Experimental Design Considerations. Theodore Huppert PhD Associate Professor Dept of Radiology University of Pittsburgh

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1 Experimental Design Considerations Theodore Huppert PhD Associate Professor Dept of Radiology University of Pittsburgh 1

2 NIRS during Wii NIRS during vestibular testing Multi-modal NIRS and MRI and MEG Infant / child studies 2

3 Considerations: Experimental design Blocked Pros:» easy to implement/subject compliance Cons:» less statistical power» saturation/adaptation effects» physiological coupling vs event-related (slow or rapid) Pros:» less physiological coupling» Less subject adaptation/expectation Cons:» need to optimally jitter for statistical analysis Number of trials/conditions Repetition is boring non-compliance, motion Many subjects/fewer repetitions vs. repeating task/few tasks Real-world studies 3

4 Experimental Set-up: Teresa Wilcox, PhD Texas A&M Jungle motif

5 Mixed Design Alternating winning and frustration blocks Blocks include 6 trials (ex. 5 win trials and 2 loss trial in win block) Online emotion rating at end of block No long rest periods between blocks Always keeping the child engaged in the task Lose ability to compare to baseline Only tests differences between condition 5

6 Considerations: Area of interest Systematic factors that could affect NIRS signal (e.g. head size, gender, atrophy, etc) Anatomy (depth of brain, blood vessels [dural sinuses], hair) Probe design Spacing ( cm) Further in forehead/temporal. Closer in occipital Probe blind spots Registration 6

7 Investigation of the sensitivity of fnirs brain imaging to anatomical variations in 5-11 year old children. Whiteman AC, Santosa H, Chen DF, Perlman S, Huppert TJ. Neurophotonics- In press 7

8 Effect of regional anatomy on Differential Pathlength Anatomy effects the diffusion of light through the tissue. Modified Beer-Lambert Law (MBLL) æ ö DOD(t)» çå e i D[ X i (t)] è ø s DPF i 8

9 Safety and Regulation NIRS in humans Non-invasive. Non-ionizing light. Light exposure limits set by ANSI (class IIM or IIIR) ANSI limit on MPE exposure comparable to sun light ( W/cm 2 ) Above class IIIB lasers regulated by FDA Permissible for child studies under (45 CFR ; not involving greater then minimum risk) OSHA/ANSI Guidelines on lasers Class I. Inherently safe at any exposure time. Example CD players Class IM. Safe unless viewed through optics (e.g. microscope or magnifying glass) Class II. Blink response will prevent damage. Must be visible. Up to 1mW. Example- cheap laser pointers Class IIM. Meets general class II unless viewed through optics Class IIIR. Safe with restricted beam viewing. Damage in 2min. Up to 5mW. Must bare warning label. Example- firearm sights and laser pointers Class IIIB mW. Damage in 1/100s. Regulated by FDA. Must have interlock and safety key. Class IV. >500mW. Industrial, military, medical lasers.

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11 Münster T2T-Converter

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13 Ye, J.C., Tak, S., Jang, K.E., Jung, J.W., Jang, J.D., NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy. NeuroImage 44,

14 Considerations: Movement Secure probe Comfort vs data quality Larger probe (generally) means more motion Systemic physiology effects 14

15 NIRS measurements of cortical control of mobility and posture

16 Dynamic Posturography Middle age: N= 15 (46+/-11 y) Older age: N= 15 (73 +/-5 y) EEI MVRC TechEn Cw6 (13 source x 20 detectors) Chia-Cheng (James) Lin, PhD, PT Now at East Carolina Univ; Dept of PT 16

17 Middle-Age Adults, Trial 4 Chia-Cheng (James) Lin, PhD, PT Now at East Carolina Univ; Dept of PT SOT IV V IV Older Adults, Trial 4 17

18 EEG [ΔPower alpha ] fnirs [ΔHbO 2 ] SOT I SOT IV T-score (N=2) left SOT II SOT V

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20 Considerations: Analysis Statistical methods (talks tomorrow) Which first/second level model to use Canonical vs deconvolution/block-average» Recommend canonical for tasks >10s duration Resting state/hyper-scanning Within vs between subjects design Real-time 20

21 Resting state NIRS/ hyperscanning Physiological/systemic noise Spatial variation in data quality Analysis model cite 21

22 Real-time NIRS (Brain Computer Interface)

23 Score Real-time NIRS BCI Read Reread Paraphrase Self-Explain Adaptive Neural Network model for continuous monitoring of brain score 0-Worst 5 10-BEST One-dimensional Neural Network

24 Score Real-time NIRS BCI Read Reread Paraphrase Self-Explain Adaptive Neural Network model for continuous monitoring of brain score 0-Worst 5 10-BEST One-dimensional Neural Network Self-explain Paraphrase Reread Time (s) true SE timing