Accurate Brain Age Prediction Model for Healthy Children and Adolescents using 3D-CNN and Dimensional Attention

2021 ◽  
Author(s):  
Guozhen Hu ◽  
Qinjian Zhang ◽  
Zhi Yang ◽  
Baobin Li
Keyword(s):  
Prediction Model ◽  
Children And Adolescents ◽  
Healthy Children ◽  
3D Cnn ◽  
Brain Age ◽  
Age Prediction

scholarly journals Review of Prediction Models to Estimate Activity-Related Energy Expenditure in Children and Adolescents

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Suzanne M. de Graauw ◽  
Janke F. de Groot ◽  
Marco van Brussel ◽  
Marjolein F. Streur ◽  
Tim Takken
Keyword(s):  
Heart Rate ◽  
Energy Expenditure ◽  
Prediction Model ◽  
Children And Adolescents ◽  
Prediction Models ◽  
Accurate Estimate ◽  
Healthy Children ◽  
Triaxial Accelerometer ◽  
Free Living ◽  
Doubly Labelled Water

Purpose. To critically review the validity of accelerometry-based prediction models to estimate activity energy expenditure (AEE) in children and adolescents.Methods. The CINAHL, EMBASE, PsycINFO, and PubMed/MEDLINE databases were searched. Inclusion criteria were development or validation of an accelerometer-based prediction model for the estimation of AEE in healthy children or adolescents (6–18 years), criterion measure: indirect calorimetry, or doubly labelled water, and language: Dutch, English or German.Results. Nine studies were included. Median methodological quality was5.5±2.0 IR (out of a maximum 10 points). Prediction models combining heart rate and counts explained 86–91% of the variance in measured AEE. A prediction model based on a triaxial accelerometer explained 90%. Models derived during free-living explained up to 45%.Conclusions. Accelerometry-based prediction models may provide an accurate estimate of AEE in children on a group level. Best results are retrieved when the model combines accelerometer counts with heart rate or when a triaxial accelerometer is used. Future development of AEE prediction models applicable to free-living scenarios is needed.

scholarly journals Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

2021 ◽  
Vol 12 ◽  
Author(s):  
Chen-Yuan Kuo ◽  
Tsung-Ming Tai ◽  
Pei-Lin Lee ◽  
Chiu-Wang Tseng ◽  
Chieh-Yu Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Analytical Framework ◽  
Support Vector ◽  
Feature Sets ◽  
Input Feature ◽  
Brain Age ◽  
Analytical Frameworks ◽  
Age Prediction

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R2 = 0.88; support vector regression, MAE = 4.42 years, R2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

scholarly journals Accelerated Brain Aging in Mild Traumatic Brain Injury: Longitudinal Pattern Recognition With White Matter Integrity

