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Yoon JH, Lee D, Lee C, Cho E, Lee S, Cazenave-Gassiot A, Kim K, Chae S, Dennis EA, Suh PG. Paradigm shift required for translational research on the brain. Exp Mol Med 2024; 56:1043-1054. [PMID: 38689090 PMCID: PMC11148129 DOI: 10.1038/s12276-024-01218-x] [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: 10/13/2023] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.
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Affiliation(s)
- Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea.
| | - Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Chany Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Eunji Cho
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Seulah Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Kipom Kim
- Research Strategy Office, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Sehyun Chae
- Neurovascular Unit Research Group, Korean Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Edward A Dennis
- Department of Pharmacology and Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0601, USA
| | - Pann-Ghill Suh
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
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2
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Zhou T, Ho YY, Lee RX, Fath AB, He K, Scott J, Bajwa N, Hartley ND, Wilde J, Gao X, Li C, Hong E, Nassar MR, Wimmer RD, Singh T, Halassa MM, Feng G. Enhancement of mediodorsal thalamus rescues aberrant belief dynamics in a mouse model with schizophrenia-associated mutation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.08.574745. [PMID: 38260581 PMCID: PMC10802391 DOI: 10.1101/2024.01.08.574745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Optimizing behavioral strategy requires belief updating based on new evidence, a process that engages higher cognition. In schizophrenia, aberrant belief dynamics may lead to psychosis, but the mechanisms underlying this process are unknown, in part, due to lack of appropriate animal models and behavior readouts. Here, we address this challenge by taking two synergistic approaches. First, we generate a mouse model bearing patient-derived point mutation in Grin2a (Grin2aY700X+/-), a gene that confers high-risk for schizophrenia and recently identified by large-scale exome sequencing. Second, we develop a computationally trackable foraging task, in which mice form and update belief-driven strategies in a dynamic environment. We found that Grin2aY700X+/- mice perform less optimally than their wild-type (WT) littermates, showing unstable behavioral states and a slower belief update rate. Using functional ultrasound imaging, we identified the mediodorsal (MD) thalamus as hypofunctional in Grin2aY700X+/- mice, and in vivo task recordings showed that MD neurons encoded dynamic values and behavioral states in WT mice. Optogenetic inhibition of MD neurons in WT mice phenocopied Grin2aY700X+/- mice, and enhancing MD activity rescued task deficits in Grin2aY700X+/- mice. Together, our study identifies the MD thalamus as a key node for schizophrenia-relevant cognitive dysfunction, and a potential target for future therapeutics.
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Affiliation(s)
- Tingting Zhou
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Yi-Yun Ho
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Ray X Lee
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Amanda B Fath
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Kathleen He
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Jonathan Scott
- Department of Neuroscience, Tufts University School of Medicine
| | - Navdeep Bajwa
- Department of Neuroscience, Tufts University School of Medicine
| | - Nolan D Hartley
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard
| | - Jonathan Wilde
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Xian Gao
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Cui Li
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Evan Hong
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | | | - Ralf D Wimmer
- Department of Neuroscience, Tufts University School of Medicine
| | - Tarjinder Singh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard
| | | | - Guoping Feng
- Yang Tan Collection and McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard
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3
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Shen FX, Baum ML, Martinez-Martin N, Miner AS, Abraham M, Brownstein CA, Cortez N, Evans BJ, Germine LT, Glahn DC, Grady C, Holm IA, Hurley EA, Kimble S, Lázaro-Muñoz G, Leary K, Marks M, Monette PJ, Jukka-Pekka O, O’Rourke PP, Rauch SL, Shachar C, Sen S, Vahia I, Vassy JL, Baker JT, Bierer BE, Silverman BC. Returning Individual Research Results from Digital Phenotyping in Psychiatry. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2024; 24:69-90. [PMID: 37155651 PMCID: PMC10630534 DOI: 10.1080/15265161.2023.2180109] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.
