Project1-Final Presentation
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This study aimed to replicate previous research identifying clinical subgroups in schizophrenia patients based on patterns of gray matter concentration (GMC) identified through structural MRI data. The study involved extracting schizophrenia patients from an MRI dataset, regressing out effects of age, sex and site, performing independent component analysis to identify relevant brain networks, and using biclustering analysis to group patients based on their brain imaging data and examine differences in clinical symptoms between the groups. Preliminary results identified two subgroups of patients but found no significant differences between the subgroups in positive or negative symptoms, though the small sample size of 30 patients may have limited the ability to detect differences. Larger samples from additional datasets will be analyzed in future work.
![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](https://image.slidesharecdn.com/385e205c-c9f1-4999-867b-7b46435f2ad8-160825002019/85/Project1-Final-Presentation-14-320.jpg)
![[Research] Detection of MCI using EEG Relative Power + DNN](https://cdn.slidesharecdn.com/ss_thumbnails/20180619a-gisteegaddiagnosisdkimconference-180622113717-thumbnail.jpg?width=560&fit=bounds)
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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
- 19. Questions
- 20. Thank you!
Editor's Notes
- #16: 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