2020 ◽  
Author(s):  
Shuoqiu Gan ◽  
Wen Shi ◽  
Shan Wang ◽  
Yingxiang Sun ◽  
Bo Yin ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Prediction Model ◽  
Mild Traumatic Brain Injury ◽  
Aging Process ◽  
Brain Aging ◽  
Post Injury ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Abstract Background: Long-term effects of mild traumatic brain injury (mTBI) resemble brain aging changes (i.e., microstructure integrity loss), which implies an accelerated age-associated process. This study aimed to develop a quantifiable neuroimaging marker to characterize the brain-aging process accelerated by mTBI from acute to chronic phases. Methods: A brain-age prediction model was defined using relevance vector regression (RVR) in 523 healthy individuals, based on fractional anisotropy metrics from diffusion-tensor imaging. The model was adopted to estimate brain-predicted age difference (brain-PAD = predicted brain age - chronological age) in 116 acute mTBI patients and 63 healthy controls (HCs). Fifty patients were followed up 6~12 month post-injury to evaluate the longitudinal changes in brain-PAD. Another mTBI group containing 70 acute patients were included as a replicated cohort. We investigated whether brain-PAD was greater in patients with elderly age, post-concussion complaints, and risky apolipoprotein E (APOE) genotype, and whether it had the potential to predict neuropsychological outcomes for information processing speed (IPS). Between-group and longitudinal comparison in brain-PAD was conducted with analysis of covariance and linear mixed-effects model, respectively. The correlation between brain-PAD and continuous variables was analyzed with Spearman rank-order correlation.Results: The RVR brain-age prediction model predicted brain age accurately (r = 0.96, R2 = 0.93). The brain age of mTBI patients was estimated to be "older" in the acute phase, with mean brain-PAD of 2.59 (± 5.97) years compared with HCs (0.12 ± 3.19 years) (P < 0.05) and replicated in another mTBI cohort (brain-PAD: 3.26 ± 4.55 years). The increased brain age in mTBI kept stable at 6-12 month post-injury (2.50 ± 4.54 years). Patients with older age or severer post-concussion complaints obtained greater brain-PAD (P < 0.001, P = 0.024), while patients with APOE ε4 didn’t obtain greater brain-PAD than those without. Additionally, brain-PAD in the acute phase predicted patients’ IPS profile at 6~12 month follow-up (rho = -0.36, P = 0.01). Conclusion: Mild TBI, even a single one, accelerates the brain-aging process. The brain-PAD can be considered as a quantitative neuroimaging marker to evaluate the susceptibility to neurodegeneration or other age-associated conditions following mTBI. Trial registration: NCT02868684.

Normal percentile curves for left atrial size in healthy children and adolescents

2019 ◽  
Vol 36 (4) ◽  
pp. 770-782 ◽  
Author(s):  
Alejandro Díaz ◽  
Yanina Zócalo ◽  
Daniel Bia
Keyword(s):  
Children And Adolescents ◽  
Left Atrial ◽  
Healthy Children ◽  
Left Atrial Size ◽  
Percentile Curves ◽  
Atrial Size

scholarly journals Folate and Cobalamin Serum Levels in Healthy Children and Adolescents and Their Association with Age, Sex, BMI and Socioeconomic Status

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 546
Author(s):  
Paulina Kreusler ◽  
Mandy Vogel ◽  
Anja Willenberg ◽  
Ronny Baber ◽  
Yvonne Dietz ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Children And Adolescents ◽  
Serum Levels ◽  
Population Based ◽  
Research Centre ◽  
Healthy Children ◽  
Serum Samples ◽  
Lms Method ◽  
Healthy Participants ◽  
Age And Sex

This study proposes age- and sex-specific percentiles for serum cobalamin and folate, and analyzes the effects of sex, age, body mass index (BMI), and socioeconomic status (SES) on cobalamin and folate concentrations in healthy children and adolescents. In total, 4478 serum samples provided by healthy participants (2 months–18.0 years) in the LIFE (Leipzig Research Centre for Civilization Diseases) Child population-based cohort study between 2011 and 2015 were analyzed by electrochemiluminescence immunoassay (ECLIA). Continuous age-and sex-related percentiles (2.5th, 10th, 50th, 90th, 97.5th) were estimated, applying Cole’s LMS method. In both sexes, folate concentrations decreased continuously with age, whereas cobalamin concentration peaked between three and seven years of age and declined thereafter. Female sex was associated with higher concentrations of both vitamins in 13- to 18-year-olds and with higher folate levels in one- to five-year-olds. BMI was inversely correlated with concentrations of both vitamins, whilst SES positively affected folate but not cobalamin concentrations. To conclude, in the assessment of cobalamin and folate status, the age- and sex-dependent dynamic of the respective serum concentrations must be considered. While BMI is a determinant of both vitamin concentrations, SES is only associated with folate concentrations.

scholarly journals Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

2021 ◽  
Vol 310 ◽  
pp. 111270
Author(s):  
Won Hee Lee ◽  
Mathilde Antoniades ◽  
Hugo G Schnack ◽  
Rene S. Kahn ◽  
Sophia Frangou
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Brain Age ◽  
Age Prediction
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