Biology subjects

Betzel, R.

Publications and source records attributed to Betzel, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Cortical Sites Critical for Speech and Language Exhibit Distinct fMRI-Derived Network Features

Direct electrocortical stimulation (ECS) is a well-established brain mapping technique that helps achieve safe and effective resection of epileptic foci, tumors or vascular malformations. Recent studies using electrocorticography (ECoG) suggest that ECS' exerted effects on brain sites are determined by the roles of those sites in larger networks. However, ECoG has limited spatial coverage. Here, we used functional magnetic resonance imaging from eighteen participants during performance of five language tasks to assess the functional network signatures of cortical sites defined as critical for speech and language by ECS. We found that critical sites causing speech arrests (SA) and language errors (LE) exhibited lower local and global connectivity than non-critical sites. LE sites showed greater connectivity across sub-networks (communities) than both non-critical and SA sites, indicating their role as connectors across functional networks. This connector profile of LE sites was most robust when considering network connectivity across the entire brain. Connector sites were concentrated primarily in temporal and inferior parietal cortices. Finally, these network features accurately predicted which sites were critical in machine learning models. These findings provide a preoperative framework for predicting cortical sites critical for speech and language function, which may ultimately help accelerate or improve brain mapping.

neuroscience↗

bioRxiv2026

Global topology of brain-wide co-fluctuations links task states, personality, and behavioral symptom dimensions

Functional connectivity in network neuroscience is traditionally characterized using time-averaged correlations between brain regions. While these summaries capture stable large-scale organization, they do not fully reflect the temporal structure of moment-to-moment interactions. Here, we investigate how the order of interaction used to represent brain dynamics shapes the organization recovered from neural data. We compare three interaction representations of fMRI dynamics: regional activation (node time series), pairwise co-fluctuations (edge time series), and higher-order triplet interactions (triangle time series); within a common topological framework using Mapper from topological data analysis (TDA). Across task and resting-state data, Mapper representations derived from pairwise co-fluctuations more distinctly segregate task conditions than activation-based or higher-order representations. This organization reflects structured coordination patterns beyond activation polarity and is driven by high-amplitude interaction events. Beyond task states, modularity quality computed across all Mapper representations is highest for edge time series and selectively associated with stable individual differences: higher modularity relates to higher conscientiousness and lower internalizing and externalizing symptom dimensions. Together, these findings suggest that behaviorally relevant information is reflected in the topology of moment-to-moment brain interactions. Topological analysis of interaction-level dynamics therefore provides a complementary and interpretable framework for linking large-scale neural coordination to cognition, personality, and mental health.

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bioRxiv2026

Individualized Mapping of Functional Brain Networks in Older Adulthood

The functional network architecture of the aging brain undergoes significant systematic and idiosyncratic changes. Emergent individualized network mapping approaches may yield better or more sensitive explanatory insight about age-related neural and behavioral variability, although most applications have focused on young adults. In the current study, we tested the validity and impact of mapping individual-specific topography in two fMRI datasets comprising 112 young (18-35 years) and 176 older adults (60-92 years). Older adults had more idiosyncratic network topography than young adults. Individualized maps from resting-state fMRI improved network homogeneity and fidelity to task fMRI activations, while also exhibiting intra-individual reliability and inter-individual discriminability over a 2-year interval. Last, traditional group-averaged (vs. individualized) network mapping had a moderate-to-large impact on individual-level estimates of network segregation, a widely-studied measure of functional brain aging. Therefore, individualized network mapping captures important heterogeneity in older adulthood and may yield more precise characterization of neurocognitive aging.

neuroscience↗

bioRxiv2025

Hierarchical Community Structure of the Adult Drosophila Connectome Reveals Conserved Circuit Archetypes

The community structure of connectomes supports functional specialization, adaptability, and cost-efficient wiring. However, little is known about communities in connectomes mapped at the level of individual neurons and synapses. Here, we analyze a whole-brain Drosophila adult connectome using a nested stochastic blockmodel to uncover its hierarchical community structure. Most of the roughly 1500 fine-scale communities-the smallest, and best resolved level of the hierarchy- are spatially compact, mostly assortative, and aligned with biological features. Nonetheless, we find evidence of nonassortative communities, spatially co-localized within the optic lobe and vision-processing pathways. Seeking "functional primitives"-small circuits with functionally narrow feature profiles-we use data-driven clustering to group communities into 45 archetypical meta-clusters based on their spatial, functional, and molecular properties, revealing modular building blocks from which larger, functionally diverse communities are composed. This work advances our understanding of how structure and function are organized in the fruit fly brain and highlights the value of statistical network models in interpreting nanoscale connectomes.

