | - Andrew Harkin
- Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
- School of Pharmacy and Pharmaceutical Sciences, Trinity College, Dublin, Ireland
| | - Gerard Clarke
- Department of Psychiatry and Neurobehavioral Science, University College Cork, Cork, Ireland
- APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Veronica O'Keane
- Department of Psychiatry, Trinity College, Dublin, Ireland
- Department of Psychiatry, Tallaght University Hospital, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
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73
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Chen AA, Beer JC, Tustison NJ, Cook PA, Shinohara RT, Shou H. Mitigating site effects in covariance for machine learning in neuroimaging data. Hum Brain Mapp 2021; 43:1179-1195. [PMID: 34904312 PMCID: PMC8837590 DOI: 10.1002/hbm.25688] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/16/2021] [Accepted: 10/03/2021] [Indexed: 12/29/2022] Open
Abstract
To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi‐site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site‐related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi‐center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within‐site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group.
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Affiliation(s)
- Andrew A Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joanne C Beer
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Davey CG, Cearns M, Jamieson A, Harrison BJ. Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission. Psychol Med 2021; 53:1-8. [PMID: 36762975 PMCID: PMC10123826 DOI: 10.1017/s0033291721004323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 08/08/2021] [Accepted: 10/04/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission. METHODS Data were obtained from 81 15- to 25-year-olds with a major depressive disorder who had participated in the YoDA-C trial, in which they had been randomised to receive cognitive behavioural therapy plus either fluoxetine or placebo. Prior to commencing treatment patients performed a functional magnetic resonance imaging (fMRI) task to assess rACC suppression. Support vector machines were trained on the fMRI data using nested cross-validation, and were similarly trained on clinical data. We further tested our fMRI model on data from the YoDA-A trial, in which participants had completed the same fMRI paradigm. RESULTS Thirty-six of 81 (44%) participants in the YoDA-C trial achieved remission. Our fMRI model was able to predict remission status (AUC = 0.777 [95% confidence interval (CI) 0.638-0.916], balanced accuracy = 67%, negative predictive value = 74%, p < 0.0001). Clinical models failed to predict remission status at better than chance levels. Testing the model on the alternative YoDA-A dataset confirmed its ability to predict remission (AUC = 0.776, balanced accuracy = 64%, negative predictive value = 70%, p < 0.0001). CONCLUSIONS We confirm that rACC activity acts as a prognostic biomarker for depression. The machine learning model can identify patients who are likely to have difficult-to-treat depression, which might direct the earlier provision of enhanced support and more intensive therapies.
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Affiliation(s)
| | - Micah Cearns
- Discipline of Psychiatry, School of Medicine, The University of Adelaide, Adelaide, Australia
| | - Alec Jamieson
- Department of Psychiatry, The University of Melbourne, Melbourne, Australia
| | - Ben J. Harrison
- Department of Psychiatry, The University of Melbourne, Melbourne, Australia
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75
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Yavi M, Henter ID, Park LT, Zarate C. Key considerations in the pharmacological management of treatment-resistant depression. Expert Opin Pharmacother 2021; 22:2405-2415. [PMID: 34252320 PMCID: PMC8648908 DOI: 10.1080/14656566.2021.1951225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 06/30/2021] [Indexed: 10/20/2022]
Abstract
Introduction: Treatment-resistant depression (TRD) is a complex, multifactorial, and biologically heterogeneous disorder with debilitating outcomes. Understanding individual reasons why patients do not respond to treatment is necessary for improving clinical recommendations regarding medication regimens, augmentation strategies, and alternative treatments.Areas covered: This manuscript reviews evidence-based treatment strategies for the clinical management of TRD. Current developments in the field and potential future recommendations for personalized treatment of TRD are also discussed.Expert opinion: Treatment guidelines for TRD are limited by the heterogeneous nature of the disorder. Furthermore, current strategies reflect this heterogeneity by emphasizing disease characteristics as well as drug trial response or failure. Developing robust biomarkers that could one day be integrated into clinical practice has the potential to advance specific treatment targets and ultimately improve treatment and remission outcomes.
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Affiliation(s)
- Mani Yavi
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental HealthNational Institutes of Health Bethesda, MD, USA
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76
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Kleinerman A, Rosenfeld A, Benrimoh D, Fratila R, Armstrong C, Mehltretter J, Shneider E, Yaniv-Rosenfeld A, Karp J, Reynolds CF, Turecki G, Kapelner A. Treatment selection using prototyping in latent-space with application to depression treatment. PLoS One 2021; 16:e0258400. [PMID: 34767577 PMCID: PMC8589171 DOI: 10.1371/journal.pone.0258400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/26/2021] [Indexed: 12/28/2022] Open
Abstract
Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.
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Affiliation(s)
| | | | - David Benrimoh
- McGill University, Montreal, Canada
- Aifred Health, Montreal, Canada
| | | | | | | | | | - Amit Yaniv-Rosenfeld
- Shalvata Mental Health Center, Hod Hasharon, Israel
- Tel-Aviv University, Tel-Aviv, Israel
| | - Jordan Karp
- University of Arizona, Tucson, Arizona, United States of America
| | - Charles F. Reynolds
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | | | - Adam Kapelner
- Queens College, New York City, NY, United States of America
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77
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Cardinale R, Menkes MW, Andrews CM, Webb CA, Jha MK, Trombello JM, Trivedi MH, McInnis MG, Deldin PJ. Preliminary Evidence for Sociotropy and Autonomy in Relation to Antidepressant Treatment Outcome. Psychiatr Q 2021; 92:1069-1077. [PMID: 33566317 PMCID: PMC9746245 DOI: 10.1007/s11126-021-09891-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/31/2021] [Indexed: 12/16/2022]
Abstract
Sociotropy and autonomy are cognitive-personality styles that have been hypothesized to confer vulnerability to different presentations of major depressive disorder (MDD), which may respond differentially to treatment. Specifically, the profile of low sociotropy and high autonomy is hypothesized to indicate a positive response to antidepressant medication. The current study examines sociotropy and autonomy in relation to sertraline treatment response in individuals with MDD. As part of an ancillary study to the larger Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) project, individuals with MDD participated in an 8-week trial of sertraline and completed a self-report questionnaire of sociotropy and autonomy. Discriminant function analyses were used to examine whether sociotropy and autonomy scores could distinguish antidepressant treatment responders (determined by a 50% or greater reduction in depressive symptoms) from non-responders. The sociotropy scale successfully discriminated sertraline treatment responders from non-responders. Further, lower sociotropy was associated with greater improvements in depressive symptomology following sertraline treatment. The current findings suggest individuals with MDD characterized by low sociotropy are more likely to benefit from sertraline. Given the promising results of the Sociotropy-Autonomy Scale in discriminating treatment responders from non-responders, the low resources necessary for administration, and the ease of translation into routine clinical care, the scale warrants further research attention.
