[KHBM] Application of network analysis based on cortical thickness to obsessive-compulsive disorder patients
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This was presented at The Biannual Meeting of Korean Society of Human Brain Mapping (KHBM), Seoul, Korea (Nov 2011). It was selected for the Excellent Oral Award.
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[KHBM] Application of network analysis based on cortical thickness to obsessive-compulsive disorder patients
- 1. BRAIN NETWORK ANALYSIS BASED ON CORTICAL THICKNESS AND ITS APPLICATION TO PATIENTS WITH OBSESSIVE-COMPULSIVE DISORDER @ KHBM, 2011. 11. 04 김승구 sgKIM Department of Brain and Cognitive Sciences, Seoul National University.
- 2. ACKNOWLEDGEMENT • 정무경 Moo K. CHUNG @ U WISC, SNU • 정휘훈, 장준환, 권준수 @ SNU, SNUH
- 3. INTRODUCTION PREVIOUS NETWORK STUDIES ON OCD
- 4. OBSESSIVE-COMPULSIVE DISORDER (OCD) • Obsessive–compulsive disorder (OCD) is an anxiety disorder characterized by intrusive, troubling thoughts or repetitive, compulsive behaviors perceived as the products of one’s own mind (American Psychiatric Association, 1994). • FRONTAL-SUBCORTICAL CIRCUITRY DYSFUNCTION : unable to modulate impulsive behaviors (Saxena, 1998) • However, hypothesis-free analysis rarely has been done. Actually, he seems to be with OCPD
- 5. A WHOLE-BRAIN NETWORK OCD controls Zhang et al. (2011) J Psychiatry Neurosci
- 6. A WHOLE-BRAIN NETWORK Zhang et al. (2011) J Psychiatry Neurosci
- 7. LOCALLY DEFINED ANATOMICAL MEASURE Slide from FreeSurfer’s workshop
- 8. CORRELATION OF THICKNESS • What’s with the correlation in anatomical measures?
- 9. CORRELATION OF THICKNESS • What’s with the correlation in anatomical measures? i j subject 1
- 10. CORRELATION OF THICKNESS • What’s with the correlation in anatomical measures? i j i j subject 1 subject 2
- 11. CORRELATION OF THICKNESS • What’s with the correlation in anatomical measures? i j i j i j ··· subject 1 subject 2 subject 3
- 12. CORRELATION OF THICKNESS • What’s with the correlation in anatomical measures? i j i j i j ··· subject 1 subject 2 subject 3
- 13. PREVIOUS THICKNESS-BASED NETWORK STUDIES He et al. (2007)
- 14. PREVIOUS THICKNESS-BASED NETWORK STUDIES He et al. (2007) Cerebral Cortex FDR q=0.05 He et al. (2007)
- 15. PREVIOUS THICKNESS-BASED NETWORK STUDIES executive auditory/language ventral visual pathway He et al. (2007) Cerebral Cortex FDR q=0.05 default mode sensorimotor/visuospatial Chen etet al. (2007) He al. (2011) NI
- 16. MOTIVATION • Structuralnetwork over whole brain in patients with OCD is still unknown. • Corticalthickness-based network studies has been gained attention. • To investigate the structural brain network based on cortical thickness is the motivation of this study.
- 17. METHOD CORTICAL THICKNESS-BASED NETWORK CONSTRUCTION USING FreeSurferTM
- 18. SUBJECTS & IMAGES • T1-weighted MRIs from 35 healthy control (HC) subjects and 32 obsessive-compulsive disorder (OCD) patients. • Mean age: 23.94 ± 3.60 (HC); 24.81 ± 6.41(OCD) • Gender: 24 M + 11 F (HC); 21 M + 11 F (OCD) • All 32 OCDs were drug-free: 23 patients were drug-naive and the other 9 patients were unmedicated by the scanning. • All right-handed.
