Annie G. Bryant

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 fewer 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 atlases for the subcortex (including thalamic nuclei and the brainstem) and cerebellum. These two-dimensional vector-based 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 twelve atlases—to our knowledge, the largest collection of non-cortical atlases in a single vector-based brain 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 region-level data below the cortical mantle.

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, neuroscience and finance. 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 accessibility and reproducibility 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.

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 fewer 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 region-level data across many popular atlases for the subcortex (including thalamic nuclei and the brainstem) and cerebellum. These two-dimensional vector-based 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 twelve atlases—to our knowledge, the largest collection of non-cortical atlases in a single vector-based brain 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 region-level 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.

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.

The OHBM Brainhack 2023 integrated educational innovation, creative expression, and scientific exploration to cultivate a dynamic and inclusive environment for learning and collaboration. The event featured two main components: the Train-Track, which provided structured and flexible hands-on training in a study group setting, and the Hack-Track, where participants engaged in open-source project development. To enhance engagement, the Buddy System facilitated peer support, ensuring newcomers felt welcomed within the Brainhack community. A novel addition to this year’s event was the Rhyming Battle, which encouraged participants to creatively express their experiences through scientific humor and artistic wordplay. Additionally, the introduction of the Mini-Grant Initiative aimed to recognize projects that prioritized open science, diversity, and interdisciplinary collaboration. While this initiative received mixed feedback due to its competitive nature, it provided valuable insights for future iterations. Here we reflect on the successes and lessons learned from Brainhack 2023, underscoring the collective impact of these initiatives.

Working memory is critical for manipulating and temporarily storing information during cognitive tasks such as problem-solving. Most models focus primarily on cortical-cortical interactions, neglecting subcortical and cerebellar contributions. Given the extensive connectivity between the cerebellum, subcortex, and cortex, we hypothesize that they contribute distinct, yet complementary, functions during working memory manipulation. To test this, we used functional Magnetic Resonance Imaging (fMRI) to measure blood oxygen-level dependent (BOLD) activity while participants performed a mental rotation task. Our results revealed a distributed network spanning the cortex, subcortex, and cerebellum that differentiates rotated from non-rotated stimuli and correct from incorrect responses. Notably, delayed responses in premotor, subcortical, and cerebellar regions during incorrect trials, suggest that their precise recruitment is crucial for successful working memory manipulation. These findings expand current models of working memory manipulation, revealing the collaborative role of subcortical and cerebellar regions in coordinating higher cognitive functions.

Homotopic functional connectivity (HoFC)—the synchronous activity between homologous regions of the left and right hemispheres—is a hallmark of inter-hemispheric brain architecture, yet its biological underpinnings remain incompletely understood. Here, we characterize spatial variation in resting-state HoFC and its relation to dominant natural axes of anatomical, functional, and transcriptomic organization across the human cerebral cortex. We show that the regional distribution of HoFC, from the lowest in association areas (especially limbic) to the highest in primary sensorimotor regions, is preserved across neuropsychiatric disorders in a separate neuroimaging cohort (including schizophrenia, bipolar I disorder, and attention-deficit hyperactivity disorder) despite disease-associated perturbations in magnitude. Regional variation in HoFC is not fully explained by properties of physical embedding or vascular innervation in the brain, suggesting involvement of alternative physiological processes. Furthermore, we find that regions with stronger HoFC are more functionally connected within and across hemispheres at rest, suggesting that homotopic functional coupling is positively associated with brain-wide functional synchrony. Our results collectively suggest that HoFC is inherently aligned with a fundamental natural axis of macroscopic cortical organization and shared global signal—advancing our understanding of the biological correlates of cortical homotopy and suggesting potential mechanisms to investigate in the future.

Identifying the neural correlates of consciousness 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 consciousness. 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 the GNWT-based model, featuring delayed ignition dynamics, better captured observed connectivity patterns than the IIT-based model, which relied on highly synchronized sensory dynamics. Beyond dataset-specific conclusions and limitations, we introduce a framework for systematically identifying and testing candidate neural correlates of consciousness in an unbiased and interpretable manner.

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, multi-functional 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 datadriven 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 structural connectome, we find that the ‘Order Statistics Local Optimization Method’ (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble and yields a seven-network decomposition of the human cortex. These modules are bridged by fifteen 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.