Cortical responses evoked by continuous wrist manipulation Alfred C. - - PowerPoint PPT Presentation

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Cortical responses evoked by continuous wrist manipulation Alfred C. - - PowerPoint PPT Presentation

Nov 14, 2023 549 likes •758 views

Cortical responses evoked by continuous wrist manipulation Alfred C. Schouten , Martijn P. Vlaar, Teodoro Solis- Escalante, Yuan Yang, Frans C.T. van der Helm a.c.schouten@tudelft.nl 1 Benchmark data based on two articles 2 Brain as

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Cortical responses evoked by continuous wrist manipulation

Alfred C. Schouten, Martijn P. Vlaar, Teodoro Solis- Escalante, Yuan Yang, Frans C.T. van der Helm a.c.schouten@tudelft.nl

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Benchmark data based on two articles

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Brain as controller of the human body

reflex modulation

mechanical interaction

cortex spinal cord muscle & sketeton

  • 2. cortical loop
  • 1. spinal reflex loop

proprio- ceptors

Scott, 2004

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Motivation

  • Stroke

– Disturbed blood supply to the brain – Hemiparesis

  • Rehabilitation therapy

– Restitution or compensation?

  • Restitution

=> focus on affected side

  • Compensation

=> focus on non-affected side

  • Initial sensory function might predict potential recovery

– Select best therapy for the individual patient

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Recording brain activity

  • EEG: electroencephalography
  • Electrodes measure electrical potential at scalp

– Noisy data – Volume conduction – Requires many parallel oriented neurons to fire synchronously

  • Preparation (0.5 - 1 hour)

– Apply cap and conductive gel

  • Data analysis

– Manually remove all trials which contain artefacts

  • Eye blinks, movements, etc.

– Analysis at:

  • electrode level or
  • source level (ICA: Independent Component Analysis)
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Experiment

  • 126 channel EEG
  • 10 subjects, 2 tasks

– Passive task (‘do nothing’), angular perturbations – Active task (‘keep position’), torque perturbations

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Experiment

  • Perturbations:

– random phase multisines with 10 frequencies – 1 s period with 1,3,5,6,9,11,13,15,19,23 Hz

  • M=7 realizations,

– each P=210 periods

  • 7*210=1470 periods of 1s

– 25 min of data per task, excl. breaks – 2 hours of recording

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Recording and analysis

  • Recorded signals (@2048Hz)

– Torque, angle, EMG (flexor&extensor), EEG (126 ch.)

  • Analysis

– SNR for each electrode – ‘best’ electrode: power in excited frequencies, even &

  • dd harmonics
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Method: detecting nonlinearities

LTI NLTI

Pintelon & Schoukens, 2012

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Cortical response

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Nonlinearities

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Intermediate summary

  • Mechanics

– 99% in excited frequencies

  • ~linear, muscle visco-elasticity
  • EMG

– ± 25% power in harmonics

  • EEG

– ± 80% power in harmonics

  • => highly nonlinear!

reflex modulation

mechanical interaction

cortex spinal cord muscle & sketeton

  • 2. cortical loop
  • 1. spinal reflex loop

proprio- ceptors

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Analysis and modelling

  • Source localization: ICA analysis
  • Analyse ‘best’ ICA

– 4 groups of frequencies

  • f1: excited frequencies
  • f2: 2nd harmonics
  • f3: 3rd harmonics (excl. f1)
  • f4: 4th harmonics (excl. f2)
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Modelling

  • h1: Best Linear Approximation (BLA)
  • h2: regularized Volterra kernels (2nd order)

Pintelon & Schoukens, 2012 Birpoutsoukis et al. Automatica, 2017.

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Results

  • Power distribution

2nd order Volterra

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Main findings

  • Quantifying nonlinear contributions (paper 1)

– Highest SNR found over contralateral cortex – SNR of electrodes with highest SNR: -14.8 dB – Cortical response is highly nonlinear (>80%)

  • Modelling the nonlinear cortical response (paper 2)

– SNR of best source (ICA component): -12.3dB – Estimated noise level 8% – Truncated Volterra model explains 46% – High similarity of the obtained models across participants – Models show high-pass behavior, suggesting velocity- sensitivity (muscle spindle)

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Main challenges

1. What is the nonlinearity?

– Efficient models without prior assumptions – Intuitive description of the identified nonlinear model – => insight into the physiological origin

2. One common model structure to fit all subjects/data

– Compare parameters between groups (healthy vs. disease) – Track stroke subjects over time (neuroplasticity) – => diagnostic/monitoring tool

3. Network connectivity

– Track the sensory signal through the brain – Connectivity between sources/components – => altered brain networks after e.g. stroke

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Available datasets

  • Small (<1Mb): input (handle angle) - output (best ICA) averaged
  • ver periods for every participant and multisine realization,

resampled at 256Hz (www.nonlinearbenchmark.org)

  • Medium (around 500Mb): as small, but not averaged over

periods and at the original sample rate of 2048Hz (www.nonlinearbenchmark.org)

  • Large (around 60Gb): Only filtered and segmented in 1 s
  • periods. Includes all 126 EEG channels at 2048Hz, and the ICA

matrix to convert channels to sources. Available at 4TU Centre for Research Data (data.4tu.nl)

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4D-EEG project team

Delft University of Technology Frans C.T. van der Helm (PI) Alfred C. Schouten Yuan Yang Martijn P. Vlaar Teodoro Solis-Escalante Mark van de Ruit Konstantina Kalogianni Lena Filatova Northwestern University Feinberg School of Medicine Chicago Julius P.A. Dewald Jun Yao VU University Medical Center Amsterdam Gert Kwakkel (Co-PI) Erwin E.H. van Wegen Jan C. de Munck Carel Meskers Caroline Winters Aukje Andringa Sarah Zandvliet Mique Saes Lucas Haring Dirk Hoevenaars VU University Amsterdam Andreas Daffertshofer