{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T20:39:28Z","timestamp":1786653568700,"version":"build-2736575974"},"reference-count":60,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Despite extensive research on various brain diseases, a few studies have focused on radiomic feature distribution in healthy brain images. The present study applied a novel radiomic framework to investigate the robustness and baseline values of radiomic features in normal brain magnetic resonance imaging (MRIs) regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>Analyses were performed on T1 and T2 images including 276 normal brains and 14 healthy volunteers were scanned with three scanners using the same protocols. The images were divided into 1024 three-dimensional nonoverlap patches with the same pixel size. Seven patches located in the thalamus, putamen, hippocampus and brain stem were selected as volume of interest (VOI). Eighty-five radiomic features were generated. To investigate the variation of features across VOIs, the analysis of variance was performed and coefficient of variation (COV) and intraclass correlation coefficient (ICC) were explored to examine the features repeatability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Thalamus (right and left) and hippocampus (left) resulted in more stable features (COV \u2264 6%) in T1 and T2 images, respectively. The inter-scanner ICC analysis demonstrated the features of T2 sequences represented more repeatable results and the brain stem and thalamus (both T1 and T2) showed particularly high repeatability (higher ICC values). Robust results (ICC \u2265 0.9) were identified for energy and range features of the first order class and several textures features across different brain regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Our results indicated the baselines of the repeatable texture features in healthy brain structural MRI highlighting inter-scanner stability. According to the findings, MRI sequencing and VOI location impact feature robustness and should be considered in brain radiomic studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmp.jmp_149_24","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T04:00:24Z","timestamp":1734580824000},"page":"608-622","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":2,"title":["A Comprehensive Evaluation of Radiomic Features in Normal Brain Magnetic Resonance Imaging: Investigating Robustness and Region Variations"],"prefix":"10.4103","volume":"49","author":[{"given":"Mahsa","family":"Shakeri","sequence":"first","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran"},{"name":"Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Mostaar","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arash Zare","family":"Sadeghi","sequence":"additional","affiliation":[{"name":"Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyyed Mohammad","family":"Hosseini","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran"},{"name":"Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Yaghobi","family":"Joybari","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Ghadiri","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran"},{"name":"Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"R1-20260424","doi-asserted-by":"crossref","first-page":"91.","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging-\u201chow-to\u201d guide and critical reflection","volume":"11","author":"van Timmeren","year":"2020","journal-title":"Insights Imaging"},{"key":"R2-20260424","doi-asserted-by":"crossref","first-page":"R150","DOI":"10.1088\/0031-9155\/61\/13\/R150","article-title":"Applications and limitations of radiomics","volume":"61","author":"Yip","year":"2016","journal-title":"Phys Med Biol"},{"key":"R3-20260424","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1111\/jon.12262","article-title":"MRI texture analysis reveals deep gray nuclei damage in amyotrophic lateral sclerosis","volume":"26","author":"de Albuquerque","year":"2016","journal-title":"J Neuroimaging"},{"key":"R4-20260424","doi-asserted-by":"crossref","first-page":"09TR03","DOI":"10.1088\/1361-6560\/ac60b9","article-title":"The stability of oncologic MRI radiomic features and the potential role of deep learning: A review","volume":"67","author":"Scalco","year":"2022","journal-title":"Phys Med Biol"},{"key":"R5-20260424","doi-asserted-by":"crossref","first-page":"393.","DOI":"10.3389\/fnagi.2018.00393","article-title":"Predicting the development of normal-appearing white matter with radiomics in the aging brain: A longitudinal clinical study","volume":"10","author":"Shao","year":"2018","journal-title":"Front Aging Neurosci"},{"key":"R6-20260424","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1148\/radiol.2016152149","article-title":"Psychoradiology: The frontier of neuroimaging in psychiatry","volume":"281","author":"Lui","year":"2016","journal-title":"Radiology"},{"key":"R7-20260424","doi-asserted-by":"crossref","first-page":"16712.","DOI":"10.1038\/s41598-022-20703-9","article-title":"Robustness of radiomics to variations in segmentation methods in multimodal brain