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Affiliation(s)
- Francis X. Shen
- Harvard Medical School
- Massachusetts General Hospital
- Harvard Law School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Mason Marks
- Harvard Law School
- Florida State University College of Law
- Yale Law School
| | | | | | | | - Scott L. Rauch
- Harvard Medical School
- McLean Hospital
- Mass General Brigham
| | | | | | | | - Jason L. Vassy
- Harvard Medical School
- Brigham and Women’s Hospital
- VA Boston Healthcare System
| | | | - Barbara E. Bierer
- Harvard Medical School
- Brigham and Women’s Hospital
- Multi-Regional Clinical Trials Center of Brigham and Women’s Hospital and Harvard
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4
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Bouzid A, Almidani A, Zubrikhina M, Kamzanova A, Ilce BY, Zholdassova M, Yusuf AM, Bhamidimarri PM, AlHaj HA, Kustubayeva A, Bernstein A, Burnaev E, Sharaev M, Hamoudi R. Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1. Neurobiol Stress 2023; 26:100555. [PMID: 37583471 PMCID: PMC10423927 DOI: 10.1016/j.ynstr.2023.100555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 08/17/2023] Open
Abstract
Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention. In this study, a mixture of AI and bioinformatics were used to mine transcriptomic data from publicly available datasets including 170 MDD patients and 121 healthy controls. Bioinformatics analysis using gene set enrichment analysis (GSEA) and machine learning (ML) algorithms were applied. The GSEA revealed that differentially expressed genes in MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways. Feature selection methods and ML provided predicted models based on MDD-altered genes with ≥75% of accuracy. The integrative analysis between the bioinformatics and ML approaches identified ten key MDD-related biomarkers including NRG1, CEACAM8, CLEC12B, DEFA4, HP, LCN2, OLFM4, SERPING1, TCN1 and THBS1. Among them, NRG1, active in synaptic plasticity and neurotransmission, was the most robust and reliable to distinguish between MDD patients and healthy controls amongst independent external datasets consisting of a mixture of populations. Further evaluation using saliva samples from an independent cohort of MDD and healthy individuals confirmed the upregulation of NRG1 in patients with MDD compared to healthy controls. Functional mapping to the human brain regions showed NRG1 to have high expression in the main subcortical limbic brain regions implicated in depression. In conclusion, integrative bioinformatics and ML approaches identified putative non-invasive diagnostic MDD-related biomarkers panel for the onset of depression.
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Affiliation(s)
- Amal Bouzid
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Abdulrahman Almidani
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Maria Zubrikhina
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Altyngul Kamzanova
- The Center for Cognitive Neuroscience, Al Farabi Kazakh National University, Kazakhstan
| | - Burcu Yener Ilce
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Manzura Zholdassova
- The Center for Cognitive Neuroscience, Al Farabi Kazakh National University, Kazakhstan
| | - Ayesha M. Yusuf
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Poorna Manasa Bhamidimarri
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Hamid A. AlHaj
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Faculty of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Almira Kustubayeva
- The Center for Cognitive Neuroscience, Al Farabi Kazakh National University, Kazakhstan
| | - Alexander Bernstein
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Evgeny Burnaev
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Maxim Sharaev
- Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| | - Rifat Hamoudi
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Faculty of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
- ASPIRE Precision Medicine Research Institute Abu Dhabi, University of Sharjah, Sharjah, United Arab Emirates
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5
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Felsky D, Cannitelli A, Pipitone J. Whole Person Modeling: a transdisciplinary approach to mental health research. DISCOVER MENTAL HEALTH 2023; 3:16. [PMID: 37638348 PMCID: PMC10449734 DOI: 10.1007/s44192-023-00041-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.
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Affiliation(s)
- Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Alyssa Cannitelli
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Jon Pipitone
- Department of Psychiatry, Queen’s University, Kingston, ON Canada
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6
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Javaheripour N, Colic L, Opel N, Li M, Maleki Balajoo S, Chand T, Van der Meer J, Krylova M, Izyurov I, Meller T, Goltermann J, Winter NR, Meinert S, Grotegerd D, Jansen A, Alexander N, Usemann P, Thomas-Odenthal F, Evermann U, Wroblewski A, Brosch K, Stein F, Hahn T, Straube B, Krug A, Nenadić I, Kircher T, Croy I, Dannlowski U, Wagner G, Walter M. Altered brain dynamic in major depressive disorder: state and trait features. Transl Psychiatry 2023; 13:261. [PMID: 37460460 DOI: 10.1038/s41398-023-02540-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/20/2023] Open
Abstract
Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.
