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REPLICATION AND IDENTIFICATION OF
CLINICAL SUBGROUPS IN A STRUCTURAL
MRI DATASET FOR SCHIZOPHRENIA
Dakarai McCoy (PM), Abeer Jihad, Edith Castellanos
Sponsors: Drs. Vince Calhoun (MRN), Jessica Turner (GSU)
Technical Mentor: Navin Cota
Objective
To replicate the results obtain by the Mind Research Network
of clinical subtyping in Schizophrenia by finding the
relationship between the Gray Matter concentration (GMC)
and clinical scores.
Purpose
The success of this research will discover reliable clinical subtype patterns
of Gray Matter Concentration (GMC) deficit, which could be targeted in
the personalized development of drugs for schizophrenia.
 Schizophrenia is a chronic brain disorder
 Delusion
 Hallucination
 Disorganized speech
 Catatonic behavior
 Negative symptoms
Structural Magnetic Resonance Imaging sMRI
 Body is composed of 70% H2O
 Spinning charged particle
 Magnetic moment
Hydrogen Atom
 Randomly oriented with no applied field
MRI components
 Primary magnet
 Gradient magnets
 Radiofrequency (RF)coils
 Computer System
Primary Magnetic Field
 Hydrogen atoms align parallel or antiparallel
to the primary field (B0)
 Precession
Gradient Coils
 Allow spatial encoding for MRI images in the x
y, and z axis
 RF pules
 Signal received by the RF coil in the transverse
plane
Computer System
White Matter (WM)light gray Cerebrospinal fluid (CSF) black
Gray Matter (GM) dark gray
Structural Magnetic Resonance Imaging sMRI
T1-Weighted
Source Based Morphometry
T1
VBM ICA
GMC
WM
CSF
Source Based Morphometry
Independent Component Analysis ICA
Maximally independent sources (two groups; Controls and Sz)
Column of loadings for a given source map(say Insula) covariation
among subjects is captured in loadings.
For a given component map (say insula) if there are two groups, if
mean of Controls in A1 is greater than mean of Sz in A1; then it
means Ct has more GMC than Sz
Top Two Components (HC>Sz) from
C.N.Gupta et al based on Effect Size,
Schizophrenia Bulletin, 2014.
Voxel Component
Insula,STG
Voxel
Subject
Subject
A1
Component
*
A2
C1
C2
Frontal
Component
Matrix
Ctr and Szs GMC
matrix regressed
of Age, Gender, site
voxelwise
Loading
Matrix
 Develop MATLAB code to extract Sz patients
from a data set.
 Data extracted: URSI, site, age, sex, SAPS/SANS.
Module 1
Extraction of patients
with Schizophrenia
 Perform multivariable regression of age and
gender to make the results more sensitive to
group differences (Cota et Al 2009)
Module 2
Regression of
covariates
. Run ICA using SBM toolbox from GIFT
Perform B-ICA in the selected components
Perform 2 sample t-test
Module 3
ICA and Biclustering
Method
Module 1 Extraction of patients with Schizophrenia
 109 Schizophrenia patients from 1st data set
 Variables extracted: Site, Sex, Age, SAPS/SANS scores
Module 2 Regression of covariates
 Extracted brain images from 109 patients
 Run regression of covariates [Age, Sex, Site]
 Smooth selected images
Picture of the brain of one subject after regression
Picture of the brain of one subject after smoothing
Module 3 ICA  Used SBM to run ICA for 109 images
 30 components. Components selected 5 and 7
Medial frontal gyrus (mFG)
Superior frontal gyrus (SFG)
Middle fontal gyrus (MFG)
Insula (I)
Inferior frontal gyrus (IFG)
Superior temporal gyrus (STG)
Component 5
Component 7
Sagittal (y) Frontal/coronal (x) Transverse/Axial (z)
Voxel Component
Insula,STG
Voxel
Subject
Subject
A1
Component
*
A2
C1
C2
Frontal
2. INTERSECTION OF ABS SORTED LOADINGS
A1
A2
3. SUBGROUPS DECIPHERED
AND A TWO SAMPLE TTEST IS
PERFORMED ON THE
CLINICAL SYMPTOMS
109 Szs GMC matrix regressed
of Age, Gender, site voxelwise
with two overlapping biclusters
S1
S2
Sinter
Sorted by Abs value. Gradient shows the
higher magnitude (negative and positive)
loadings pushed up
Rule of thumb: Dissect Subject
number by 4 and do an intersection
of sorted subject names in A1 and
A2
Loading
Matrix
Component
Matrix
Biclustering (B-ICA)
Preliminary Results
 30 subjects from top quarter
 S1 = 16, S2 = 16 , S(inter) = 11 , S(total) = 27
 Scale for the Assessment of Positive Symptoms (SAPS)
 H = 0, p < 0.6875
 Scale for the Assessment of Negative Symptoms (SANS)
 H = 0, p < 0.736
Future Work
 Increase number of subjects  (4 more data sets)
 Integration into Genetics project
Questions
Thank you!