neuroscience↗

bioRxiv2025

Edge communities in functional brain networks reveal heterogeneous, overlapping organization across the human lifespan

Understanding changes in functional brain organization and their implications in development and aging is one of the central questions in neuroscience. In this study, we used an edge-centric approach to examine cross-sectional differences in functional brain network organization across the human lifespan using resting state functional MRI data from the Nathan Kline Institute - Rockland Sample dataset. By creating edge time series - a framewise multiplication of nodal time series - and clustering them based on their temporal similarities, we were able to identify clusters of edges instead of nodes. This method naturally allows multiple community affiliations per node (brain region), providing a nuanced perspective on network participation compared to conventional hard-partition approaches. To do so, we created age-neutral templates of edge communities - or "eFC lures" - that, when applied, yielded consistent edge communities across non-overlapping subsamples of data. The communities of edges revealed a trajectory of desegregation with aging, suggested to be linked to neural dedifferentiation of activity and cognitive decline in older adults. Additionally, age group-specific lures significantly enhanced the detection of edge community organization compared to the age-neutral version. Combined, these results offer new insights into the heterogeneous, event cluster-level shifts in brain functional organization as well as underscore the importance of age-targeted analytical frameworks throughout the human lifespan.

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bioRxiv2025

Functional network architecture and dynamics of the somato-cognitive action network

The somato-cognitive-action-network (SCAN) is a recently discovered brain network. SCAN interdigitates somatomotor networks and is functionally connected to the cingulo-opercular/action-mode network. SCAN is therefore thought to play a role in motor/action planning. This view of SCAN, however, is based on its "static" functional connectivity-i.e. FC estimated using data pooled over an entire scan session or dataset. However, this approach necessarily overlooks changes in network activity and connectivity that occurs over shorter timescales. In this report, we extend analyses of SCANs static architecture, demonstrating that, at the whole-brain level, SCAN vertices exhibit overlapping network membership, such that, while they form a cohesive sub-network, they may also couple transiently and dynamically with other networks. We examine these potential co-activations by focusing on SCAN dynamics and through the identification of distinct coupling modes - co-activation patterns (CAPs). We show that CAPs differentially contribute to SCANs static architecture and that their frequency varies across following limb immobilization.

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bioRxiv2024

The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction

Functional connectivity (FC) is frequently operationalized as a correlation. Many studies have examined changes in correlation networks across time, claiming to link time-varying fluctuations to ongoing mental operations and physiological processes. Other studies, however, have called these results into question, noting that statistically indistinguishable patterns of time-varying fluctuations can be obtained by windowing synthetic time series generated from ground-truth stationary correlation structure. Recently, we developed a technique for tracking rapid (framewise) fluctuations in network connectivity over time. Here, we show that these "edge time series" are mathematically equivalent to interaction terms in a specific family of general linear models. We exploit this fact to further demonstrate that time-varying connectivity carries explanatory power above and beyond brain activations. This observation suggests that time-varying connectivity is likely more than a statistical artifact. SUMMARYBrain activity and connectivity have been linked to ongoing behavior and mentation but usually in isolation and almost never in the same model. Here, we show that "edge time series" - a recently proposed method for tracking moment-to-moment connectivity changes - are equivalent to an interaction term in a linear model. By including terms for activations in the same model, it provides an elegant framework for assessing the relative explanatory power of edges and activations. In our work, we use this modeling framework to study time-varying behavior in zebrafish, worms, and humans. We find that connectivity contains unique explanatory power above and beyond activity.

neuroscience↗

bioRxiv2024

Dynamic adaptation to novelty in the brain is related to arousal and intelligence

How does the human brain respond to novelty? Here, we address this question using fMRI data wherein human participants watch the same movie scene four times. On the first viewing, this movie scene is novel, and on later viewings it is not. We find that brain activity is lower-dimensional in response to novelty. At a finer scale, we find that this reduction in the dimensionality of brain activity is the result of increased coupling in specific brain systems, most specifically within and between the control and dorsal attention systems. Additionally, we found that novelty induced an increase in between-subject synchronization of brain activity in the same brain systems. We also find evidence that adaptation to novelty, herein operationalized as the difference between baseline coupling and novelty-response coupling, is related to fluid intelligence. Finally, using separately collected out-of-sample data, we find that the above results may be linked to psychological arousal.

neuroscience↗

bioRxiv2024

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as "default" or "visual". Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

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bioRxiv2024

Multimodal identification of the mouse brain using simultaneous Ca2+ imaging and fMRI

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.

