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Quiñones I, Gisbert-Muñoz S, Amoruso L, Manso-Ortega L, Mori U, Bermudez G, Robles SG, Pomposo I, Carreiras M. Unveiling the neuroplastic capacity of the bilingual brain: insights from healthy and pathological individuals. Brain Struct Funct 2024:10.1007/s00429-024-02846-9. [PMID: 39289268 DOI: 10.1007/s00429-024-02846-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 07/29/2024] [Indexed: 09/19/2024]
Abstract
Research on the neural imprint of dual-language experience, crucial for understanding how the brain processes dominant and non-dominant languages, remains inconclusive. Conflicting evidence suggests either similarity or distinction in neural processing, with implications for bilingual patients with brain tumors. Preserving dual-language functions after surgery requires considering pre-diagnosis neuroplastic changes. Here, we combine univariate and multivariate fMRI methodologies to test a group of healthy Spanish-Basque bilinguals and a group of bilingual patients with gliomas affecting the language-dominant hemisphere while they overtly produced sentences in either their dominant or non-dominant language. Findings from healthy participants revealed the presence of a shared neural system for both languages, while also identifying regions with distinct language-dependent activation and lateralization patterns. Specifically, while the dominant language engaged a more left-lateralized network, speech production in the non-dominant language relied on the recruitment of a bilateral basal ganglia-thalamo-cortical circuit. Notably, based on language lateralization patterns, we were able to robustly decode (AUC: 0.80 ± 0.18) the language being used. Conversely, bilingual patients exhibited bilateral activation patterns for both languages. For the dominant language, regions such as the cerebellum, thalamus, and caudate acted in concert with the sparsely activated language-specific nodes. In the case of the non-dominant language, the recruitment of the default mode network was notably prominent. These results demonstrate the compensatory engagement of non-language-specific networks in the preservation of bilingual speech production, even in the face of pathological conditions. Overall, our findings underscore the pervasive impact of dual-language experience on brain functional (re)organization, both in health and disease.
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Affiliation(s)
- Ileana Quiñones
- Biogipuzkoa Health Research Institute, Basque Foundation for Science, San Sebastian, 20009, Spain.
- Ikerbasque, Basque Foundation for Science, Bilbao, 48009, Spain.
| | - Sandra Gisbert-Muñoz
- ESIC Business and Marketing School, Valencia, Spain.
- University of the Basque Country, UPV/EHU, Bilbao, 48940, Spain.
| | - Lucía Amoruso
- Ikerbasque, Basque Foundation for Science, Bilbao, 48009, Spain.
- BCBL, Basque Center on Cognition, Brain, and Language, San Sebastian, 20009, Spain.
| | - Lucia Manso-Ortega
- University of the Basque Country, UPV/EHU, Bilbao, 48940, Spain
- BCBL, Basque Center on Cognition, Brain, and Language, San Sebastian, 20009, Spain
| | - Usue Mori
- University of the Basque Country, UPV/EHU, Bilbao, 48940, Spain
| | - Garazi Bermudez
- Biobizkaia Health Research Institute, Bilbao, 48015, Spain
- Department of Neurosurgery, Hospital Cruces, Bilbao, 48903, Spain
| | - Santiago Gil Robles
- Biobizkaia Health Research Institute, Bilbao, 48015, Spain
- Department of Neurosurgery, Hospital Quirón salud, Madrid, 28223, Spain
| | - Iñigo Pomposo
- Biobizkaia Health Research Institute, Bilbao, 48015, Spain
- Department of Neurosurgery, Hospital Cruces, Bilbao, 48903, Spain
| | - Manuel Carreiras
- Ikerbasque, Basque Foundation for Science, Bilbao, 48009, Spain
- University of the Basque Country, UPV/EHU, Bilbao, 48940, Spain
- BCBL, Basque Center on Cognition, Brain, and Language, San Sebastian, 20009, Spain
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Wu Y, Gao H, Zhang C, Ma X, Zhu X, Wu S, Lin L. Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review. Tomography 2024; 10:1238-1262. [PMID: 39195728 PMCID: PMC11359833 DOI: 10.3390/tomography10080093] [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: 07/18/2024] [Revised: 08/09/2024] [Accepted: 08/09/2024] [Indexed: 08/29/2024] Open
Abstract
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.
