scholarly journals Individual variation underlying brain age estimates in typical development

2020 ◽  
Author(s):  
Gareth Ball ◽  
Claire E Kelly ◽  
Richard Beare ◽  
Marc L Seal
Keyword(s):  
Machine Learning ◽  
Brain Development ◽  
Machine Learning Algorithms ◽  
Brain Maturation ◽  
Childhood And Adolescence ◽  
Typically Developing ◽  
Feature Importance ◽  
Brain Age ◽  
Age Estimates ◽  
Age Prediction

AbstractTypical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed ‘brain age’, this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis.In a large cohort of n=768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to estimate sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regional brain development. However, while several regions are found to contribute to brain age prediction, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample.Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.

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

2020 ◽  
Author(s):  
Won Hee Lee ◽  
Mathilde Antoniades ◽  
Hugo G Schnack ◽  
Rene S. Kahn ◽  
Sophia Frangou
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Chronological Age ◽  
Lasso Regression ◽  
Healthy Individuals ◽  
Ols Regression ◽  
Regional Regression ◽  
The Mean ◽  
Brain Age ◽  
Age Prediction

AbstractBackgroundSchizophrenia has been associated with lifelong deviations in the normative trajectories of brain structure. These deviations can be captured using the brain-predicted age difference (brainPAD), which is the difference between the biological age of an individual’s brain, as inferred from neuroimaging data, and their chronological age. Various machine learning algorithms are currently used for this purpose but their comparative performance has yet to be systematically evaluated.MethodsSix linear regression algorithms, ordinary least squares (OLS) regression, ridge regression, least absolute shrinkage and selection operator (Lasso) regression, elastic-net regression, linear support vector regression (SVR), and relevance vector regression (RVR), were applied to brain structural data acquired on the same 3T scanner using identical sequences from patients with schizophrenia (n=90) and healthy individuals (n=200). The performance of each algorithm was quantified by the mean absolute error (MAE) and the correlation (R) between predicted brain-age and chronological age. The inter-algorithm similarity in predicted brain-age, brain regional regression weights and brainPAD were compared using correlation analyses and hierarchical clustering.ResultsIn patients with schizophrenia, ridge regression, Lasso regression, elastic-net regression, and RVR performed very similarly and showed a high degree of correlation in predicted brain-age (R>0.94) and brain regional regression weights (R>0.66). By contrast, OLS regression, which was the only algorithm without a penalty term, performed markedly worse and showed a lower similarity with the other algorithms. The mean brainPAD was higher in patients than in healthy individuals but varied by algorithm from 3.8 to 5.2 years although all analyses were performed on the same dataset.ConclusionsLinear machine learning algorithms, with the exception of OLS regression, have comparable performance for age prediction on the basis of a combination of cortical and subcortical structural measures. However, algorithm choice introduced variation in brainPAD estimation, and therefore represents an important source of inter-study variability.

Using neuroimaging to predict brain age: insights into typical and atypical development and risk for psychopathology

2020 ◽  
Vol 124 (2) ◽  
pp. 400-403
Author(s):  
Carmela Díaz-Arteche ◽  
Divyangana Rakesh
Keyword(s):  
Learning Algorithm ◽  
Brain Maturation ◽  
Future Research ◽  
Childhood And Adolescence ◽  
Brain Structure And Function ◽  
Atypical Development ◽  
And Function ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Childhood and adolescence are characterized by complex patterns of changes in brain structure and function. Recently, Truelove-Hill et al. (Truelove-Hill M, Erus G, Bashyam V, Varol E, Sako C, Gur RC, Gur RE, Koutsouleris N, Zhuo C, Fan Y, Wolf DH, Satterthwaite TD, Davatzikos C. J Neurosci 40: 1265–1275, 2020) used a novel machine learning algorithm to capture the subtle nuances of brain maturation during adolescence in two indices based on predicted brain age. In this article, we present an overview of the brain age prediction model used, provide further insight into the utility of this multimodal index to explore typical and atypical development, and discuss avenues for future research.

