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Статті в журналах з теми "Brain-age prediction"
Xiong, Min, Lan Lin, Yue Jin, Wenjie Kang, Shuicai Wu, and Shen Sun. "Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults." Sensors 23, no. 7 (March 30, 2023): 3622. http://dx.doi.org/10.3390/s23073622.
Повний текст джерелаZhang, Biao, Shuqin Zhang, Jianfeng Feng, and Shihua Zhang. "Age-level bias correction in brain age prediction." NeuroImage: Clinical 37 (2023): 103319. http://dx.doi.org/10.1016/j.nicl.2023.103319.
Повний текст джерелаGómez-Ramírez, Jaime, Miguel A. Fernández-Blázquez, and Javier J. González-Rosa. "Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation." Brain Sciences 12, no. 5 (April 29, 2022): 579. http://dx.doi.org/10.3390/brainsci12050579.
Повний текст джерелаde Lange, Ann-Marie G., and James H. Cole. "Commentary: Correction procedures in brain-age prediction." NeuroImage: Clinical 26 (2020): 102229. http://dx.doi.org/10.1016/j.nicl.2020.102229.
Повний текст джерелаDunås, Tora, Anders Wåhlin, Lars Nyberg, and Carl-Johan Boraxbekk. "Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance." Cerebral Cortex 31, no. 7 (March 5, 2021): 3393–407. http://dx.doi.org/10.1093/cercor/bhab019.
Повний текст джерелаCole, James H., Robert Leech, and David J. Sharp. "Prediction of brain age suggests accelerated atrophy after traumatic brain injury." Annals of Neurology 77, no. 4 (March 25, 2015): 571–81. http://dx.doi.org/10.1002/ana.24367.
Повний текст джерелаLombardi, Angela, Nicola Amoroso, Domenico Diacono, Alfonso Monaco, Sabina Tangaro, and Roberto Bellotti. "Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction." Brain Sciences 10, no. 6 (June 11, 2020): 364. http://dx.doi.org/10.3390/brainsci10060364.
Повний текст джерелаKassani, Peyman Hosseinzadeh, Alexej Gossmann, and Yu-Ping Wang. "Multimodal Sparse Classifier for Adolescent Brain Age Prediction." IEEE Journal of Biomedical and Health Informatics 24, no. 2 (February 2020): 336–44. http://dx.doi.org/10.1109/jbhi.2019.2925710.
Повний текст джерелаPeng, Han, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, and Stephen M. Smith. "Accurate brain age prediction with lightweight deep neural networks." Medical Image Analysis 68 (February 2021): 101871. http://dx.doi.org/10.1016/j.media.2020.101871.
Повний текст джерелаLam, Pradeep, Alyssa Zhu, Lauren Salminen, Sophia Thomopoulos, Neda Jahanshad, and Paul Thompson. "Comparison of Deep Learning Methods for Brain Age Prediction." Biological Psychiatry 87, no. 9 (May 2020): S374—S375. http://dx.doi.org/10.1016/j.biopsych.2020.02.959.
Повний текст джерелаДисертації з теми "Brain-age prediction"
Valkama, M. (Marita). "Prediction of neurosensory disability in very low birth weight preterm infants:structural and functional brain imaging and hearing screening at term age and follow-up of infants to a corrected age of 18 months." Doctoral thesis, University of Oulu, 2001. http://urn.fi/urn:isbn:9514259157.
Повний текст джерелаPläschke, Rachel N. [Verfasser], Simon B. [Akademischer Betreuer] Eickhoff, and Tobias [Gutachter] Kalenscher. "Altered Functional Brain Networks in Schizophrenia, Parkinson’s Disease and Advanced Age: Insights from Applying Machine Learning for Connectivity-based Predictions / Rachel N. Pläschke ; Gutachter: Tobias Kalenscher ; Betreuer: Simon B. Eickhoff." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2021. http://d-nb.info/1227706820/34.
Повний текст джерелаChen, Chang-Le, and 陳長樂. "Prediction of Brain Age Based on Cerebral White Matter Microstructural Properties and Its Potential Clinical Applications." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ddzn3r.
