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 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 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

scholarly journals Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

2020 ◽  
Author(s):  
Xin Niu ◽  
Alexei Taylor ◽  
Russell T. Shinohara ◽  
John Kounios ◽  
Fengqing Zhang
Keyword(s):  
Psychiatric Disorders ◽  
Robust Regression ◽  
Developmental Trajectories ◽  
Brain Structures ◽  
Brain Regions ◽  
Developmental Trajectory ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

AbstractBrain regions change in different ways and at different rates. This staggered developmental unfolding is determined by genetics and postnatal experience and is implicated in the progression of psychiatric and neurological disorders. Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, the unidimensional brain-age estimates provided by previous methods do not capture the divergent developmental trajectories of various brain structures. Here we propose and illustrate an analytic pipeline to compute an index of multidimensional brain-age that provides regional age predictions. First, using a database of 556 subjects that includes psychiatric and neurological patients as well as healthy controls we conducted robust regression to characterize the developmental trajectory of each MRI-based brain-imaging feature. We then utilized cluster analysis to identify subgroups of imaging features with a similar developmental trajectory. For each identified cluster, we obtained a brain-age prediction by applying machine-learning models with imaging features belonging to each cluster. Brain-age predictions from multiple clusters form a multidimensional brain-age index (MBAI). The MBAI is more sensitive to alterations in brain structures and captured distinct regional change patterns. In particular, the MBAI provided a more flexible analysis of brain age across brain regions that revealed changes in specific structures in psychiatric disorders that would otherwise have been combined in a unidimensional brain age prediction. More generally, brain-age prediction using a subset of homogeneous features circumvents the curse of dimensionality in neuroimaging data.

scholarly journals Increased Brain Age Gap Estimate (BrainAGE) in Young Adults After Premature Birth

2021 ◽  
Vol 13 ◽  
Author(s):  
Dennis M. Hedderich ◽  
Aurore Menegaux ◽  
Benita Schmitz-Koep ◽  
Rachel Nuttall ◽  
Juliana Zimmermann ◽  
...  
Keyword(s):  
Premature Birth ◽  
Brain Aging ◽  
Structural Mri ◽  
Full Term ◽  
Intracranial Volume ◽  
Age Gap ◽  
Increased Risk ◽  
Aging Rates ◽  
Brain Age ◽  
Premature Born

Recent evidence suggests increased metabolic and physiologic aging rates in premature-born adults. While the lasting consequences of premature birth on human brain development are known, its impact on brain aging remains unclear. We addressed the question of whether premature birth impacts brain age gap estimates (BrainAGE) using an accurate and robust machine-learning framework based on structural MRI in a large cohort of young premature-born adults (n = 101) and full-term (FT) controls (n = 111). Study participants are part of a geographically defined population study of premature-born individuals, which have been followed longitudinally from birth until young adulthood. We investigated the association between BrainAGE scores and perinatal variables as well as with outcomes of physical (total intracranial volume, TIV) and cognitive development (full-scale IQ, FS-IQ). We found increased BrainAGE in premature-born adults [median (interquartile range) = 1.4 (−1.3–4.7 years)] compared to full-term controls (p = 0.002, Cohen’s d = 0.443), which was associated with low Gestational age (GA), low birth weight (BW), and increased neonatal treatment intensity but not with TIV or FS-IQ. In conclusion, results demonstrate elevated BrainAGE in premature-born adults, suggesting an increased risk for accelerated brain aging in human prematurity.

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 Shared Component Analysis

2020 ◽  
Author(s):  
Alain de Cheveigné
Keyword(s):  
Dimensionality Reduction ◽  
Large Fraction ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Sensors ◽  
Current Spread ◽  
Neuroimaging Data ◽  
Orthogonal Components ◽  
Shared Component ◽  
Deep Sources

AbstractThis paper proposes Shared Component Analysis (SCA) as an alternative to Principal Component Analysis (PCA) for the purpose of dimensionality reduction of neuroimaging data. The trend towards larger numbers of recording sensors, pixels or voxels leads to richer data, with finer spatial resolution, but it also inflates the cost of storage and computation and the risk of overfitting. PCA can be used to select a subset of orthogonal components that explain a large fraction of variance in the data. This implicitly equates variance with relevance, and for neuroimaging data such as electroencephalography (EEG) or magnetoencephalography (MEG) that assumption may be inappropriate if (latent) sources of interest are weak relative to competing sources. SCA instead assumes that components that contribute to observable signals on multiple sensors are of likely interest, as may be the case for deep sources within the brain as a result of current spread. In SCA, steps of normalization and PCA are applied iteratively, linearly transforming the data such that components more widely shared across channels appear first in the component series. The paper explains the motivation, defines the algorithm, evaluates the outcome, and sketches a wider strategy for dimensionality reduction of which this algorithm is an example. SCA is intended as a plug-in replacement for PCA for the purpose of dimensionality reduction.

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 Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019

2020 ◽  
Vol 11 ◽  
Author(s):  
Pedro F. Da Costa ◽  
Jessica Dafflon ◽  
Walter H. L. Pinaya
Keyword(s):  
Machine Learning ◽  
Brain Aging ◽  
Predictive Analytics ◽  
Neurological Diseases ◽  
Absolute Error ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Neuroimaging Data ◽  
Brain Age

As we age, our brain structure changes and our cognitive capabilities decline. Although brain aging is universal, rates of brain aging differ markedly, which can be associated with pathological mechanism of psychiatric and neurological diseases. Predictive models have been applied to neuroimaging data to learn patterns associated with this variability and develop a neuroimaging biomarker of the brain condition. Aiming to stimulate the development of more accurate brain-age predictors, the Predictive Analytics Competition (PAC) 2019 provided a challenge that included a dataset of 2,640 participants. Here, we present our approach which placed between the top 10 of the challenge. We developed an ensemble of shallow machine learning methods (e.g., Support Vector Regression and Decision Tree-based regressors) that combined voxel-based and surface-based morphometric data. We used normalized brain volume maps (i.e., gray matter, white matter, or both) and features of cortical regions and anatomical structures, like cortical thickness, volume, and mean curvature. In order to fine-tune the hyperparameters of the machine learning methods, we combined the use of genetic algorithms and grid search. Our ensemble had a mean absolute error of 3.7597 years on the competition, showing the potential that shallow methods still have in predicting brain-age.

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