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Affiliation(s)
- Ryan Cardinale
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Margo W Menkes
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA.
- Department of Psychiatry, University of Michigan/Michigan Medicine, Ann Arbor, MI, USA.
| | - Carolyn M Andrews
- Department of Psychiatry, University of Michigan/Michigan Medicine, Ann Arbor, MI, USA
| | - Christian A Webb
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Manish K Jha
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Joseph M Trombello
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan/Michigan Medicine, Ann Arbor, MI, USA
| | - Patricia J Deldin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan/Michigan Medicine, Ann Arbor, MI, USA
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Blain SD, Sassenberg TA, Xi M, Zhao D, DeYoung CG. Extraversion but not depression predicts reward sensitivity: Revisiting the measurement of anhedonic phenotypes. J Pers Soc Psychol 2021; 121:e1-e18. [PMID: 33119388 PMCID: PMC8081762 DOI: 10.1037/pspp0000371] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Recently, increasing efforts have been made to define and measure dimensional phenotypes associated with psychiatric disorders. One example is a probabilistic reward task developed by Pizzagalli, Jahn, and O'Shea (2005) to assess anhedonia, by measuring response to a differential reinforcement schedule. This task has been used in many studies, which have connected blunted reward response in the task to depressive symptoms, across clinical groups and in the general population. The current study attempted to replicate these findings in a large community sample and also investigated possible associations with Extraversion, a personality trait linked to reward sensitivity. Participants (N = 299) completed the probabilistic reward task, as well as the Beck Depression Inventory, Personality Inventory for the DSM-5, Big Five Inventory, and Big Five Aspect Scales. Our direct replication attempts used bivariate correlations and analysis of variance models. Follow-up and extension analyses used structural equation models to assess relations among reward sensitivity, depression, Extraversion, and Neuroticism. No significant associations were found between reward sensitivity and depression, thus failing to replicate previous findings. Reward sensitivity (both modeled as response bias aggregated across blocks and as response bias controlling for baseline) showed positive associations with Extraversion, but not Neuroticism. Findings suggest reward sensitivity as measured by this task may be related primarily to Extraversion and its pathological manifestations, rather than to depression per se, consistent with existing models that conceptualize depressive symptoms as combining features of Neuroticism and low Extraversion. Findings are discussed in broader contexts of dimensional psychopathology frameworks, replicable science, and behavioral task reliability. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Scott D Blain
- Department of Psychology, University of Minnesota, Twin Cities
| | | | - Muchen Xi
- Department of Psychology, University of Minnesota, Twin Cities
| | - Daiqing Zhao
- Department of Psychology, University of Minnesota, Twin Cities
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, Twin Cities
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Ip CT, Olbrich S, Ganz M, Ozenne B, Köhler-Forsberg K, Dam VH, Beniczky S, Jørgensen MB, Frokjaer VG, Søgaard B, Christensen SR, Knudsen GM. Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study. Eur Neuropsychopharmacol 2021; 49:101-112. [PMID: 33910154 DOI: 10.1016/j.euroneuro.2021.03.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 02/24/2021] [Accepted: 03/30/2021] [Indexed: 02/04/2023]
Abstract
Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response. Registration number: NCT02869035.
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Affiliation(s)
- Cheng-Teng Ip
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark; Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Olbrich
- Department of Psychiatry, Psychotherapy and Psychosomatic, University Zürich, Switzerland
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Martin B Jørgensen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Birgitte Søgaard
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark
| | | | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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80
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Oathes DJ, Balderston NL, Kording KP, DeLuisi JA, Perez GM, Medaglia JD, Fan Y, Duprat RJ, Satterthwaite TD, Sheline YI, Linn KA. Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 12:e1553. [PMID: 33470055 DOI: 10.1002/wcs.1553] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.
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Affiliation(s)
- Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Konrad P Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joseph A DeLuisi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Gianna M Perez
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Romain J Duprat
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Kristin A Linn
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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81
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Jha MK, Fava M, Minhajuddin A, Chin Fatt C, Mischoulon D, Wakhlu N, Trombello JM, Cusin C, Trivedi MH. Anger attacks are associated with persistently elevated irritability in MDD: findings from the EMBARC study. Psychol Med 2021; 51:1355-1363. [PMID: 32138798 DOI: 10.1017/s0033291720000112] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND This report tests the association of self-reported symptoms of irritability with overt behavior of anger attacks (uncharacteristic sudden bouts of anger that are disproportionate to situation and associated with autonomic activation). METHODS Participants of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care study who completed Massachusetts General Hospital Anger Attacks questionnaire were included (n = 293). At each visit, the 17-item Hamilton Depression Rating Scale and the 16-item Concise Associated Symptom Tracking scale were used to measure depression, anxiety, and irritability. In those with anger attacks present v. those without anger attacks, separate t tests and mixed model analyses compared afore-mentioned symptoms at baseline and changes with treatment respectively. As anger attacks may occur without aggressive behaviors, analyses were repeated based only on the presence of aggressive behaviors. RESULTS At baseline, those with anger attacks (n = 109) v. those without anger attacks (n = 184) had similar levels of depression but higher levels of irritability [effect size (d) = 0.80] and anxiety (d = 0.32). With acute-phase treatment, participants with anger attacks experienced a greater reduction in irritability (p < 0.001) but not in depression (p = 0.813) or anxiety (p = 0.771) as compared to those without anger attacks. Yet, irritability levels at week-8 were higher in those with anger attacks (d = 0.32) than those without anger attacks. Similar results were found in participants with aggressive behaviors. CONCLUSIONS The presence of anger attacks in outpatients with major depressive disorder may identify a sub-group of patients with persistently elevated irritability.
