Age Prediction Based on Brain MRI Image: A Survey

Sajedi, Hedieh; Pardakhti, Nastaran

doi:10.1007/s10916-019-1401-7

Cited by 56 publications

(42 citation statements)

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inflammatory biomarkers are commonly dysregulated in MDD and negative relationships between levels of inflammatory cytokine (e.g., interleukin-6) and cortical thickness have been found in medication-free, first-episode MDD patients 94 , suggesting that inflammation may be a common biological mechanism between MDD and brain aging. Notably, brain-PAD has been shown to be a general predictor of psychiatric and neurological disorders, with low clinical disease specificity 95 .…”

Section: Brain Aging In Mddmentioning

confidence: 99%

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

et al. 2020

View full text Add to dashboard Cite

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of largescale data sharing for mental health research.

show abstract

Section: Brain Aging In Mddmentioning

confidence: 99%

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It is important to predict the brain age reliably and accurately for brain development analysis and brain disease diagnosis in pediatric patients. Basically, methods for predicting brain age can be divided into two categories: shallow learning algorithms and deep learning algorithms (38). So far, numerous shallow learning algorithms have been developed, such as gaussian processes regression (GPR) (29,39,40), support vector regression (SVR) (41,42), partial least squares (PLS) regression (43), relevance vector regression (RVR) (44), hidden Markov model (HMM) (45), and Bayesian linear discriminant analysis (46).…”

Section: High Reliability and Accuracy Of 3d Cnn For Brain Age Predicmentioning

confidence: 99%

“…The manual interventions in preprocessing lead to high intra-observer and inter-observer variability, which easily biased the final interpretation. Comparing to the traditional machine learning methods, CNN-based methods are an endto-end system that uses the raw MR image data as the input and output the age value without manual interventions, showing higher reliability and improving clinical practice (38).…”

Section: High Reliability and Accuracy Of 3d Cnn For Brain Age Predicmentioning

confidence: 99%

Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

et al. 2020

View full text Add to dashboard Cite

Predicting brain age of children accurately and quantitatively can give help in brain development analysis and brain disease diagnosis. Traditional methods to estimate brain age based on 3D magnetic resonance (MR), T1 weighted imaging (T1WI), and diffusion tensor imaging (DTI) need complex preprocessing and extra scanning time, decreasing clinical practice, especially in children. This research aims at proposing an end-to-end AI system based on deep learning to predict the brain age based on routine brain MR imaging. We spent over 5 years enrolling 220 stacked 2D routine clinical brain MR T1-weighted images of healthy children aged 0 to 5 years old and randomly divided those images into training data including 176 subjects and test data including 44 subjects. Data augmentation technology, which includes scaling, image rotation, translation, and gamma correction, was employed to extend the training data. A 10-layer 3D convolutional neural network (CNN) was designed for predicting the brain age of children and it achieved reliable and accurate results on test data with a mean absolute deviation (MAE) of 67.6 days, a root mean squared error (RMSE) of 96.1 days, a mean relative error (MRE) of 8.2%, a correlation coefficient (R) of 0.985, and a coefficient of determination (R 2) of 0.971. Specially, the performance on predicting the age of children under 2 years old with a MAE of 28.9 days, a RMSE of 37.0 days, a MRE of 7.8%, a R of 0.983, and a R 2 of 0.967 is much better than that over 2 with a MAE of 110.0 days, a RMSE of 133.5 days, a MRE of 8.2%, a R of 0.883, and a R 2 of 0.780.

show abstract

“…However, it is an imperfect predictor of disease risk or of healthy individuals' functional capability (1). A growing field of research has been focusing on identifying biological correlates of age (e.g., from telomere length, methylation site, brain structure, and function) to derive measures of biological age (2)(3)(4)(5)(6). Promises of biological age rely on the assumption that it would capture specific physiological or biological aspects of aging, which may allow predicting mortality and could supersede chronological age in predicting diseases or functional state (5,7).…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

et al. 2020

View full text Add to dashboard Cite

We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.

show abstract

Age Prediction Based on Brain MRI Image: A Survey

Cited by 56 publications

References 98 publications

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

Contact Info

Product

Resources

About