scholarly journals MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide

Brain ◽  
2020 ◽  
Vol 143 (7) ◽  
pp. 2312-2324 ◽  
Author(s):  
Vishnu M Bashyam ◽  
Guray Erus ◽  
Jimit Doshi ◽  
Mohamad Habes ◽  
Ilya M Nasrallah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Brain Network ◽  
Brain Diseases ◽  
Normal Brain ◽  
Brain Health ◽  
Brain Ageing ◽  
Brain Age ◽  
Age Estimates ◽  
Deep Brain

Abstract Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer’s disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

scholarly journals Deep learning based brain age prediction uncovers associated sequence variants

2019 ◽  
Author(s):  
B.A. Jonsson ◽  
G. Bjornsdottir ◽  
T.E. Thorgeirsson ◽  
L.M. Ellingsen ◽  
G. Bragi Walters ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Brain Diseases ◽  
Age Difference ◽  
Sequence Variants ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Brain Ageing ◽  
Brain Age

AbstractMachine learning algorithms trained to recognize age-related structural changes in magnetic resonance images (MRIs) of healthy individuals can be used to predict biological brain age in independent samples. The difference between predicted and chronological age, predicted age difference (PAD), is a phenotype holding promise for the study of normal brain ageing and brain diseases, and genetic discoveryviagenome-wide association studies (GWASs). Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders (N= 1264) and tested on two datasets, the IXI (N= 544) and UK Biobank (N= 12395) datasets, utilizing transfer learning to improve accuracy on new sites. A GWAS of PAD in the UK Biobank data (discovery set: N=12395, replication set: N=4453) yielded two sequence variants, rs1452628-T (β=-0.08,P= 1.15 · 10−9) and rs2435204-G (β=0.102,P= 9.73 · 10−12). The former is nearKCNK2and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2). The genetic association analysis was also confined to variants known to associate with brain structure, yielding three additional sequence variants associating with PAD.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals Women with a history of previous childbirths show less evident white matter brain ageing

2020 ◽  
Author(s):  
Irene Voldsbekk ◽  
Claudia Barth ◽  
Ivan I. Maximov ◽  
Tobias Kaufmann ◽  
Dani Beck ◽  
...  
Keyword(s):  
White Matter ◽  
Nulliparous Women ◽  
Brain Ageing ◽  
Maternal Brain ◽  
History Of ◽  
The Uk ◽  
Age Range ◽  
Brain Age ◽  
Age Estimates ◽  
Age Prediction

AbstractMaternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter (WM) microstructure and WM ageing trajectories later in life. Utilising global and regional brain-age prediction based on multi-shell diffusion MRI data, we investigated the association between previous childbirths and WM brain age in 8,895 women in the UK Biobank cohort (age range = 54 - 81 years). The results showed that a higher number of previous childbirths was associated with lower WM brain age, in line with previous studies showing less evident grey matter (GM) brain ageing in parous relative to nulliparous women. Both global WM and GM brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global WM association with previous childbirths, and showed a stronger relationship relative to several other tracts. While our findings demonstrate a link between reproductive history and brain WM characteristics later in life, longitudinal studies are required to understand how parity influences women’s WM trajectories across the lifespan.

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 A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhijian Yang ◽  
Ilya M. Nasrallah ◽  
Haochang Shou ◽  
Junhao Wen ◽  
Jimit Doshi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Structure ◽  
Brain Diseases ◽  
Normal Brain ◽  
Generative Adversarial Network ◽  
Cognitively Normal ◽  
Learning Framework ◽  
Adversarial Network ◽  
Normal Individuals ◽  
Disease Subtypes

AbstractHeterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.

scholarly journals Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

2020 ◽  
Vol 30 (11) ◽  
pp. 5844-5862
Author(s):  
Chen-Yuan Kuo ◽  
Pei-Lin Lee ◽  
Sheng-Che Hung ◽  
Li-Kuo Liu ◽  
Wei-Ju Lee ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Aging ◽  
Neurological Diseases ◽  
Brain Network ◽  
Machine Learning Algorithms ◽  
Model Complexity ◽  
Brain Health ◽  
Structural Covariance ◽  
Neuropsychiatric Diseases ◽  
Brain Age

Abstract The aging process is accompanied by changes in the brain’s cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework’s ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer’s disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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