About

Richard Betzel, PhD, is an Associate Professor in the Department of Neuroscience at the University of Minnesota. His research program focuses on characterizing connectomes, which are network maps of the anatomical and functional connections between neural elements. He investigates the relationship of these connectomes to large-scale brain dynamics, human behavior, disease, and cognition. A major component of his work involves the use of complex network models and analysis, employing a diverse set of mathematical tools including graph theory, information theory, and dynamical systems theory.

Research topics

• Computer Science
• Artificial Intelligence
• Data Mining
• Biology
• Neuroscience
• Psychology
• Physics
• Database
• Computer vision
• Cognitive science
• Data science
• Computer network
• Medicine
• Evolutionary biology
• Radiology
• World Wide Web

Selected publications

• White matter pathways mediating dorsolateral prefrontal TMS therapy for depression

  Nature Neuroscience · 2026-04-14

  article
  PublisherDOI

• Assessing Reliability of Metrics of Criticality in Resting State Brain Networks at High Field and Ultra High Field fMRI

  Open MIND · 2026-01-01

  otherOpen accessSenior author

  This research aims to assess reliability of metrics of criticality within resting state brain networks (RSNs) derived from the same individuals at 3T and 7T fMRI using BOLD data.

  PublisherOA PDFDOI

• Erratum: Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer’s disease

  Network Neuroscience · 2025-01-01

  erratumOpen access

  In the paper “Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer’s disease” published in Network Neuroscience, 8(4), there are errors in the captions for the following figures:Figure 2.Where it read “Mean (lower triangle) and standard deviation (upper triangle) of the Fisher Z-transformed FC matrix”, we have corrected it to “Mean of the Fisher Z-transformed FC matrix”.Figure 3(A).“(right) cartoon illustrating that strength is calculated by summing the weights across the connected edges” was corrected to “(right) cartoon illustrating the definition of strength”.Figure 4(A).“(middle) distribution of within-strengthZ-score (Z) across the NC match 1 group” was corrected to “(middle) distribution of within-module strengthZ-score (Z) across the NC match 1 group” and “Module centers are nodes with a high Z-score” was corrected to “Module centers are nodes with a high Z”.None of the changes affect the results or conclusions.

  PublisherOA PDFDOI

• Diverging Network Architecture of the <i>C. elegans</i> Connectome and Signaling Network

  PRX Life · 2025-09-10 · 2 citations

  preprintOpen access

  The connectome describes the complete set of synaptic contacts through which neurons communicate. While the architecture of the connectome has been extensively characterized, much less is known about the organization of causal signaling networks arising from functional interactions between neurons. Understanding how effective communication pathways relate to or diverge from the underlying structure is a central question in neuroscience. Here we analyze the modular architecture of the signal propagation network, measured via calcium imaging and optogenetics, and compare it to the underlying anatomical wiring measured by electron microscopy. Compared to the connectome, we find that signaling modules are not aligned with the modular boundaries of the anatomical network, highlighting an instance where function deviates from structure. However, we find that some of the most striking features of the anatomical network are preserved, as exemplified by the pharynx, which is delineated into a separate community in both anatomy and signaling. We analyze the cellular compositions of the signaling architecture and find that its modules are enriched for specific cell types and functions, suggesting that the network modules are neurobiologically relevant. Lastly, we identify a “rich club” of hub neurons in the signaling network. The membership of the signaling rich club differs from the rich club detected in the anatomical network, challenging the view that structural hubs occupy positions of influence in functional (signaling) networks. The only overlap between the two rich clubs is given by neurons AVEL/R, which have some of the highest degrees in the anatomical network, again illustrating the preservation of the most pronounced features of the network. Our results provide new insight into the interplay between brain structure, in the form of a complete synaptic-level connectome, and brain function, in the form of a system-wide causal signal propagation atlas.

  PublisherDOI

• Variation in high-amplitude events across the human lifespan

  Network Neuroscience · 2025-10-30 · 1 citations

  articleOpen accessSenior author

  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. However, whether events and their patterns change or persist throughout the human lifespan has not been investigated. 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 which 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 nonevents in predicting phenotypes regarding human intelligence and achievement. Collectively, our findings fill several gaps in current knowledge about cofluctuation 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.

