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

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.

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Individual variation underlying brain age estimates in typical development

10.1101/2020.11.30.405290 ◽

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.

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

Psychiatry Research Neuroimaging ◽

10.1016/j.pscychresns.2021.111270 ◽

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

Human Brain Mapping ◽

10.1002/hbm.25368 ◽

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

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A machine learning-based model for predicting intensive care unit length of stay in patients with cardiac surgery (Preprint)

10.2196/preprints.32887 ◽

2021 ◽

Author(s):

Nianyue Wu ◽

Siru Liu ◽

Haotian Zhang ◽

Xiaomin Hou ◽

Ping Zhang ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Length Of Stay ◽

Confidence Interval ◽

Medical Information ◽

Lasso Regression ◽

Icu Length Of Stay ◽

Data Source ◽

The Mean ◽

Selection Operator

BACKGROUND The intensive care unit (ICU) length of stay is significant to evaluate the effect of cardiac surgical treatment inpatient. OBJECTIVE This research aims to accurately predict the ICU length of stay in patients with cardiac surgery. Methods: We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. METHODS We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. RESULTS The mean accuracy are 0.603 (95% confidence interval (CI): [0.602-0.604]), 0.687 (95% confidence interval (CI): [0.687-0.688]) and 0.688 (95% confidence interval (CI): [0.687-0.689]) for the logistic regression (LR) with all variables, the gradient boosted decision tree (GBDT) with important variables and the GBDT with all variables respectively. CONCLUSIONS The GBDT model with important predictors partly overestimated patients whose length of stay was less than 3 days and underestimated patients whose length of stay was longer than 3 days. But the better prediction performance of GBDT facilitates early intervention of ICU patients with a long period of hospitalization.

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Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

Information ◽

10.3390/info13010035 ◽

2022 ◽

Vol 13 (1) ◽

pp. 35

Author(s):

Jibouni Ayoub ◽

Dounia Lotfi ◽

Ahmed Hammouch

Keyword(s):

Machine Learning ◽

Link Prediction ◽

Learning Algorithms ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Actual State ◽

The Future ◽

Auc Value ◽

The Mean ◽

Analysis Of Social Networks

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

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Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training

10.1101/687079 ◽

2019 ◽

Cited By ~ 1

Author(s):

Geneviève Richard ◽

Knut Kolskår ◽

Kristine M. Ulrichsen ◽

Tobias Kaufmann ◽

Dag Alnæs ◽

...

Keyword(s):

Cognitive Training ◽

Outcome Prediction ◽

Cognitive Intervention ◽

Chronological Age ◽

Double Blind Study ◽

Stroke Patients ◽

Brain Morphometry ◽

Age Gap ◽

Brain Age ◽

Age Prediction

AbstractCognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS<7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age – chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.

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

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v7.i10.2019.408 ◽

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%.

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Bayesian Optimisation for Neuroimaging Pre-processing in Brain Age Prediction

10.1101/201061 ◽

2017 ◽

Author(s):

Jenessa Lancaster ◽

Romy Lorenz ◽

Rob Leech ◽

James H Cole

Keyword(s):

Absolute Error ◽

Processing Parameters ◽

Training Dataset ◽

Chronological Age ◽

Support Vector ◽

Voxel Size ◽

Smoothing Kernel ◽

And Performance ◽

Brain Age ◽

Age Prediction

AbstractNeuroimaging-based age predictions using machine learning have been shown to relate to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalisation to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimising resampling parameters using Bayesian optimisation.Using data on N=2001 healthy individuals (aged 16-90 years) we trained support vector machines to i) distinguish between young (<50 years) and old (>50 years) brains and ii) predict chronological age, with accuracy assessed using cross-validation. We also evaluated model generalisability to the Cam-CAN dataset (N=648, aged 18-88 years). Bayesian optimisation was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values.When distinguishing between young and old brains a classification accuracy of 96.25% was achieved, with voxel size = 11.5mm3 and smoothing kernel = 2.3mm. For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, with voxel size = 3.73mm3 and smoothing kernel = 3.68mm. This was compared to performance using default values of 1.5mm3 and 4mm respectively, which gave a MAE = 5.48 years, a 7.3% improvement. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimisation framework to the new dataset, out-performing the parameters optimised for the initial training dataset.Our study demonstrates the proof-of-principle that neuroimaging models for brain age prediction can be improved by using Bayesian optimisation to select more appropriate pre-processing parameters. Our results suggest that different parameters are selected and performance improves when optimisation is conducted in specific contexts. This motivates use of optimisation techniques at many different points during the experimental process, which may result in improved statistical sensitivity and reduce opportunities for experimenter-led bias.

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Correlation Constraints for Regression Models: Controlling Bias in Brain Age Prediction

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.615754 ◽

2021 ◽

Vol 12 ◽

Author(s):

Matthias S. Treder ◽

Jonathan P. Shock ◽

Dan J. Stein ◽

Stéfan du Plessis ◽

Soraya Seedat ◽

...

Keyword(s):

Regression Models ◽

Optimization Problem ◽

Prediction Models ◽

Chronological Age ◽

Training Procedure ◽

Constrained Optimization Problem ◽

The Difference ◽

Model Training ◽

Brain Age ◽

Age Prediction

In neuroimaging, the difference between chronological age and predicted brain age, also known as brain age delta, has been proposed as a pathology marker linked to a range of phenotypes. Brain age delta is estimated using regression, which involves a frequently observed bias due to a negative correlation between chronological age and brain age delta. In brain age prediction models, this correlation can manifest as an overprediction of the age of young brains and an underprediction for elderly ones. We show that this bias can be controlled for by adding correlation constraints to the model training procedure. We develop an analytical solution to this constrained optimization problem for Linear, Ridge, and Kernel Ridge regression. The solution is optimal in the least-squares sense i.e., there is no other model that satisfies the correlation constraints and has a better fit. Analyses on the PAC2019 competition data demonstrate that this approach produces optimal unbiased predictive models with a number of advantages over existing approaches. Finally, we introduce regression toolboxes for Python and MATLAB that implement our algorithm.

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Predicting brain-age from multimodal imaging data captures cognitive impairment

10.1101/085506 ◽

2016 ◽

Author(s):

Franziskus Liem ◽

Gaël Varoquaux ◽

Jana Kynast ◽

Frauke Beyer ◽

Shahrzad Kharabian Masouleh ◽

...

Keyword(s):

Cognitive Impairment ◽

Biological Information ◽

Future Application ◽

Chronological Age ◽

Imaging Data ◽

Adult Lifespan ◽

Training Models ◽

Brain Data ◽

Brain Age ◽

Age Prediction

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.

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