Michael W. Cole

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.

This study identifies distributed cortical networks involved in binocular convergent eye movements using resting-state functional connectivity and task-evoked activations.

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.

The paper shows that network geometry influences how task representations change across different cortical regions, with low-dimensional connectivity compressing and high-dimensional connectivity expanding representations.

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.

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

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

The paper investigates how cortical regions influence arousal systems to predict cognitive abilities using fMRI and cognitive assessments.

This paper develops a framework to model brain dynamics from time-series data, revealing individual differences in alpha and beta oscillations through excitation-inhibition balance.

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

The paper investigates how thalamocortical interactions support cognitive task activity by analyzing fMRI data from 127 tasks and showing thalamic hubs transform activity to cortical representations.

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

The paper examines regional neural redundancy in the hippocampus and frontal cortex in relation to Alzheimer's pathology and cognitive performance.

The paper estimates latent functional connectivity across multiple brain states and finds it better captures state-general intrinsic connectivity than resting-state measures.

This paper improves task fMRI preprocessing by filtering out pre-event intrinsic activity using individualized MINDy models, enhancing statistical power and behavioral prediction.

The paper shows that global connectivity patterns in the brain predict the domain generality of cognitive control regions using resting-state fMRI data.

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

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.

The paper quantifies the functional importance of task-state functional connectivity in shaping cognitive task activations and finds it improves prediction accuracy beyond resting-state connectivity.

The paper constructs neural network models from fMRI data to reveal how conjunction hubs support adaptive behavior through integrative computations.

The paper finds that whole-brain functional network organization is similar across mental disorders and healthy controls, but subtle differences predict diagnosis.

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

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

The paper examines the relationship between localized task activity and distributed functional connectivity across the cortex, revealing a negative correlation and hierarchical differences in intrinsic timescales and myelin content.

This paper introduces MINDy, a method to create individualized dynamic brain models from resting state fMRI data, showing they are predictive and better at uncovering mechanisms than functional connectivity methods.

The paper introduces a combined functional connectivity method that improves causal inferences by integrating bivariate and partial correlation approaches.

This study shows that tACS at 10 Hz reduces BOLD adaptation in the human motion area and increases functional connectivity with the dorsal attention network.

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.

The paper creates a large-scale functional network map of the entire human brain, including cortical and subcortical regions, and releases it as an open resource for future research.