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Affiliation(s)
- Manish K Jha
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Abu Minhajuddin
- Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cherise Chin Fatt
- Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - David Mischoulon
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Nausheen Wakhlu
- Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Joseph M Trombello
- Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cristina Cusin
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Madhukar H Trivedi
- Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
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82
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Park H, Petkova E, Tarpey T, Ogden RT. A constrained single-index regression for estimating interactions between a treatment and covariates. Biometrics 2021; 77:506-518. [PMID: 32573759 PMCID: PMC7755672 DOI: 10.1111/biom.13320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 06/16/2020] [Indexed: 11/28/2022]
Abstract
We consider a single-index regression model, uniquely constrained to estimate interactions between a set of pretreatment covariates and a treatment variable on their effects on a response variable, in the context of analyzing data from randomized clinical trials. We represent interaction effect terms of the model through a set of treatment-specific flexible link functions on a linear combination of the covariates (a single index), subject to the constraint that the expected value given the covariates equals 0, while leaving the main effects of the covariates unspecified. We show that the proposed semiparametric estimator is consistent for the interaction term of the model, and that the efficiency of the estimator can be improved with an augmentation procedure. The proposed single-index regression provides a flexible and interpretable modeling approach to optimizing individualized treatment rules based on patients' data measured at baseline, as illustrated by simulation examples and an application to data from a depression clinical trial.
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Affiliation(s)
- Hyung Park
- Division of Biostatistics, Department of Population Health, New York University, New York, New York
| | - Eva Petkova
- Division of Biostatistics, Department of Population Health, New York University, New York, New York
| | - Thaddeus Tarpey
- Division of Biostatistics, Department of Population Health, New York University, New York, New York
| | - R. Todd Ogden
- Department of Biostatistics, Columbia University, New York, New York
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83
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Fan L, Duan Q, Luo S. Self-Dependent Neural Variability Predicts Recovery from Depressive Symptoms. Soc Cogn Affect Neurosci 2021; 16:962-971. [PMID: 33990844 PMCID: PMC8421703 DOI: 10.1093/scan/nsab050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/20/2021] [Accepted: 05/14/2021] [Indexed: 11/28/2022] Open
Abstract
Researchers have increasingly paid attention to the neural dynamics of depression. This study examined whether self-dependent neural variability predicts recovery from depressive symptoms. Sixty adults with depressive symptoms who were not officially diagnosed with major depressive disorder participated in this study. Participants completed functional magnetic resonance imaging (fMRI) scanning, including a resting-state and a self-reflection task. The fMRI data were used to estimate neural variability, which refers to the temporal variability in regional functional connectivity patterns. Participants then completed the Self-Construal Scale and the Beck Depression Inventory (BDI). The change in BDI scores over 3 months indicated the degree of recovery from depressive symptoms. Self-construal moderated the effects of general neural variability on predicting recovery from depressive symptoms. Interdependent individuals became less depressive with higher general neural variability, but the relationship was not significant in independent individuals. The differences in neural variability between self-related and other-related conditions also predicted recovery from depressive symptoms. The regions contributing to the prediction were mainly distributed in the default-mode network. Based on these results, the harmony between individuals’ neural dynamics and self-concept is important for recovery from depressive symptoms, which might be a foundation for individualized treatment and counseling.
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Affiliation(s)
- Leyi Fan
- Department of Psychology, Guangdong Key Laboratory of Social Cognitive Neuroscience and Mental Health, Guangdong Provincial Key Laboratory of Brain Function and Disease, Sun Yat-sen University, Guangzhou 510006, China
| | - Qin Duan
- Department of Psychology, Guangdong Key Laboratory of Social Cognitive Neuroscience and Mental Health, Guangdong Provincial Key Laboratory of Brain Function and Disease, Sun Yat-sen University, Guangzhou 510006, China
| | - Siyang Luo
- Department of Psychology, Guangdong Key Laboratory of Social Cognitive Neuroscience and Mental Health, Guangdong Provincial Key Laboratory of Brain Function and Disease, Sun Yat-sen University, Guangzhou 510006, China
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84
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Jha MK, Schatzberg A, Minhajuddin A, Chin Fatt C, Mayes TL, Trivedi MH. Cross-Sectional Associations Among Symptoms of Pain, Irritability, and Depression and How These Symptoms Relate to Social Functioning and Quality of Life: Findings From the EMBARC and STRIDE Studies and the VitalSign6 Project. J Clin Psychiatry 2021; 82:20m13740. [PMID: 34000130 PMCID: PMC9578176 DOI: 10.4088/jcp.20m13740] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
OBJECTIVE The aim of this report was to evaluate the psychometric properties of the Pain Frequency, Intensity, and Burden Scale (P-FIBS), a brief measure of pain, as well as the association of pain with irritability and depression and how these symptoms relate to functional impairments. METHODS Participants of 2 randomized controlled trials (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care [EMBARC; n = 251 with DSM-IV diagnosis of major depressive disorder; study duration: August 2011-December 2015] and STimulant Reduction Intervention Using Dosed Exercise [STRIDE; n = 302 with DSM-IV diagnosis of stimulant abuse or dependence; study-duration: July 2010-February 2013]) and treatment-seeking patients in primary care clinics from an ongoing quality-improvement project (VitalSign6; n = 4,370; project duration: August 2014-July 2019) were included. Psychometric properties of the P-FIBS were evaluated with confirmatory factor and item response theory analyses in EMBARC and VitalSign6. The approach of Baron and Kenny was used to assess whether irritability accounted for the effect of pain on depression. RESULTS Cronbach α (0.84-0.89) and model fits for single-factor structure of P-FIBS were acceptable. Pain was positively correlated with irritability (r = 0.22-0.29) and depression (r = 0.10-0.33). Irritability accounted for 40.7%-65.5% of the effect of pain on depression. Higher irritability and depression were associated with poorer social functioning, quality of life, and productivity in work- and non-work-related activities. Pain was associated with non-work-related activity impairments even after controlling for irritability and depression. CONCLUSIONS The P-FIBS is a brief and reliable measure of pain. Irritability is associated with pain and accounts for a large proportion of the effect of pain on depression. Symptoms of pain, irritability, and depression are associated with functional impairments. TRIAL REGISTRATION ClinicalTrials.gov identifiers: NCT01407094 (EMBARC), NCT01141608 (STRIDE).
