scholarly journals Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

2020 ◽  
Author(s):  
Jaroslav Rokicki ◽  
Thomas Wolfers ◽  
Wibeke Nordhøy ◽  
Natalia Tesli ◽  
Daniel S. Quintana ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Neurological Disorders ◽  
Prediction Accuracy ◽  
Brain Aging ◽  
Autism Spectrum ◽  
Clinical Group ◽  
Operating Characteristics ◽  
Prediction Approach ◽  
Brain Age ◽  
Age Prediction

BackgroundThe deviation between chronological age and age predicted using brain MRI is a putative marker of brain health and disease-related deterioration. Age prediction based on structural MRI data shows high accuracy and sensitivity to common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the biological processes involved. Here, we implemented a multimodal age prediction approach and tested the predictive value across patients with a range of disorders with distinct etiologies and clinical features.MethodsWe implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) calculated from functional arterial spin labeling (ASL) data. For each of the 11 models we assessed the age prediction accuracy in HC n=761 and compared the resulting brain age gaps (BAGs) between each clinical group and age-matched subsets of HC in patients with Alzheimer’s disease (AD, n=54), mild cognitive impairment (MCI, n=88), subjective cognitive impairment (SCI, n=55), schizophrenia (SZ, n=156), bipolar disorder (BD, n=136), autism spectrum disorder (ASD, n=28).ResultsAmong the 11 models, we found highest age prediction accuracy in HC when integrating all modalities (mean absolute error=6.5 years). Beyond this global BAG, the area under the curve for the receiver-operating characteristics based on two-group case-control classifications showed strongest effects for AD and ASD in global T1-weighted BAG (T1w-BAG), while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs.ConclusionsCombining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and controls were often larger for BAGs based on single modalities. These findings demonstrate that multidimensional phenotyping provides a mapping of overlapping and distinct pathophysiology in common disorders of the brain, and specifically suggest metabolic and neurovascular aberrations in SZ and at-risk and early stage dementia.

scholarly journals Deep learning-based brain age prediction in normal aging and dementia

2021 ◽  
Author(s):  
Jeyeon Lee ◽  
Brian Burkett ◽  
Hoon-Ki Min ◽  
Matthew Senjem ◽  
Emily Lundt ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Brain Aging ◽  
Lewy Body Dementia ◽  
Normal Aging ◽  
Specific Pattern ◽  
Normal Brain ◽  
Age Gap ◽  
Brain Age ◽  
Age Prediction

Abstract Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging at respective age ranges. Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative cognitive syndromes including mild cognitive impairment, AD, frontotemporal dementia, and Lewy body dementia. Occlusion analysis, performed to facilitate interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap in dementia cohorts was highly correlated with the cognitive impairment and AD biomarker. However, regions generating brain age gaps were different for each diagnosis group of which the AD continuum showed similar patterns to normal aging in the CU.

scholarly journals Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

2020 ◽  
Author(s):  
Ann-Marie G. de Lange ◽  
Melis Anatürk ◽  
Tobias Kaufmann ◽  
James H. Cole ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
White Matter ◽  
Functional Connectivity ◽  
Prediction Accuracy ◽  
Brain Aging ◽  
Training Sample ◽  
Resting State Functional Connectivity ◽  
White Matter Microstructure ◽  
Brain Age ◽  
Age Prediction

AbstractBrain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II MRI cohort using machine learning and imaging-derived measures of gray matter morphology, diffusion-based white matter microstructure, and resting state functional connectivity. Ten-fold cross validation yielded multimodal and modality-specific brain age estimates for each participant, and additional predictions based on a separate training sample was included for comparison. The results showed equivalent age prediction accuracy between the multimodal model and the gray and white matter models (R2 of 0.34, 0.31, and 0.31, respectively), while the functional connectivity model showed a lower prediction accuracy (R2 of 0.01). Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with more apparent brain aging, with consistent associations across modalities.

scholarly journals History of childbirths relates to region-specific brain aging patterns in middle and older-aged women

2020 ◽  
Author(s):  
Ann-Marie G. de Lange ◽  
Claudia Barth ◽  
Tobias Kaufmann ◽  
Melis Anatürk ◽  
Sana Suri ◽  
...  
Keyword(s):  
Brain Aging ◽  
Reward System ◽  
Regional Brain ◽  
Pregnancy And Postpartum ◽  
Maternal Brain ◽  
History Of ◽  
Novel Applications ◽  
Prospective Longitudinal ◽  
Brain Age ◽  
Age Prediction

AbstractPregnancy involves maternal brain adaptations, but little is known about how parity influences women’s brain aging trajectories later in life. In this study, we replicated previous findings showing less apparent brain aging in women with a history of childbirths, and identified regional brain aging patterns linked to parity in 19,787 middle and older-aged women. Using novel applications of brain-age prediction methods, we found that a higher number of previous childbirths was linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens – a key region in the mesolimbic reward system, which plays an important role in maternal behavior. While only prospective longitudinal studies would be conclusive, our findings indicate that subcortical brain modulations during pregnancy and postpartum may be traceable decades after childbirth.