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Affiliation(s)
- Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
| | - Lejla Colic
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Nils Opel
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
| | - Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, 40225, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, 52425, Jülich, Germany
| | - Tara Chand
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
- Department of Clinical Psychology, Friedrich Schiller University Jena, Am Steiger 3-1, 07743, Jena, Germany
| | - Johan Van der Meer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marina Krylova
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Igor Izyurov
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Ilona Croy
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
- Department of Clinical Psychology, Friedrich Schiller University Jena, Am Steiger 3-1, 07743, Jena, Germany
- Department of Psychotherapie and Psychosomatic Medicine, Carl Gustav Carus University Hospital Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany.
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany.
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany.
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany.
- German Center for Mental Health (DZPG), Jena, Germany.
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany.
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
- Leibniz Institute for Neurobiology, Magdeburg, Germany.
- Center for Behavioral Brain Sciences, Magdeburg, Germany.
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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7
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Rosenblau G, Frolichs K, Korn CW. A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders. Neurosci Biobehav Rev 2023; 149:105181. [PMID: 37062494 PMCID: PMC10236440 DOI: 10.1016/j.neubiorev.2023.105181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/14/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023]
Abstract
Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.
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Affiliation(s)
- Gabriela Rosenblau
- Department of Psychological and Brain Sciences, George Washington University, Washington DC, USA; Autism and Neurodevelopmental Disorders Institute, George Washington University, Washington DC, USA.
| | - Koen Frolichs
- Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany; Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph W Korn
- Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany; Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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8
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Hampel H, Caruso G, Nisticò R, Piccioni G, Mercuri NB, Giorgi FS, Ferrarelli F, Lemercier P, Caraci F, Lista S, Vergallo A. Biological Mechanism-based Neurology and Psychiatry: A BACE1/2 and Downstream Pathway Model. Curr Neuropharmacol 2023; 21:31-53. [PMID: 34852743 PMCID: PMC10193755 DOI: 10.2174/1570159x19666211201095701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 02/04/2023] Open
Abstract
In oncology, comprehensive omics and functional enrichment studies have led to an extensive profiling of (epi)genetic and neurobiological alterations that can be mapped onto a single tumor's clinical phenotype and divergent clinical phenotypes expressing common pathophysiological pathways. Consequently, molecular pathway-based therapeutic interventions for different cancer typologies, namely tumor type- and site-agnostic treatments, have been developed, encouraging the real-world implementation of a paradigm shift in medicine. Given the breakthrough nature of the new-generation translational research and drug development in oncology, there is an increasing rationale to transfertilize this blueprint to other medical fields, including psychiatry and neurology. In order to illustrate the emerging paradigm shift in neuroscience, we provide a state-of-the-art review of translational studies on the β-site amyloid precursor protein cleaving enzyme (BACE) and its most studied downstream effector, neuregulin, which are molecular orchestrators of distinct biological pathways involved in several neurological and psychiatric diseases. This body of data aligns with the evidence of a shared genetic/biological architecture among Alzheimer's disease, schizoaffective disorder, and autism spectrum disorders. To facilitate a forward-looking discussion about a potential first step towards the adoption of biological pathway-based, clinical symptom-agnostic, categorization models in clinical neurology and psychiatry for precision medicine solutions, we engage in a speculative intellectual exercise gravitating around BACE-related science, which is used as a paradigmatic case here. We draw a perspective whereby pathway-based therapeutic strategies could be catalyzed by highthroughput techniques embedded in systems-scaled biology, neuroscience, and pharmacology approaches that will help overcome the constraints of traditional descriptive clinical symptom and syndrome-focused constructs in neurology and psychiatry.
