Edward F. Chang

This study decodes chronic pain states from intracranial recordings and identifies network-specific neural signatures.

The paper presents a cloud-based pipeline for analyzing and visualizing neuronal electrophysiology data from HD-MEAs, demonstrating its utility in simplifying data analysis and understanding neural activity.

This study investigates how slow and fast theta oscillations in the hippocampal-orbitofrontal network affect working memory performance, finding opposing effects on response times and accuracy.

This paper introduces vascularized cortical assembloids (vCAs) that model neurovascular co-development and show improved cellular specialization and connectivity compared to standard cortical organoids.

The paper investigates whether speech processing in the frontal lobe involves direct parallel pathways from low-level auditory areas, finding evidence of short-latency speech responses and spectrotemporal encoding similar to lateral temporal cortex.

This paper develops a statistics-driven workflow using stereoelectroencephalography to identify personalized deep brain stimulation targets through power analysis and effect size calculations.

The paper identifies sensorimotor theta oscillations as an intrinsic timing mechanism coordinating speech motor control through phase coherence and rhythmic activation patterns.

This paper introduces a dual-path framework for reconstructing high-fidelity speech from neural recordings, achieving high intelligibility with limited ECoG data.

This study maps age-related changes in aperiodic neural activity across brain regions using iEEG data from children and adults, linking these changes to cognitive development and memory performance.

The paper identifies speech-related neural activity in IDH-mutant gliomas using electrocorticography, showing task-based and resting-state distinctions that could improve surgical outcomes.

The paper investigates how glioma infiltration affects the aperiodic slope of neural dynamics, finding it correlates with excitation dominance and semantic naming impairments.

This study investigates how the brain encodes auditory word forms in speech processing, finding distinct neural resets at word boundaries and temporal scaffolding for variable word lengths.

The paper identifies enhancer-AAV tools for specific neuronal subtypes and validates them across species.

DREDge improves motion correction in high-density extracellular recordings across species, enhancing reliability and reducing errors in electrophysiological data.

This study identifies a premotor frontal cortex network involved in inhibitory control of speech, showing distinct neural activity for stopping speech compared to speech production.

This study shows that neural representations for imagined movements remain stable over time, allowing long-term neuroprosthetic control without recalibration.

This study compares how speech and language DNN models align with neural activity during speech processing, finding shared components and distinct spatiotemporal dynamics.

The paper investigates neural activity in the human precentral gyrus during speech tasks, finding neurons that encode both listening and speaking phases, including mirror and bridge neurons.

This paper proposes a pre-trained encoder-decoder framework to re-synthesize natural speech from neural recordings, achieving a phoneme error rate of 28.6% and 71.6% word recognition.

The paper uses high-density electrophysiology to study epileptiform activity in human hippocampal slices from epilepsy patients, revealing unexpected network oscillations in dentate gyrus slices.

This paper develops a method to detect brain stimulation artifacts in iEEG using synthetic data and a ResNet classifier, achieving high accuracy on real data.

The paper identifies electrophysiological activity in the anterior insular cortex associated with anxiety-related behaviors and suggests it as a potential therapeutic target.

This study investigates how different dimensions of melody are encoded in the human auditory cortex, finding both music-specific and domain-general representations.

The paper identifies a cross-species emotional biomarker based on beta-frequency communication between the ventral hippocampus and basolateral amygdala, showing it causally modulates anxiety-related behaviors in mice and correlates with human mood and anxiety.

This paper introduces two algorithms, GridFit and CEPA, for accurate and automatic localization of intracranial electrodes and brain-shift compensation, validated on real patient data.

This study uses deep neural networks to model neural coding in the human auditory pathway and finds that DNN representations align with biological auditory processing.

The paper shows that neural activity in auditory cortex encodes temporal context in speech through low-dimensional latent states, with timing cues capturing more variance than phonetic features.

This study links neuroanatomical features of the left STG to auditory processing abilities and behavioral outcomes in children with and without developmental dyslexia.

This study investigates whether neural phase locking to speech amplitude envelopes reflects oscillatory entrainment or evoked responses to acoustic edges, finding support for the latter.

The paper investigates how human auditory speech cortex processes vowel formants, finding localized formant tuning and population-level vowel category decoding.