Повний текст джерела國立臺灣大學
腦與心智科學研究所
105
Imaging-based brain-age prediction provides a promising approach to assess an individual’s brain age relative to healthy populations. The estimated “brain-age” potentially offers clinically relevant biomarkers of neurodegenerative diseases which often manifest accelerated aging process. Studies on brain-age prediction depend on morphological changes of cerebral macrostructure, like volumes in gray or white matter. However, some studies suggest that alterations of white matter microstructure, like tract integrity, are more sensitive to aging effects. Therefore, we aimed to develop a brain-age prediction model based on white matter microstructure, using diffusion spectrum imaging to acquire characteristics of white matter. In addition, the disparity between chronological age and the corresponding predicted brain age might signal the presence of neurodegenerative disease. Studies reported that mild cognitive impairment (MCI) and schizophrenia were both engaged in accelerated aging of the brain. Therefore, to explore the potential clinical applications of the prediction model, we applied the prediction model to MCI and schizophrenia patients. We aimed to investigate whether the overestimated brain age could be observed in these two patient groups, reflecting the effect of accelerated aging. Four independent samples were recruited in the study: 192 healthy controls (age: 31–92 years) as the training set, 30 healthy controls (age: 31–80 years) as the model testing set, 35 MCI patients (age: 61–83 years) and 44 patients with schizophrenia (age: 32–62 years) as the clinical testing sets. MAP-MRI framework was used to reconstruct diffusion spectrum imaging (DSI) datasets into 7 diffusion indices, namely generalized fractional anisotropy (GFA), axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), non-Gaussianity (NG), NG orthogonal (NGO), and NG parallel (NGP). Whole-brain tract-based automatic analysis was implemented to obtain 3D-connectograms of the 7 diffusion indices. To extract age-related features, general linear models were estimated at each step of the connectograms using linear and quadratic age as the independent variables. Continuous steps with significant aging effect were selected as segments. The segments underwent principal component analysis to reduce the dimensions. Gaussian process regression (GPR) models were employed to fit each diffusion index using the age as the response variable and the principal segments as the predictors. An integrative model was defined to integrate the GPR models for each diffusion index into a unified model. Six-fold cross-validation on the training set was conducted to validate the robustness of the model. Model performance was assessed by Pearson’s correlation coefficient and mean absolute error (MAE) between the predicted age and chronological age. In the model test for clinical applications, predicted age difference (PAD) was calculated by subtracting chronological age from predicted age. The higher the PAD, the more overestimation of the brain age is. The PAD scores were used to test group differences among the three study groups using analysis of covariance (ANCOVA), controlling sex. In the model assessment, Pearson’s correlation coefficients and MAE in the training and testing sets were r = 0.86, MAE = 5.6 years, and r = 0.92, MAE = 4.3 years, respectively. In the model test for clinical applications, Pearson’s correlation coefficients and MAE in the MCI and schizophrenia groups were, r = 0.69, MAE = 6.8 years and r = 0.61, MAE = 9.4 year, respectively. Compared to the healthy controls (-1.92 years), the MCI and schizophrenia groups had significantly increased PAD by 2.96 and 8.65 years, respectively Our results showed that the GPR modeling approach achieved equally high accuracy in the training group and the testing group of healthy controls. In the MCI and schizophrenia groups, the average predicted brain age was overestimated with respect to their chronological age. The results are consistent with previous studies that MCI and schizophrenia may accelerate the aging process. In summary, a model of brain age prediction based on white matter microstructural properties was developed with high accuracy in the healthy population, allowing brain age assessment on individual basis. Moreover, this model might be helpful in detecting individuals with accelerated aging effects.
Sreenivasan, Varsha. "Structural connectivity correlates of human cognition explored with diffusion MRI and tractography." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5228.
Повний текст джерелаКниги з теми "Brain-age prediction"
Kagan, Jerome. Five Constraints on Predicting Behavior. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262036528.001.0001.
Повний текст джерелаHough, Catherine L. Chronic critical illness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0377.
Повний текст джерелаDowker, Ann. Individual Differences in Arithmetical Abilities. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.034.
Повний текст джерелаЧастини книг з теми "Brain-age prediction"
Dular, Lara, and Žiga Špiclin. "Mixup Augmentation Improves Age Prediction from T1-Weighted Brain MRI Scans." In Predictive Intelligence in Medicine, 60–70. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16919-9_6.
Повний текст джерелаHan, Hongfang, Xingliang Xiong, Jianfeng Yan, Haixian Wang, and Mengting Wei. "The Evaluation of Brain Age Prediction by Different Functional Brain Network Construction Methods." In Neural Information Processing, 122–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63836-8_11.
Повний текст джерелаSchulz, Marc-Andre, Alexander Koch, Vanessa Emanuela Guarino, Dagmar Kainmueller, and Kerstin Ritter. "Data Augmentation via Partial Nonlinear Registration for Brain-Age Prediction." In Lecture Notes in Computer Science, 169–78. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17899-3_17.
Повний текст джерелаRachmadi, Muhammad Febrian, Maria del C. Valdés-Hernández, and Taku Komura. "Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI." In PRedictive Intelligence in MEdicine, 85–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00320-3_11.
Повний текст джерелаBrown, Colin J., Kathleen P. Moriarty, Steven P. Miller, Brian G. Booth, Jill G. Zwicker, Ruth E. Grunau, Anne R. Synnes, Vann Chau, and Ghassan Hamarneh. "Prediction of Brain Network Age and Factors of Delayed Maturation in Very Preterm Infants." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 84–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66182-7_10.
