Questions for the Peanut Gallery
1. Standard developed by 3 or 4 researchers – ideas?
2. Stronger paper if “released” with an actual tool?
3. Other feedback?
Paperize
Background
• Goal: diagnosis and subtyping of neuropsychiatric disorder from functional brain networks
• Landscape: exploding publicly available data call for data-driven, automatic methods.
• ICA: derives independent signal: networks and noise
• Current Task: separate noise from networks
Background
Overview of Method
Methods
time
DATA
135 Spatial + 111 Temporal Features
• 53 resting BOLD functional
magnetic resonance imaging
data-sets
• 24 Healthy Control / 29
Schizophrenia
Preprocess Functional
MRI Data
motion correction
segmentation
normalization
smoothing
Extract Components with
ICA
N = 1518 components:
component = spatial maps + timecourse
patterns of noise and brain networks
Define Features of
Components
Create Standards of
Component Types
Machine Learning to Predict
Component Type Based on
Features
Creating the “Gold Standard”
Methods
1. Select data and save labels
2. Select next / previous
3. Label component
4. Spatial map
5. Temporal timecourse
6. Distribution of signal (FFT)
For the following component types:
Methods
N = 818 (700) N=46 (1472) N=40 (1478
N=67 (1451) N=48 (1470)
A B C
D E A.Noise
B.Eyeballs
C.Head motion
D.White matter
E.Parieto-occipital
F.cortex
ALL NOISE EYEBALLS HEAD MOTION
WHITE MATTER PARIETO OCCIPITAL CORTEX
Percent total activation skull
% activation voxels LR symmetric
Power band 0.0 to 0.008 Hz
% total activation MNI152 all
edges
Avg distance btw 10 local max
Hpsd bin 2 freq0 to pi 0.038312
Percent total activation in CSF
Four lag auto correlation
Hpsd bin 3 freq0 to pi 0.076624
three lag auto correlation
Percent total activation in CSF
Percent total activation MNI152 edges
kurtosis measure outlier-prone ts
Caudate R
Percent total activation skull
Caudate L
Percent activation in eyeballs
Paracentral Lobule L
Spatial Entropy of IC distribution
Percent total activation spinal cord
Percent total activation ventricles
Percent total activation in WM
Caudate R
Cingulum Post R
Caudate L
Thalamus L, Thalamus R
Max cluster size 10 local max region
growing thresh 2.5 < .5 overlap
power band 0.05 to 0.1 Hz
Cingulum Post L
Occipital Sup R
Occipital Mid R
Parietal Sup L
Percentage activation voxels LR
symmetric
Occipital Mid L
Occipital Sup L
Parietal Inf L
Parietal Sup R
Occipital Inf L
Parietal Inf R
LASSO most highly weighted features predict component types
Results
Percent activation in eyeballs
Spatial Entropy of IC distribution
Avg distance btw 10 local max
Skewness of IC distribution
% activation voxels LR symmetric
Rectus L
Olfactory R
Percent total activation in WM
Perfect total activation in GM
Evaluation of classifiers is very good!
Evaluation
Lasso L1 constrained linear regression selects features to distinguish component types (N=1518)
with cross validation accuracies of .8689, .9834, .9808, .9675, and .9695 respectively.
A B C D E
A. Noise
B. Eyeballs
C. Head motion
D. White matter
E. Parieto-occipital
cortex
Conclusion
Noisy components can be computationally defined using
spatial and temporal features to allow for automatic filtration
of large functional MRI databases, an improvement over
current manual methods.
Selected features provide researchers with semantic
understanding of component types.
This method can be extended to identify features of
functional brain networks.
Conclusion
Goals for Writing into Paper:
• Journal Choice: NeuroImage
• Time: before end of Spring Quarter (before Quals)
• My first paper!
Paperize
How I'm Fixing it
• Separating SZ and HC data
• Build model with HC, test on SZ
• New datasets: • Age and gender matched
• Different institution, same scanner • Different institution, different scanner • Standard developed by 3 or 4 researchers
• Original SZ model successful, or model built with subset of new data successful on second portion.
Paperize
My progress so far:
1. Separating SZ and HC data
2. Build model with HC 3. Test on SZ
4. New dataset: Different institution, same scanner 5. Different institution, different scanner 6. Standard developed by 3 or 4 researchers 7. Original SZ model successful, or model built with
subset of new data successful on second portion.
Paperize
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