Variation in extracerebral layer (ECL) thickness within a subject and between subjects

1. Variation in extracerebral layer (ECL) thickness within a subject and between subjects Tae Sun Yoo Department of Medical Biophysics University of Western Ontario Supervisor: Dr. Keith St Lawrence and Ph.D. candidate Jonathan Elliott

2. Near Infrared Spectroscopy & Principles • Near infrared spectroscopy is a powerful optical technique used to measure cerebral blood flow (CBF) and cerebral blood oxygenation. Blood Flow Technique: Bolus injection light-absorbing dye, indocyanine green (ICG) Monitor passage of ICG through brain by NIRS 3) CBF determined by shape of time-concentration data

3. Clinical Relevance of NIRS • Brain injury is a leading cause of deaths and disability in Canada. • NIRS can be used to assess brain health at the bedside of head-trauma patients • NIRS works well with infants, but not with adults • Problem is signal contamination from extracerebral layer • Consequence is reduced sensitivity to brain leading to underestimation of CBF.

4. Approach • MRIcro was used to import MRI images and Image J was used to obtain length of extracerebral layer (ECL) thickness values across the circumference of the head (from 0 to 360˚)

5. Hypothesis • Variations in ECL thickness across the subject will be sufficient to affect the NIRS brain attenuation signal to measure accurate cerebral blood flow (CBF).

6. Method • Brain MRI images were acquired from five young healthy subjects & viewed by MRIcro. • Anterior Commissure (AC) was chosen as the reference location for all subjects • MRI images were exported in 2 transverse slices per subject.

7. Method • Brain MRI images were loaded onto “ImageJ”. • By using a line tool and fill function, outlines were drawn from 0 to 360˚ within the image. • Color picker function was used with pencil tool. land markers were made at two locations. • Total (ECL) thickness measurements were done by moving a line tool at 10˚increment with use of measurement function.

8. 90˚ Angle (θ) Position 180˚ 0, 360˚ 270˚ Note: MR images were calibrated by 1 x 1 mm ECL thickness Scalp Angle (θ) from 0° to 360° Record: Total ECL thickness made by Δ10° increment Skull

9. Results

10. *p < 0.05 ∆ in ECL thickness between AC & AC +20mm:  ECL thickness was slightly thinner at AC above 20mm!

11. Subject 3, AC, Region I 9% • As ECL thickness increased, % of brain signal is reduced

12. ICG concentration (uM) Pure brain curve Pure scalp curve Time (s) ICG concentration (uM) ICG concentration (uM) Time (s) Time (s) Mean, AC+20, Region I Brain contribution: 35% Subject 3, AC, Region I Brain contribution: 9%

13. Discussion Main limitations: There isn’t a decent imaging tool which resolves different ECL layers separately. • Source of errors: . Small sample size (5 subjects) . Resolution of MR image quality (reduced with “zoom in” function)

14. Discussion Future works on identifying ECL thickness variability: • By standardizing variation of ECL thickness, more precise and reliable CBF measurements can be made on adults. • In near future, individualized approach on making an adjustment on CBF measurements are possible by removing ECL (skull and scalp) contamination.

15. Conclusion • Mean ECL thickness across the circumference from subject 1 to 5 at AC+20mm was thinner by 2mm than at AC. • Based on regions (I-IV), forehead region I at AC above 20mm was shown to be the thinnest mean ECL thickness. • Thus, thinner the ECL thickness, better the CBF measurements by increased in contribution of brain signal (more light propagates into brain and reduced ECL contamination).