Richard F. Betzel

This study compares functional brain network estimation using resting state crosshair fixation and movie watching in adolescents, finding similar network topography with minor border shifts.

The paper identifies speech and language critical cortex using fMRI network features, finding critical sites have distinct connectivity patterns compared to non-critical sites.

The paper analyzes the Drosophila connectome to reveal hierarchical community structures and identifies functional circuit archetypes.

The paper demonstrates that edge time series, a method for tracking connectivity changes, are equivalent to interaction terms in a linear model and show connectivity has unique explanatory power beyond brain activity.

The paper uses topological analysis of fMRI dynamics to show that pairwise co-fluctuations better capture task states and link to personality and symptom dimensions than other interaction representations.

The paper simulates sensory signal propagation in the Drosophila connectome to study modality-specific cascades and neuron classification based on dynamic response profiles.

The paper examines how the brain adapts to novelty using fMRI data from repeated movie viewings, finding reduced brain activity dimensionality and links to intelligence and arousal.

This paper adapts network control theory to model spatially diffuse input signals in the human brain, showing reduced energy for state transitions and matching existing neurobiological maps.

This study challenges the idea that the rich club controls brain dynamics, showing low-degree regions are more effective in controlling the human connectome across various brain states and parameters.

This study shows that individualized functional brain network mapping in older adults reveals more heterogeneity and improves network fidelity compared to group-averaged approaches.

The paper identifies social envirotypes in the ABCD study, showing they are associated with distinct brain connectivity patterns and diverging developmental outcomes.

The paper analyzes high-amplitude events in edge time series across the human lifespan, finding age-related changes in their patterns and associations with cognitive phenotypes.

This study identifies age-related changes in brain network organization using edge communities from resting state fMRI data across the human lifespan.

This paper examines the dynamic functional architecture of the somato-cognitive-action network (SCAN) and identifies coupling patterns that contribute to its static structure.

This study uses diffusion imaging and tractography to analyze structural connectivity changes in a rat model of Parkinson's Disease, identifying lesion-specific and compensatory neural networks.

The paper uses simultaneous calcium imaging and fMRI in mice to identify cell-type specific signals in the brain with high spatiotemporal resolution.

The paper develops a framework using optimal control theory to estimate brain structure-function interactions from MRI data, showing correlations between self-inhibition parameters and neural timescales across species.

The paper introduces the Network Correspondence Toolbox (NCT) to evaluate spatial correspondence between novel neuroimaging results and existing brain atlases, enabling standardized reporting and comparison across studies.

The paper investigates how connectome architecture supports dual communication modes, showing that modular networks enable within-module diffusion and between-module routing across multiple organisms.

The paper shows that selecting specific time points from fMRI data improves brain-behavior correlations compared to full scan data across multiple behaviors.

This paper introduces a simulated annealing algorithm for randomizing weighted networks while preserving strength sequences, outperforming existing methods and showing broad applicability in neuroscience and beyond.

The paper analyzes a larval Drosophila connectome to identify hierarchical communities linked to cell type and function, revealing assortative and non-assortative interactions and rich-club structures.

The paper introduces edge community entropy as a measure of neural dedifferentiation in aging, showing it correlates with age and fluid cognition, particularly in older adults.

This study identifies bursty dynamics and modular subgraphs in mouse and human brain networks, showing cross-species consistency in co-fluctuation patterns and anatomical connectivity.

This paper introduces a generative model of the connectome that simulates dynamic axon growth, producing brain-like network structures and revealing how axonal guidance principles shape macroscopic connectomes.

This study shows hub disruption in ADAD follows a targeted attack pattern, detectable pre-clinically and linked to amyloid beta changes and neuronal activity.

This study examines how network integration and centrality in the medial temporal lobe during NREM sleep relate to emotional memory retention in older adults.

The paper analyzes brain network events during movie-watching, identifying three synchronization classes and their distinct characteristics.

The paper uses machine learning to decode cognitive states and identify individuals from fMRI data, achieving up to 99% accuracy.

This commentary critiques Pang et al.'s 2023 study, arguing that their geometric mode approach to brain dynamics lacks specificity to brain shape and orientation, and does not sufficiently establish geometry as a more fundamental constraint than connectivity.