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Affiliation(s)
- Harald Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France
| | | | - Robert Nisticò
- Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, Rome, Italy
- School of Pharmacy, University of Rome “Tor Vergata”, Rome, Italy
| | - Gaia Piccioni
- Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, Rome, Italy
- Department of Physiology and Pharmacology “V.Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Nicola B. Mercuri
- Department of Systems Medicine, University of Rome “Tor Vergata”, Rome, Italy
- IRCCS Santa Lucia Foundation, Rome, Italy
| | - Filippo Sean Giorgi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh, School of Medicine, Pittsburgh, PA, USA
| | - Pablo Lemercier
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France
| | - Filippo Caraci
- Oasi Research Institute-IRCCS, Troina, Italy
- Department of Drug Sciences, University of Catania, Catania, Italy
| | - Simone Lista
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France
- Memory Resources and Research Center (CMRR), Neurology Department, Gui de Chauliac University Hospital, Montpellier, France
| | - Andrea Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France
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9
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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10
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Barriers to genetic testing in clinical psychiatry and ways to overcome them: from clinicians' attitudes to sociocultural differences between patients across the globe. Transl Psychiatry 2022; 12:442. [PMID: 36220808 PMCID: PMC9553897 DOI: 10.1038/s41398-022-02203-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 09/15/2022] [Accepted: 09/23/2022] [Indexed: 11/08/2022] Open
Abstract
Genetic testing has evolved rapidly over recent years and new developments have the potential to provide insights that could improve the ability to diagnose, treat, and prevent diseases. Information obtained through genetic testing has proven useful in other specialties, such as cardiology and oncology. Nonetheless, a range of barriers impedes techniques, such as whole-exome or whole-genome sequencing, pharmacogenomics, and polygenic risk scoring, from being implemented in psychiatric practice. These barriers may be procedural (e.g., limitations in extrapolating results to the individual level), economic (e.g., perceived relatively elevated costs precluding insurance coverage), or related to clinicians' knowledge, attitudes, and practices (e.g., perceived unfavorable cost-effectiveness, insufficient understanding of probability statistics, and concerns regarding genetic counseling). Additionally, several ethical concerns may arise (e.g., increased stigma and discrimination through exclusion from health insurance). Here, we provide an overview of potential barriers for the implementation of genetic testing in psychiatry, as well as an in-depth discussion of strategies to address these challenges.
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11
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Manzone ML, Barbieri A, Orlandini F. Commentary: Perspective on excellence in forensic mental health services: What we can learn from oncology and other medical services. Front Public Health 2022; 10:951337. [PMID: 36072377 PMCID: PMC9444131 DOI: 10.3389/fpubh.2022.951337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/20/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Maria Laura Manzone
- Department of Mental Health, Psychiatric Emergency Service, Asl4 Liguria, Chiavari, Italy
- *Correspondence: Maria Laura Manzone
| | - Antonella Barbieri
- Independent Researcher, Milan, Italy
- Independent Researcher, London, United Kingdom
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12
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Akinyelu AA, Zaccagna F, Grist JT, Castelli M, Rundo L. Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey. J Imaging 2022; 8:205. [PMID: 35893083 PMCID: PMC9331677 DOI: 10.3390/jimaging8080205] [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: 04/10/2022] [Revised: 06/20/2022] [Accepted: 07/12/2022] [Indexed: 02/01/2023] Open
Abstract
Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.
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Affiliation(s)
- Andronicus A. Akinyelu
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal;
- Department of Computer Science and Informatics, University of the Free State, Phuthaditjhaba 9866, South Africa
| | - Fulvio Zaccagna
- Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum-University of Bologna, 40138 Bologna, Italy;
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, 40139 Bologna, Italy
| | - James T. Grist
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, UK;
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Oxford Centre for Clinical Magnetic Research Imaging, University of Oxford, Oxford OX3 9DU, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2SY, UK
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal;
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
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13
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Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. Eur Neuropsychopharmacol 2022; 60:100-116. [PMID: 35671641 DOI: 10.1016/j.euroneuro.2022.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022]
Abstract
Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.