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Affiliation(s)
| | | | | | | | | | | | - Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (H.G.); (C.Z.); (X.M.); (X.Z.); (S.W.)
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3
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Edelstein R, Gutterman S, Newman B, Van Horn JD. Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics? Neuroinformatics 2024:10.1007/s12021-024-09680-8. [PMID: 39078562 DOI: 10.1007/s12021-024-09680-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2024] [Indexed: 07/31/2024]
Abstract
Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.
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Affiliation(s)
- Rachel Edelstein
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA.
| | - Sterling Gutterman
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA
| | - Benjamin Newman
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, 409 McCormick Road Gilmer Hall Room 304, Charlottesville, VA, 22904, USA
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Dagnew TM, Tseng CEJ, Yoo CH, Makary MM, Goodheart AE, Striar R, Meyer TN, Rattray AK, Kang L, Wolf KA, Fiedler SA, Tocci D, Shapiro H, Provost S, Sultana E, Liu Y, Ding W, Chen P, Kubicki M, Shen S, Catana C, Zürcher NR, Wey HY, Hooker JM, Weiss RD, Wang C. Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study. iScience 2024; 27:110159. [PMID: 39021792 PMCID: PMC11253155 DOI: 10.1016/j.isci.2024.110159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/13/2024] [Accepted: 05/29/2024] [Indexed: 07/20/2024] Open
Abstract
Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-environment interaction in AUD remain largely unknown. In this proof-of-concept study, we showed, for the first time, the neuroepigenetic biomarker capability of non-invasive imaging of class I histone deacetylase (HDAC) epigenetic enzymes in the in vivo brain for classifying AUD patients from healthy controls using a machine learning approach in the context of precision diagnosis. Eleven AUD patients and 16 age- and sex-matched healthy controls completed a simultaneous positron emission tomography-magnetic resonance (PET/MR) scan with the HDAC-binding radiotracer [11C]Martinostat. Our results showed lower HDAC expression in the anterior cingulate region in AUD. Furthermore, by applying a genetic algorithm feature selection, we identified five particular brain regions whose combined [11C]Martinostat relative standard uptake value (SUVR) features could reliably classify AUD vs. controls. We validate their promising classification reliability using a support vector machine classifier. These findings inform the potential of in vivo HDAC imaging biomarkers coupled with machine learning tools in the objective diagnosis and molecular translation of AUD that could complement the current diagnostic and statistical manual of mental disorders (DSM)-based intervention to propel precision medicine forward.
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Affiliation(s)
- Tewodros Mulugeta Dagnew
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chieh-En J. Tseng
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chi-Hyeon Yoo
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Meena M. Makary
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt
| | - Anna E. Goodheart
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Robin Striar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tyler N. Meyer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anna K. Rattray
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Leyi Kang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kendall A. Wolf
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephanie A. Fiedler
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Darcy Tocci
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hannah Shapiro
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Scott Provost
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Eleanor Sultana
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Yan Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Ding
- Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA
| | - Ping Chen
- Department of Engineering, University of Massachusetts Boston, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Shiqian Shen
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicole R. Zürcher
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hsiao-Ying Wey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jacob M. Hooker
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Roger D. Weiss
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Changning Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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5
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Wider W, Mutang JA, Chua BS, Pang NTP, Jiang L, Fauzi MA, Udang LN. Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions. Front Hum Neurosci 2024; 18:1339444. [PMID: 38799297 PMCID: PMC11116792 DOI: 10.3389/fnhum.2024.1339444] [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: 11/22/2023] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Introduction This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments. Methods It examined 3,626 journal articles from the Web of Science (WoS) using co-citation and co-word methods. Results The co-citation analysis identified three major clusters: "Real-Time fMRI Neurofeedback and Self-Regulation of Brain Activity," "EEG Neurofeedback and Cognitive Performance Enhancement," and "Treatment of ADHD Using Neurofeedback." The co-word analysis highlighted four key clusters: "Neurofeedback in Mental Health Research," "Brain-Computer Interfaces for Stroke Rehabilitation," "Neurofeedback for ADHD in Youth," and "Neural Mechanisms of Emotion and Self-Regulation with Advanced Neuroimaging. Discussion This in-depth bibliometric study significantly enhances our understanding of the dynamic field of neurofeedback, indicating its potential in treating ADHD and improving performance. It offers non-invasive, ethical alternatives to conventional psychopharmacology and aligns with the trend toward personalized medicine, suggesting specialized solutions for mental health and rehabilitation as a growing focus in medical practice.