neuroscience↗

bioRxiv2024

Variation in high-amplitude events across the human lifespan

Edge time series decompose functional connections into their fine-scale, framewise contributions. Previous studies have demonstrated that global high-amplitude "events" in edge time series can be clustered into distinct patterns. To date, however, it is unknown whether events and their patterns change or persist throughout the human lifespan. Here, we directly address this question by clustering event frames using the Nathan Kline Institute-Rockland sample that includes subjects with ages spanning the human lifespan. We find evidence of two main clusters that appear across subjects and age groups. We also find that these patterns of clusters systematically change in magnitude and frequency with age. Our results also demonstrate that such event clusters have distinct, heterogeneous relationships with structural connectivity-derived communication measures, which change with age. Finally, event clusters were found to outperform non-events in predicting phenotypes regarding human intelligence and achievement. Collectively, our findings fill several gaps in current knowledge about co-fluctuation patterns in edge time series and human aging, setting the stage for future investigation into the causal origins of changes in functional connectivity throughout the human lifespan.

neuroscience↗

bioRxiv2024

A simulated annealing algorithm for randomizing weighted networks

Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate the prominence of brain network features, empirical measures are compared against null statistics computed in randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here, to adapt network null models to weighted network inference, we propose a simulated annealing procedure for generating strength sequence-preserving randomized networks. This model outperforms other commonly used rewiring algorithms in preserving weighted degree (strength). We show that these results generalize to directed networks as well as a wide range of real-world networks, making them generically applicable in neuroscience and in other scientific disciplines. Furthermore, we introduce morphospace representation as a tool for the assessment of null network ensemble variability and feature preservation. Finally, we show how the choice of a network null model can yield fundamentally different inferences about established organizational features of the brain such as the rich-club phenomenon and lay out best practices for the use of rewiring algorithms in brain network inference. Collectively, this work provides a simple but powerful inferential method to meet the challenges of analyzing richly detailed next-generation connectomics datasets.

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bioRxiv2023

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels--a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasnt definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruptions severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in A{beta} PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

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bioRxiv2023

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectomes topological properties-i.e. features defined from connectivity alone-with an organisms neurobiology. One approach for doing so is to compare connectome properties with maps of metabolic, functional, and neurochemical annotations. This type of analysis is popular at the meso-/macro-scale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of neuron-to-neuron connectivity, these datasets are accompanied by rich annotations, documenting cell type and function. Here, we use a hierarchical and weighted variant of the stochastic blockmodel to detect multi-level communities in a recently published larval Drosophila connectome. We find that these communities partition neurons based on function and cell type. We find that communities mostly interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact non-assortatively. The neurons that make up these communities also form a "rich-club", composed mostly of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping neuron-to-neuron communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between the system-level architecture of an organisms complete neuronal wiring network and the precise biological function and classification of its individual neurons. We envision our study as an important step towards bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

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bioRxiv2023

Edge Community Entropy is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition

Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogues of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of nodal specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. Global entropy uniquely covaried with age when controlling for network segregation. Importantly, relationships between both metrics and fluid cognition were age-dependent, although entropys relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.

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bioRxiv2023

Commentary on Pang et al. (2023) Nature

Pang et al. (2023) present novel analyses demonstrating that brain dynamics can be understood as resulting from the excitation of geometric modes, derived from the shape of the brain. Notably, they demonstrate that linear combinations of geometric modes can reconstruct patterns of fMRI data more accurately, and with fewer dimensions, than comparable connectivity-derived modes. Equipped with these results, and underpinned by neural field theory, the authors contend that the geometry of the cortical surface provides a more parsimonious explanation of brain activity than structural brain connectivity. This claim runs counter to prevailing theories of information flow in the brain, which emphasize the role of long-distance axonal projections and fasciculated white matter in relaying signals between cortical regions (Honey et al. 2009; Deco et al. 2011; Seguin et al., 2023). While we acknowledge that cortical geometry plays an important role in shaping human brain function, we feel that the presented work falls short of establishing that the brains geometry is "a more fundamental constraint on dynamics than complex interregional connectivity" (Pang et al. 2023). Here, we provide 1) a brief critique of the papers framing and 2) evidence showing that their methodology lacks specificity to the brains orientation and shape. Ultimately, we recognize that the geometric mode approach is a powerful representational framework for brain dynamics analysis, but we also believe that there are key caveats to consider alongside the claims made in the manuscript.

neuroscience↗

bioRxiv2023

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation "events" in mouse and human brains

Previous studies have adopted an edge-centric framework to study fine-scale dynamics in human fMRI. To date, however, no studies have applied this same framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

neuroscience↗

bioRxiv2023

Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease

Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brains network architecture. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies.

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