Повний текст джерелаLombardi, Angela, Domenico Diacono, Nicola Amoroso, Alfonso Monaco, Sabina Tangaro, and Roberto Bellotti. "Embedding Explainable Artificial Intelligence in Clinical Decision Support Systems: The Brain Age Prediction Case Study." In Recent Advances in AI-enabled Automated Medical Diagnosis, 81–95. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003176121-6.
Повний текст джерелаBento, Mariana, Roberto Souza, Marina Salluzzi, and Richard Frayne. "Normal Brain Aging: Prediction of Age, Sex and White Matter Hyperintensities Using a MR Image-Based Machine Learning Technique." In Lecture Notes in Computer Science, 538–45. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_61.
Повний текст джерелаDular, Lara, and Žiga Špiclin. "Improving Across Dataset Brain Age Predictions Using Transfer Learning." In Predictive Intelligence in Medicine, 243–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87602-9_23.
Повний текст джерелаMore, Shammi, Simon B. Eickhoff, Julian Caspers, and Kaustubh R. Patil. "Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67670-4_1.
Повний текст джерелаPotts, Matthew B., John H. Chi, Michele Meeker, Martin C. Holland, J. Claude Hemphill III, and Geoffrey T. Manley. "Predictive values of age and the Glasgow Coma Scale in traumatic brain injury patients treated with decompressive craniectomy." In Acta Neurochirurgica Supplements, 109–12. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85578-2_22.
Повний текст джерелаТези доповідей конференцій з теми "Brain-age prediction"
Ren, Yanhao, Qiang Luo, Weikang Gong, and Wenlian Lu. "Transfer Learning Models on Brain Age Prediction." In the Third International Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3364836.3364893.
Повний текст джерелаLam, Pradeep K., Vigneshwaran Santhalingam, Parth Suresh, Rahul Baboota, Alyssa H. Zhu, Sophia I. Thomopoulos, Neda Jahanshad, and Paul M. Thompson. "Accurate brain age prediction using recurrent slice-based networks." In 16th International Symposium on Medical Information Processing and Analysis, edited by Jorge Brieva, Natasha Lepore, Eduardo Romero Castro, and Marius G. Linguraru. SPIE, 2020. http://dx.doi.org/10.1117/12.2579630.
Повний текст джерелаRay, Bhaskar, Kuaikuai Duan, Jiayu Chen, Zening Fu, Pranav Suresh, Sarah Johnson, Vince D. Calhoun, and Jingyu Liu. "Multimodal Brain Age Prediction with Feature Selection and Comparison." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9631007.
Повний текст джерелаMorita, Ren, Saya Ando, Daisuke Fujita, Manabu Nii, Kumiko Ando, Reiichi Ishikura, and Syoji Kobashi. "Brain Devlopment Age Prediction Using Convolutnal Neural Network on Pediatrics Brain Ct Mages." In 2021 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2021. http://dx.doi.org/10.1109/icmlc54886.2021.9737254.
Повний текст джерелаPardakhti, Nastaran, and Hedieh Sajedi. "Age prediction based on brain MRI images using feature learning." In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2017. http://dx.doi.org/10.1109/sisy.2017.8080565.
Повний текст джерелаBarbano, Carlo Alberto, Benoit Dufumier, Edouard Duchesnay, Marco Grangetto, and Pietro Gori. "Contrastive Learning for Regression in Multi-Site Brain Age Prediction." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, 2023. http://dx.doi.org/10.1109/isbi53787.2023.10230733.
Повний текст джерелаAfshar, Leila Keshavarz, and Hedieh Sajedi. "Age Prediction based on Brain MRI Images using Extreme Learning Machine." In 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS). IEEE, 2019. http://dx.doi.org/10.1109/cfis.2019.8692156.
Повний текст джерелаBregu, Ornela, Nuha Zamzami, and Nizar Bouguila. "Human Age Prediction Based on Brain MRI Using Density-Based Regression." In 2023 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2023. http://dx.doi.org/10.1109/icit58465.2023.10143160.
Повний текст джерелаMorita, Ren, Saya Ando, Daisuke Fujita, Sho Ishikawa, Koji Onoue, Kumiko Ando, Reiichi Ishikura, and Syoji Kobashi. "Pediatric Brain CT Image Segmentation Methods for Effective Age Prediction Models." In 2022 World Automation Congress (WAC). IEEE, 2022. http://dx.doi.org/10.23919/wac55640.2022.9934508.
Повний текст джерелаBengs, Marcel, Finn Behrendt, Max-Heinrich Laves, Julia Krüger, Roland Opfer, and Alexander Schlaefer. "Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin, Karen Drukker, Maciej A. Mazurowski, Hongbing Lu, Chisako Muramatsu, and Ravi K. Samala. SPIE, 2022. http://dx.doi.org/10.1117/12.2608120.
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