Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

The cortex is organised into broadly hierarchical functional systems with distinct neuroanatomical characteristics reflected by macroscopic measures of cortical morphology. Diffusion‐weighted magnetic resonance imaging allows the delineation of areal connectivity, changes to which reflect the ongoing maturation of white matter tracts. These developmental processes are intrinsically linked with timing coincident with the development of cognitive function. In this study, we use a data‐driven multivariate approach, nonnegative matrix factorisation, to define cortical regions that co‐vary together across a large paediatric cohort (n = 456) and are associated with specific subnetworks of cortical connectivity. We find that age between 3 and 21 years is associated with accelerated cortical thinning in frontoparietal regions, whereas relative thinning of primary motor and sensory regions is slower. Together, the subject‐specific weights of the derived set of cortical components can be combined to predict chronological age. Structural connectivity networks reveal a relative increase in strength in connection within, as opposed to between hemispheres that vary in line with cortical changes. We confirm our findings in an independent sample.

Section: Discussionsupporting

confidence: 65%

Section: Reconstruction Error Cross-validation and Age Predictionmentioning

confidence: 99%

Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence

Ball

Beare

Seal

2019

“…It is important to remark that the value of MAE of BCA equal to 5.89 years is no better than the performance of some other prediction models using merely volumetric data (Cole, Poudel, et al, 2017;Mwangi et al, 2015). Next, the significant features are pooled to create an optimized linear regression model capable to assess the biological brain age of a given connectome with minimal error.…”

Section: Network Homeostasis: Increased Fc In Combination With Decrmentioning

confidence: 99%

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Bonifazi

Erramuzpe

Diez

et al. 2018

Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age—an age estimator resulting from a multi‐scale methodology applied to the structure–function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto‐striato‐thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural–functional connectivity patterns correlating to other biomarkers than ChA.

“…With recent advances in deep learning, DNNs are expected to improve prediction performance (Cole & Franke, ). However, most studies showed DNN yielded similar prediction performance to traditional ML methods (Aycheh et al, ; Cole et al, ). For example, a convolutional neural network (CNN) achieved comparable prediction accuracy ( r = .96) to GPR (Cole et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…However, most studies showed DNN yielded similar prediction performance to traditional ML methods (Aycheh et al, ; Cole et al, ). For example, a convolutional neural network (CNN) achieved comparable prediction accuracy ( r = .96) to GPR (Cole et al, ). Though prediction performance was not significantly improved, one advantage is that DNN is directly applicable to raw imaging data.…”

Section: Introductionmentioning

confidence: 99%

Improved prediction of brain age using multimodal neuroimaging data

Niu

Zhang

Kounios

et al. 2019

116

108

Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs‐fMRI from a large data set with 839 subjects. Our study is a follow‐up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs‐fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.