More Related Content

Project1-Final Presentation

  • 1. REPLICATION AND IDENTIFICATION OF CLINICAL SUBGROUPS IN A STRUCTURAL MRI DATASET FOR SCHIZOPHRENIA Dakarai McCoy (PM), Abeer Jihad, Edith Castellanos Sponsors: Drs. Vince Calhoun (MRN), Jessica Turner (GSU) Technical Mentor: Navin Cota
  • 2. Objective To replicate the results obtain by the Mind Research Network of clinical subtyping in Schizophrenia by finding the relationship between the Gray Matter concentration (GMC) and clinical scores.
  • 3. Purpose The success of this research will discover reliable clinical subtype patterns of Gray Matter Concentration (GMC) deficit, which could be targeted in the personalized development of drugs for schizophrenia. Schizophrenia is a chronic brain disorder Delusion Hallucination Disorganized speech Catatonic behavior Negative symptoms
  • 4. Structural Magnetic Resonance Imaging sMRI Body is composed of 70% H2O Spinning charged particle Magnetic moment
  • 5. Hydrogen Atom Randomly oriented with no applied field MRI components Primary magnet Gradient magnets Radiofrequency (RF)coils Computer System
  • 6. Primary Magnetic Field Hydrogen atoms align parallel or antiparallel to the primary field (B0) Precession
  • 7. Gradient Coils Allow spatial encoding for MRI images in the x y, and z axis RF pules Signal received by the RF coil in the transverse plane
  • 8. Computer System White Matter (WM)light gray Cerebrospinal fluid (CSF) black Gray Matter (GM) dark gray
  • 9. Structural Magnetic Resonance Imaging sMRI T1-Weighted
  • 10. Source Based Morphometry T1 VBM ICA GMC WM CSF Source Based Morphometry
  • 11. Independent Component Analysis ICA Maximally independent sources (two groups; Controls and Sz) Column of loadings for a given source map(say Insula) covariation among subjects is captured in loadings. For a given component map (say insula) if there are two groups, if mean of Controls in A1 is greater than mean of Sz in A1; then it means Ct has more GMC than Sz Top Two Components (HC>Sz) from C.N.Gupta et al based on Effect Size, Schizophrenia Bulletin, 2014. Voxel Component Insula,STG Voxel Subject Subject A1 Component * A2 C1 C2 Frontal Component Matrix Ctr and Szs GMC matrix regressed of Age, Gender, site voxelwise Loading Matrix
  • 12. Develop MATLAB code to extract Sz patients from a data set. Data extracted: URSI, site, age, sex, SAPS/SANS. Module 1 Extraction of patients with Schizophrenia Perform multivariable regression of age and gender to make the results more sensitive to group differences (Cota et Al 2009) Module 2 Regression of covariates . Run ICA using SBM toolbox from GIFT Perform B-ICA in the selected components Perform 2 sample t-test Module 3 ICA and Biclustering Method
  • 13. Module 1 Extraction of patients with Schizophrenia 109 Schizophrenia patients from 1st data set Variables extracted: Site, Sex, Age, SAPS/SANS scores
  • 14. Module 2 Regression of covariates Extracted brain images from 109 patients Run regression of covariates [Age, Sex, Site] Smooth selected images Picture of the brain of one subject after regression Picture of the brain of one subject after smoothing
  • 15. Module 3 ICA Used SBM to run ICA for 109 images 30 components. Components selected 5 and 7 Medial frontal gyrus (mFG) Superior frontal gyrus (SFG) Middle fontal gyrus (MFG) Insula (I) Inferior frontal gyrus (IFG) Superior temporal gyrus (STG) Component 5 Component 7 Sagittal (y) Frontal/coronal (x) Transverse/Axial (z)
  • 16. Voxel Component Insula,STG Voxel Subject Subject A1 Component * A2 C1 C2 Frontal 2. INTERSECTION OF ABS SORTED LOADINGS A1 A2 3. SUBGROUPS DECIPHERED AND A TWO SAMPLE TTEST IS PERFORMED ON THE CLINICAL SYMPTOMS 109 Szs GMC matrix regressed of Age, Gender, site voxelwise with two overlapping biclusters S1 S2 Sinter Sorted by Abs value. Gradient shows the higher magnitude (negative and positive) loadings pushed up Rule of thumb: Dissect Subject number by 4 and do an intersection of sorted subject names in A1 and A2 Loading Matrix Component Matrix Biclustering (B-ICA)
  • 17. Preliminary Results 30 subjects from top quarter S1 = 16, S2 = 16 , S(inter) = 11 , S(total) = 27 Scale for the Assessment of Positive Symptoms (SAPS) H = 0, p < 0.6875 Scale for the Assessment of Negative Symptoms (SANS) H = 0, p < 0.736
  • 18. Future Work Increase number of subjects (4 more data sets) Integration into Genetics project

Editor's Notes

  1. Coordinates of the components? http://headneckbrainspine.com/web_flash/newmodules/Brain%20MRI.swf https://www.imaios.com/en/e-Anatomy/Head-and-Neck/Brain-MRI-in-axial-slices http://es.slideshare.net/hytham_nafady/surface-anatomy-of-the-brain?next_slideshow=1