MRI","volume":"12","author":"Poirot","year":"2022","journal-title":"Sci Rep"},{"key":"R8-20260424","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.schres.2020.09.009","article-title":"Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics","volume":"223","author":"Park","year":"2020","journal-title":"Schizophr Res"},{"key":"R9-20260424","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1148\/radiol.2017170226","article-title":"Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: A radiomics analysis","volume":"287","author":"Sun","year":"2018","journal-title":"Radiology"},{"key":"R10-20260424","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1093\/cercor\/bhz152","article-title":"Classification of unmedicated bipolar disorder using whole-brain functional activity and connectivity: A radiomics analysis","volume":"30","author":"Wang","year":"2020","journal-title":"Cereb Corte\u00d7"},{"key":"R11-20260424","doi-asserted-by":"crossref","first-page":"682777.","DOI":"10.3389\/fnins.2021.682777","article-title":"Thalamus radiomics-based disease identification and prediction of early treatment response for schizophrenia","volume":"15","author":"Cui","year":"2021","journal-title":"Front Neurosci"},{"key":"R12-20260424","doi-asserted-by":"crossref","first-page":"5432.","DOI":"10.3390\/jcm12165432","article-title":"Radiomics and artificial intelligence for the diagnosis and monitoring of Alzheimer\u2019s disease: A systematic review of studies in the field","volume":"12","author":"Bevilacqua","year":"2023","journal-title":"J Clin Med"},{"key":"R13-20260424","doi-asserted-by":"crossref","first-page":"58213","DOI":"10.1109\/ACCESS.2018.2871977","article-title":"Deep radiomic analysis of MRI related to Alzheimer\u2019s disease","volume":"6","author":"Chaddad","year":"2018","journal-title":"IEEE Access"},{"key":"R14-20260424","doi-asserted-by":"crossref","first-page":"16421.","DOI":"10.1038\/s41598-023-43544-6","article-title":"Texture-based brain networks for characterization of healthy subjects from MRI","volume":"13","author":"da Silveira","year":"2023","journal-title":"Sci Rep"},{"key":"R15-20260424","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.3348\/kjr.2018.0070","article-title":"Reproducibility and generalizability in radiomics modeling: Possible strategies in radiologic and statistical perspectives","volume":"20","author":"Park","year":"2019","journal-title":"Korean J Radiol"},{"key":"R16-20260424","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1177\/0962280214537344","article-title":"Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment","volume":"24","author":"Raunig","year":"2015","journal-title":"Stat Methods Med Res"},{"key":"R17-20260424","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1097\/RLI.0000000000000530","article-title":"Robustness and reproducibility of radiomics in magnetic resonance imaging: A phantom study","volume":"54","author":"Bae\u00dfler","year":"2019","journal-title":"Invest Radiol"},{"key":"R18-20260424","doi-asserted-by":"crossref","first-page":"19.","DOI":"10.1186\/s42492-019-0025-6","article-title":"Robustness of radiomic features in magnetic resonance imaging: Review and a phantom study","volume":"2","author":"Cattell","year":"2019","journal-title":"Vis Comput Ind Biomed Art"},{"key":"R19-20260424","doi-asserted-by":"crossref","first-page":"3973.","DOI":"10.1038\/s41598-021-83593-3","article-title":"Radiomics feature robustness as measured using an MRI phantom","volume":"11","author":"Lee","year":"2021","journal-title":"Sci Rep"},{"key":"R20-20260424","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.phro.2022.05.006","article-title":"Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study","volume":"22","author":"Saltybaeva","year":"2022","journal-title":"Phys Imaging Radiat Oncol"},{"key":"R21-20260424","doi-asserted-by":"crossref","first-page":"10170.","DOI":"10.3390\/app112110170","article-title":"Robustness of PET radiomics features: Impact of Co-registration with MRI","volume":"11","author":"Stefano","year":"2021","journal-title":"Appl Sci"},{"key":"R22-20260424","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.ejmp.2022.03.002","article-title":"Robustness and reproducibility of radiomics in T2 weighted images from magnetic resonance image guided linear accelerator in a phantom study","volume":"96","author":"Sun","year":"2022","journal-title":"Phys Med"},{"key":"R23-20260424","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1016\/j.ijrobp.2018.05.053","article-title":"Repeatability and reproducibility of radiomic features: A systematic review","volume":"102","author":"Traverso","year":"2018","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"R24-20260424","doi-asserted-by":"crossref","first-page":"2196.","DOI":"10.3390\/diagnostics12092196","article-title":"Phantom study on the robustness of MR radiomics features: Comparing the applicability of 3D printed and biological phantoms","volume":"12","author":"Veres","year":"2022","journal-title":"Diagnostics (Basel)"},{"key":"R25-20260424","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.1002\/mp.14173","article-title":"Multicenter evaluation of MRI-based radiomic