Nelson J. Trujillo‐Barreto

The paper investigates how thalamic functional controllability contributes to cognitive impairment in MS beyond structural damage, finding it independently predicts cognitive performance.

The paper examines how thalamic nuclei's structural and functional properties relate to cognitive performance, finding that functional controllability contributes uniquely to specific cognitive domains beyond structural metrics.

The paper identifies distinct neural dynamics in resting state alpha band activity associated with different dimensions of hallucination proneness.

This study uses fMRI and DCMs to compare frontoparietal network connectivity during working memory in NF1 adolescents and neurotypical controls, finding increased intrinsic connectivity in NF1 with mixed modulatory effects.

This study shows that reduced thalamic network controllability in MS patients predicts cognitive impairment better than thalamic volume.

This study identifies altered functional connectivity and network controllability in NF1 adolescents during working memory tasks, which predict cognitive performance and distinguish NF1 from controls.

This study investigates how non-invasive brain stimulation affects effective connectivity during working memory in NF1 patients, finding changes in fronto-striatal connectivity and associations with GABA levels.

This study evaluates how different brain parcellation resolutions affect predictive models of cognition and demographics, finding that low-resolution functional parcellations perform well but no single scheme is optimal across all domains.

The paper uses EEG resting state alpha dynamics to predict individual proneness to auditory hallucinations by analyzing brain state occupancy and duration.

The paper evaluates the reliability and sensitivity of Hidden semi-Markov models (HsMM) in analyzing resting-state EEG data from Parkinson's Disease patients and healthy controls to classify brain dynamics and assess training effects.

The paper introduces a model that efficiently estimates time-dependent brain connectivity using structural connectivity constraints and sparse MAR parameters.

This paper shows that combining structural and functional brain connectivity improves predictions of specific cognitive domains, with domain-specific benefits.

The paper develops a mean-field model to link neurotransmitter dynamics at the microscopic level to fMRS signals, showing how neural activity affects observed MRS signals.

This study tests if visual alpha stimulation increases alpha power in chronic musculoskeletal pain patients and reduces pain, finding increased alpha power but no significant pain reduction.

This paper introduces Hidden Semi-Markov Models (HSMM) to better model brain state durations in EEG and MEG data compared to traditional HMMs.