- 19. Naive Surface
- 20. Naive Surface
- 21. Naive Surface RESAMPLING based on curvature:
- 22. Naive Surface RESAMPLING based on curvature: Template Surface 40962-vertices per hemisphere
- 23. HEAT KERNEL SMOOTHING 1 X - K ⇤ Y(p) = e j j (p) j=0 Implemented using cotan discretization (Seo, 2010, MICCAI)
- 24. NAIVE SPACES
- 25. NAIVE SPACES TEMPLATE (FSAVG6) SPACES
- 26. NAIVE SPACES HEAT KERNEL SMOOTHING with FWHM=10mm
- 27. THICKNESS COMPARISON thicness = 0 + 1 · age + 2 · gender + 3 · diagnosis + noise • Testing the significance of diagnosis (group effect). • Multiplecomparison correction by random field theory using SurfStat MATLAB toolbox. • You can download SurfStat from: http://galton.uchicago.edu/ faculty/InMemoriam/worsley/research/surfstat/index.htm
- 28. Regions-of-interest (ROIs) as nodes • Total 148 ROIs chosen excepting “medial wall” and “unknown” for each hemisphere. Destrieux et al. (2009) OHBM
- 29. Regions-of-interest (ROIs) as nodes • Total 148 ROIs chosen excepting “medial wall” and “unknown” for each hemisphere. Destrieux et al. (2009) OHBM Centroids as node positions
- 30. PARTIAL CORRELATION • Tocompute partial correlation controlling age and gender, we fit a GLM and use residual for [cij ] 2 R148⇥148 residual = thickness - ( c + c · age + c · gender) 0 1 2 cij = CORR(residuali , residualj )
- 31. STATISTICAL INFERENCES • Local inference on correlations: OCD vs. HC OCD HC Z(cij - cij ) >h where Z is Fisher transform, h is FDR threshold with q=0.01 • Global inference on undirected, unweighted graphs : Binarization for adjacency matrix [aij ] 2 R148⇥148 aij = 1 if Z(cij ) > h, otherwise aij = 0
- 32. RESULTS THICKNESS ANALYSIS AND NETWORK ANALYSIS
- 33. THICKNESS COMPARISON max |T|= 3.92 min P= 0.58
- 34. THICKNESS COMPARISON OCD-HC max |T| = 4.47 max |T|= 3.92 Shin et al. (2007) HBM min P= 0.58
- 35. LOCAL INFERENCE significance at FDR q=0.01 OCD > HC: 124 pairs HC > OCD: 32 pairs
- 36. OCD > HC: 124 pairs HC > OCD: 32 pairs Degree Degree
- 37. GLOBAL INFERENCE
- 38. GLOBAL INFERENCE Threshold @ FDR=0.01 HC OCD LH LH RH RH LH RH LH RH ADJACENCY MATRIX
- 39. Degree Degree
- 40. Degree Degree 0.25 Normalized Frequency HC (p<0.001) 0.2 OCD (p<0.001) HC (p>0.05) 0.15 OCD (p>0.05) 0.1 p-values computed via 10,000 permutations 0.05 0 0 2 4 6 8 10 12 Degree
- 41. TM MATLAB DEMO THRESHOLD-VARYING NETWORKS
- 42. CONCLUSION • We did NOT find any group difference by univariate tests on cortical thickness between OCD and HC. • However, weDID find significantly different correlations from number of pairs of nodes. • We also found differences in degree distributions. • Threshold-free investigation such as graph-filtration is needed as a further study.
Editor's Notes
- Hello, my name is Seung-Goo Kim.\nI&#x2019;m a PhD student at the department of Brain and Cognitive Sciences, SNU.\nand I&#x2019;ve been working with Moo.\n\nToday, I&#x2019;d like to talk about a brain network analysis based on cortical thickness,\nand its application to patients with obsessive-compulsive disorder.\n\nThe bottom line is that, this is a very simple analysis, and more like a preliminary result, so I hope many feedbacks from you.\n
- These are my colleagues.\n\nMoo Chung formulated and implemented heat-kernel smoothing based on Laplace-Beltrami eigenfunction.\n\nWi Hoon JUNG, Joon Hwan JANG, Jun Soo KWON collected patients&#x2019; data and did the processing with FreeSurfer.\n
- Now, I&#x2019;m going to explain our application on OCD.\n
- Obsessive-compulsive disorder is defined by obsessive thoughts or acts and/or compulsive behaviors.\n
- But, quite interestingly, this is the only one study I could find, that examines global characteristic of brain network in OCD. These networks are also based on fMRI.\n\n//\n\nThese are gamma and lambda for various graph densities.\n\n//\n\nOverall densities, OCD shows higher gamma, relative clustering coefficients.\n
- As I explained earlier, we quantifies the cortical thickness by the distance between two surfaces.\n
- To compute correlation matrix, factoring out confounding effect of age and gender,\nwe correlated residuals of such a linear model.\n\nLocal inference is made simply using Fisher&#x2019;s transformation and false discovery rate of .01.\n\nTo get a undirected, unweighted graph, we thresholded each positive correlation matrix at FDR .01.\n
- To compute correlation matrix, factoring out confounding effect of age and gender,\nwe correlated residuals of such a linear model.\n\nLocal inference is made simply using Fisher&#x2019;s transformation and false discovery rate of .01.\n\nTo get a undirected, unweighted graph, we thresholded each positive correlation matrix at FDR .01.\n
