Explainable Brain Age Prediction using coVariance Neural Networks

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 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 perspective to the task of brain age prediction.Comment: arXiv admin note: substantial text overlap with arXiv:2305.0180

Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques

Abstract The aim of this paper was to investigate the impact of the fly ash concentration on the infiltration process and to assess the potential of five soft computing techniques such as artificial neural network, Gaussian process, support vector machine (SVM), random forest, and M5P model tree and compare with two popular conventional models, SCS and Kostiakov mode, to estimate the cumulative infiltration of fly-ash-mixed soils. Laboratory experiment was carried out with the different combinations of the sand, clay, and fly ash by using mini disk infiltrometer. The combination consists of the different concentrations of sand (25–45%), clay (25–45%), and fly ash (10–50%). The total observation consists of the 138 field measurement. The cumulative infiltration increase with an increment in the concentration of the fly ash, but it decreases when fly ash concentration increases 40–50% in the soil. On the other hand, the cumulative infiltration increases with the decrease in the concentration of clay in samples. The predictive modeling technique, SVM with RBF kernel, is the best technique to predict the cumulative infiltration with minimum error. Results suggest that SVM with RBF kernel is the best-fit modeling technique among other soft computing techniques as well as conventional models to find the impact of fly ash on infiltration characteristics for the given combination of the sand, clay and fly ash

coVariance Neural Networks

Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the projection of data on the eigenspace of the covariance matrix and draws similarities with the graph convolutional filters in GNNs. Motivated by this observation, we propose a GNN architecture, called coVariance neural network (VNN), that operates on sample covariance matrices as graphs. We theoretically establish the stability of VNNs to perturbations in the covariance matrix, thus, implying an advantage over standard PCA-based data analysis approaches that are prone to instability due to principal components associated with close eigenvalues. Our experiments on real-world datasets validate our theoretical results and show that VNN performance is indeed more stable than PCA-based statistical approaches. Moreover, our experiments on multi-resolution datasets also demonstrate that VNNs are amenable to transferability of performance over covariance matrices of different dimensions; a feature that is infeasible for PCA-based approaches

Structural Modularity is a Feature of C9orf72 Intrinsic Network Degeneration

Background Genetic forms of frontotemporal degeneration (FTD) and amyotrophic lateral sclerosis (ALS) provide an important target for early therapeutic interventions. However, pre‐symptomatic biomarkers to monitor disease progression are lacking. Prior neuroimaging studies suggest disruption of functional and structural networks are an early feature of neurodegenerative disease. Network modularity quantifies the degree of segregation between sub‐networks. We hypothesize that structural network modularity is a marker of neurodegeneration in C9orf72 carriers. Method We evaluated diffusion magnetic resonance imaging (dMRI) in two independent cohorts of C9orf72 carriers and controls: University of Pennsylvania Prodromal Study (Penn‐C9) including symptomatic C9orf72 carriers (SC9; N=27; FTD=20; ALS=7), asymptomatic C9orf72 carriers (AC9; N=24), and healthy controls (HC; N=29); and Human Connectome Project‐FTD (HCP‐FTD) also including SC9 (N=12), AC9 (N=12), and HC (N=20) individuals. We quantified modularity in 7 intrinsic networks of 400 nodes from the Schaefer Atlas using participation coefficient (PC), where lower PC corresponds to higher modularity, and also investigated its constituent measures: within‐network connectivity for each network and pairwise between‐network connectivity. Modularity metrics were residualized in the Penn‐C9 dataset using linear regression to adjust for age. Statistical analyses on residualized scores were first performed in Penn‐C9 and cross‐validated in the HCP‐FTD dataset. Result ANCOVAs on residualized scores in Penn‐C9 revealed group differences in PC for limbic network with SC9 lower than HC (partial‐η2=0.12, p=0.011). We also observed group differences in within‐module connectivity for limbic (partial‐η2=0.16, p=0.0016) and dorsal‐attention (partial‐η2=0.1, p=0.019) networks with SC9 lower than HC. Cross‐validation in HCP‐FTD revealed group differences in modularity metrics for limbic network (PC: partial‐η2=0.29, p=0.0012 and within module connectivity:partial‐η2=0.28, p=0.0016) with both metrics lower in SC9 relative to HC. The group differences were significant between AC9 and HC in Penn‐C9 and the means of aforementioned metrics for AC9 were between HC and SC9 in both datasets, suggesting an intermediate state towards modularity. Conclusion We observed cross‐validated evidence of greater network modularity, in limbic network, for SC9 relative to HC and intermediate evidence of modularity in AC9. Limbic network is plausible as earliest loci of TDP‐43 pathology in FTD. We suggest that structural network modularity may be an early feature of C9orf72 disease progression