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Affiliation(s)
- Manish K Jha
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychiatry and Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Alan Schatzberg
- Department of Psychiatry and Behavioral Science, Stanford University, Stanford, California
| | - Abu Minhajuddin
- Department of Psychiatry and Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Cherise Chin Fatt
- Department of Psychiatry and Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Taryn L Mayes
- Department of Psychiatry and Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Madhukar H Trivedi
- Department of Psychiatry and Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, Texas
- Corresponding author: Madhukar H. Trivedi, MD, Department of Psychiatry, Professor of Psychiatry, Julie K. Hersh Chair for Depression Research and Clinical Care, Betty Jo Hay Distinguished Chair in Mental Health, Founding Director, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9119
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85
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Zhang J, Troxel AB, Petkova E. Robust index of confidence weighted learning for optimal individualized treatment rule estimation. Stat (Int Stat Inst) 2021. [DOI: 10.1002/sta4.374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jinchun Zhang
- Biostatistics and Research Decision Sciences, MRL Merck & Co., Inc. Kenilworth New Jersey USA
| | - Andrea B. Troxel
- Department of Population Health New York University Grossman School of Medicine New York New York USA
| | - Eva Petkova
- Department of Population Health New York University Grossman School of Medicine New York New York USA
- Nathan Kline Institute for Psychiatric Research Orangeburg New York USA
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86
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Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, Gordon J, Jeong J, Astolfi L, Shpigel E, Longwell P, Sarhadi K, El-Said D, Li Y, Cooper C, Chin-Fatt C, Arns M, Goodkind MS, Trivedi MH, Marmar CR, Etkin A. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng 2021; 5:309-323. [PMID: 33077939 PMCID: PMC8053667 DOI: 10.1038/s41551-020-00614-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 08/24/2020] [Indexed: 12/21/2022]
Abstract
The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.
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Affiliation(s)
- Yu Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Russell T Toll
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sharon Naparstek
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Mallissa Watts
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Joseph Gordon
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Jisoo Jeong
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering "Antonio Ruberti", University of Rome Sapienza, Rome, Italy
- IRCCF Fondazione Santa Lucia, Rome, Italy
| | - Emmanuel Shpigel
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Parker Longwell
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Kamron Sarhadi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Dawlat El-Said
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Pazhou Lab, Guangzhou, China
| | - Crystal Cooper
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cherise Chin-Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
- neuroCare Group, Munich, Germany
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Location AMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
- O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Charles R Marmar
- Steven and Alexandra Cohen Veterans Center for Post-traumatic Stress and Traumatic Brain Injury, New York University Langone School of Medicine, New York, NY, USA
- Center for Alcohol Use Disorder and PTSD, New York University Langone School of Medicine, New York, NY, USA
- Department of Psychiatry, New York University Langone School of Medicine, New York, NY, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Alto Neuroscience, Inc., Los Altos, CA, USA.
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87
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Chenji S, Cox E, Jaworska N, Swansburg RM, MacMaster FP. Body mass index and variability in hippocampal volume in youth with major depressive disorder. J Affect Disord 2021; 282:415-425. [PMID: 33422817 DOI: 10.1016/j.jad.2020.12.176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 10/31/2020] [Accepted: 12/24/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND The hippocampus has been implicated in major depressive disorder (MDD), in both adults and youth. However, possible sources of variability for the hippocampus have not been well delineated. Here, we explored the relationship between body mass index (BMI) and hippocampal volume in youth with MDD. METHODS Twenty-two controls (9 male, 13 female, 12-24 years), 24 youth with MDD and normal BMI (12 male, 12 female, 14-24 years), and 20 youth with MDD and high BMI (14 male, 6 female, 13-22 years) underwent magnetic resonance (MR) imaging and spectroscopy (1H-MRS). Hippocampal volume was determined through manual tracing of high-resolution anatomical T1 scans, and LCModel quantified neurochemical concentrations. Intracranial volume was used as a covariate in analysis to control for effects of brain volume on hippocampus. RESULTS In youth with MDD and normal BMI, right hippocampal volume was reduced (p = 0.006, Bonferroni) and a trend for reduced left hippocampal volume was noted when compared to healthy controls (p = 0.054, Bonferroni). Left hippocampal volumes were negatively associated with BMI in youth with MDD and high BMI group (r = -0.593, p = 0.006). No associations were found between the right hippocampus and BMI and there were no group differences for metabolite concentrations. LIMITATIONS Larger sample sizes would enable researchers to explore overweight vs obese groups and effect of sex in MDD-BMI groups. CONCLUSIONS BMI may account for some of the variability observed in previous studies of hippocampal volume in MDD, and therefore BMI impacts should be considered in future analyses.
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Affiliation(s)
- Sneha Chenji
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Alberta, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Alberta, Canada
| | - Emily Cox
- Department of Clinical Neurosciences, University of Calgary, Alberta, Canada
| | - Natalia Jaworska
- University of Ottawa Institute of Mental Health Research, Ontario, Canada
| | - Rose M Swansburg
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Alberta, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Alberta, Canada
| | - Frank P MacMaster
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Alberta, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Alberta, Canada; Addictions and Mental Health Strategic Clinical Network, Alberta, Canada.
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88
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Chin Fatt CR, Cooper CM, Jha MK, Minhajuddin A, Rush AJ, Trombello JM, Fava M, McInnis M, Weissman M, Trivedi MH. Differential response to SSRI versus Placebo and distinct neural signatures among data-driven subgroups of patients with major depressive disorder. J Affect Disord 2021; 282:602-610. [PMID: 33445082 DOI: 10.1016/j.jad.2020.12.102] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/19/2020] [Accepted: 12/24/2020] [Indexed: 11/19/2022]
Abstract
OBJECTIVE To identify data-driven subgroups in Major Depressive Disorder (MDD) in order to elucidate underlying neural correlates and determine if these subgroups have utility in predicting response to antidepressant versus placebo. METHODS Using 27 clinical measures at baseline of Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study, participants with MDD (n=244) were sub grouped using principal component (PC) analysis. Baseline-to-week-8 changes in depression severity with sertraline versus placebo were compared in these subgroups. Resting-state functional connectivity of these subgroups were compared to those of healthy controls (n=38). RESULTS Eight subgroups were identified from four PCs: (PC1) severity of depression-associated symptoms, (PC2) sub-threshold mania and anhedonia, (PC3) childhood trauma, medical comorbidities, and sexual dysfunction, and (PC4) personality traits of openness and agreeableness. Participants with high childhood trauma experienced greater improvement with sertraline (Cohen's d=0.87), whereas those with either higher levels of subthreshold hypomanic symptoms (Cohen's d=0.67) or with lower levels of agreeableness and openness experienced greater improvement with placebo (Cohen's d=0.71). Participants with high childhood trauma had greater connectivity between salience and dorsal attention networks, whereas those with higher levels of subthreshold hypomanic symptoms and lower levels of agreeableness and openness had greater connectivity within limbic network and that of visual network with hippocampus and dorsal attention network. CONCLUSION Assessing history of childhood trauma, presence of subthreshold hypomanic symptoms and personality traits may help to identify subgroups of patients with MDD who respond differentially to sertraline or placebo and have distinct neural signatures.
