Knowledge Vault 3/76 - G.TEC BCI & Neurotechnology Spring School 2024 - Day 8
Decoding pain relief: electrophysiological markers of chronic
pain in spinal cord stimulation
Ilknur Telkes, Florida Atlantic University (USA)
<Resume Image >

Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Llama 3:

graph LR classDef pain fill:#f9d4d4, font-weight:bold, font-size:14px; classDef scs fill:#d4f9d4, font-weight:bold, font-size:14px; classDef eeg fill:#d4d4f9, font-weight:bold, font-size:14px; classDef ml fill:#f9f9d4, font-weight:bold, font-size:14px; classDef resources fill:#f9d4f9, font-weight:bold, font-size:14px; A[Ilknur Telkes] --> B[neural engineering,
signal processing for pain. 1] A --> C[Lab: neural pain markers,
tools, neuromodulation tech. 2] A --> D[Chronic pain:
prevalent, reduces QoL. 3] D --> E[SCS: FDA-approved
pain treatment. 4] E --> F[SCS limitations: brain effects,
metrics, manual programming. 5] E --> G[Lack of pain measurements
can mean failure. 6] A --> H[Pain: sensory, cognitive,
emotional processes. 7] H --> I[EEG: biomarker potential,
SCS compatible. 8] I --> J[EEG studies show
chronic pain differences. 9] I --> K[Limited human SCS
EEG effect studies. 10] I --> L[Study: 60-ch EEG in
SCS implant patients. 11] L --> M[Preop: responder vs
non-responder differences. 12] M --> N[Non-responders: reduced alpha,
increased theta. 13] L --> O[ML with preop data
predicted SCS response. 14] L --> P[Intraop: EEG differences
between SCS waveforms. 15] P --> Q[High freq SCS: alpha
shifts, power changes. 16] L --> R[Postop changes indicate
waveform pathway differences. 17] L --> S[Intraop ML: 88%
SCS response prediction. 18] L --> T[Long-term EEG: sustained
changes in responder. 19] A --> U[SCS lead type, placement
impact stimulation. 20] U --> V[High-res paddle lead: distinct
activation patterns. 21] V --> W[High-res lead may enable
better focal targeting. 22] A --> X[Alzheimer's pain: prevalent,
untreated, hard to assess. 23] X --> Y[Pilot: EEG, eye-tracking
to assess pain. 24] Y --> Z[Multimodal setup: resting state,
cognitive task data. 25] A --> AA[SCS approach depends on
clinical decision making. 26] A --> AB[Potential EEG pain biomarkers:
peak alpha, alpha/theta, activation. 27] A --> AC[Resources for learning EEG
signal processing. 28] A --> AD[SCS research: funding,
continuing at U Arizona. 30] class B,C pain; class D,E,F,G,U,V,W,AA,AD scs; class H,I,J,K,L,M,N,O,P,Q,R,S,T,X,Y,Z,AB eeg; class O,S ml; class AC,AD resources;


1.- Ilknur Telkes studies decoding pain relief in chronic pain patients using neural engineering and signal processing methods.

2.- The lab aims to identify neural markers of chronic pain, develop computational tools, and new technologies for neuromodulation implants.

3.- Chronic pain affects 10% of the world population and 50-100 million in the US. It reduces quality of life.

4.- Spinal cord stimulation (SCS) is an FDA-approved surgical treatment that relieves pain better than just pharmacological treatments.

5.- SCS limitations include unknown brain effects, lack of objective metrics for waveform selection, and open-loop manual programming.

6.- Lack of objective pain measurements can result in therapy failure. SCS market is projected to grow to $4.12 billion by 2027.

7.- Pain involves sensory, cognitive and emotional processes. EEG provides biomarker potential with high temporal resolution and SCS compatibility.

8.- Prior EEG studies show differences between chronic pain patients and controls in spectral power, peak alpha frequency and localization.

9.- Limited studies have evaluated neural effects of SCS with EEG in humans, showing changes in frequency content and power.

10.- The study recorded 60-channel EEG preop, postop and intraop in chronic pain patients undergoing SCS implantation surgery.

11.- In the preop stage, 17 patients were grouped into SCS responders and non-responders. Demographics were not significantly different.

12.- Preop EEGs showed stronger alpha in parietal-occipital and frontal areas. Non-responders had reduced alpha and increased theta.

13.- Machine learning using preop subjective measures predicted SCS response with 70-81% accuracy. EEG features improved prediction to 86%.

14.- Intraop, EEG was recorded from 9 patients during SCS testing. Significant differences were found between waveforms.

15.- Peak alpha frequency shifted faster and alpha-theta power changed in somatosensory and prefrontal areas with high frequency SCS.

16.- Postop at 3 months, EEG changed from preop, with faster rhythms in responders. Waveforms induced different changes.

17.- A machine learning model combining EEG and subjective features predicted SCS response with 88% accuracy intraoperatively.

18.- Postop spectral changes indicate SCS waveforms affect different pain pathways. Alpha changes correlate with pain relief.

19.- In 2 long-term SCS patients, EEG showed sustained faster rhythms in the responder compared to non-responder.

20.- SCS lead type and placement impact stimulation options. Recent evidence suggests lateral stimulation may allow focal pain treatment.

21.- A high-resolution 60-channel paddle lead was developed and tested intraoperatively. EMG recordings mapped spinal motor responses.

22.- The high-res lead showed distinct lower extremity activation patterns compared to commercial leads, potentially enabling better focal targeting.

23.- Over 6 million Americans have Alzheimer's/dementia. Over 50% experience pain daily, which goes untreated due to self-reporting limitations.

24.- A pilot study is testing EEG and eye-tracking to objectively assess pain in Alzheimer's patients. Preliminary results are promising.

25.- The multimodal setup synchronizes EEG, eye-tracking and accelerometer data during resting state and a cognitive task showing images.

26.- SCS electrode placement approach depends on clinical decision making. EEG seems promising for long-term objective pain monitoring.

27.- Peak alpha frequency, alpha/theta ratio and topographical activation are potential EEG biomarkers of chronic pain and SCS response.

28.- EEGlab, MATLAB toolboxes, YouTube videos and online lectures are good resources for learning EEG signal processing.

29.- SCS is a well-accepted treatment. Study protocols add minimal time/risk. Stimulation testing is done at the end of surgery.

30.- The speaker received funding and awards for the SCS research and is continuing projects at the University of Arizona.

Knowledge Vault built byDavid Vivancos 2024