features: A phantom study","volume":"47","author":"Rai","year":"2020","journal-title":"Med Phys"},{"key":"R26-20260424","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1002\/jmri.27635","article-title":"Prospective evaluation of repeatability and robustness of radiomic descriptors in healthy brain tissue regions in vivo across systematic variations in T2-weighted magnetic resonance imaging acquisition parameters","volume":"54","author":"Eck","year":"2021","journal-title":"J Magn Reson Imaging"},{"key":"R27-20260424","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1002\/jmri.27349","article-title":"Normative baseline for radiomics in brain MRI: Evaluating the robustness, regional variations, and reproducibility on FLAIR images","volume":"53","author":"Pandey","year":"2021","journal-title":"J Magn Reson Imaging"},{"key":"R28-20260424","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1038\/nrn756","article-title":"The problem of functional localization in the human brain","volume":"3","author":"Brett","year":"2002","journal-title":"Nat Rev Neurosci"},{"key":"R30-20260424","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1002\/mp.14038","article-title":"T2w-MRI signal normalization affects radiomics features reproducibility","volume":"47","author":"Scalco","year":"2020","journal-title":"Med Phys"},{"key":"R31-20260424","doi-asserted-by":"crossref","first-page":"106332.","DOI":"10.1016\/j.compbiomed.2022.106332","article-title":"The multi-level classification network (MCN) with modified residual U-Net for ischemic stroke lesions segmentation from ATLAS","volume":"151","author":"Alquhayz","year":"2022","journal-title":"Comput Biol Med"},{"key":"R32-20260424","doi-asserted-by":"crossref","first-page":"2426475.","DOI":"10.1155\/2017\/2426475","article-title":"Patch based multiple instance learning algorithm for object tracking","volume":"2017","author":"Wang","year":"2017","journal-title":"Comput Intell Neurosci"},{"key":"R33-20260424","doi-asserted-by":"crossref","first-page":"654.","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat Commun"},{"key":"R34-20260424","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","article-title":"DeepIGeoS: A deep interactive geodesic framework for medical image segmentation","volume":"41","author":"Wang","year":"2019","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"R35-20260424","doi-asserted-by":"crossref","first-page":"665929.","DOI":"10.3389\/fonc.2021.665929","article-title":"Automatic pancreatic ductal adenocarcinoma detection in whole slide images using deep convolutional neural networks","volume":"11","author":"Fu","year":"2021","journal-title":"Front Oncol"},{"key":"R37-20260424","doi-asserted-by":"crossref","first-page":"73436","DOI":"10.1109\/ACCESS.2019.2920005","article-title":"Hippocampus localization using a two-stage ensemble Hough convolutional neural network","volume":"7","author":"Basher","year":"2019","journal-title":"IEEE Access"},{"key":"R38-20260424","first-page":"1","article-title":"\u201cDeep ICE: A Deep learning approach for MRI Intracranial Cavity Extraction","author":"Manj\u00f3n","year":"2020","journal-title":"arXiv preprint arXiv:2001.05720"},{"key":"R39-20260424","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1093\/neuros\/nyx349","article-title":"Thalamic glioblastoma: Clinical presentation, management strategies, and outcomes","volume":"83","author":"Esquenazi","year":"2018","journal-title":"Neurosurgery"},{"key":"R40-20260424","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11060-021-03898-1","article-title":"Thalamic gliomas in adults: A systematic review of clinical characteristics, treatment strategies, and survival outcomes","volume":"155","author":"Palmisciano","year":"2021","journal-title":"J Neurooncol"},{"key":"R41-20260424","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1177\/0004867415585857","article-title":"The thalamus as a putative biomarker in neurodegenerative disorders","volume":"49","author":"Power","year":"2015","journal-title":"Aust N Z J Psychiatry"},{"key":"R42-20260424","doi-asserted-by":"crossref","first-page":"576.","DOI":"10.3389\/fnins.2018.00576","article-title":"MRI Asymmetry index of hippocampal subfields increases through the continuum from the mild cognitive impairment to the Alzheimer\u2019s disease","volume":"12","author":"Sarica","year":"2018","journal-title":"Front Neurosci"},{"key":"R43-20260424","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.expneurol.2006.01.008","article-title":"Transcriptional changes in multiple system atrophy and Parkinson\u2019s disease putamen","volume":"199","author":"Vogt","year":"2006","journal-title":"Exp Neurol"},{"key":"R44-20260424","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1101\/lm.73304","article-title":"The hippocampus and memory for \u201cwhat,\u201d \u201cwhere,\u201d and \u201cwhen\u201d","volume":"11","author":"Ergorul","year":"2004","journal-title":"Learn Mem"},{"key":"R45-20260424","article-title":"Image biomarker standardisation initiative","author":"Zwanenburg","year":"2019","journal-title":"arXiv preprint arXiv:1612.07003"},{"key":"R46-20260424","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J