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Affiliation(s)
- Cherise R Chin Fatt
- The University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Department of Psychiatry, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - Crystal M Cooper
- The University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Department of Psychiatry, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - Manish K Jha
- The University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Department of Psychiatry, 5323 Harry Hines Blvd., Dallas, TX, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place. Box 1230. New York, NY, 10029, USA
| | - Abu Minhajuddin
- The University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Department of Psychiatry, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - A John Rush
- Department of Psychiatry and Behavioral Sciences, Duke-National University of Singapore, Singapore, 169857; Department of Psychiatry, Duke University Medical School, Durham, NC, USA; Department of Psychiatry, Texas Tech University, Health Science Center, Permian Basin, Midland, TX, USA
| | - Joseph M Trombello
- The University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Department of Psychiatry, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02144, USA
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan School of Medicine, 4250 Plymouth Road, Ann Arbor, MI 48109-2700, USA
| | - Myrna Weissman
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, 1051 Riverside Drive, New York, NY 10032, USA
| | - Madhukar H Trivedi
- The University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Department of Psychiatry, 5323 Harry Hines Blvd., Dallas, TX, USA.
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89
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Malik S, Singh R, Arora G, Dangol A, Goyal S. Biomarkers of Major Depressive Disorder: Knowing is Half the Battle. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2021; 19:12-25. [PMID: 33508785 PMCID: PMC7851463 DOI: 10.9758/cpn.2021.19.1.12] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 07/02/2020] [Accepted: 09/15/2020] [Indexed: 12/18/2022]
Abstract
Major depressive disorder (MDD) is a heterogeneous disease which is why there are currently no specific methods to accurately test the severity, endophenotype or therapy response. This lack of progress is partly attributed to the com-plexity and variability of depression, in association with analytical variability of clinical literature and the wide number of theoretically complex biomarkers. The literature accessible, indicates that markers involved in inflammatory, neuro-trophic and metabolic processes and components of neurotransmitters and neuroendocrine systems are rather strong indicators to be considered clinically and can be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomics and neuroimaging assessments. Promising biologic systems/markers found were i.e., growth biomarkers, endocrine markers, oxidant stress markers, proteomic and chronic inflammatory markers, are discussed in this review. Several lines of evidence suggest that a portion of MDD is a dopamine agonist-responsive subtype. This review analyzes concise reports on the pathophysiological biomarkers of MDD and therapeutic reactions via peripheral developmental factors, inflammative cytokines, endocrine factors and metabolic markers. Various literatures also support that endocrine and metabolism changes are associated with MDD. Accumulating evidence suggests that at least a portion of MDD patients show characteristics pathological changes regarding different clinical pathological biomarkers. By this review we sum up all the different biomarkers playing an important role in the detection or treatment of the different patients suffering from MDD. The review also gives an overview of different biomarker's playing a potential role in modulating effect of MDD.
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Affiliation(s)
- Sahil Malik
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Ravinder Singh
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Govind Arora
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Akriti Dangol
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Sanjay Goyal
- Department of Internal Medicine, Government Medical College, Patiala, India
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90
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Li L, Su YA, Wu YK, Castellanos FX, Li K, Li JT, Si TM, Yan CG. Eight-week antidepressant treatment reduces functional connectivity in first-episode drug-naïve patients with major depressive disorder. Hum Brain Mapp 2021; 42:2593-2605. [PMID: 33638263 PMCID: PMC8090770 DOI: 10.1002/hbm.25391] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 01/29/2021] [Accepted: 02/17/2021] [Indexed: 01/31/2023] Open
Abstract
Previous neuroimaging studies have revealed abnormal functional connectivity of brain networks in patients with major depressive disorder (MDD), but findings have been inconsistent. A recent big‐data study found abnormal intrinsic functional connectivity within the default mode network in patients with recurrent MDD but not in first‐episode drug‐naïve patients with MDD. This study also provided evidence for reduced default mode network functional connectivity in medicated MDD patients, raising the question of whether previously observed abnormalities may be attributable to antidepressant effects. The present study (ClinicalTrials.gov identifier: NCT03294525) aimed to disentangle the effects of antidepressant treatment from the pathophysiology of MDD and test the medication normalization hypothesis. Forty‐one first‐episode drug‐naïve MDD patients were administrated antidepressant medication (escitalopram or duloxetine) for 8 weeks, with resting‐state functional connectivity compared between posttreatment and baseline. To assess the replicability of the big‐data finding, we also conducted a cross‐sectional comparison of resting‐state functional connectivity between the MDD patients and 92 matched healthy controls. Both Network‐Based Statistic analyses and large‐scale network analyses revealed intrinsic functional connectivity decreases in extensive brain networks after treatment, indicating considerable antidepressant effects. Neither Network‐Based Statistic analyses nor large‐scale network analyses detected significant functional connectivity differences between treatment‐naïve patients and healthy controls. In short, antidepressant effects are widespread across most brain networks and need to be accounted for when considering functional connectivity abnormalities in MDD.
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Affiliation(s)
- Le Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing, China
| | - Yun-Ai Su
- Peking University Institute of Mental Health, Peking University Sixth Hospital & National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)/NHC Key Laboratory of Mental Health (Peking University), Beijing, China
| | - Yan-Kun Wu
- Peking University Institute of Mental Health, Peking University Sixth Hospital & National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)/NHC Key Laboratory of Mental Health (Peking University), Beijing, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, New York, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, USA
| | - Ke Li
- Department of Radiology, 306 Hospital of People's Liberation Army, Beijing, China
| | - Ji-Tao Li
- Peking University Institute of Mental Health, Peking University Sixth Hospital & National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)/NHC Key Laboratory of Mental Health (Peking University), Beijing, China
| | - Tian-Mei Si
- Peking University Institute of Mental Health, Peking University Sixth Hospital & National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)/NHC Key Laboratory of Mental Health (Peking University), Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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91
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Kim DJ, Bartlett EA, DeLorenzo C, Parsey RV, Kilts C, Cáceda R. Examination of structural brain changes in recent suicidal behavior. Psychiatry Res Neuroimaging 2021; 307:111216. [PMID: 33129637 PMCID: PMC9227957 DOI: 10.1016/j.pscychresns.2020.111216] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 11/17/2022]
Abstract
We aimed to identify brain structural changes in cortical and subcortical regions linked to recent suicidal behavior. We performed secondary analyses of structural MRI data of two independent studies, namely the Establishing Moderators/Biosignatures of Antidepressant Response - Clinical Care (EMBARC) study and a Little Rock study on acute suicidal behavior. Study 1 (EMBARC, N = 187), compared individuals with remote suicide attempts (Remote-SA), individuals with lifetime suicide ideation but no attempts (Lifetime-SI only), and depressed individuals without lifetime suicide ideation or attempts (non-suicidal depressed). Study 2 (Little Rock data, N = 34) included patients recently hospitalized for suicide ideation or attempt constituted by: patients who recently attempted suicide (Recent-SA), individuals with remote suicide attempts (Remote-SA), and Lifetime-SI only. Study 3 combined the EMBARC and Little Rock datasets including Recent-SA, Remote-SA, Lifetime-SI only and non-suicidal depressed individuals. In Study 1 and Study 2, no significant differences were observed between groups. In Study 3, significantly lower middle temporal gyrus thickness, insular surface area, and thalamic volume and higher volume in the nucleus accumbens were observed in Recent-SA. This pattern of structural abnormalities may underlie pain and emotion dysregulation, which have been linked to the transition from suicidal thoughts to action.
