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Gotowe bibliografie tematyczne / Brain-age prediction

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Artykuły w czasopismach

Gotowa bibliografia na temat „Brain-age prediction”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 7 września 2023

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Artykuły w czasopismach na temat "Brain-age prediction"

1

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 (2023): 3622. http://dx.doi.org/10.3390/s23073622.

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Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population.

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2

Zhang, 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.

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3

Gómez-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 (2022): 579. http://dx.doi.org/10.3390/brainsci12050579.

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Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance

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4

de 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.

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5

Dunås, 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 (2021): 3393–407. http://dx.doi.org/10.1093/cercor/bhab019.

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Abstract Maintaining a youthful brain structure and function throughout life may be the single most important determinant of successful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain age predictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, and diffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal prediction models, based on 7 regression methods. The models were compared on mean absolute error and whether they were related to ph

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6

Cole, 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 (2015): 571–81. http://dx.doi.org/10.1002/ana.24367.

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7

Lombardi, 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 (2020): 364. http://dx.doi.org/10.3390/brainsci10060364.

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Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. Initiatives to share open access morphological data contributed significantly to the advance in brain structure characterization. Indeed, such initiatives allow large brain morphology multi-site datasets to be shared, which increases the statistical sensitivity of the outcomes. However, using neuroimaging data from multi-site studies requires harmonizing data across the site to avoid bias. In

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8

Kassani, 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 (2020): 336–44. http://dx.doi.org/10.1109/jbhi.2019.2925710.

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9

Peng, 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.

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10

Lam, 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 (2020): S374—S375. http://dx.doi.org/10.1016/j.biopsych.2020.02.959.

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