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 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 Contributing Factors to Advanced Brain Aging in Depression and Anxiety Disorders

2020 ◽  
Author(s):  
Laura K.M. Han ◽  
Hugo G. Schnack ◽  
Rachel M. Brouwer ◽  
Dick J. Veltman ◽  
Nic J.A. van der Wee ◽  
...  
Keyword(s):  
Brain Aging ◽  
Antidepressant Medication ◽  
Structural Mri ◽  
Depression Symptoms ◽  
Depression And Anxiety ◽  
Contributing Factors ◽  
Major Depressive ◽  
Antidepressant Use ◽  
Brain Age ◽  
Age Prediction

ABSTRACTBrain aging has shown to be more advanced in patients with Major Depressive Disorder (MDD). This study examines which factors underlie this older brain age. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pre-trained brain age prediction model based on >2,000 samples from the ENIGMA consortium was applied to predict age in 65 controls and 220 patients with current MDD and/or anxiety disorder. Brain-predicted age differences (brain-PAD) were calculated (predicted brain age minus chronological age) and associated with clinical, psychological, and biological factors. After correcting for antidepressant use, brain-PAD was significantly higher in MDD (+2.78 years) and anxiety patients (+2.91 years) compared to controls. Findings further indicate unique contributions of higher severity of somatic depression symptoms to advanced brain aging and a potential protective effect of antidepressant medication (-2.53 years).

scholarly journals Accurate brain age prediction using recurrent slice-based networks

2020 ◽  
Author(s):  
Pradeep K. Lam ◽  
Vigneshwaran Santhalingam ◽  
Parth Suresh ◽  
Rahul Baboota ◽  
Alyssa H. Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Brain Aging ◽  
Convergence Time ◽  
Information Theoretic ◽  
Current State ◽  
Proposed Model ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

ABSTRACTBrainAge (a subject’s apparent age predicted from neuroimaging data) is an important biomarker of brain aging. The deviation of BrainAge from true age has been associated with psychiatric and neurological disease, and has proven effective in predicting conversion from mild cognitive impairment (MCI) to dementia. Conventionally, 3D convolutional neural networks and their variants are used for brain age prediction. However, these networks have a larger number of parameters and take longer to train than their 2D counterparts. Here we propose a 2D slice-based recurrent neural network model, which takes in an ordered sequence of sagittal slices as input to predict the brain age. The model consists of two components: a 2D convolutional neural network (CNN), which encodes the relevant features from the slices, and a recurrent neural network (RNN) that learns the relationship between slices. We compare our method to other recently proposed methods, including 3D deep convolutional regression networks, information theoretic models, and bag-of-features (BoF) models (such as BagNet) - where the classification is based on the occurrences of local features, without taking into consideration their global spatial ordering. In our experiments, our proposed model performs comparably to, or better than, the current state of the art models, with nearly half the number of parameters and a lower convergence time.

scholarly journals Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques

2019 ◽  
Author(s):  
Alba Xifra-Porxas ◽  
Arna Ghosh ◽  
Georgios D. Mitsis ◽  
Marie-Hélène Boudrias
Keyword(s):  
Dimensionality Reduction ◽  
Brain Aging ◽  
Principal Component ◽  
Structural Mri ◽  
Prediction Performance ◽  
Subcortical Structures ◽  
Mri Features ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

AbstractBrain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N=613, age 18-88 yrs) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 yrs) yielded worse performance when compared to using MRI features (MAE of 5.33 yrs), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 yrs). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan.

scholarly journals Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

2020 ◽  
Author(s):  
Jaroslav Rokicki ◽  
Thomas Wolfers ◽  
Wibeke Nordhøy ◽  
Natalia Tesli ◽  
Daniel S. Quintana ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Multimodal Imaging ◽  
Brain Age ◽  
Age Prediction

scholarly journals From a deep learning model back to the brain - inferring morphological markers and their relation to aging

2019 ◽  
Author(s):  
Gidon Levakov ◽  
Gideon Rosenthal ◽  
Ilan Shelef ◽  
Tammy Riklin Raviv ◽  
Galia Avidan
Keyword(s):  
Deep Learning ◽  
Brain Aging ◽  
Absolute Error ◽  
Structural Mri ◽  
Brain Regions ◽  
Population Based ◽  
List Type ◽  
Brain Images ◽  
Brain Age ◽  
Age Prediction

AbstractWe present a Deep Learning framework for the prediction of chronological age from structural MRI scans. Previous findings associate an overestimation of brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in ‘explanation maps’ that were found noisy and unreliable. To address this problem, we developed an inference framework for combining these maps across subjects, thus creating a population-based rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects’ age from raw T1 brain images of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r=0.98. Using the inference method, we revealed that cavities containing CSF, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability contributed the most to the prediction.HighlightsCNNs ensemble is shown to estimate “brain age” from sMRI with an MAE of ∼3.1 yearsA novel framework enables to highlight brain regions contributing to the predictionThis framework results in explanation maps showing consistency with the literatureAs sample size increases, these maps show higher inter-sample replicabilityCSF cavities reflecting general atrophy were found as a prominent aging biomarker

Sign in / Sign up

Export Citation Format

Share Document