Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional fluctuations emerge remarkable cognition is still elusive. Meanwhile, tremendous efforts have been made in the realm of machine learning to establish the nonlinear mapping between neuroimaging data and phenotypic traits. However, the absence of neuroscience insight in the current approaches poses significant challenges in understanding cognitive behavior from transient neural activities. To address this challenge, we put the spotlight on the coupling mechanism of structural connectivity (SC) and functional connectivity (FC) by formulating such network neuroscience question into an expressive graph representation learning problem for high-order topology. Specifically, we introduce the concept of topological detour to characterize how a ubiquitous instance of FC (direct link) is supported by neural pathways (detour) physically wired by SC, which forms a cyclic loop interacted by brain structure and function. In the cliché of machine learning, the multi-hop detour pathway underlying SC-FC coupling allows us to devise a novel multi-head self-attention mechanism within Transformer to capture multi-modal feature representation from paired graphs of SC and FC. Taken together, we propose a biological-inspired deep model, coined as NeuroPath, to find putative connectomic feature representations from the unprecedented amount of neuroimages, which can be plugged into various downstream applications such as task recognition and disease diagnosis. We have evaluated NeuroPath on large-scale public datasets including Human Connectome Project (HCP) and UK Biobank (UKB) under different experiment settings of supervised and zero-shot learning, where the state-of-the-art performance by our NeuroPath indicates great potential in network neuroscience.

SIDER is a dataset for predicting side effect from the small molecule structure. It contains 27 classification tasks, corresponding to the 27 system organ classes following MedDRA classifications [1]. If taking a closer look at the MedDRA classification on the system organ level on its website, we can find a claim of "System Organ Classes (SOCs) which are groupings by aetiology (e.g. In addition, there is a SOC to contain issues pertaining to products and one to contain social circumstances." In fact, the two tasks among the 27 tasks are named "Social circumstances" and "Product issues", that corresponds to the claims above. Predicting such label from molecular structure alone is futile and therefore does not serve the purpose of a benchmarking dataset. The other problematic example in MoleculeNet is the PCBA dataset, originally used in [44]. However, as claimed in the original paper, "It should be noted that we did not perform any preprocessing of our datasets, such as removing potential experimental artifacts". And we have demonstrated the importance of removing the experimental artifacts in the data processing pipeline in the main text. There are more example issues with MoleculeNet that can be found in [52]. For Therapeutics Data Commons (TDC) [24], we used filters in our pipeline on small molecule-related tasks on and found issues with them. The promiscuity filter is not applied due to the long running time.

With all the advances in both the science of aging and artificial intelligence (AI), we are in a propitious position to accurately and precisely determine who is at high risk of developing Alzheimer's disease years before signs of even mild cognitive deficit. It takes at least 20 years for aggregates of misfolded β-amyloid and tau proteins to accumulate in the brain along with neuroinflammation that they incite. This provides a long window of opportunity to get ahead of the pathobiological process, both for prediction and prevention. A family history of Alzheimer's and the presence of genetic variants in the APOE4 (apolipoprotein E4) allele indicate an increased risk, as does a polygenic risk score that is based on the combined influence of many genetic variants. However, each of these clues provides little insight about when initial symptoms would likely present.

Medical time series (MedTS) data, such as Electroencephalography (EEG) and Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases. Existing methods for MedTS classification primarily rely on handcrafted biomarkers extraction and CNN-based models, with limited exploration of transformer-based models. In this paper, we introduce Medformer, a multi-granularity patching transformer tailored specifically for MedTS classification. Our method incorporates three novel mechanisms to leverage the unique characteristics of MedTS: cross-channel patching to leverage inter-channel correlations, multi-granularity embedding for capturing features at different scales, and two-stage (intra-and inter-granularity) multi-granularity self-attention for learning features and correlations within and among granularities. We conduct extensive experiments on five public datasets under both subject-dependent and challenging subject-independent setups. Results demonstrate Medformer's superiority over 10 baselines, achieving top averaged ranking across five datasets on all six evaluation metrics. These findings underscore the significant impact of our method on healthcare applications, such as diagnosing Myocardial Infarction, Alzheimer's, and Parkinson's disease.

This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. To address this, we introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes. Our approach involves the integration of the pairwise graph disparity error and a tailored loss function into a nonsmooth multi-objective optimization problem, striving to achieve fairness across different sensitive groups while maintaining the effectiveness of the GMs. Experimental evaluations on synthetic and real-world datasets demonstrate that our framework effectively mitigates bias without undermining GMs' performance.

We generate 24000 data points in total, from which n=2000 are sampled for each randomly seeded run of experiments; the context space consists of 8 attributes, and the decision space consists of 4 rocket actions.

Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise. In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to successfully improve the cognitive impairment prediction using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort data.

This network takes a 3D brain MRI as its input and produces a cortex ribbon segmentation map. It is worth noting that the white matter (WM) segmentation refers to the interior of the WM surface, which contains cortical WM, deep gray matter, ventricle, hippocampus, and other tissues within the surface. Similarly, grey matter (GM) segmentation refers to the interior of the pial surface. The output map consists of 5 classes, namely left hemisphere WM and GM, right hemisphere WM and GM, and background. In the encoder, at each level, we apply a 3 3 3 convolutional layer with a stride of 2 to downsample the features. In the decoder, upsampling is applied to the features at each scale by 2, and the features are concatenated with their counterparts of the encoder via a skip connection, in conjunction with feature fusion using a 3 3 3 convolutional layer with a stride of 1. From the input, features are learned with a 3 3 3 convolutional layer with a stride of 1.