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Affiliation(s)
- Walton Wider
- Faculty of Business and Communications, INTI International University, Nilai, Negeri Sembilan, Malaysia
| | - Jasmine Adela Mutang
- Faculty of Psychology and Education, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
| | - Bee Seok Chua
- Faculty of Psychology and Education, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
| | - Nicholas Tze Ping Pang
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
| | - Leilei Jiang
- Faculty of Education and Liberal Arts, INTI International University, Nilai, Negeri Sembilan, Malaysia
| | - Muhammad Ashraf Fauzi
- Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia
| | - Lester Naces Udang
- Faculty of Liberal Arts, Shinawatra University, Pathumthani, Thailand
- College of Education, University of the Philippines, Diliman, Philippines
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6
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Kiyak C, Ijezie OA, Ackah JA, Armstrong M, Cowen J, Cetinkaya D, Burianová H, Akudjedu TN. Topographical Distribution of Neuroanatomical Abnormalities Following COVID-19 Invasion : A Systematic Literature Review. Clin Neuroradiol 2024; 34:13-31. [PMID: 37697012 PMCID: PMC10881816 DOI: 10.1007/s00062-023-01344-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/14/2023] [Indexed: 09/13/2023]
Abstract
PURPOSE This systematic review is aimed at synthesising the literature base to date on the frequency and topographical distribution of neuroanatomical changes seen on imaging following COVID-19 invasion with a focus on both the acute and chronic phases of the disease. METHODS In this study, 8 databases were systematically searched to identify relevant articles published from December 2019 to March 2022 and supplemented with a manual reference search. Data were extracted from the included studies and narrative synthesis was employed to integrate the findings. RESULTS A total of 110 studies met the inclusion criteria and comprised 119,307 participants (including 31,073 acute and 143 long COVID-19 patients manifesting neurological alterations) and controls. Considerable variability in both the localisation and nature of neuroanatomical abnormalities are noted along the continuum with a wide range of neuropathologies relating to the cerebrovascular/neurovascular system, (sub)cortical structures (including deep grey and white matter structures), brainstem, and predominant regional and/or global alterations in the cerebellum with varying degrees of spinal involvement. CONCLUSION Structural regional alterations on neuroimaging are frequently demonstrated in both the acute and chronic phases of SARS-CoV‑2 infection, particularly prevalent across subcortical, prefrontal/frontal and cortico-limbic brain areas as well as the cerebrovascular/neurovascular system. These findings contribute to our understanding of the acute and chronic effects of the virus on the nervous system and has the potential to provide information on acute and long-term treatment and neurorehabilitation decisions.