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Affiliation(s)
- Diane J Kim
- Renaissance School of Medicine at Stony Brook University, Department of Psychiatry and Behavioral Health, Stony Brook, New York, United States.
| | - Elizabeth A Bartlett
- Columbia University College of Physicians and Surgeons, Department of Psychiatry, New York, NY, United States; New York State Psychiatric Institute, Division of Molecular Imaging and Neuropathology, New York, New York, United States
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Department of Psychiatry and Behavioral Health, Stony Brook, New York, United States; Stony Brook University, Department of Biomedical Engineering, Stony Brook, New York, United States
| | - Ramin V Parsey
- Renaissance School of Medicine at Stony Brook University, Department of Psychiatry and Behavioral Health, Stony Brook, New York, United States
| | - Clinton Kilts
- University of Arkansas for Medical Sciences, Psychiatric Research Institute, Little Rock, Arkansas, United States
| | - Ricardo Cáceda
- Renaissance School of Medicine at Stony Brook University, Department of Psychiatry and Behavioral Health, Stony Brook, New York, United States
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92
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Fan S, Nemati S, Akiki TJ, Roscoe J, Averill CL, Fouda S, Averill LA, Abdallah CG. Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder. ACTA ACUST UNITED AC 2021; 4:2470547020984726. [PMID: 33458556 PMCID: PMC7783890 DOI: 10.1177/2470547020984726] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 12/10/2020] [Indexed: 12/04/2022]
Abstract
Background Major depressive disorder (MDD) treatment is characterized by low remission
rate and often involves weeks to months of treatment. Identification of
pretreatment biomarkers of response may play a critical role in novel drug
development, in enhanced prognostic predictions, and perhaps in providing
more personalized medicine. Using a network restricted strength predictive
modeling (NRS-PM) approach, the goal of the current study was to identify
pretreatment functional connectome fingerprints (CFPs) that (1) predict
symptom improvement regardless of treatment modality and (2) predict
treatment specific improvement. Methods Functional magnetic resonance imaging and behavioral data from unmedicated
patients with MDD (n = 200) were investigated. Participants were randomized
to daily treatment of sertraline or placebo for 8 weeks. NRS-PM with 1000
iterations of 10 cross-validation were implemented to identify brain
connectivity signatures that predict percent improvement in depression
severity at week-8. Results The study identified a pretreatment CFP that significantly predicts symptom
improvement independent of treatment modality but failed to identify a
treatment specific CFP. Regardless of treatment modality, improved
antidepressant response was predicted by high pretreatment connectivity
between modules in the default mode network and the rest of the brain, but
low external connectivity in the executive network. Moreover, high
pretreatment internal nodal connectivity in the bilateral caudate predicted
better response. Conclusions The identified CFP may contribute to drug development and ultimately to
enhanced prognostic predictions. However, the results do not assist with
providing personalized medicine, as pretreatment functional connectivity
failed to predict treatment specific response.
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Affiliation(s)
- Siyan Fan
- National Center for PTSD—Clinical Neuroscience Division, US
Department of Veterans Affairs, West Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine,
New Haven, Connecticut
| | - Samaneh Nemati
- National Center for PTSD—Clinical Neuroscience Division, US
Department of Veterans Affairs, West Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine,
New Haven, Connecticut
| | - Teddy J. Akiki
- National Center for PTSD—Clinical Neuroscience Division, US
Department of Veterans Affairs, West Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine,
New Haven, Connecticut
- Center for Behavioral Health—Neurological Institute, Cleveland
Clinic, Cleveland, Ohio
| | - Jeremy Roscoe
- National Center for PTSD—Clinical Neuroscience Division, US
Department of Veterans Affairs, West Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine,
New Haven, Connecticut
| | - Christopher L. Averill
- Michael E. DeBakey, VA Medical Center, Houston, Texas
- Menninger Department of Psychiatry and Behavioral Sciences,
Baylor College of Medicine, Houston, Texas
| | - Samar Fouda
- National Center for PTSD—Clinical Neuroscience Division, US
Department of Veterans Affairs, West Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine,
New Haven, Connecticut
| | - Lynnette A. Averill
- National Center for PTSD—Clinical Neuroscience Division, US
Department of Veterans Affairs, West Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine,
New Haven, Connecticut
- Michael E. DeBakey, VA Medical Center, Houston, Texas
- Menninger Department of Psychiatry and Behavioral Sciences,
Baylor College of Medicine, Houston, Texas
| | - Chadi G. Abdallah
- National Center for PTSD—Clinical Neuroscience Division, US
Department of Veterans Affairs, West Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine,
New Haven, Connecticut
- Michael E. DeBakey, VA Medical Center, Houston, Texas
- Menninger Department of Psychiatry and Behavioral Sciences,
Baylor College of Medicine, Houston, Texas
- Chadi G. Abdallah, Menninger Department of
Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler
Blvd, E4187, Houston, TX 77030, USA.
