Danielle S. Bassett

This paper introduces a multi-layer network method to analyze brain network modularity across individuals and time, revealing scale-specific variability linked to cognitive performance.

The paper shows working memory involves brain-wide state switching, with dopamine D1/D2 receptor expression affecting stability and state transitions, and schizophrenia linked to altered network control properties.

This study shows that increased task-related activity and reduced connectivity between non-targeted brain regions predict better performance during BCI learning.

This paper introduces a dynamical systems framework to estimate both intrinsic brain activity and external stimuli, showing task-related changes and non-stationarities in cortical dynamics.

This study investigates how dopamine affects dynamic striatal-cortical connectivity during learning in Parkinson’s disease patients, finding dopamine-dependent changes in network flexibility and connectivity.

The paper uses generative network models to identify spatial and topological mechanisms underlying altered structural brain connectivity in schizophrenia and its relatives.

The paper uses a pairwise maximum entropy model to analyze intracranial EEG data and shows it captures neural dynamics and identifies white matter connections.

This study shows how network topology influences neural dynamics, enabling long-lasting stimulus propagation and recovery through specific network motifs and control theory principles.

This study predicts ictal and preictal brain network dynamics in epilepsy using anatomical, connectivity, and genetic features, finding white matter plays a greater role in seizure dynamics than geometry or genetics.

The paper uses machine learning to analyze brain states from fMRI data and shows that white matter connectivity influences their dynamics and development.

This study quantifies structure-function coupling between white matter connections and iEEG in epilepsy patients, finding increased coupling during seizures and its dependence on short-range structural connections.

The paper identifies brain-based dimensions of psychopathology using sCCA in youths, finding four dimensions linked to functional dysconnectivity that replicate in an independent dataset.

The paper identifies cognitive control subgraphs in brain networks using fMRI data and shows their temporal expression relates to cognitive demands and task performance.

The paper examines community structure in functional brain networks during rest and tasks, finding non-assortative communities during tasks and their correlation with cognitive performance.

This study links accelerated cortical thinning in structural brain networks to irritability in youth, highlighting associations in emotion-related brain regions.

This study uses network control theory to predictably reconfigure brain network interactions through targeted neurostimulation in humans.

The paper uses fMRI and dynamic network neuroscience to study how striatal-cortical circuits change during reinforcement learning and finds that dynamic network coupling correlates with learning parameters.

This paper applies community detection algorithms to brain graphs derived from neuroimaging data to uncover patterns of coordinated neural activity and their implications for brain function.

The paper predicts future motor learning from baseline sensorimotor network connectivity in humans.

The paper introduces a method to detect hierarchical 3D genome domain reconfiguration using network modularity analysis across cellular states.

This paper uses network control theory to model how white matter tracts guide electrical stimulation in the brain, showing that real white matter structures better predict state transitions than artificial models.

This study uses network control theory to show that structural connectivity deficits in bipolar disorder and high-risk individuals lead to impaired brain network controllability in regions associated with cognitive and emotional control.

This study uses machine learning to uncover dynamic subgraphs in brain networks, revealing how they constrain modular organization and explain inter-individual differences in network reconfiguration.

The paper shows that temporal structure in network traversal affects how well learners infer network architecture.

This paper evaluates how in-scanner head motion affects structural connectivity estimates from diffusion tractography in a developmental cohort, showing motion biases connectivity strength in a consistency and length-dependent manner.

This paper reviews modeling approaches for dynamic brain networks and discusses statistical testing and interpretation of dynamic graph architecture.

The paper investigates how social exclusion affects brain connectivity in adolescents and links these changes to the density of their friendship networks.

The paper uses machine learning to analyze brain network dynamics in epilepsy patients, finding similar subgraph structures in seizure and baseline states and identifying predictive patterns for seizure onset.