Relevant bibliographies by topics / Brain-age prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Brain-age prediction'

Author: Grafiati
Published: 6 September 2023
Last updated: 7 September 2023

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Journal articles on the topic "Brain-age prediction"

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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.

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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. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.
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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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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 (April 29, 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 analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.
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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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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 (March 5, 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 physical fitness and cognitive ability, measured both currently and longitudinally, as well as study attrition and years of education. Multimodal prediction models performed at a similar level as single-modal models, and the choice of regression method did not significantly affect the results. Correlation with the BAPG was found for current physical fitness, current cognitive ability, and study attrition. Correlations were also found for retrospective physical fitness, measured 10 years prior to imaging, and slope for cognitive ability during a period of 15 years. The results suggest that maintaining a high physical fitness throughout life contributes to brain maintenance and preserved cognitive ability.
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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 (March 25, 2015): 571–81. http://dx.doi.org/10.1002/ana.24367.

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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 (June 11, 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 this work we evaluated three different harmonization techniques on the Autism Brain Imaging Data Exchange (ABIDE) dataset for age prediction analysis in two groups of subjects (i.e., controls and autism spectrum disorder). We extracted the morphological features from T1-weighted images of a mixed cohort of 654 subjects acquired from 17 sites to predict the biological age of the subjects using three machine learning regression models. A machine learning framework was developed to quantify the effects of the different harmonization strategies on the final performance of the models and on the set of morphological features that are relevant to the age prediction problem in both the presence and absence of pathology. The results show that, even if two harmonization strategies exhibit similar accuracy of predictive models, a greater mismatch occurs between the sets of most age-related predictive regions for the Autism Spectrum Disorder (ASD) subjects. Thus, we propose to use a stability index to extract meaningful features for a robust clinical validation of the outcomes of multiple harmonization strategies.
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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 (February 2020): 336–44. http://dx.doi.org/10.1109/jbhi.2019.2925710.

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

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Dissertations / Theses on the topic "Brain-age prediction"

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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.

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Abstract The objectives were to study ultrasound (US), magnetic resonance imaging (MRI), single photon emission tomography (SPET) and brainstem auditory evoked potentials (BAEP) as structural and functional imaging methods for the prediction of later neuromotor outcome and to assess the reliability of auditory brainstem responses (ABR), transient evoked otoacoustic emissions (TEOAE) and free-field auditory behavioural responses (FF) for the prediction of permanent hearing loss. The series comprised 51 surviving very low birth weight preterm infants born at < 34 gestational weeks with a birth weight < 1500 grams, taking 52 full-term infants as controls with respect to hearing screening and 21 with respect to brainstem function. The imaging examinations and hearing screening were performed at term age and follow-up continued to a corrected age of 18 months for the evaluation of neurodevelopment and hearing. MRI images were analysed with regard to the degree of myelination, parenchymal lesions, ventricular-brain ratios and widths of the extracerebral spaces, and the predictive value of the findings for later neuromotor development was assessed by comparison with US. In the SPET examinations (on 34 infants) relative regional perfusion levels and hemispheric asymmetries were evaluated in slices. The predictive value of perfusion defects in SPET was similarly assessed relative to US abnormalities. Brainstem size was measured by MRI, and brainstem function evaluated by BAEP, and results being used to predict neurosensory disability. Hearing was screened by means of TEOAE, ABR and FF, and the results used to predict permanent hearing loss. Parenchymal lesions in MRI predicted cerebral palsy (CP) with a sensitivity of 82% and a specificity of 97%, the corresponding figures for US being 58% and 100%. Delayed myelination, ventricular-brain ratios and widths of the extracerebral spaces failed to predict CP. The sensitivity of perfusion defects in SPET for predicting CP was 82% and the specificity 70%, and correspondingly US attained a sensitivity of 73% and a specificity of 83%. The best brainstem dimensions for predicting neurosensory disability reached at sensitivity of 23-31% and a specificity of 97-100%. The best predictors in BAEP gave the sensitivity of 93% with a specificity of 57-59%. Bilateral failure in TEOAE predicted hearing loss with a sensitivity of 50% and with a specificity of 84%, and that in ABR with a sensitivity of 100% and a specificity of 98%. The FF examination showed a sensitivity of 50% and a specificity of 98%. In conclusion, out of the brain imaging methods used here MRI was the best for predicting abnormal neuromotor outcome. Brainstem dimensions in MRI appear to predict neurosensory disability poorly, however, whereas BAEP shows a better prediction value, but is limited by a low specificity. ABR seems to be the best hearing screening method because it includes retrocochlear involvements in preterm infants.
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Pläschke, 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.