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93
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Benrimoh D, Tanguay-Sela M, Perlman K, Israel S, Mehltretter J, Armstrong C, Fratila R, Parikh SV, Karp JF, Heller K, Vahia IV, Blumberger DM, Karama S, Vigod SN, Myhr G, Martins R, Rollins C, Popescu C, Lundrigan E, Snook E, Wakid M, Williams J, Soufi G, Perez T, Tunteng JF, Rosenfeld K, Miresco M, Turecki G, Gomez Cardona L, Linnaranta O, Margolese HC. Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction. BJPsych Open 2021; 7:e22. [PMID: 33403948 PMCID: PMC8058891 DOI: 10.1192/bjo.2020.127] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. AIMS Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction. METHOD Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. RESULTS All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction. CONCLUSIONS The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University, Canada; Aifred Heath Inc., Montreal, Canada; and Faculty of Medicine, McGill University, Canada
| | - Myriam Tanguay-Sela
- Montreal Neurological Institute, McGill University, Canada; and Aifred Health Inc., Montreal, Canada
| | - Kelly Perlman
- Douglas Mental Health University Institute, Montreal, Canada; and Aifred Health Inc., Montreal, Canada
| | | | - Joseph Mehltretter
- Department of Computer Science, University of Southern California, Los Angeles, USA; and Aifred Health Inc., Montreal, Canada
| | - Caitrin Armstrong
- School of Computer Science, McGill University, Canada; and Aifred Health Inc., Montreal, Canada
| | | | | | - Jordan F Karp
- Department of Psychiatry, University of Pittsburgh, USA
| | | | - Ipsit V Vahia
- Department of Psychiatry, McLean Hospital/Harvard University, USA
| | | | | | | | - Gail Myhr
- Department of Psychiatry, McGill University, Canada
| | - Ruben Martins
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
| | - Colleen Rollins
- Department of Psychiatry, University of Cambridge, UK; and Aifred Health Inc., Montreal, Canada
| | - Christina Popescu
- Douglas Mental Health University Institute, Montreal, Canada; and Aifred Health Inc., Montreal, Canada
| | - Eryn Lundrigan
- Department of Anatomy and Cell Biology, McGill University, Canada
| | - Emily Snook
- Faculty of Medicine, University of Toronto, Canada
| | - Marina Wakid
- Douglas Mental Health University Institute, Montreal, Canada
| | | | | | - Tamara Perez
- Department of Experimental Medicine, McGill University, Canada
| | | | | | - Marc Miresco
- Department of Psychiatry, McGill University, Canada
| | - Gustavo Turecki
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
| | - Liliana Gomez Cardona
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
| | - Outi Linnaranta
- Douglas Mental Health University Institute, Montreal, Canada; and Department of Psychiatry, McGill University, Canada
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94
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Olbrich S, Brunovsky M. The way ahead for predictive EEG biomarkers in treatment of depression. Clin Neurophysiol 2021; 132:616-617. [PMID: 33386211 DOI: 10.1016/j.clinph.2020.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland.
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany, Czech Republic; Third Faculty of Medicine, Charles University, Prague, Czech Republic
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95
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Jha MK, Fava M, Minhajuddin A, Chin Fatt C, Mischoulon D, Cusin C, Trivedi MH. Association of anger attacks with suicidal ideation in adults with major depressive disorder: Findings from the EMBARC study. Depress Anxiety 2021; 38:57-66. [PMID: 33038902 DOI: 10.1002/da.23095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/06/2020] [Accepted: 09/09/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND This report evaluates whether anger attacks (sudden uncharacteristic bouts of anger that are associated with autonomic arousal and/or aggression) in patients with major depressive disorder (MDD) are associated with elevated suicidal ideation (SI; active suicidal thoughts and plans). METHODS Participants of Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study who completed Massachusetts General Hospital Anger Attack Questionnaire (AAQ) at baseline were included (n = 293). Levels of SI (suicidal thoughts factor of Concise Health Risk Tracking) were compared at baseline with generalized linear models, and during Stage 1 (baseline-to-week-8) and Stage 2 (week-8-to-week-16) with repeated-measures mixed model analyses. Covariates included age, sex, race, ethnicity, site, and treatment arm. RESULTS At baseline, participants with (n = 109) versus without anger attacks (n = 184) had higher levels of SI (Cohen's d effect size [d] = 1.20). Those with ≥9 anger attacks in the past month had significantly higher SI than those with 1-2 (d = 1.21), 3-4 (d = 1.48), and 5-8 (d = 0.94) anger attacks in the past month. Furthermore, participants with anger attacks at baseline reported higher SI at each post-baseline visit (both Stages 1 and 2) of EMBARC study (d = 0.39-0.77; all p < .05). Associations between anger attacks and SI were significant even after controlling for irritability, hostility, anxious arousal, depression, suicide propensity, and self-reported pain at baseline and lifetime suicidal tendencies. Similar results were found in participants with aggressive behaviors. CONCLUSION Anger attacks in outpatients with MDD may be associated with chronically elevated SI. Clinical Trials Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC); NCT01407094; https://clinicaltrials.gov/ct2/show/NCT01407094.
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Affiliation(s)
- Manish Kumar Jha
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Psychiatry, Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Abu Minhajuddin
- Department of Psychiatry, Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, Texas, USA
| | - David Mischoulon
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Christina Cusin
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Madhukar H Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, UT Southwestern Medical Center, Dallas, Texas, USA
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96
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A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression. Mol Psychiatry 2021; 26:7372-7383. [PMID: 34088979 PMCID: PMC8873015 DOI: 10.1038/s41380-021-01176-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 05/07/2021] [Accepted: 05/17/2021] [Indexed: 02/05/2023]
Abstract
Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.
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97
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Taylor JJ, Kurt HG, Anand A. Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review. Front Psychiatry 2021; 12:565136. [PMID: 33841196 PMCID: PMC8032870 DOI: 10.3389/fpsyt.2021.565136] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 02/26/2021] [Indexed: 12/24/2022] Open
Abstract
There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.