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14
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Gaur L, Bhandari M, Razdan T, Mallik S, Zhao Z. Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data. Front Genet 2022; 13:822666. [PMID: 35360838 PMCID: PMC8964286 DOI: 10.3389/fgene.2022.822666] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Abstract
Cancer research has seen explosive development exploring deep learning (DL) techniques for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We have observed a substantial gap in explanation, interpretability, and high accuracy for DL models. Consequently, we propose an explanation-driven DL model by utilising a convolutional neural network (CNN), local interpretable model-agnostic explanation (LIME), and Shapley additive explanation (SHAP) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an MRI image dataset. Unlike previous models, our model used a dual-input CNN approach to prevail over the classification challenge with images of inferior quality in terms of noise and metal artifacts by adding Gaussian noise. Our CNN training results reveal 94.64% accuracy as compared to other state-of-the-art methods. We used SHAP to ensure consistency and local accuracy for interpretation as Shapley values examine all future predictions applying all possible combinations of inputs. In contrast, LIME constructs sparse linear models around each prediction to illustrate how the model operates in the immediate area. Our emphasis for this study is interpretability and high accuracy, which is critical for realising disparities in predictive performance, helpful in developing trust, and essential in integration into clinical practice. The proposed method has a vast clinical application that could potentially be used for mass screening in resource-constraint countries.
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Affiliation(s)
- Loveleen Gaur
- Amity International Business School, Amity University, Noida, India
| | - Mohan Bhandari
- Nepal College of Information Technology, Lalitpur, Nepal
| | - Tanvi Razdan
- Amity International Business School, Amity University, Noida, India
| | - Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- *Correspondence: Zhongming Zhao,
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15
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Grant CW, Barreto EF, Kumar R, Kaddurah-Daouk R, Skime M, Mayes T, Carmody T, Biernacka J, Wang L, Weinshilboum R, Trivedi MH, Bobo WV, Croarkin PE, Athreya AP. Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder. J Pers Med 2022; 12:jpm12030412. [PMID: 35330412 PMCID: PMC8949112 DOI: 10.3390/jpm12030412] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 02/24/2022] [Accepted: 03/02/2022] [Indexed: 01/14/2023] Open
Abstract
Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.
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Grants
- R01 MH124655 NIMH NIH HHS
- R01 MH113700 NIMH NIH HHS
- K23 AI143882 NIAID NIH HHS
- U19GM61388, R01GM028157, R01AA027486, R01MH108348, R24GM078233, RC2GM092729, U19AG063744, N01MH90003, R01AG04617, U01AG061359, RF1AG051550, R01MH113700, R01MH124655, K23AI143882 NIH HHS
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Affiliation(s)
- Caroline W. Grant
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55901, USA; (C.W.G.); (L.W.); (R.W.)
| | - Erin F. Barreto
- Department of Pharmacy, Mayo Clinic, Rochester, MN 55901, USA;
| | - Rakesh Kumar
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55901, USA; (R.K.); (M.S.)
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27701, USA;
- Department of Medicine, Duke University, Durham, NC 27708, USA
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27710, USA
| | - Michelle Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55901, USA; (R.K.); (M.S.)
| | - Taryn Mayes
- Department of Psychiatry, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA; (T.M.); (M.H.T.)
| | - Thomas Carmody
- Department Population and Data Sciences, University of Texas Southwestern Medical Center in Dallas, Dallas, TX 75390, USA;
| | - Joanna Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55901, USA;
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55901, USA; (C.W.G.); (L.W.); (R.W.)
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55901, USA; (C.W.G.); (L.W.); (R.W.)
| | - Madhukar H. Trivedi
- Department of Psychiatry, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA; (T.M.); (M.H.T.)
| | - William V. Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55901, USA; (R.K.); (M.S.)
- Correspondence: (P.E.C.); (A.P.A.); Tel.: +1-507-422-6073 (A.P.A.)
| | - Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55901, USA; (C.W.G.); (L.W.); (R.W.)
- Correspondence: (P.E.C.); (A.P.A.); Tel.: +1-507-422-6073 (A.P.A.)
