1. conflicts of interest to report.
Conflict of Interest Disclosure Amanda F. Khan, MSc. Medical Biophysics
Has no real or apparent
conflicts of interest to report.
I have no conflict of interests to report.

2. Ventricle Sub-Region Segmentation Utilizing MRI as a Structural Biomarker of Alzheimer’s Disease
Amanda F. Khan
Department of Medical Biophysics
Imaging Research Laboratories
Robarts Research Institute
The University of Western Ontario
Supervisors: Dr. Michael Borrie, Dr. Robert Bartha
Alzheimer’s Disease International – March 28th, 2011

3. The Ventricular System
Lateral ventricles
structures containing CSF in the midbrain
atrophy of surrounding tissues leads to increase in CSF volume
increase in CSF = increase in lateral ventricles (surrogate measure)
capture this increase on MRI, sometimes years before cognitive decline can be measured
3D image adapted from: The Biodidac

4. Atrophy as Captured on MRI
Normal AD
Images adapted from: The Alzheimer's Disease Research Center, Florida

5. NIH: AD Biomarker Criteria
Development of Specific AD Treatment Strategies Requires
Ventricular Enlargement as a Biomarker 1
Dx in early stages when intervention is most effective
Can detect very early brain atrophy before cognitive decline can be measured 2
Treatment efficacy can be monitored
Serial MRI can measure atrophy (or lack thereof) over time in clinical trials in a way cognitive tests cannot
Source: NIH -Ways Towards an Early Diagnosis in Alzheimer’s Disease

6. Hypothesis
That sub-region ventricular volume expansion, particularly that of the temporal horns, may be a more sensitive biomarker of disease progression than total ventricular volume
Normal Elderly Control
AD Patient

7. Alzheimer’s Disease Neuroimaging Initiative
ADNI
6 year multi-site study of NEC, MCI & AD
55 participating sites
imaging, clinical + cognitive measures, biological samples
MRI
1.5T (T1 –weighted)
MP-RAGE pulse sequence
Source of Map: The Alzheimer’s Disease Neuroimaging Initiative

8. Methods Baseline Month 12 Month 24 97 subjects total
blinded segmentation
lateral ventricle volumes extracted with software
NEC
n=26
MCI
n=42 AD
n=29

9. Brain Ventricle Quantification (BVQ)

10. Ventricle Sub-Regions
Left and Right Hemispheres
Ventricle Sub-Region
Lateral Ventricle
lateral anterior (LA)
lateral middle (LM)
lateral posterior (LP)
Temporal Horn
anterior horn (AH)
posterior horn (PH)

11. Preliminary Statistical Analysis
Procedure
Data Used
Evaluate
Repeated-measures ANOVA
Conducted on each sub-region over the 3 time periods
Sub-region volume
longitudinal significance
Paired t-tests
Post-hoc analysis to ANOVAs
Pair-wise significance between any two time points

12. Normal Elderly Controls (NEC)
Temporal Horn
Sub-Region
Significant?
Type of Pairwise Significance
LPH
YES
Baseline and M24
Superior view of lateral (shades of red) and temporal horn (green) regions

13. Mild Cognitive Impairment (MCI)
Temporal Horn
Sub-Region
Significant?
Type of Pairwise Significance
LAH, RAH, LPH, RPH
YES
Baseline and M24
Superior view of lateral (shades of red) and temporal horn (green) regions

14. Alzheimer’s Disease (AD)
Temporal Horn
Sub-Region
Significant?
Type of Pairwise Significance
LAH, RAH, LPH, RPH
YES
Baseline and M24
Superior view of lateral (shades of red) and temporal horn (green) regions

15. Total Ventricle vs. Horn Volume
Normal controls:
NO significant temporal horn enlargement

16. Calculated Sample Sizes
Estimated sample size required to detect a 25% reduction in the mean annual rate of atrophy in a two-sided test with α=0.05 for a two-arm study over one year
Measure
Patient Classification
Calculated n Number
Temporal Horn Only AD
284
Total Ventricle
226
MCI
1547
420
ADAS-cog
3237
2066
Equation source: The ADNI Biostatistics Core

17. Summary
NEC  AD more sub-regions show significant growth in more pair-wise comparisons
MCI & AD significant enlargement in temporal horns, NEC do not
Temporal horn: discriminate patients based on normal age-related atrophy and AD
Smaller sample sizes for total ventricle than horn volumes but significantly smaller for both measures compared to ADAS-cog

18. Acknowledgements Supervisors: Sources of Funding and Collaboration:
Dr. Robert Bartha
Dr. Michael Borrie
Collaborators:
Matthew Smith
Yun-Hee Choi
Support:
Michael Marynowski
Henry Betta
Vaishali Karnik
Sources of Funding and
Collaboration: