Author name cluster

Howard Poizner

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

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JBHI Journal 2015 Journal Article

EEG Activity During Movement Planning Encodes Upcoming Peak Speed and Acceleration and Improves the Accuracy in Predicting Hand Kinematics

Lingling Yang
Howard Leung
Markus Plank
Joe Snider
Howard Poizner

The relationship between movement kinematics and human brain activity is an important and fundamental question for the development of neural prosthesis. The peak velocity and the peak acceleration could best reflect the feedforward-type movement; thus, it is worthwhile to investigate them further. Most related studies focused on the correlation between kinematics and brain activity during the movement execution or imagery. However, human movement is the result of the motor planning phase as well as the execution phase and researchers have demonstrated that statistical correlations exist between EEG activity during the motor planning and the peak velocity and the peak acceleration using grand-average analysis. In this paper, we examined whether the correlations were concealed in trial-to-trial decoding from the low signal-to-noise ratio of EEG activity. The alpha and beta powers from the movement planning phase were combined with the alpha and beta powers from the movement execution phase to predict the peak tangential speed and acceleration. The results showed that EEG activity from the motor planning phase could also predict the peak speed and the peak acceleration with a reasonable accuracy. Furthermore, the decoding accuracy of the peak speed and the peak acceleration could both be improved by combining band powers from the motor planning phase with the band powers from the movement execution.

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YNICL Journal 2013 Journal Article

Dopaminergic therapy in Parkinson's disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control

Jobi S. George
Jon Strunk
Rachel Mak-McCully
Melissa Houser
Howard Poizner
Adam R. Aron

It is not yet well understood how dopaminergic therapy improves cognitive and motor function in Parkinson's disease (PD). One possibility is that it reduces the pathological synchronization within and between the cortex and basal ganglia, thus improving neural communication. We tested this hypothesis by recording scalp electroencephalography (EEG) in PD patients when On and Off medication, during a brief resting state epoch (no task), and during performance of a stop signal task that is thought to engage two partially overlapping (or different) frontal-basal-ganglia circuits. For resting state EEG, we measured pair-wise coherence between scalp electrodes in several frequency bands. Consistent with previous studies, in the Off medication state, those patients with the greatest clinical impairment had the strongest coherence, especially in the beta band, indicating pathological over-synchronization. Dopaminergic medication reduced this coherence. For the stop signal task, On vs. Off medication increased beta band power over right frontal cortex for successful stopping and over bilateral sensorimotor cortex for going, especially for those patients who showed greater clinical improvement. Thus, medication reduced pathological coherence in beta band at rest and increased task related beta power for two potentially dissociable cortico-basal ganglia circuits. These results support the hypothesis that dopaminergic medication in PD improves neural communication both at rest and for executive and motor function.

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YNIMG Journal 2011 Journal Article

Choice modulates the neural dynamics of prediction error processing during rewarded learning

David A. Peterson
Daniel T. Lotz
Eric Halgren
Terrence J. Sejnowski
Howard Poizner

Our ability to selectively engage with our environment enables us to guide our learning and to take advantage of its benefits. When facing multiple possible actions, our choices are a critical aspect of learning. In the case of learning from rewarding feedback, there has been substantial theoretical and empirical progress in elucidating the associated behavioral and neural processes, predominantly in terms of a reward prediction error, a measure of the discrepancy between actual versus expected reward. Nevertheless, the distinct influence of choice on prediction error processing and its neural dynamics remains relatively unexplored. In this study we used a novel paradigm to determine how choice influences prediction error processing and to examine whether there are correspondingly distinct neural dynamics. We recorded scalp electroencephalogram while healthy adults were administered a rewarded learning task in which choice trials were intermingled with control trials involving the same stimuli, motor responses, and probabilistic rewards. We used a temporal difference learning model of subjects' trial-by-trial choices to infer subjects' image valuations and corresponding prediction errors. As expected, choices were associated with lower overall prediction error magnitudes, most notably over the course of learning the stimulus–reward contingencies. Choices also induced a higher-amplitude relative positivity in the frontocentral event-related potential about 200ms after reward signal onset that was negatively correlated with the differential effect of choice on the prediction error. Thus choice influences the neural dynamics associated with how reward signals are processed during learning. Behavioral, computational, and neurobiological models of rewarded learning should therefore accommodate a distinct influence for choice during rewarded learning.

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