Accurate identification of brain disorders enables timely intervention and improved patient outcomes. While numerous studies have developed AI models for resting-state functional magnetic resonance imaging (rs-fMRI) analysis, most rely on supervised learning, which can overlook hidden patterns that are less discriminatively associated with labels and require large annotated datasets. To address these limitations, we propose leveraging normative modeling, an unsupervised approach that constructs a model of normality based on healthy controls' data. Deviations from normality indicate potential disorders. However, applying normative modeling to rs-fMRI faces two significant challenges: constructing normality and ensuring explainability. To tackle these challenges, we propose BRAINEXA, a novel framework enhancing normative modeling for rs-fMRI-based brain disorder identification. Specifically, to construct accurate and stable normality, BRAINEXA introduces a training strategy that predicts more informative regions from less informative regions, discouraging trivial self-supervised learning solutions and improving representation learning without additional overhead. Furthermore, we incorporate spatiotemporal mutual information regularization to preserve distinctiveness between more informative regions and less informative regions during latent encoding, preventing potential representational distortions. For interpretability, BRAINEXA extracts normality-defining (ND) subregions, the core regions that characterize normal brain function. By combining ND subregions with anomaly scores, BRAINEXA can offer region- and connection-wise explanations that help identify clinically meaningful disruptions of normality in an unsupervised setting. We demonstrate the effectiveness of BRAINEXA on four public rs-fMRI datasets: REST-meta-MDD, ABIDE I, ADHD-200, and OASIS-3. Our code is available at https://github.com/ku-milab/BRAINEXA.

Causal inference involves determining how interventions affect outcomes and explaining the underlying mechanisms, and it holds critical importance across various fields. A key assumption in causal inference is that the measured covariates form a sufficient adjustment set. However, this assumption often fails due to unobserved confounders, as confounding mechanisms are rarely fully captured by measured covariates alone. Recent research has attempted to address this challenge using variational autoencoders (VAEs), but these approaches face practical limitations, including unidentifiability and bias toward proxy variables. To overcome these issues, we propose a novel method that incorporates quantized factor identifiability into VAEs for causal effect estimation. This integration mitigates unidentifiability and reduces the dominance of proxy variables, thereby enhancing consistency and accuracy in causal inference. Extensive experiments on both simulated and real-world datasets demonstrate the robustness and effectiveness of our method, establishing a new benchmark in deep causal modeling.

Functional magnetic resonance imaging (fMRI) leverages the blood-oxygen-level-dependent (BOLD) signals to gauge functional brain activation. Specifically, task-fMRI has become a predominant tool to investigate specific cerebral regions associated with diverse cognitive processes. A myriad of studies employing task-fMRI have harnessed both static functional connectivity (FC) and dynamic functional connectivity (dFC) to identify task-related biomarkers. However, while FC and dFC have proven their efficacy, they mostly necessitate manual determination of factors, including window size, stride, and FC metrics, which potentially undermine their analysis reliability. In response to these challenges, one can detect and classify task-related patterns directly from the fMRI signals without using connectivity matrices (i.e., FC and dFC). Here, we propose a novel eigendecomposition-based attention mechanism (EAM) that emphasizes task-related regions and time points in the original signal space. By virtue of this approach, our proposed model allow us to effectively extract a refined integrated feature representation for identifying the brain cognitive states from the fMRI signals and also lessens the burden of making heuristic decisions in measuring both FC and dFC. We validate the superiority and effectiveness of our proposed method with comprehensive evaluations conducted on the Human Connectome Project (HCP) dataset, which covers seven distinct cognitive tasks.

Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. To cover these limitations, we propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework that identifies non-linear high-order functional associations among brain regions by leveraging an explainable artificial intelligence technique and selects class-discriminative regions for brain disease identification. The proposed framework includes three steps: (i) inter-regional relation learning to estimate non-linear relations through random seed-based network masking, (ii) explainable connection-wise relevance score estimation to explore high-order relations between functional connections, and (iii) non-linear high-order FC-based diagnosis-informative ROI selection and classifier learning to identify ASD. We validated the effectiveness of our proposed method by conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE) dataset, demonstrating that the proposed method outperforms other comparative methods in terms of various evaluation metrics. Furthermore, we qualitatively analyzed the selected ROIs and identified ASD subtypes linked to previous neuroscientific studies.

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

Large amounts of fMRI data are essential to building generalized predictive models for brain disease diagnosis. In order to conduct extensive data analysis, it is often necessary to gather data from multiple organizations. However, the site variation inherent in multisite resting-state functional magnetic resonance imaging (rs-fMRI) leads to unfavorable heterogeneity in data distribution, negatively impacting the identification of biomarkers and the diagnostic decision. Several existing methods have alleviated this shift of domain distribution (i.e., multisite problem). Statistical tuning schemes directly regress out site disparity factors from the data prior to model training. Such methods have a limitation in processing data each time through variance estimation according to the added site. In the model adjustment approaches, domain adaptation (DA) methods adjust the features or models of the source domain according to the target domain during model training. Thus, it is inevitable that it needs updating model parameters according to the samples of a target site, causing great limitations in practical applicability. Meanwhile, the approach of domain generalization (DG) aims to create a universal model that can be quickly adapted to multiple domains. In this study, we propose a novel framework for disease diagnosis that alleviates the multisite problem by adaptively calibrating site-specific features into site-invariant features. Specifically, it applies directly to samples from unseen sites without the need for fine-tuning. With a learning-to-learn strategy that learns how to calibrate the features under the various domain shift environments, our novel modulation mechanism extracts site-invariant features. In our experiments over the Autism Brain Imaging Data Exchange (ABIDE I and II) dataset, we validated the generalization ability of the proposed network by improving diagnostic accuracy in both seen and unseen multisite samples.