Simon J Little

This study shows that device-embedded accelerometry better tracks Parkinsonian motor states than neural signals during deep brain stimulation.

This study identifies neural states in Parkinson's disease patients using Hidden Markov Models and links them to bradykinesia severity through spectral and temporal features.

This study investigates how motor learning in Parkinson's Disease patients involves coordinated low-frequency cortico-basal ganglia activity to optimize preplanning of motor sequences.

The paper shows that REM sleep duration and delta activity in Parkinson's disease patients correlate with reduced daytime basal ganglia-cortical network pathology and improved alertness.

The paper analyzes beta burst characteristics and coupling between sensorimotor cortical and subthalamic nucleus (STN) regions in Parkinson's disease patients during naturalistic behaviors.

The paper examines neural oscillations in human basal ganglia and prefrontal cortex during decision-making tasks, finding distinct roles for beta and theta oscillations in tracking effort and previous reward.

This study uses multi-night recordings to show that Parkinson's disease affects NREM sleep by altering cortico-basal slow wave activity and subcortical beta power, with DBS enhancing these changes and predicting spontaneous awakenings.

This study develops a model to predict cortical gamma oscillation entrainment to DBS in Parkinson's patients and validates it in three subjects.

The paper uses high precision MEG to show R-IFG beta power increases during response conflict, suggesting a generalized role in response inhibition beyond simple stopping.

This paper identifies artifacts in neural signals from a DBS device during stimulation and presents an offline removal method using template subtraction.

This study uses wrist-worn accelerometers to classify bradykinesia fluctuations in Parkinson's patients at the single-minute timescale using individual and group models.

This study identifies laminar dynamics of beta bursts in human motor cortex using high precision MEG and a neocortical circuit model, showing distinct excitatory inputs to deep and superficial layers.

The paper introduces an open-source Matlab toolbox for processing and analyzing data from the Medtronic Summit RC+S neurostimulation system.

The paper investigates how implant location affects ECG contamination in neurostimulation systems, finding higher contamination in left-sided subclavicular implants compared to right-sided ones.

This study compares acute effects of adaptive DBS (aDBS) with conventional DBS (cDBS) and no stimulation in Parkinson's disease patients, finding aDBS reduces motor symptoms more than cDBS and has fewer speech side effects.

This paper describes a wireless neural interface for long-term home monitoring of neural activity in Parkinson's disease patients, enabling adaptive stimulation and behavioral state decoding.

The paper investigates transient beta bursts in motor cortex and finds they predict movement onset and delay after errors, suggesting roles in goal specification and error monitoring.

This study shows that phase-dependent electrical stimulation can suppress beta oscillations in Parkinson's patients, offering a potential therapeutic strategy.

The paper evaluates MEG source reconstruction methods using resting state data, finding that the Empirical Bayesian Beamformer provides the best anatomical discrimination.

This paper compares MEG source inversion algorithms and fit metrics to determine the laminar origin of neural activity, finding that sparsity-constrained methods and specific fit metrics improve discrimination accuracy.

The paper uses MEG to show laminar-specific frequency activity in human visual and sensorimotor cortices, with distinct roles for alpha/beta and gamma bands.