In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of brain age for an individual. Importantly, the discordance between brain age and chronological age (referred to as brain age gap) can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix. Together, these observations facilitate an explainable and anatomically interpretable perspective to the task of brain age prediction.

MRI-based brain age estimation models aim to assess a subject's biological brain age based on information, such as neuroanatomical features. Various factors, including neurodegenerative diseases, can accelerate brain aging and measuring this phenomena could serve as a potential biomarker for clinical applications. While deep learning (DL)-based regression has recently attracted major attention, existing approaches often fail to capture the continuous nature of neuromorphological changes, potentially resulting in sub-optimal feature representation and results. To address this, we propose to use supervised contrastive learning with the recent Rank-N-Contrast (RNC) loss to estimate brain age based on widely used T1w structural MRI for the first time and leverage Grad-RAM to visually explain regression results. Experiments show that our proposed method achieves a mean absolute error (MAE) of 4.27 years and an $R^2$ of 0.93 with a limited dataset of training samples, significantly outperforming conventional deep regression with the same ResNet backbone while performing better or comparably with the state-of-the-art methods with significantly larger training data. Furthermore, Grad-RAM revealed more nuanced features related to age regression with the RNC loss than conventional deep regression. As an exploratory study, we employed the proposed method to estimate the gap between the biological and chronological brain ages in Alzheimer's Disease and Parkinson's disease patients, and revealed the correlation between the brain age gap and disease severity, demonstrating its potential as a biomarker in neurodegenerative disorders.

Seven genes have been linked to particularly fast ageing of the brain – but 13 drugs and supplements might reduce their effects. The activity of many genes contributes to the difference between our actual age and the biological age of our brains, defined by how old our cells indicate we are, which creates what is known as a brain age gap. Restoring the brain's mitochondria could slow ageing and end dementia To find genes that accelerate brain ageing and widen this gap, Zhengxing Huang at Zhejiang University in China and his colleagues trained a deep-learning model called 3D-ViT on some medical records and used others to check it gave accurate responses. They then used it to analyse data from nearly 39,000 people who had health, genetic and lifestyle information, along with biological samples, stored in the UK Biobank. These participants were 64 years old, on average, and about half were women.

In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix.

Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing \textit{brain age gap} characterized by an elevated brain age relative to the chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. Hence, brain age gap is a promising biomarker for monitoring brain health. However, black-box machine learning approaches to brain age gap prediction have limited practical utility. Recent studies on coVariance neural networks (VNN) have proposed a relatively transparent deep learning pipeline for neuroimaging data analyses, which possesses two key features: (i) inherent \textit{anatomically interpretablity} of derived biomarkers; and (ii) a methodologically interpretable perspective based on \textit{linkage with eigenvectors of anatomic covariance matrix}. In this paper, we apply the VNN-based approach to study brain age gap using cortical thickness features for various prevalent neurodegenerative conditions. Our results reveal distinct anatomic patterns for brain age gap in Alzheimer's disease, frontotemporal dementia, and atypical Parkinsonian disorders. Furthermore, we demonstrate that the distinct anatomic patterns of brain age gap are linked with the differences in how VNN leverages the eigenspectrum of the anatomic covariance matrix, thus lending explainability to the reported results.

Researchers trained an artificial intelligence model to gauge people's ages from their brain scans The abundance of 13 proteins in your blood seems to be a strong indicator of how rapidly your brain is ageing. This suggests that blood tests could one day help people track and even boost their brain health. Most previous studies that have looked at protein markers of brain ageing in the blood have involved fewer than 1000 people, says Nicholas Seyfried at Emory University in Atlanta, Georgia, who wasn't involved in the new research. To get a broader idea of the impact of these proteins, Wei-Shi Liu at Fudan University in China and his colleagues analysed MRI brain scan data from nearly 11,000 adults from the UK Biobank project, whose ages ranged from around 50 to 80 at the time of imaging. Using data from 70 per cent of the participants, Liu's team trained an artificial intelligence model to predict how old the participants were based on features of the brain images, such as the size of different brain regions and how distinct parts connected to each other.

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data are necessary for model building and optimization. To help overcome such limitations in the context of brain MRI, we present NeuroSynth: a collection of generative models of normative regional volumetric features derived from structural brain imaging. NeuroSynth models are trained on real brain imaging regional volumetric measures from the iSTAGING consortium, which encompasses over 40,000 MRI scans across 13 studies, incorporating covariates such as age, sex, and race. Leveraging NeuroSynth, we produce and offer 18,000 synthetic samples spanning the adult lifespan (ages 22-90 years), alongside the model's capability to generate unlimited data. Experimental results indicate that samples generated from NeuroSynth agree with the distributions obtained from real data. Most importantly, the generated normative data significantly enhance the accuracy of downstream machine learning models on tasks such as disease classification.

It may be possible to delay your brain from aging. And no, this isn't the beginning of a pitch from an after-hours informercial -- the science behind this concept is surprisingly real. A recent study in Nature Neuroscience merged three fields to make strides in this research: longevity, neuroscience, and machine learning. An algorithm that can predict your brain age from MRI scans. Brain age refers to how well your brain is aging compared to your chronological age. Humans run the gamut in this context; we've all encountered a spry 70-year-old who seemed surprisingly sharp for their age.

Delaying "brain age" may sound like the latest quick-fix gimmick on a late-night infomercial, but the science underlying the concept is very real. Rather than reflecting the average functional state of your chronological age, brain age looks at how well your brain is aging relative to how many birthdays you've celebrated. We all know people that seem sharper and act much younger than their age--that incredulous moment when you realize the 40-year-old you've been chatting with on the plane is actually a grandma in her 70s. Brain age, as a concept, hopes to capture the biological intricacies behind that cognitive dissociation. Longevity researchers have increasingly realized that how long you've lived isn't the best predictor of overall health.