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Affiliation(s)
- Joseph J Taylor
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Hatice Guncu Kurt
- Center for Behavioral Health, Cleveland Clinic, Cleveland, OH, United States
| | - Amit Anand
- Center for Behavioral Health, Cleveland Clinic, Cleveland, OH, United States
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98
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Abdallah CG, Ahn KH, Averill LA, Nemati S, Averill CL, Fouda S, Ranganathan M, Morgan PT, D’Souza DC, Mathalon DH, Krystal JH, Driesen NR. A robust and reproducible connectome fingerprint of ketamine is highly associated with the connectomic signature of antidepressants. Neuropsychopharmacology 2021; 46:478-485. [PMID: 32967000 PMCID: PMC7852889 DOI: 10.1038/s41386-020-00864-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 08/15/2020] [Accepted: 09/10/2020] [Indexed: 12/13/2022]
Abstract
Over the past decade, various N-methyl-D-aspartate modulators have failed in clinical trials, underscoring the challenges of developing novel rapid-acting antidepressants based solely on the receptor or regional targets of ketamine. Thus, identifying the effect of ketamine on the brain circuitry and networks is becoming increasingly critical. In this longitudinal functional magnetic resonance imaging study of data from 265 participants, we used a validated predictive model approach that allows the full assessment of brain functional connectivity, without the need for seed selection or connectivity summaries. First, we identified a connectome fingerprint (CFP) in healthy participants (Cohort A, n = 25) during intravenous infusion of a subanesthetic dose of ketamine, compared to normal saline. We then demonstrated the robustness and reproducibility of the discovered ketamine CFP in two separate healthy samples (Cohort B, n = 22; Cohort C, n = 18). Finally, we investigated the ketamine CFP connectivity at 1-week post treatment in major depressive disorder patients randomized to 8 weeks of sertraline or placebo (Cohort D, n = 200). We found a significant, robust, and reproducible ketamine CFP, consistent with reduced connectivity within the primary cortices and within the executive network, but increased connectivity between the executive network and the rest of the brain. Compared to placebo, the ketamine CFP connectivity changes at 1 week predicted response to sertraline at 8 weeks. In each of Cohorts A-C, ketamine significantly increased connectivity in a previously identified antidepressant CFP. Investigating the brain connectivity networks, we successfully identified a robust and reproducible ketamine biomarker that is related to the mechanisms of antidepressants.
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Affiliation(s)
- Chadi G. Abdallah
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA ,grid.413890.70000 0004 0420 5521Michael E. DeBakey VA Medical Center, Houston, TX USA ,grid.39382.330000 0001 2160 926XMenninger Department of Psychiatry, Baylor College of Medicine, Houston, TX USA
| | - Kyung-Heup Ahn
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Lynnette A. Averill
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Samaneh Nemati
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Christopher L. Averill
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Samar Fouda
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Mohini Ranganathan
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Peter T. Morgan
- grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Deepak C. D’Souza
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Daniel H. Mathalon
- grid.266102.10000 0001 2297 6811San Francisco VA Medical Center, University of California, San Francisco, CA USA
| | - John H. Krystal
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
| | - Naomi R. Driesen
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, CT USA ,grid.47100.320000000419368710Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
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Chen Y, Wang Y, Zeng D. Synthesizing independent stagewise trials for optimal dynamic treatment regimes. Stat Med 2020; 39:4107-4119. [PMID: 32804414 PMCID: PMC7814466 DOI: 10.1002/sim.8712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/29/2020] [Accepted: 07/09/2020] [Indexed: 11/09/2022]
Abstract
Dynamic treatment regimes (DTRs) adaptively prescribe treatments based on patients' intermediate responses and evolving health status over multiple treatment stages. Data from sequential multiple assignment randomization trials (SMARTs) are recommended to be used for learning DTRs. However, due to re-randomization of the same patients over multiple treatment stages and a prolonged follow-up period, SMARTs are often difficult to implement and costly to manage, and patient adherence is always a concern in practice. To lessen such practical challenges, we propose an alternative approach to learn optimal DTRs by synthesizing independent trials over different stages. Specifically, at each stage, data from a single randomized trial along with patients' natural medical history and health status in previous stages are used. We use a backward learning method to estimate optimal treatment decisions at a particular stage, where patients' future optimal outcome increments are estimated using data observed from independent trials with future stages' information. Under some conditions, we show that the proposed method yields consistent estimation of the optimal DTRs and we obtain the same learning rates as those from SMARTs. We conduct simulation studies to demonstrate the advantage of the proposed method. Finally, we learn optimal DTRs for treating major depressive disorder (MDD) by stagewise synthesis of two randomized trials. We perform a validation study on independent subjects and show that the synthesized DTRs lead to the greatest MDD symptom reduction compared to alternative methods.
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Affiliation(s)
- Yuan Chen
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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100
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Jha MK, Minhajuddin A, Chin Fatt C, Kircanski K, Stringaris A, Leibenluft E, Trivedi MH. Association between irritability and suicidal ideation in three clinical trials of adults with major depressive disorder. Neuropsychopharmacology 2020; 45:2147-2154. [PMID: 32663842 PMCID: PMC7784964 DOI: 10.1038/s41386-020-0769-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/19/2020] [Accepted: 07/08/2020] [Indexed: 12/19/2022]
Abstract
Irritability in pediatric samples is associated with higher rates of subsequent suicide-related outcomes. No study, to date, has evaluated the longitudinal association between irritability and suicidal ideation (SI) in adults with major depressive disorder (MDD). This report evaluated whether irritability is associated with SI at the same visit (i.e., concurrently) and whether early changes in irritability with antidepressant treatment predict subsequent levels of SI. Participants of Combining Medications to Enhance Depression Outcomes (CO-MED, n = 665), Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC, n = 296), and Suicide Assessment Methodology Study (SAMS, n = 266) were included. Repeated-measures mixed model analyses evaluated concurrent association throughout the trial between irritability (five-item irritability domain of Concise Associated Symptom Tracking scale) and SI (three-item suicidal thoughts factor of Concise Health Risk Tracking scale) after controlling for overall depression (excluding suicidality-related item), and predicted subsequent levels of SI (repeated observations from week-2-to-week-8) based on early (baseline-to-week-2) changes in irritability after controlling for early changes in overall depression. Higher irritability was associated with higher SI concurrently; estimates (standard error) were 0.18 (0.02, p < 0.0001), 0.64 (0.02, p < 0.0001), and 0.26 (0.04, p < 0.0001) in CO-MED, EMBARC, and SAMS respectively. Greater baseline-to-week-2 reductions in irritability predicted lower levels of subsequent SI; estimates (standard errors) were -0.08 (0.03, p = 0.023), -0.50 (0.05, p < 0.0001), and -0.12 (0.05, p = 0.024) in CO-MED, EMBARC, and SAMS, respectively. Controlling for anxiety or insomnia produced similar results. In conclusion, irritability and SI were consistently linked in adults with MDD. These findings support careful assessment of irritability in suicide risk assessment.
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Affiliation(s)
- Manish K Jha
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA
- Center for Depression Research and Clinical Care, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9119, USA
| | - Abu Minhajuddin
- Center for Depression Research and Clinical Care, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9119, USA
| | - Cherise Chin Fatt
- Center for Depression Research and Clinical Care, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9119, USA
| | | | - Argyris Stringaris
- National Institute of Mental Health, 10 Center Dr, Bethesda, MD, 20814, USA
| | - Ellen Leibenluft
- National Institute of Mental Health, 10 Center Dr, Bethesda, MD, 20814, USA
| | - Madhukar H Trivedi
- Center for Depression Research and Clinical Care, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9119, USA.
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