Ravi D. Mill

Hierarchical and non-hierarchical network flows generate complementary representational dynamics in human visual cortex

The paper investigates the functional roles of hierarchical and direct connectivity pathways in human visual cortex using empirical neural network models and 7T MRI data, finding they generate complementary representational dynamics.

Dynamically shifting from compositional to conjunctive brain representations supports cognitive task learning

The paper shows that during cognitive task learning, neural representations shift from compositional to conjunctive formats, improving behavior and pattern separation through cortical-subcortical dynamics.

Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding

This paper introduces regularized partial correlation methods to improve functional connectivity estimates in fMRI by reducing noise and confounding, showing higher reliability and accuracy compared to standard methods.

Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation

The paper investigates theta phase resetting in the human parahippocampal gyrus during navigation, finding it correlates with BOLD activation in specific PHG clusters.

Distributed network flows generate localized category selectivity in human visual cortex

The paper demonstrates that distributed network flows can generate localized visual category selectivity in human visual cortex using fMRI data and computational models.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations

The paper introduces causally informed activity flow models to better understand network-generated cognitive activations by using causal functional connectivity measures.

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

The paper introduces a dynamic network modeling approach to predict neural information dynamics supporting human cognitive behavior using non-invasive brain imaging data.

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

The paper investigates how abnormal activity flow in brain networks contributes to cognitive dysfunction in schizophrenia using fMRI data and activity flow mapping.

Brain Network Mechanisms of Visual Shape Completion

The paper identifies brain networks involved in visual shape completion using fMRI and shows that the secondary visual network is central, coordinated by the dorsal attention network.

Task-evoked activity quenches neural correlations and variability across cortical areas

The paper shows that task states reduce neural correlations and variability across cortical areas in both primate and human data, increasing information coding capacity.

Predicting dysfunctional age-related task activations from resting-state network alterations

The paper predicts dysfunctional age-related task activations from resting-state network alterations in Alzheimer's disease risk groups using activity flow modeling.

Task activations produce spurious but systematic inflation of task functional connectivity estimates

The paper shows that task-evoked activations inflate task-state functional connectivity estimates and demonstrates that flexible modeling of hemodynamic responses effectively corrects this inflation.

Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in the general population

The paper examines the relationship between depression symptoms and frontoparietal cognitive control network connectivity in healthy individuals, finding negative correlations with global connectivity.

Cognitive task information is transferred between brain regions via resting-state network topology

The paper introduces information transfer mapping to show resting-state network topology predicts cognitive task information transfer between brain regions.