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Affiliation(s)
- Ceyda Kiyak
- Faculty of Science and Technology, Bournemouth University, Bournemouth, UK
- School of Psychology, University of East Anglia, Norwich, UK
| | | | - Joseph A Ackah
- Institute of Medical Imaging and Visualisation, Faculty of Health and Social Sciences, Bournemouth University, 8 8GP, Bournemouth, UK
| | - Matthew Armstrong
- Department of Rehabilitation & Sports Sciences, Faculty of Health and Social Sciences, Bournemouth University, Bournemouth, UK
| | - Jake Cowen
- Department of Radiology, Queen Alexandra Hospital, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Deniz Cetinkaya
- Faculty of Science and Technology, Bournemouth University, Bournemouth, UK
| | - Hana Burianová
- Faculty of Science and Technology, Bournemouth University, Bournemouth, UK
| | - Theophilus N Akudjedu
- Institute of Medical Imaging and Visualisation, Faculty of Health and Social Sciences, Bournemouth University, 8 8GP, Bournemouth, UK.
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Calomino C, Quattrone A, Bianco MG, Nisticò R, Buonocore J, Crasà M, Vaccaro MG, Sarica A, Quattrone A. Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor. Front Neurol 2024; 15:1372262. [PMID: 38585347 PMCID: PMC10995929 DOI: 10.3389/fneur.2024.1372262] [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: 01/17/2024] [Accepted: 02/14/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET. Methods We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients. Results ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms. Conclusion This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.
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Affiliation(s)
- Camilla Calomino
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Andrea Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy
| | - Maria Giovanna Bianco
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Rita Nisticò
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Jolanda Buonocore
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy
| | - Marianna Crasà
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Maria Grazia Vaccaro
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Alessia Sarica
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
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Zimmer L. Recent applications of positron emission tomographic (PET) imaging in psychiatric drug discovery. Expert Opin Drug Discov 2024; 19:161-172. [PMID: 37948046 DOI: 10.1080/17460441.2023.2278635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Psychiatry is one of the medical disciplines that suffers most from a lack of innovation in its therapeutic arsenal. Many failures in drug candidate trials can be explained by pharmacological properties that have been poorly assessed upstream, in terms of brain passage, brain target binding and clinical outcomes. Positron emission tomography can provide pharmacokinetic and pharmacodynamic data to help select candidate-molecules for further clinical trials. AREAS COVERED This review aims to explain and discuss the various methods using positron-emitting radiolabeled molecules to trace the cerebral distribution of the drug-candidate or indirectly measure binding to its therapeutic target. More than an exhaustive review of PET studies in psychopharmacology, this article highlights the contributions this technology can make in drug discovery applied to psychiatry. EXPERT OPINION PET neuroimaging is the only technological approach that can, in vivo in humans, measure cerebral delivery of a drug candidate, percentage and duration of target binding, and even the pharmacological effects. PET studies in a small number of subjects in the early stages of the development of a psychotropic drug can therefore provide the pharmacokinetic/pharmacodynamic data required for subsequent clinical evaluation. While PET technology is demanding in terms of radiochemical, radiopharmacological and nuclear medicine expertise, its integration into the development process of new drugs for psychiatry has great added value.
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Affiliation(s)
- Luc Zimmer
- Lyon Neuroscience Research Center, Université Claude Bernard, Lyon, France
- CERMEP, Hospices Civils de Lyon, Lyon, France
- Institut National des Sciences et Technologies Nucléaire, Saclay, France
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Buchman DZ, Imahori D, Lo C, Hui K, Walker C, Shaw J, Davis KD. The Influence of Using Novel Predictive Technologies on Judgments of Stigma, Empathy, and Compassion among Healthcare Professionals. AJOB Neurosci 2024; 15:32-45. [PMID: 37450417 DOI: 10.1080/21507740.2023.2225470] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND Our objective was to evaluate whether the description of a machine learning (ML) app or brain imaging technology to predict the onset of schizophrenia or alcohol use disorder (AUD) influences healthcare professionals' judgments of stigma, empathy, and compassion. METHODS We randomized healthcare professionals (N = 310) to one vignette about a person whose clinician seeks to predict schizophrenia or an AUD, using a ML app, brain imaging, or a psychosocial assessment. Participants used scales to measure their judgments of stigma, empathy, and compassion. RESULTS Participants randomized to the ML vignette endorsed less anger and more fear relative to the psychosocial vignette, and the brain imaging vignette elicited higher pity ratings. The brain imaging and ML vignettes evoked lower personal responsibility judgments compared to the psychosocial vignette. Physicians and nurses reported less empathy than clinical psychologists. CONCLUSIONS The use of predictive technologies may reinforce essentialist views about mental health and substance use that may increase specific aspects of stigma and reduce others.
