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Multimodal Neuroimaging Training Program NIRS module

Multimodal Neuroimaging Training Program NIRS module. Anna Manelis Department of Psychology, CNBC Carnegie Mellon University Faculty Instructor: Theodore Huppert, PhD Technical Adviser: Nancy Beluk. July 14, 2011. portable relatively non-invasive low cost

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Multimodal Neuroimaging Training Program NIRS module

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  1. Multimodal Neuroimaging Training ProgramNIRS module Anna Manelis Department of Psychology, CNBC Carnegie Mellon University Faculty Instructor: Theodore Huppert, PhD Technical Adviser: Nancy Beluk July 14, 2011

  2. portable relatively non-invasive low cost has low sensitivity to subjects’ motion able to measure both oxy- hemoglobin and deoxy- hemoglobin as a function of near-infrared wavelengths NIRS

  3. CW6 system

  4. Registration

  5. sources detectors Find a right spot

  6. 4 experiments • Median nerve stimulation (2 subjects) • Finger tapping (1 subject) • Words encoding and recognition (1 subject) • Working memory (2 subjects) • the measurements were taken at two wavelengths (690nm and 830nm).

  7. Finger tapping 15s on + 15s off 5 blocks Right hand Unilateral probe

  8. Finger tapping detectors detectors sources

  9. Finger tappingLeft motor cortex 0 50 100 150 200 0 50 100 150 200 ΔOD – changes in optical Density at 830 nm Raw data Optical density = -log (I1/I0)

  10. 0 50 100 150 200 Finger tappingLeft motor cortex hp=70s, GF=2s

  11. 0 50 100 150 200 Finger tappingLeft motor cortex hp=70s, GF=2s

  12. 0 50 100 150 200 Finger tappingLeft motor cortex hp=70s, GF=2s

  13. Memory Studies Right

  14. 0 50 100 150 200 Verbal memory encoding recognition 690nm 830nm 0 50 100 150 200 time (sec) time (sec)

  15. Verbal memory HbR HbO HbT encoding recognition 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 time (sec) time (sec)

  16. N-back predictionsfMRI results Owen et al., 2005 (HBM)

  17. 3-back 2-back 1-back 0 10 20 30 40 50 60 70 time (sec) N-back load effect

  18. Summary NIRS can detect changes in brain activity in various tasks that include simple sensory-motor and higher cognitive functions tasks

  19. Limitations • Three types of noise in NIRS data: • instrument noise • - sometimes difficult to detect • - not much support from the companies • - may have different distribution across channels and wavelengths • physiological noise • experiment error • - cap motion (especially problematic for bilateral caps) • - cap placement

  20. 690 nm vs. 830 nm 690 nm 830 nm

  21. Noise in the data

  22. Limitations • Three types of noise in NIRS data: • instrument noise • - sometimes difficult to detect • - not much support from the companies • - may have different distribution across channels and wavelengths • physiological noise • experiment error • - cap motion (especially problematic for bilateral caps) • - cap placement

  23. Limitations • Methods for data analysis and registration are not well developed (i.e., work in progress) • NIRS is sensitive to • the changes in the scalp thickness over time • between-subject variability within the brain stuctures

  24. Acknowledgements Seong-Gi Kim, PhD Bill Eddy, PhD Theodore Huppert, PhD Nancy Beluk Tomika Cohen MNTP Faculty, Staff, and Teaching Assistants University of Pittsburgh Medical Center Carnegie Mellon Center for Neural Basis of Cognition NIH R90DA02342 5T32-MH019983-12

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