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

scholarly journals Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data

2021 ◽  
Author(s):  
Lea Baecker ◽  
Jessica Dafflon ◽  
Pedro F. Costa ◽  
Rafael Garcia‐Dias ◽  
Sandra Vieira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Morphometric Data ◽  
Brain Age ◽  
Machine Learning Models ◽  
Age Prediction

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

scholarly journals Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

2021 ◽  
Author(s):  
Mandana Modabbernia ◽  
Heather C Whalley ◽  
David Glahn ◽  
Paul M. Thompson ◽  
Rene S. Kahn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Efficiency ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sensitivity Analyses ◽  
Gradient Boosting ◽  
Support Vector ◽  
Age Related ◽  
Extreme Gradient Boosting ◽  
Brain Age

Application of machine learning algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the machine learning approach in estimating brain-age in children and adolescents is important because age-related brain changes in these age-groups are dynamic. However, the comparative performance of the multiple machine learning algorithms available has not been systematically appraised. To address this gap, the present study evaluated the accuracy (Mean Absolute Error; MAE) and computational efficiency of 21 machine learning algorithms using sMRI data from 2,105 typically developing individuals aged 5 to 22 years from five cohorts. The trained models were then tested in an independent holdout datasets, comprising 4,078 pre-adolescents (aged 9-10 years). The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, and sample size. The best performing algorithms were Extreme Gradient Boosting (MAE of 1.25 years for females and 1.57 years for males), Random Forest Regression (MAE of 1.23 years for females and 1.65 years for males) and Support Vector Regression with Radial Basis Function Kernel (MAE of 1.47 years for females and 1.72 years for males) which had acceptable and comparable computational efficiency. Findings of the present study could be used as a guide for optimizing methodology when quantifying age-related changes during development.

COMPARATIVE ANALYSIS OF VARIOUS MACHINE LEARNING ALGORITHMS FOR BIOLOGICAL AGE PREDICTION

2020 ◽  
Vol 8 (3) ◽  
pp. 1
Author(s):  
VAIDYA CHANDU ◽  
RANGARI SAJAN ◽  
WALKE TANMAY ◽  
PHADNIS TANMAY ◽  
DESHMUKH YASH ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Biological Age ◽  
Age Prediction

scholarly journals Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

2021 ◽  
Vol 11 ◽  
Author(s):  
Angela Lombardi ◽  
Alfonso Monaco ◽  
Giacinto Donvito ◽  
Nicola Amoroso ◽  
Roberto Bellotti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Morphological Changes ◽  
Structure Characterization ◽  
Feature Importance ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

scholarly journals Gaining Hydrological Insights Through Wilk's Feature Importance: A Test-Statistic Interpretation method for Reliable and Robust Inference

2021 ◽  
Author(s):  
Kailong Li ◽  
Guohe Huang ◽  
Brian Baetz
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Yellow River ◽  
Yellow River Basin ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Test Statistic ◽  
Relative Significance ◽  
Testing Dataset ◽  
Feature Importance

Abstract. Feature importance has been a popular approach for machine learning models to investigate the relative significance of model predictors. In this study, we developed a Wilk's feature importance (WFI) method for hydrological inference. Compared with conventional feature importance methods such as permutation feature importance (PFI) and mean decrease in impurity (MDI), the proposed WFI aims to provide more reliable importance scores that could partially address the equifinality problem in hydrology. To achieve this, the WFI measures the importance scores based on Wilk's Ʌ (a test-statistic that can be used to distinguish the differences between two or more groups of variables) throughout a decision tree. The WFI has an advantage over PFI and MDI as it does not account for predictive accuracy so the risk of overfitting will be greatly reduced. The proposed WFI was applied to three interconnected irrigated watersheds located in the Yellow River Basin, China. By employing the recursive feature elimination approach, our results indicated that the WFI could generate more stable relative importance scores in response to the reduction of irrelevant predictors, as compared with PFI and MDI embedded in three different machine learning algorithms. In addition, the comparative study also shows that the predictors identified by WFI achieved the highest predictive accuracy on the testing dataset, which indicates the proposed WFI could identify more informative predictors among many irrelevant ones. We also extended the WFI to the local importance scores for reflecting the varying characteristics of a predictor in the hydrological processes. The related findings could help to gain insights into different hydrological behaviours.

scholarly journals Regressor Fitting Of Feature Importance For Customer Segment Prediction With Ensembling Schemes Using Machine Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 952-956 ◽  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Manufacturing Companies ◽  
Data Set ◽  
Feature Importance ◽  
Customer Segment ◽  
Feature Scaling

Prediction of client behavior and their feedback remains as a challenging task in today’s world for all the manufacturing companies. The companies are struggling to increase their profit and annual turnover due to the lack of exact prediction of customer like and dislike. This leads to the accomplishment of machine learning algorithms for the prediction of customer demands. This paper attempts to identify the important features of the wine data set extracted from UCI Machine learning repository for the prediction of customer segment. The important features are extracted for the various ensembling methods like Ada boost regressor, Ada boost classifier, Random forest regressor, Extra Trees Regressor, Gradient booster regressor. The extracted feature importance of each of the ensembling methods is then fitted with logistic regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. The Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). Experimental results shows that after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 94%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.

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