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Affiliation(s)
- Daniel Z Buchman
- Centre for Addiction and Mental Health
- Dalla Lana School of Public Health, University of Toronto
- University of Toronto Joint Centre for Bioethics
| | | | - Christopher Lo
- Dalla Lana School of Public Health, University of Toronto
- Temerty Faculty of Medicine, University of Toronto
- College of Healthcare Sciences, James Cook University, Singapore
| | - Katrina Hui
- Centre for Addiction and Mental Health
- Temerty Faculty of Medicine, University of Toronto
| | | | - James Shaw
- University of Toronto Joint Centre for Bioethics
- Temerty Faculty of Medicine, University of Toronto
| | - Karen D Davis
- Temerty Faculty of Medicine, University of Toronto
- Krembil Brain Institute, University Health Network
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10
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Quattrone A, Sarica A, Buonocore J, Morelli M, Bianco MG, Calomino C, Aracri F, De Maria M, Vescio B, Vaccaro MG, Quattrone A. Differentiating between common PSP phenotypes using structural MRI: a machine learning study. J Neurol 2023; 270:5502-5515. [PMID: 37507502 PMCID: PMC10576703 DOI: 10.1007/s00415-023-11892-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
BACKGROUND Differentiating Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data. METHODS Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes. RESULTS MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients. CONCLUSION The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials.
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Affiliation(s)
- Andrea Quattrone
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy
| | - Alessia Sarica
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Jolanda Buonocore
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy
| | - Maurizio Morelli
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy
| | - Maria Giovanna Bianco
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Camilla Calomino
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Federica Aracri
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Marida De Maria
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | | | - Maria Grazia Vaccaro
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Aldo Quattrone
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Viale Europa, Germaneto, 88100, Catanzaro, Italy.
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11
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Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare. Cureus 2023; 15:e43262. [PMID: 37692617 PMCID: PMC10492220 DOI: 10.7759/cureus.43262] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.
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Affiliation(s)
- Madhan Jeyaraman
- Orthopedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | - Sangeetha Balaji
- Orthopedics, Government Medical College, Omandurar Government Estate, Chennai, IND
| | - Naveen Jeyaraman
- Orthopedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | - Sankalp Yadav
- Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
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12
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Poikonen H, Zaluska T, Wang X, Magno M, Kapur M. Nonlinear and machine learning analyses on high-density EEG data of math experts and novices. Sci Rep 2023; 13:8012. [PMID: 37198273 DOI: 10.1038/s41598-023-35032-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/11/2023] [Indexed: 05/19/2023] Open
Abstract
Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.
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Affiliation(s)
- Hanna Poikonen
- Learning Sciences and Higher Education, ETH Zurich, Clausiusstrasse 59 RZ J2, 8092, Zurich, Switzerland.
