Fanny Quandt

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Oscillatory dynamics track motor learning in human cortex

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Authors:

  • S. Durschmid

  • Fanny Quandt

  • Ulrike M. Krämer

  • Hermann Hinrichs

  • R. T. Schultz

  • H. Pannek

  • Edward F. Chang

  • Robert T. Knight

Date: 2014

DOI: 10.1371/journal.pone.0089576

PubMed: 24586885

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Abstract:

Improving performance in motor skill acquisition is proposed to be supported by tuning of neural networks. To address this issue we investigated changes of phase-amplitude cross-frequency coupling (paCFC) in neuronal networks during motor performance improvement. We recorded intracranially from subdural electrodes (electrocorticogram; ECoG) from 6 patients who learned 3 distinct motor tasks requiring coordination of finger movements with an external cue (serial response task, auditory motor coordination task, go/no-go). Performance improved in all subjects and all tasks during the first block and plateaued in subsequent blocks. Performance improvement was paralleled by increasing neural changes in the trial-to-trial paCFC between theta ([Formula: see text]; 4-8 Hz) phase and high gamma (HG; 80-180 Hz) amplitude. Electrodes showing this covariation pattern (Pearson's r ranging up to .45) were located contralateral to the limb performing the task and were observed predominantly in motor brain regions. We observed stable paCFC when task performance asymptoted. Our results indicate that motor performance improvement is accompanied by adjustments in the dynamics and topology of neuronal network interactions in the [Formula: see text] and HG range. The location of the involved electrodes suggests that oscillatory dynamics in motor cortices support performance improvement with practice.

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Single trial discrimination of individual finger movements on one hand: A combined MEG and EEG study

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Authors:

  • Fanny Quandt

  • Christoph Reichert

  • Hermann Hinrichs

  • Hans-Jochen Heinze

  • Robert T. Knight

  • Jochem W. Rieger

Date: 2011

DOI: 10.1016/j.neuroimage.2011.11.053

PubMed: 22155040

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Abstract:

It is crucial to understand what brain signals can be decoded from single trials with different recording techniques for the development of Brain-Machine Interfaces. A specific challenge for non-invasive recording methods are activations confined to small spatial areas on the cortex such as the finger representation of one hand. Here we study the information content of single trial brain activity in non-invasive MEG and EEG recordings elicited by finger movements of one hand. We investigate the feasibility of decoding which of four fingers of one hand performed a slight button press. With MEG we demonstrate reliable discrimination of single button presses performed with the thumb, the index, the middle or the little finger (average over all subjects and fingers 57%, best subject 70%, empirical guessing level: 25.1%). EEG decoding performance was less robust (average over all subjects and fingers 43%, best subject 54%, empirical guessing level 25.1%). Spatiotemporal patterns of amplitude variations in the time series provided best information for discriminating finger movements. Non-phase-locked changes of mu and beta oscillations were less predictive. Movement related high gamma oscillations were observed in average induced oscillation amplitudes in the MEG but did not provide sufficient information about the finger's identity in single trials. Importantly, pre-movement neuronal activity provided information about the preparation of the movement of a specific finger. Our study demonstrates the potential of non-invasive MEG to provide informative features for individual finger control in a Brain-Machine Interface neuroprosthesis.