Robert Mark Richardson

The paper investigates how basal ganglia activity encodes speech production and ambient noise levels in humans, finding distinct neural populations for each.

This study investigates how sleep spindles interact with epileptic activity in thalamocortical circuits, finding bidirectional relationships and suggesting spindles stabilize network stability.

The paper maps human cortical connectivity using es-fMRI and finds that functional similarity, not distance, primarily determines anatomical connections.

This paper presents the first extensive reconstruction of human subcortical fiber pathways using ultra-high-resolution dMRI, providing a high-definition atlas with clinical applications.

The paper introduces a reinforcement learning model for face perception that incorporates environmental feedback, showing it matches supervised and unsupervised models in capturing neural responses to faces.

The paper uses electrophysiological data from the nucleus accumbens to guide deep brain stimulation for opioid use disorder, showing suppression of drug-related cravings with targeted stimulation.

This study identifies somatic Ras-MAPK pathway variants in hippocampal tissue from patients with drug-resistant epilepsy, showing their association with clonal competition and worse surgical outcomes.

Patch2MAP integrates patch-clamp electrophysiology with super-resolution imaging to analyze subcellular protein expression in human neurons without genetic modification.

This study examines how parkinsonism affects movement-related activity in the GPi of macaques, finding altered timing and magnitude of neuronal responses.

This study examines the cross-regional interplay of sleep oscillations in memory consolidation using intracranial recordings from epilepsy patients and finds that specific oscillation combinations and epileptic spikes affect memory outcomes.

This study uses deep learning models to analyze neural activity during conversation, showing distributed and context-dependent patterns for speech production and comprehension.

The paper shows that spike-phase coupling between subthalamic nucleus neurons and posterior opercular cortex predicts speech sound accuracy during speech production.

This study decodes lexical semantic information from natural speech using intracranial EEG in participants during spontaneous conversation, achieving 21% accuracy.

This study shows that high-performing ANNs and brains converge on similar representations, and identifies stimuli that modulate model-to-brain alignment.

The paper investigates how subthalamic nucleus neurons encode phoneme and sequence features of speech in Parkinson's patients during deep brain stimulation.

This study investigates how theta-burst stimulation affects neural activity in human brain networks using intracranial EEG during direct electrical stimulation.

The paper introduces an HFO-sequencing approach to analyze spontaneous HFOs in epilepsy patients, revealing propagation pathways and improving localization of epileptic tissue.

The paper analyzes aperiodic components of local field potentials in cortical and subcortical regions, finding differences in aperiodic exponents and the absence of aperiodic knees in subcortical areas.

This paper introduces a photothrombotic stroke model in mice that reproduces human epilepsy electrographic biomarkers, showing increased spike ripples and delta power in the stroke hemisphere.

The paper introduces a high-channel-count flexible electrode for deep brain recording, capable of capturing single-unit activity from up to 128 channels.

The paper develops a supervised spatial filter to remove speech artifacts from intracranial EEG recordings while preserving neural activity.

This study uses intracranial EEG and machine learning to show dynamic, activity-silent mechanisms maintain auditory working memory in humans.

This study identifies neural mechanisms of human conscious visual perception using report-independent methods and shows involvement of large-scale subcortical and cortical networks.

The paper describes an immersive paradigm for studying natural social behavior and its neural correlates in epilepsy patients using synchronized behavioral and neurophysiological data.

The paper maps gradients of neural timescales and functional connectivity in the cortex and shows their relationship to perceptual behavior.

This study examines ketamine-induced brain dynamics in humans using intracranial recordings, identifying oscillatory changes linked to antidepressant and dissociative effects.

This study identifies hyperdirect connectivity between opercular speech regions and the subthalamic nucleus in humans using intracranial recordings during deep brain stimulation.

The paper identifies speech-induced artifacts in intracranial recordings and shows they correlate with speech audio and vibration patterns.

This paper describes a new high-density Neuropixels probe enabling single-cell resolution recordings from over 200 cortical neurons in humans during surgery, identifying eight distinct single unit classes.

This study uses machine learning and spectral graph theory to show that DBS increases high beta-gamma synchrony in a cortical network involving motor and cognitive areas.