Dynamic functional connectivity (dFC) analysis investigates how the functional interactions between brain regions change over time by identifying recurring connectivity patterns, known as dFC states, and tracking transitions between them. Non-negative matrix factorization (NMF) has been used in dFC analysis because it produces non-negative dFC states and coefficients, interpreting dFC states and their transitions straightforwardly. However, existing NMF-based methods are limited to processing dFC data with exclusively positive values, failing to align with the functional correlations and anti-correlations between brain regions. This paper proposes an orthogonal semi-nonnegative matrix factorization (OSemiNMF) method, extending NMF to directly handle mixed-sign dFC data. Furthermore, an orthogonality constraint on the bases (i.e., dFC states) is incorporated to enhance the uniqueness of dFC states. For 10 simulated datasets with varying properties, our method outperforms comparison methods, supporting its superior ability to capture dFC states and state transitions. Using four resting-state fMRI datasets consisting of 708 healthy controls (HCs) and 537 schizophrenia patients (SZs), our method identifies reproducible dFC states and state transitions across datasets. Further, our findings reveal that SZs spend less time in high-connectivity states compared to HCs. Our study identifies meaningful and reproducible biomarkers of schizophrenia, mainly involving the connectivity associated with the sub-cortical domain. In summary, the OSemiNMF method facilitates the dFC analysis for understanding brain dynamics.

The subjective nature of diagnosing mental disorders complicates achieving accurate diagnoses. The complex relationship among disorders further exacerbates this issue, particularly in clinical practice where conditions like bipolar disorder (BP) and schizophrenia (SZ) can present similar clinical symptoms and cognitive impairments. To address these challenges, this paper proposes a mutualistic multi-network noisy label learning (MMNNLL) method, which aims to enhance diagnostic accuracy by leveraging neuroimaging data under the presence of potential clinical diagnosis bias or errors. MMNNLL effectively utilizes multiple deep neural networks (DNNs) for learning from data with noisy labels by maximizing the consistency among DNNs in identifying and utilizing samples with clean and noisy labels. Experimental results on public CIFAR-10 and PathMNIST datasets demonstrate the effectiveness of our method in classifying independent test data across various types and levels of label noise. Additionally, our MMNNLL method significantly outperforms state-of-the-art noisy label learning methods. When applied to brain functional connectivity data from BP and SZ patients, our method identifies two biotypes that show more pronounced group differences, and improved classification accuracy compared to the original clinical categories, using both traditional machine learning and advanced deep learning techniques. In summary, our method effectively addresses the possible inaccuracy in nosology of mental disorders and achieves transdiagnostic classification through robust noisy label learning via multi-network collaboration and competition.

Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.

Functional magnetic resonance imaging (fMRI) is a technique which produces complex data; however the vast majority of functional magnetic resonance imaging analyses utilize only magnitude images. In this paper, we derive a complex-valued independent component analysis (ICA) algorithm using the infomax approach which we then apply to fMRI analysis. Theoretical and empirical results demonstrate an improved sensitivity to functional changes when utilizing the complex data. Additionally, the complex infomax algorithm developed provides a powerful method for exploratory analysis of fMRI data.