| | - Tomasz Zaluska
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Xiaying Wang
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Michele Magno
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Manu Kapur
- Learning Sciences and Higher Education, ETH Zurich, Clausiusstrasse 59 RZ J2, 8092, Zurich, Switzerland
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13
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Bianco MG, Quattrone A, Sarica A, Aracri F, Calomino C, Caligiuri ME, Novellino F, Nisticò R, Buonocore J, Crasà M, Vaccaro MG, Quattrone A. Cortical involvement in essential tremor with and without rest tremor: a machine learning study. J Neurol 2023:10.1007/s00415-023-11747-6. [PMID: 37145157 DOI: 10.1007/s00415-023-11747-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/04/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
INTRODUCTION There is some debate on the relationship between essential tremor with rest tremor (rET) and the classic ET syndrome, and only few MRI studies compared ET and rET patients. This study aimed to explore structural cortical differences between ET and rET, to improve the knowledge of these tremor syndromes. METHODS Thirty-three ET patients, 30 rET patients and 45 control subjects (HC) were enrolled. Several MR morphometric variables (thickness, surface area, volume, roughness, mean curvature) of brain cortical regions were extracted using Freesurfer on T1-weighted images and compared among groups. The performance of a machine learning approach (XGBoost) using the extracted morphometric features was tested in discriminating between ET and rET patients. RESULTS rET patients showed increased roughness and mean curvature in some fronto-temporal areas compared with HC and ET, and these metrics significantly correlated with cognitive scores. Cortical volume in the left pars opercularis was also lower in rET than in ET patients. No differences were found between ET and HC. XGBoost discriminated between rET and ET with mean AUC of 0.86 ± 0.11 in cross-validation analysis, using a model based on cortical volume. Cortical volume in the left pars opercularis was the most informative feature for classification between the two ET groups. CONCLUSION Our study demonstrated higher cortical involvement in fronto-temporal areas in rET than in ET patients, which may be linked to the cognitive status. A machine learning approach based on MR volumetric data demonstrated that these two ET subtypes can be distinguished using structural cortical features.
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Affiliation(s)
- Maria Giovanna Bianco
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Andrea Quattrone
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Alessia Sarica
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Federica Aracri
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Camilla Calomino
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Maria Eugenia Caligiuri
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Fabiana Novellino
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Rita Nisticò
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Jolanda Buonocore
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Marianna Crasà
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Maria Grazia Vaccaro
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy
| | - Aldo Quattrone
- Department of Medical and Surgical Sciences, Neuroscience Research Center, University "Magna Graecia", Catanzaro, Italy.
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14
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Jirsaraie RJ, Gorelik AJ, Gatavins MM, Engemann DA, Bogdan R, Barch DM, Sotiras A. A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility. PATTERNS (NEW YORK, N.Y.) 2023; 4:100712. [PMID: 37123443 PMCID: PMC10140612 DOI: 10.1016/j.patter.2023.100712] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
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Affiliation(s)
- Robert J. Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron J. Gorelik
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M. Gatavins
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Undergraduate Neuroscience Program, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Denis A. Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M. Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Corresponding author
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15
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Vedaei F, Mashhadi N, Zabrecky G, Monti D, Navarreto E, Hriso C, Wintering N, Newberg AB, Mohamed FB. Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques. Front Neurosci 2023; 16:1099560. [PMID: 36699521 PMCID: PMC9869678 DOI: 10.3389/fnins.2022.1099560] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration [clinicaltrials.gov], identifier [NCT03241732].
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Najmeh Mashhadi
- Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B. Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
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Rosano C. A training program for researchers in population neuroimaging: Early experiences. FRONTIERS IN NEUROIMAGING 2022; 1:896350. [PMID: 37555144 PMCID: PMC10406197 DOI: 10.3389/fnimg.2022.896350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/28/2022] [Indexed: 08/10/2023]
Abstract
Recent advances in neuroimaging create groundbreaking opportunities to better understand human neurological and psychiatric diseases, but also bring new challenges. With the advent of more and more sophisticated and efficient multimodal image processing software, we can now study much larger populations and integrate information from multiple modalities. In consequence, investigators that use neuroimaging techniques must also understand and apply principles of population sampling and contemporary data analytic techniques. The next generation of neuroimaging researchers must be skilled in numerous previously distinct disciplines and so a new integrated model of training is needed. This tutorial presents the rationale for such a new training model and presents the results from the first years of the training program focused on population neuroimaging of Alzheimer's Disease. This approach is applicable to other areas of population neuroimaging.
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Affiliation(s)
- Caterina Rosano
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
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