multimodal neuroimaging training program nirs module
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Multimodal Neuroimaging Training ProgramNIRS module
Anna ManelisDepartment of Psychology, CNBC
Carnegie Mellon University
Faculty Instructor: Theodore Huppert, PhDTechnical Adviser: Nancy Beluk
July 14, 2011
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NIRS
• 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
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CW6 system
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Registration
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Find a right spot
detectorssources
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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).
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Finger tapping
15s on + 15s off
5 blocks
Right hand
Unilateral probe
QuickTime™ and a decompressor
are needed to see this picture.
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Finger tapping
sources
detectors
detectors
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Finger tappingLeft motor cortex
ΔOD – changes in optical Density at 830 nm
Raw data
Optical density = -log (I1/I0)
0 50 100 150 2000 50 100 150 200
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Finger tappingLeft motor cortex
hp=70s, GF=2s
0 50 100 150 200
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Finger tappingLeft motor cortex
hp=70s, GF=2s
0 50 100 150 200
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Finger tappingLeft motor cortex
hp=70s, GF=2s
0 50 100 150 200
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Memory Studies
Right
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Verbal memory
690nm
830nm
encoding recognition
0 50 100 150 200 0 50 100 150 200time (sec) time (sec)
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Verbal memoryencoding recognition
HbRHbO
HbT
0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70time (sec) time (sec)
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N-back predictionsfMRI results
Owen et al., 2005 (HBM)
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N-back load effect
1-back
2-back
3-back
0 10 20 30 40 50 60 70
time (sec)
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Summary
NIRS can detect changes in brain activity in various tasks that include simple sensory-motor and higher cognitive functions tasks
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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
Limitations
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690 nm 830 nm
690 nm vs. 830 nm
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Noise in the data
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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
Limitations
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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
Limitations
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Acknowledgements
24
Seong-Gi Kim, PhDBill Eddy, PhDTheodore Huppert, PhDNancy BelukTomika CohenMNTP Faculty, Staff, and Teaching AssistantsUniversity of Pittsburgh Medical CenterCarnegie Mellon Center for Neural Basis of CognitionNIH R90DA023425T32-MH019983-12