Chiropr Med"},{"key":"R47-20260424","doi-asserted-by":"crossref","first-page":"10.","DOI":"10.1186\/s42492-022-00106-3","article-title":"Acquisition repeatability of MRI radiomics features in the head and neck: A dual-3D-sequence multi-scan study","volume":"5","author":"Xue","year":"2022","journal-title":"Vis Comput Ind Biomed Art"},{"key":"R48-20260424","doi-asserted-by":"crossref","first-page":"12340.","DOI":"10.1038\/s41598-020-69298-z","article-title":"Standardization of brain MR images across machines and protocols: Bridging the gap for MRI-based radiomics","volume":"10","author":"Carr\u00e9","year":"2020","journal-title":"Sci Rep"},{"key":"R49-20260424","doi-asserted-by":"crossref","first-page":"14248.","DOI":"10.1038\/s41598-021-93756-x","article-title":"Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging","volume":"11","author":"Bernatz","year":"2021","journal-title":"Sci Rep"},{"key":"R50-20260424","doi-asserted-by":"crossref","first-page":"055002","DOI":"10.1088\/2057-1976\/ace4ce","article-title":"Could normalization improve robustness of abdominal MRI radiomic features?","volume":"9","author":"Giannini","year":"2023","journal-title":"Biomed Phys Eng Express"},{"key":"R51-20260424","first-page":"12","article-title":"Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer\u2019s disease and mild cognitive impairment","author":"Li","year":"2019","journal-title":"Therapeutic Advances in Neurological Disorders"},{"key":"R52-20260424","doi-asserted-by":"crossref","first-page":"e102107.","DOI":"10.1371\/journal.pone.0102107","article-title":"Robust radiomics feature quantification using semiautomatic volumetric segmentation","volume":"9","author":"Parmar","year":"2014","journal-title":"PLoS One"},{"key":"R53-20260424","doi-asserted-by":"crossref","first-page":"9249","DOI":"10.1007\/s13369-019-03967-8","article-title":"Brain tumor detection and segmentation in MR images using deep learning","volume":"44","author":"Sajid","year":"2019","journal-title":"Arab J Sci Eng"},{"key":"R54-20260424","doi-asserted-by":"crossref","first-page":"1055.","DOI":"10.3390\/brainsci11081055","article-title":"Brain image segmentation in recent years: A narrative review","volume":"11","author":"Fawzi","year":"2021","journal-title":"Brain Sci"},{"key":"R55-20260424","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"Van Griethuysen","year":"2017","journal-title":"Cancer Res"},{"key":"R56-20260424","doi-asserted-by":"crossref","first-page":"4041.","DOI":"10.1038\/s41598-017-04151-4","article-title":"Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters","volume":"7","author":"Brynolfsson","year":"2017","journal-title":"Sci Rep"},{"key":"R57-20260424","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1118\/1.3081408","article-title":"Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: An application-oriented study","volume":"36","author":"Mayerhoefer","year":"2009","journal-title":"Med Phys"},{"key":"R58-20260424","doi-asserted-by":"crossref","first-page":"16791.","DOI":"10.1038\/s41598-023-43715-5","article-title":"Effects of MRI scanner manufacturers in classification tasks with deep learning models","volume":"13","author":"Kushol","year":"2023","journal-title":"Sci Rep"},{"key":"R59-20260424","doi-asserted-by":"crossref","first-page":"746982.","DOI":"10.3389\/fnagi.2021.746982","article-title":"Inter- and intra-scanner variability of automated brain volumetry on three magnetic resonance imaging systems in Alzheimer\u2019s disease and controls","volume":"13","author":"Wittens","year":"2021","journal-title":"Front Aging Neurosci"},{"key":"R60-20260424","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.phro.2021.11.001","article-title":"Intensity standardization methods in magnetic resonance imaging of head and neck cancer","volume":"20","author":"Wahid","year":"2021","journal-title":"Phys Imaging Radiat Oncol"},{"key":"R61-20260424","doi-asserted-by":"crossref","first-page":"842.","DOI":"10.3390\/jpm11090842","article-title":"Making radiomics more reproducible across scanner and imaging protocol variations: A review of harmonization methods","volume":"11","author":"Mali","year":"2021","journal-title":"J Pers Med"},{"key":"R62-20260424","doi-asserted-by":"crossref","first-page":"303.","DOI":"10.3390\/jimaging8110303","article-title":"Harmonization strategies in multicenter MRI-based radiomics","volume":"8","author":"Stamoulou","year":"2022","journal-title":"J Imaging"}],"container-title":["Journal of Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jmp.jmp_149_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T19:39:21Z","timestamp":1786649961000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jmp.jmp_149_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":60,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.4103\/jmp.jmp_149_24","relation":{},"ISSN":["0971-6203","1998-3913"],"issn-type":[{"value":"0971-6203","type":"print"},{"value":"1998-3913","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"2024-08-30","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-01","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}