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Chen, 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.

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碩士
國立臺灣大學
腦與心智科學研究所
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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.
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Sreenivasan, Varsha. "Structural connectivity correlates of human cognition explored with diffusion MRI and tractography." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5228.

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Intact structural connectivity among brain regions is critical to cognition. Structural connectivity forms the substratum for information flow between brain regions, and its plasticity is a hallmark of learning in the brain. Moreover, structural connectivity markers constitute a heritable phenotype. Investigating neuroanatomical connectivity in the human brain is, therefore, critical not only for uncovering the neural underpinnings of behavior but also for understanding connectomic bases of neurodevelopmental and neurodegenerative disorders, such as autism and Alzheimer’s Disease. Diffusion magnetic resonance imaging (dMRI) and tractography are among the only techniques, at present, that enable estimation of anatomical connectivity in the human brain, in-vivo. By tracking the anisotropic diffusion of water molecules in white matter, dMRI and tractography enable post hoc reconstruction of contiguous fascicles between distal brain regions. How accurately can dMRI and tractography track these connections to match ground-truth in the brain? Are structural connections between specific pairs of brain regions informative about subjects’ cognitive capacities, like attention? Could changes in these connections indicate mechanisms of cognitive decline, both in healthy and pathologically aging populations? In this thesis, I report results from three studies, each of which addresses one of these key questions. In the first study, I explored how the midbrain contributes to attention, by combining model-based analysis of behavior with dMRI-tractography. Specifically, I examined the role of the superior colliculus (SC), a vertebrate midbrain structure, in attention. Does the SC control perceptual sensitivity to attended information, does it enable biasing choices toward attended information, or both? I mapped structural connections of the human SC with neocortical regions and found that the strengths of these connections correlated with, and were strongly predictive of, individuals’ choice bias, but not sensitivity. Taken together with previous studies, these results indicate that the human SC may play an evolutionarily conserved role in controlling choice bias during visual attention. In the second study, I developed a novel approach, implemented on GPUs, for pruning structural connectomes, at scale. First, I identified key limitations of a state-of-the-art connectome pruning technique, Linear Fascicle Evaluation (LiFE), and introduced a GPU-based implementation that achieves >100x speedups over conventional CPU-based implementations. Leveraging these speedups, I advanced LiFE’s algorithm by imposing a regularization constraint on estimated fiber weights. This regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal, and enables rapid and accurate connectome evaluation at scale. In the third study, I demonstrated several real-world applications of the ReAl-LiFE technique for analysis of large datasets. First, I showed that structural connectivity estimated with ReAl-LiFE predicts behavioral scores across a range of cognitive tasks in a cohort with 200 healthy human volunteers from the Human Connectome Project database. Moreover, ReAl-LiFE pruned connection weights provided a more reliable marker for structural connectivity strength than the number of fibers in the unpruned connectome. Second, ReAl-LiFE connection weights effectively predicted both chronological age, as well as age-related decline in cognitive factor scores in a cohort of 101 healthy, aged volunteers whose data were acquired as part of the Tata longitudinal study on aging at IISc. Finally, analyzing nearly 100 dMRI scans from the ADNI database, I showed that ReAl-LiFE outperformed competing approaches in terms of its accuracy with classifying patients with Alzheimer’s Dementia from healthy, age-matched controls, based on cortico-hippocampal connection weights. In summary, these findings show that diffusion MRI and tractography can serve as powerful tools for addressing key questions regarding brain-behavior relationships. In this thesis, I developed a technique to reliably estimate structural connectivity between distal brain regions, identified the role of subcortical structural connections in attention, and showed that cortical connectivity can be used to predict behavioral scores and cognitive decline. Broadly, these results will be relevant for understanding the connectomic basis of various cognitive processes, like attention, in healthy populations, and its dysfunction in diseased patients.
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Books on the topic "Brain-age prediction"

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Kagan, Jerome. Five Constraints on Predicting Behavior. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262036528.001.0001.

