Dario J. Englot

The paper generates lifespan normative charts for cervical spinal cord morphometry using MRI data, revealing non-linear changes and sex differences across ages 0-100.

This study characterizes white matter asymmetry across the lifespan using a large dataset, revealing dynamic changes in asymmetry patterns with age.

The paper develops normative reference charts for white matter microstructure and macrostructure across the lifespan using diffusion MRI data from 50 studies.

The paper evaluates the variability of GAMLSS-based brain charts using analytic and empirical methods, finding low coefficient of variation across most white matter features.

This study examines how arousal states affect brain network switching rates and their relationship with cognitive task performance using fMRI, EEG, and eye tracking.

This paper introduces a framework for longitudinal quantitative streamline tractography that reduces spurious changes in white matter connectivity estimates by fixing trajectories and varying density weights between sessions.

KenazLBM is a model that characterizes and predicts brain states from raw iEEG data, generalizing across subjects with heterogeneous recording channels.

This paper analyzes peri-ictal network dynamics in drug-resistant epilepsy patients to show that NIZs increase inhibitory signaling to SOZs during seizures, but this suppression is insufficient to prevent seizure onset.

This study identifies reward circuitry activity predictive of risk-taking in humans using local field potential recordings during a gambling task.

Daily intermittent basal forebrain stimulation improves working memory in senescent monkeys and retains benefits over time.

This study links neural activity during laboratory working memory tasks to neuropsychological test performance in human epilepsy patients.

This study examines how connectivity patterns of arousal regulation centers change with vigilance states using EEG and fMRI, showing distinct network correlations in low and high alertness.

The paper examines how motor control affects mesolimbic local field potentials using electrophysiological recordings during a stop-signal task.

This paper introduces BrainState, a self-supervised method to model complex brain states from EEG data, applied to epilepsy patients to capture pre-seizure states and neuromodulation effects.

The paper analyzes LFP activity in the human brain during visual working memory tasks to identify task-related oscillatory patterns across different brain regions.

This paper develops a deep learning model to accurately segment the nucleus basalis of Meynert on 3T MRI, showing improved performance over standard atlases in both healthy subjects and TLE patients.

The paper tests the interictal suppression hypothesis in focal epilepsy using electrographic and structural data to identify seizure onset zones and develop a classification model with high accuracy.

This study shows that PVNH tissue is functionally coupled with the neocortex during resting and task states, indicating integration into cortical circuits.

This paper develops a deep learning model to classify seizure onset zones using cortico-cortical evoked potentials from intracranial neurostimulation recordings.