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16
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Kalgotra P, Sharda R. When will I get out of the Hospital? Modeling Length of Stay using Comorbidity Networks. J MANAGE INFORM SYST 2022. [DOI: 10.1080/07421222.2021.1990618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Pankush Kalgotra
- Harbert College of Business, Auburn University Auburn, AL 36849 US
| | - Ramesh Sharda
- Vice Dean, Watson Graduate School of Management, Regents Professor of Management Science and Information Systems, Spears School of Business, Oklahoma State University, OK 74078, USA
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17
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Kung B, Chiang M, Perera G, Pritchard M, Stewart R. Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study. Sci Rep 2021; 11:22426. [PMID: 34789827 PMCID: PMC8599474 DOI: 10.1038/s41598-021-01954-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
Abstract
Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of symptoms that do not reflect the disorder's heterogeneity. By contrast, clinicians often write about patients in depth, creating descriptions that may better characterize depression. However, clinical text is not commonly used to this end. Here we show that clinically relevant depressive subtypes can be derived from unstructured electronic health records. Five subtypes were identified amongst 18,314 patients with depression treated at a large mental healthcare provider by using unsupervised machine learning: severe-typical, psychotic, mild-typical, agitated, and anergic-apathetic. Subtypes were used to place patients in groups for validation; groups were found to be associated with future outcomes and characteristics that were consistent with the subtypes. These associations suggest that these categorizations are actionable due to their validity with respect to disease prognosis. Moreover, they were derived with automated techniques that might theoretically be widely implemented, allowing for future analyses in more varied populations and settings. Additional research, especially with respect to treatment response, may prove useful in further evaluation.
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Affiliation(s)
| | | | - Gayan Perera
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Megan Pritchard
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Robert Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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18
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Fernandes BS, Quevedo J, Zhao Z. Fostering precision psychiatry through bioinformatics. ACTA ACUST UNITED AC 2021; 44:119-120. [PMID: 34378746 PMCID: PMC9041962 DOI: 10.1590/1516-4446-2021-2083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 06/20/2021] [Indexed: 11/22/2022]
Affiliation(s)
- Brisa S Fernandes
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - João Quevedo
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, UTHealth, Houston, TX, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA.,Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, UTHealth, Houston, TX, USA.,Human Genetics Center, School of Public Health, UTHealth, Houston, TX, USA
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19
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Remington G, Hahn MK, Agarwal SM, Chintoh A, Agid O. Schizophrenia: Antipsychotics and drug development. Behav Brain Res 2021; 414:113507. [PMID: 34352293 DOI: 10.1016/j.bbr.2021.113507] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 07/25/2021] [Accepted: 07/28/2021] [Indexed: 12/21/2022]
Abstract
The introduction of chlorpromazine and the work that ensued provided the foundation to reposition schizophrenia as a biological illness. The present paper follows the evolution of antipsychotics and their shift from 'typical' to 'atypical'. Atypicality is reviewed in reference to its original definition, clozapine's role, and developments that now leave the concept's utility in question. In a similar fashion, drug development is reviewed in the context of the illness' multiple symptom domains, as well as differences captured by clinical staging and phenotyping. Collectively, the evidence argues for a more nuanced approach to drug development that aligns with the illness' heterogeneity and complexity. Just as 'atypical' as a descriptor for antipsychotics may be outdated, it may be time to set aside the notion of developing drugs that treat 'schizophrenia'.
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Affiliation(s)
- Gary Remington
- University of Toronto, Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.
| | - Margaret K Hahn
- University of Toronto, Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Sri Mahavir Agarwal
- University of Toronto, Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Araba Chintoh
- University of Toronto, Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Ofer Agid
- University of Toronto, Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
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20
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Blom JMC, Colliva C, Benatti C, Tascedda F, Pani L. Digital Phenotyping and Dynamic Monitoring of Adolescents Treated for Cancer to Guide Intervention: Embracing a New Era. Front Oncol 2021; 11:673581. [PMID: 34262863 PMCID: PMC8273734 DOI: 10.3389/fonc.2021.673581] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Johanna M. C. Blom
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
| | - Chiara Colliva
- Azienda Unità Sanitaria Locale di Modena, Distretto di Carpi, Carpi, Italy
| | - Cristina Benatti
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Fabio Tascedda
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Luca Pani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
- Department of Psychiatry and Behavioral Sciences, University of Miami, Miami, FL, United States
- VeraSci., Durham, NC, United States
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