Danielle S. Bassett

The paper uses machine learning to analyze functional topography in youth brains, finding sex differences linked to X-linked genes and specific brain networks.

The paper models cortical brain deviations from normative neurodevelopment to predict psychiatric symptoms, finding that transdiagnostic psychopathology dimensions better predict symptoms than specific disorder measures.

This study uses network control theory to analyze brain dynamics in 22q11DS, finding altered persistence control energy and reduced dynamic efficiency in brain states.

This paper introduces transparent graphene microelectrodes to simultaneously record microelectrocorticography and calcium fluorescence in vivo, revealing spatiotemporal seizure dynamics through non-negative matrix factorization.

The paper uses persistent homology to analyze the growth of the C. elegans connectome, revealing topological cavities and their sensitivity to neuron birth times.

This study develops an NMDAR-Ab-mediated rat seizure model and shows that reducing excitatory neurotransmission, not inhibitory, causes seizures, which can be mitigated by a neurosteroid.

In five top neuroscience journals, papers with name-inferred first and last authors of color are under-cited, mostly by White-led teams, and the gap is growing.

This study identifies three neurocognitive subtypes of depression in youth using machine learning, showing distinct neural signatures despite similar clinical symptoms.

QSIPrep is a software platform for preprocessing diffusion MRI data that supports multiple sampling schemes and ensures reproducibility through standardized workflows and quality measures.

This paper defines transdiagnostic disease categories using histopathological data and predicts cluster membership from CSF and genotype data with high accuracy.

The paper examines how brain connectivity and social network structure influence response inhibition in adolescents, finding that social brain systems compensate for weaker response inhibition systems and that social context strengthens this relationship.

The paper investigates dynamic network properties of cognitive control networks in humans using a high-control-demand task, revealing distinct roles for the frontoparietal and cingulo-opercular networks.

The paper introduces MemorySeq, a method to identify gene expression fluctuations persisting across cell divisions, revealing non-genetic, heritable cellular behaviors in cancer cell lines.

The paper identifies functional activation patterns during pattern separation in the hippocampus and neocortex, showing alignment with intrinsic connectivity gradients and performance differences in patients.

This study examines how brain network modularity changes during working memory training using fMRI, finding increased modularity and altered integration between key systems.

The paper shows that dynamic changes in brain functional connectivity correlate with learning adjustments in uncertain environments, particularly increased integration between fronto-parietal and other systems.

The paper uses network control theory to show that structural brain networks mature during adolescence to reduce the energy cost of activating the frontoparietal system, supporting executive function development.

This paper introduces MSNR, a multi-scale network regression method that models brain networks at multiple scales, showing improved interpretability and performance in linking brain connectivity to phenotypes.

This study investigates how brain activity and connectivity reorganize during learning, finding distinct patterns in early and later stages of value learning.

This paper shows deep neural networks outperform linear models in predicting behavior from brain connectivity, especially in connector hubs, and demonstrates their interpretability using multislice network models.

This study shows how functional topography of brain networks changes with age and relates to executive function and brain organization in youth.

The paper identifies hub and community structures in brain networks driven by long-distance functional connections, showing primary sensory cortices as more hub-like and community structures spanning multiple levels.

The paper uses TMS-EEG and dynamic community detection to analyze how brain network reconfigurations differ in alpha and beta frequency bands following stimulation.

This study examines how structure-function coupling in brain networks develops during youth, finding it aligns with cortical hierarchies and supports cognitive improvements.

The paper uses a virtual lesion approach to simulate the disconnectome caused by progressive ischemic periventricular white matter lesions and validates it using DTI and fMRI data from elderly subjects.

This paper identifies disrupted basal ganglia-thalamocortical loops in patients with focal to bilateral tonic-clonic seizures, linking these disruptions to increased seizure generalization risk.

The paper analyzes spontaneous neural activity in mouse auditory cortex over time using network science, showing modular structure and a stable core of correlated cells.

This study evaluates DTI, NODDI, and MAPL in youth to assess their associations with age and motion, finding that multi-shell models offer advantages for neurodevelopmental studies despite motion sensitivity.

The paper uses machine learning to rank cognitive domains based on brain data to predict schizophrenia in a multi-site dataset.

This study evaluates how sensitive functional connectivity is to electrode resampling in intracranial EEG and proposes an algorithm to assess model confidence in drug-resistant epilepsy.