Aaron D. Boes

This study examines how domain-general cognition (g) affects lesion-deficit mapping by analyzing 473 patients with focal brain damage using structural and functional lesion network mapping.

This study maps TMS-induced discomfort across the whole brain using TMS-fMRI and shows it accounts for 12-25% of TMS-evoked responses.

This study shows that TMS to the dlPFC modulates single-neuron activity in humans, with facilitation of inhibitory firing and suppression of excitatory drive in specific brain networks.

This study uses TMS combined with iEEG and fMRI to show that parietal TMS guided by hippocampal connectivity engages and modulates hippocampal activity, with effects related to individual FC strength and theta oscillations.

The paper defends lesion network mapping by showing that it provides meaningful differences through specificity testing, refuting criticisms that it reflects normative connectome properties.

This paper presents a preprocessing pipeline for TMS-iEEG data, showing it reduces artifacts and noise, and highlights methodological choices that impact results.

This study identifies brainstem and thalamus lesions as necessary and sufficient for coma in acute ischemic stroke, highlighting lesion location over volume in coma pathogenesis.

This study shows that TMS to the DLPFC reduces high-frequency neural activity in the sgACC, suggesting a mechanism for its antidepressant effects.

This study uses TMS-iEEG to show that dlPFC stimulation evokes activity in sgACC, providing electrophysiologic evidence of its connectivity.

The paper introduces an open-source MATLAB toolkit for inferential and predictive modeling of imaging-behavior and lesion-deficit relationships, demonstrating its use in analyzing language impairments linked to brain lesions.

The paper identifies a brain network associated with religious fundamentalism using lesion data from two independent datasets, showing strong right hemisphere lateralization and reproducibility.

This paper introduces a novel unsupervised method, Causal Feature Learning (CFL), to discover lesion-behavior maps by learning the appropriate level of analysis and the relation between brain lesions and cognitive outcomes.

This study investigates how TMS affects neural rhythms in different brain regions using intracranial recordings, finding both low-frequency increases and high-frequency decreases in response to stimulation.

This paper improves MVAR model estimation for electrophysiological data by incorporating fMRI priors, reducing data needs and improving model accuracy.

This study compares data-driven and theory-based biomarkers for predicting chronic motor outcomes after stroke, finding data-driven models perform better, especially when using structural disconnection measures.

The paper examines brain activity patterns in stroke patients, finding that increased time in a frontoparietal brain state correlates with better motor recovery in those with dominant-hand corticospinal tract damage.

This study identifies brain regions and networks associated with time disorientation in patients with focal brain lesions.

The paper maps auditory cortical networks using resting state intracranial electrophysiology, revealing hierarchical organization across spatial scales and frequency-dependent hub identities.

This study uses intracranial electrocorticography to evaluate the effects of transcranial magnetic stimulation in humans, showing localized and network-level brain responses.

This study uses graph matching to analyze functional connectivity reorganization in stroke patients, showing it correlates with motor recovery and structural disconnection.

The paper investigates thalamic hubs in the human brain and finds that lesions in highly connected subregions cause broader cognitive deficits than those in less connected regions.

This study uses machine learning to predict motor improvement in chronic stroke patients after therapy, finding that pre-intervention assessments and motor thresholds are key predictors.