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

2019 ◽  
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.

scholarly journals Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training

2020 ◽  
Vol 25 ◽  
pp. 102159 ◽  
Author(s):  
Geneviève Richard ◽  
Knut Kolskår ◽  
Kristine M. Ulrichsen ◽  
Tobias Kaufmann ◽  
Dag Alnæs ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Cognitive Training ◽  
Stroke Patients ◽  
Brain Age ◽  
Age Prediction ◽  
Limited Sensitivity

scholarly journals Grey Matter Age Prediction as a Biomarker for Risk of Dementia: A Population-based Study

2019 ◽  
Author(s):  
Johnny Wang ◽  
Maria J. Knol ◽  
Aleksei Tiulpin ◽  
Florian Dubost ◽  
Marleen de Bruijne ◽  
...  
Keyword(s):  
Grey Matter ◽  
Proportional Hazards ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Chronological Age ◽  
Population Based Study ◽  
Age Gap ◽  
Incident Dementia ◽  
Brain Age ◽  
Age Prediction

Key PointsQuestionIs the gap between brain age predicted from MRI and chronological age associated with incident dementia in a general population of Dutch adults?FindingsBrain age was predicted using a deep learning model, using MRI-derived grey matter density maps. In a population based study including 5496 participants, the observed gap was significantly associated with the risk of dementia.MeaningThe gap between MRI-brain predicted and chronological age is potentially a biomarker for dementia risk screening.AbstractImportanceThe gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as biomarker for early-stage neurodegeneration and potentially as a risk indicator for dementia. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link.ObjectiveWe aimed to investigate the utility of such a gap as a risk biomarker for incident dementia in a general Dutch population, using a deep learning approach for predicting brain age based on MRI-derived grey matter maps.DesignData was collected from participants of the cohort-based Rotterdam Study who underwent brain magnetic resonance imaging between 2006 and 2015. This study was performed in a longitudinal setting and all participant were followed up for incident dementia until 2016.SettingThe Rotterdam Study is a prospective population-based study, initiated in 1990 in the suburb Ommoord of in Rotterdam, the Netherlands.ParticipantsAt baseline, 5496 dementia- and stroke-free participants (mean age 64.67±9.82, 54.73% women) were scanned and screened for incident dementia. During 6.66±2.46 years of follow-up, 159 people developed dementia.Main outcomes and measuresWe built a convolutional neural network (CNN) model to predict brain age based on its MRI. Model prediction performance was measured in mean absolute error (MAE). Reproducibility of prediction was tested using the intraclass correlation coefficient (ICC) computed on a subset of 80 subjects. Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for years of education, ApoEε4 allele carriership, grey matter volume and intracranial volume. Additionally, we computed the attention maps of CNN, which shows which brain regions are important for age prediction.ResultsMAE of brain age prediction was 4.45±3.59 years and ICC was 0.97 (95% confidence interval CI=0.96-0.98). Logistic regression and Cox proportional hazards models showed that the age gap was significantly related to incident dementia (odds ratio OR=1.11 and 95% confidence intervals CI=1.05-1.16; hazard ratio HR=1.11 and 95% CI=1.06-1.15, respectively). Attention maps indicated that grey matter density around the amygdalae and hippocampi primarily drive the age estimation.Conclusion and relevanceWe show that the gap between predicted and chronological brain age is a biomarker associated with risk of dementia development. This suggests that it can be used as a biomarker, complimentary to those that are known, for dementia risk screening.

scholarly journals Bayesian Optimisation for Neuroimaging Pre-processing in Brain Age Prediction

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.

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

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.

scholarly journals Predicting brain-age from multimodal imaging data captures cognitive impairment

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.

scholarly journals Brain Age in Early Stages of Bipolar Disorders or Schizophrenia

2017 ◽  
Vol 45 (1) ◽  
pp. 190-198 ◽  
Author(s):  
Tomas Hajek ◽  
Katja Franke ◽  
Marian Kolenic ◽  
Jana Capkova ◽  
Martin Matejka ◽  
...  
Keyword(s):  
Bipolar Disorders ◽  
Structural Mri ◽  
Chronological Age ◽  
First Episode ◽  
Schizophrenia Spectrum Disorders ◽  
Early Stages ◽  
Age Gap ◽  
Gap Estimate ◽  
Brain Age ◽  
The Brain

Abstract Background The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P &lt; .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.

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