Semi‑quantitative analysis cerebrospinal fluid chemistry and cellularity using
Copy of NCSU Workshop - DTI.1
1. Diffusion Tensor Imaging of Multiple
Sclerosis Along the Corpus
Callosum
Erin DeNardo, Washington University in St. Louis
Delaram Farzanfar, University of Toronto
Joanna Jeon, University of Virginia
Shivangi Mistry, University of Virginia
Ainslee Neu, University of Minnesota
Katie Santo, University of Minnesota
Tinashe Tapera, Drexel University
May 27th, 2016
2. Research Question
Do water diffusion profiles along the corpus callosum fibers
correlate with the diagnosis of multiple sclerosis ?
○ Understand how diffusion properties are changing along the callosal fiber
tracts
○ Predict the likelihood of having MS based on the FA values and gender
3. Multiple Sclerosis
● Damage to myelin sheath; protective
tissue covering axons
● Transmission of nerve impulses
impaired
● Damage may be caused by
inflammation, lesions, scars, plaques
scattered throughout the CNS
● Common symptoms include
autonomic, visual, motor & sensory
problems
4. What is Diffusion Tensor Imaging…?
● MRI based imaging method
● Sensitive to microscopic
motion of water molecules
● Water molecule movement
is different in different
tissues
● Sensitive to disruption of
tissue microstructure
Higher FALower FA
www.na-mic.org
Closer to 0 Closer to 1
6. Data Visualisation: Mean Data and Differences of Means
Data Source: MRI/DTI data were collected at Johns Hopkins University and Kennedy-Krieger Institute
10. Methodical Approach
1. All Data Points 2. Three equal segments 3. Every 10th Point
● Independent T-tests at each location
● Logistic Regression Models
○ 1. Overall (all 93 points)
○ 2. Grouped (split into 3 sections of 31 points)
○ 3. Alternating (every 10th point)
○ 4. Functional Model
4. Functional Model
11. Logistic Regression
We have a binary output variable MS, and we want to model the conditional probability
Pr(MS = 1 | X = x) as a function of x.
12. Train & Test Validation
Data Set
Training Set Test Set
Learned modelLearning
Prediction Accuracy Estimate
5%95%
#
Train
on
Test on Correct
1 S2...S93 S1 90/93
2
S1,S3...
S93
S2 75/93
14. Overall Model (Using All Points)
● Assumes that all points along corpus callosum are predictive
● There is high collinearity
● High standard errors
● Very large P-values
● No points are significant
● Due to high correlation between adjacent points
● Prediction accuracy 66%
15. Grouped Model (3 Equal Segments)
● We originally suspected that the peaks may represent specific regions
● May be biologically informed
● For practical purposes, we simplified the segments into equal lengths
● Each of the three predictors were significant
○ Region 1: p < 0.001
○ Region 2: p < 0.001
○ Region 3: p < 0.001
● Prediction accuracy 76%
16. Alternating Model (Every 10th)
● Reduce the number of predictors
● Increase the distance between the points
● Two points 13 (p > 0.037) and 73 (p > 0.024) were significant
● Prediction accuracy 71%
17. Functional Model
● Functional data analysis
● Model: Functional
Generalized Additive Model
(FGAM)
● CCA data as function as
predictor of case status
Equation that FGAM is based on:
18. Model Prediction Accuracy
Model Prediction
Accuracy
AIC
Overall 66% 190
Grouped 76% 4281*
Alternating 71% 141
*To be discussed
19. Limitations
● Age not taken into consideration
● Model not informed by intrinsic variability along the posterior-anterior direction
● No information on the approximate location of the start and end points
● Only the first scan analyzed for patients with multiple scans
20. Future Directions
● Find the normal range of variability between different
portions of the tract
● Develop better diagnostic criteria for MS
○ Faster diagnosis for earlier treatment course
● Presence of periventricular lesions as a hallmark of MS
○ Lower FA values in the middle section related to proximity to
Lateral ventricles ?
○ How does this data relate to variability along the medial plane ? Aldasoro-Cáceres V, et al. 2014 Jan 31;89(1):31-4.