  PublisherOA PDFDOI

• Personalized Adaptive Cortical Electro-stimulation (PACE) in Treatment-Resistant Depression

  2025-08-09 · 2 citations

  preprintOpen access

  Treatment-resistant depression (TRD) is a leading cause of premature death. For decades investigators have assessed the clinical efficacy of direct brain stimulation for TRD. Outcomes have been inconsistent due to imprecise brain targeting. Minimally invasive Personalized Adaptive Cortical Electro-stimulation (PACE) uses fMRI-based precision functional mapping (PFM) to target patient-specific network anomalies with brain surface electrodes. We utilized this novel technology in an n-of-1 study to treat a 44-year-old man (TRD-1) with over 30 years of severe unipolar depression. PFM revealed a 400% expansion in cortical area of the Salience Network (SN) and a 25% reduction in default mode (DMN) and frontoparietal (FP) networks compared to a group average controls. Stimulation paddles were implanted targeting patient-specific SN, DMN, and FP networks. Postoperative stimulation testing (within 24h) revealed immediate electrode-specific mood and cognition responses that matched the underlying functional networks. Stimulation parameters were iteratively optimized using ‘Bayes Tuning’ based on patient feedback. TRD-1’s suicidal ideations ceased within 7 weeks. Full remission of symptoms was achieved within 9 months and maintained at 30 months. PACE therapy appears to be a scalable, safe, and cost-effective approach to treat TRD.

  PublisherDOI

• Time-varying co-activity and connectivity of the somato-cognitive-action-network in densely sampled brains

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-18

  preprintOpen access1st authorCorresponding

  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 SCAN’s 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 SCAN’s static architecture and that their frequency varies across following limb immobilization.

  PublisherDOI

• Inferring macroscopic intrinsic neural timescales using optimal control theory

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-27

  preprintOpen access

  The temporal evolution of brain activity relies on complex interactions within and between brain regions that are mediated by neurobiology and connectivity. To understand these interactions, many large-scale efforts have measured structural connectivity, neural activity, gene expression, and cognition across multiple modalities and species. However, data-driven discovery of large-scale activity models remains difficult owing to the lack of flexible quantitative frameworks for estimating the interplay between brain structure and function while preserving biophysical realism. Here, we provide such a framework by integrating network control theory (NCT) with automatic differentiation to estimate model parameters with greater biophysical realism from data. Specifically, we estimate the structural form of regional self-inhibition—a quantity that is experimentally difficult to measure—from MRI data. Next, we demonstrate that the resulting model-based self-inhibition parameters correlate significantly with regions’ intrinsic neural timescales (INTs), neurobiological measures of gene expression and cell-type densities, as well as behavioral measures of cognition. We demonstrate consistent results across multiple datasets and species. Finally, we demonstrate that our self-inhibition parameters enable the efficient control of brain dynamics from fewer brain regions. Taken together, our results provide a simple and flexible quantitative framework that more accurately captures the interplay between brain structure, function, and dynamics with greater biophysical realism.

  PublisherOA PDFDOI

• Biopsychosocial and Demographic Predictors of Functional Brain Network Specialization and Segregation Across the Adult Lifespan

  Neurobiology of Aging · 2025-11-21

  articleOpen access
  PublisherOA PDFDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-30

  preprintOpen accessSenior authorCorresponding

  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.

  PublisherOA PDFDOI

Recent grants

• NCS-FO: Edge-centric maps of functional brain network organization and dynamics

  NSF · $737k · 2020–2024

• Dynamic approaches to understanding social cognitive aging: A social network neuroscience approach

  NIH · $2.2M · 2022–2027

• CRCNS: US-France Data Sharing Proposal: Lowering the barrier of entry to network neuroscience

  NIH · $546k · 2019–2024

Frequent coauthors

• Danielle S. Bassett

  McGill University

  79 shared

• Olaf Sporns

  69 shared

• Bratislav Mišić

  Montreal Neurological Institute and Hospital

  60 shared

• Joshua Faskowitz

  National Institute of Mental Health

  57 shared

• Jacob Tanner

  Indiana University

  28 shared

• Theodore D. Satterthwaite

  Children's Hospital of Philadelphia

  26 shared

• Farnaz Zamani Esfahlani

  University of Oklahoma

  25 shared

• Raquel E. Gur

  Children's Hospital of Philadelphia

  25 shared

Labs

• Institute for Translational NeurosciencePI