Annie G. Bryant

Brain networks exhibit non-trivial modular organization, with groups of densely connected areas participating in specialized functions. Traditional community detection algorithms assign each node to one module, but this representation cannot capture integrative, multifunctional nodes that span multiple communities. Despite the increasing availability of overlapping community detection algorithms (OCDAs) to capture such integrative nodes, there is no objective procedure for selecting the most appropriate method and its parameters for a given problem. Here, we overcome this limitation by introducing a data-driven method for selecting an OCDA and its parameters from performance on a tailored ensemble of generated benchmark networks, assessing 22 unique algorithms and parameter settings. Applied to the human right-hemisphere structural connectome, we find that the “order statistics local optimization method” (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble, yielding a seven-network decomposition of the right-hemisphere cortex. These modules are bridged by 15 overlapping regions that generally sit at the apex of the putative cortical hierarchy—suggesting integrative, higher order function—with network participation increasing along the cortical hierarchy, a finding not supported using a non-overlapping modular decomposition. This data-driven approach to selecting OCDAs is applicable across domains, opening new avenues to detecting and quantifying informative structures in complex real-world networks.

Despite a rich and interdisciplinary literature on time-series analysis, the brain’s complex distributed dynamics are typically quantified using only a limited set of manually selected representations and statistics. This leaves open the possibility that alternative dynamical properties may outperform those reported for a given application. Here we address this limitation by introducing a systematic procedure to compare diverse, interpretable features of functional magnetic resonance imaging (fMRI) data, drawn from comprehensive algorithmic libraries to capture both dynamical properties of individual brain regions (intra-regional activity) and patterns of statistical dependence between regions (inter-regional functional coupling). We apply our data-driven approach to investigate alterations to dynamical structures in resting-state fMRI in participants with schizophrenia (SCZ), bipolar 1 disorder (BP), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Our findings broadly support the use of linear time-series analysis techniques for fMRI-based case–control analysis, while also identifying novel informative dynamical structures that have previously received less attention. While some simple statistical representations of fMRI dynamics perform surprisingly well (e.g., properties within a single brain region), we found performance improvements through combining intra-regional properties with inter-regional coupling—consistent with distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The data-driven approach here enables the systematic identification and interpretation of disorder-specific dynamical signatures, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

Recent years have seen an expanding repertoire of code-based tools for visualizing neuroimaging data, promoting reproducibility and interpretability of brain-mapping findings. However, most open-source visualization packages for the human brain are geared toward the cerebral cortex, and the comparatively few options for the subcortex and cerebellum are limited in scope (i.e., atlas support) and flexibility. We address this critical gap by introducing subcortex_visualization, an open-source package offered in both Python and R, that provides a unified and accessible framework for programmatically visualizing non-cortical data across many popular subcortical and cerebellar parcellation atlases. These visuals are inspired by the ggseg R package for cortical data, which implements standardized rendering conventions to facilitate comparison across atlases in a vectorized two-dimensional format. In addition to the vectorized versions of nine subcortical and cerebellar atlases—to our knowledge, the most comprehensive collection of non-cortical atlases in a single visualization toolbox—we also provide a step-by-step tutorial for users to generate custom vector-based visualizations from any given brain segmentation, enabling flexible extension to new atlases and structures. Collectively, subcortex_visualization and the accompanying documentation support reproducible and interpretable visualization of neuroimaging data below the cortical mantle.

Identifying the neural correlates of conscious visual perception remains a major challenge in neuroscience, requiring theories that bridge between subjective experience and measurable neural correlates. However, theoretical interpretation of empirical evidence is often post hoc and susceptible to confirmation bias. Building upon the adversarial collaboration mediated by the COGITATE Consortium, we present a generalizable approach for the data-driven identification, evaluation, and theoretical modeling of connectivity-based neural correlates of conscious visual perception. Using the same magnetoencephalography (MEG) dataset and accompanying pre-registered hypotheses from the COGITATE Consortium, we systematically compared 246 functional connectivity (FC) measures between regions predicted to underlie conscious vision by Integrated Information Theory (IIT) and/or Global Neuronal Workspace Theory (GNWT). We identified a family of FC measures based on the barycenter—tracking the ‘center of mass’ between two signals—as the top-performing stimulus decoding measures that generalize across regions central to predictions of both IIT and GNWT. To interpret these findings within a theoretical framework, we developed neural mass models that recapitulate the neural dynamics hypothesized to underlie conscious perception by each theory. Comparing simulated barycenter values from these models against empirically measured MEG data revealed that both the GNWT-based model, featuring delayed ignition dynamics, and the IIT-based model, which relied on synchronous sensory dynamics, captured the observed connectivity patterns. These results lend tentative support to GNWT, as the presence of ignition dynamics independent of task-demand conditions contradicts the predictions of IIT. Beyond dataset-specific conclusions and limitations, we introduce a framework for systematically identifying and testing candidate neural correlates of conscious visual perception in an unbiased and interpretable manner.

Information theory is a powerful framework for quantifying complexity, uncertainty, and dynamical structure in time-series data, with widespread applicability across disciplines such as physics, finance, and neuroscience. However, the literature on these measures remains fragmented, with domain-specific terminologies, inconsistent mathematical notation, and disparate visualization conventions that hinder interdisciplinary integration. This work addresses these challenges by unifying key information-theoretic time-series measures through shared semantic definitions, standardized mathematical notation, and cohesive visual representations. We compare these measures in terms of their theoretical foundations, computational formulations, and practical interpretability – mapping them onto a common conceptual space through an illustrative case study with functional magnetic resonance imaging time series in the brain. This case study exemplifies the complementary insights these measures offer in characterizing the dynamics of complex neural systems, such as signal complexity and information flow. By providing a structured synthesis, our work aims to enhance interdisciplinary dialogue and methodological adoption, which is particularly critical for reproducibility and interoperability in computational neuroscience. More broadly, our framework serves as a resource for researchers seeking to navigate and apply information-theoretic time-series measures to diverse complex systems.