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Scientists were unable to study the relation of brain to mind until the invention of technologies that measured the brain activity accompanying psychological processes. Yet even with these new tools, conclusions are tentative or simply wrong. This book describes five conditions that place serious constraints on the ability to predict mental or behavioral outcomes based on brain data: the setting in which evidence is gathered, the expectations of the subject, the source of the evidence that supports the conclusion, the absence of studies that examine patterns of causes with patterns of measures, and the habit of borrowing terms from psychology. The book describes the importance of context, and how the experimental setting—including the room, the procedure, and the species, age, and sex of both subject and examiner—can influence the conclusions. It explains how subject expectations affect all brain measures; considers why brain and psychological data often yield different conclusions; argues for relations between patterns of causes and outcomes rather than correlating single variables; and criticizes the borrowing of psychological terms to describe brain evidence. Brain sites cannot be in a state of “fear.” A deeper understanding of the brain's contributions to behavior, the book argues, requires investigators to acknowledge these five constraints in the design or interpretation of an experiment.
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Hough, Catherine L. Chronic critical illness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0377.

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Chronic critical illness (CCI) is common and describes a state of prolonged critical illness, in which patients have persisting organ failures requiring treatment in an intensive care setting. There are many different definitions of CCI, with most including prolonged (> 96 hours) mechanical ventilation. Advanced age, higher severity of illness, and poor functional status prior to critical illness are all important risk factors, but prediction of CCI is imperfect. Although requirement for mechanical ventilation is the hallmark, CCI encompasses much more than the respiratory system, with effects on metabolism, skin, brain, and neuromuscular function. During CCI, patients have a high burden of symptoms and impaired capacity to communicate their needs. Mortality and quality of life are generally poor, but highly variable, with 1-year mortality over 50% and most survivors suffering permanent cognitive impairment and functional dependence. Patients at highest and lowest risk for mortality can be identified using a simple prediction rule. Caring for the chronically critically ill is a substantial burden both to patients’ families and to the health care system as a whole. Further research is needed in order to improve care and outcomes for CCI patients and their families.
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Dowker, 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.

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This chapter discusses individual differences in arithmetic. It deals relatively briefly with the findings about the general large extent of such differences in both children and adults. It then discusses findings that indicate that it is inadequate to speak of arithmetical ability as a single characteristic. Rather, it is made up of many components, which may correlate, but also show significant functional independence. Discrepancies between any two such components, in both directions, can be frequently observed. There is evidence for this from many sources, including studies of patients with acquired dyscalculia, brain imaging studies, cross-cultural studies, and studies of both typically developing children and those with mathematical difficulties. The chapter then discusses questions about when such between- and within-individual differences begin, and whether numerical ability is componential from infancy or starts as a single ability and then differentiates. There is certainly evidence that it is already componential in preschoolers. The need for more longitudinal and intervention studies is emphasized, if we are to understand whether differences in specific components are consistent over time, and whether specific components at an early age have specific predictive relationships to specific components found later on.
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Book chapters on the topic "Brain-age prediction"

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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.

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Han, 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.

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Schulz, 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.

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Rachmadi, 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.

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Brown, 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.

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Lombardi, 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.

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Bento, 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.

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Dular, 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.

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More, 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.

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AbstractMachine learning (ML) methods are increasingly being used to predict pathologies and biological traits using neuroimaging data. Here controlling for confounds is essential to get unbiased estimates of generalization performance and to identify the features driving predictions. However, a systematic evaluation of the advantages and disadvantages of available alternatives is lacking. This makes it difficult to compare results across studies and to build deployment quality models. Here, we evaluated two commonly used confound removal schemes–whole data confound regression (WDCR) and cross-validated confound regression (CVCR)–to understand their effectiveness and biases induced in generalization performance estimation. Additionally, we study the interaction of the confound removal schemes with Z-score normalization, a common practice in ML modelling. We applied eight combinations of confound removal schemes and normalization (pipelines) to decode sex from resting-state functional MRI (rfMRI) data while controlling for two confounds, brain size and age. We show that both schemes effectively remove linear univariate and multivariate confounding effects resulting in reduced model performance with CVCR providing better generalization estimates, i.e., closer to out-of-sample performance than WDCR. We found no effect of normalizing before or after confound removal. In the presence of dataset and confound shift, four tested confound removal procedures yielded mixed results, raising new questions. We conclude that CVCR is a better method to control for confounding effects in neuroimaging studies. We believe that our in-depth analyses shed light on choices associated with confound removal and hope that it generates more interest in this problem instrumental to numerous applications.
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Potts, 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.

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Conference papers on the topic "Brain-age prediction"

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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.

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Lam, 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.

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Ray, 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.

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Morita, 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.

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Pardakhti, 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.

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Barbano, 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.

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Afshar, 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.

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Bregu, 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.

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Morita, 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.

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Bengs, 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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