- To compute correlation matrix, factoring out confounding effect of age and gender,\nwe correlated residuals of such a linear model.\n\nLocal inference is made simply using Fisher&#x2019;s transformation and false discovery rate of .01.\n\nTo get a undirected, unweighted graph, we thresholded each positive correlation matrix at FDR .01.\n
- To compute correlation matrix, factoring out confounding effect of age and gender,\nwe correlated residuals of such a linear model.\n\nLocal inference is made simply using Fisher&#x2019;s transformation and false discovery rate of .01.\n\nTo get a undirected, unweighted graph, we thresholded each positive correlation matrix at FDR .01.\n
- To compute correlation matrix, factoring out confounding effect of age and gender,\nwe correlated residuals of such a linear model.\n\nLocal inference is made simply using Fisher&#x2019;s transformation and false discovery rate of .01.\n\nTo get a undirected, unweighted graph, we thresholded each positive correlation matrix at FDR .01.\n
- To compute correlation matrix, factoring out confounding effect of age and gender,\nwe correlated residuals of such a linear model.\n\nLocal inference is made simply using Fisher&#x2019;s transformation and false discovery rate of .01.\n\nTo get a undirected, unweighted graph, we thresholded each positive correlation matrix at FDR .01.\n
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- Then the motivation of this study is to learn about global signature of structural network in OCD.\n
- Now, let me show you how we did this application.\n
- We used T1-weighted MRIs of 35 controls and 32 OCD patients.\n\nAge, gender, handness are matched.\n\nAll OCD patients were not taking drugs by the time of scanning.\n
- Once you measure thickness in the original space, \n//\nyou can map it to a corresponding sphere.\n//\nThen by the curvature-mapping, it can be resampled onto another sphere,\n//\nthat corresponds to template surface.\n\n\n
- Once you measure thickness in the original space, \n//\nyou can map it to a corresponding sphere.\n//\nThen by the curvature-mapping, it can be resampled onto another sphere,\n//\nthat corresponds to template surface.\n\n\n
- Once you measure thickness in the original space, \n//\nyou can map it to a corresponding sphere.\n//\nThen by the curvature-mapping, it can be resampled onto another sphere,\n//\nthat corresponds to template surface.\n\n\n
- Once you measure thickness in the original space, \n//\nyou can map it to a corresponding sphere.\n//\nThen by the curvature-mapping, it can be resampled onto another sphere,\n//\nthat corresponds to template surface.\n\n\n
- Then to increase signal to noise ratio and statistical power, we apply heat kernel smoothing.\n\nThis smoothing technique is based on Laplace-Beltrami eigenfunction, which forms orthonormal basis on an arbitrary surface.\n\nShown here as Psy is a basis function over cortical surface. By multiplying exponential term, we can get a Kernel convoluted measure as a sum of weighted Fourier series.\n
- By doing that, \n\\\\\nyou can map individual measures onto a common surface.\n
- By doing that, \n\\\\\nyou can map individual measures onto a common surface.\n
- By doing that, \n\\\\\nyou can map individual measures onto a common surface.\n
- The smoothed thickness on each vertex is linearly modeled, and computed t-statistics with a contrast on the diagnosis term.\n\nMultiple comparison correction is made by random field theory using surfstat matlab toolbox.\n\n\n
- Then using a ROI atlas available in FreeSurer,\nwe averaged measure into 148 ROIs.\n\\\\\nFor visualization, centriods within each ROI are used as nodes.\n
- To compute correlation matrix, factoring out confounding effect of age and gender,\nwe correlated residuals of such a linear model.\n\nLocal inference is made simply using Fisher&#x2019;s transformation and false discovery rate of .01.\n\nTo get a undirected, unweighted graph, we thresholded each positive correlation matrix at FDR .01.\n
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- Now, I present the result with an univariate analysis for comparison.\n
- absolute diff=0.2 mm\nt-test without covariates: p=0.65\n\nShin: without covariates.\n
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- The spatial dispositions of the previous network is like this.\n\\\\\nAnd the degree distribution is like this.\nActually we found significant differences at degree 1, 4 and others using ten thousand permutations.\n\nWe can see more densely connected nodes in OCD than in health controls.\n
- Now I&#x2019;ll show you changing networks by the varying threshold.\n
- I think it is a quite interesting result, because it shows that we can find the significant differences from the network analysis, even when we cannot find any differences in the univariate analysis.\n\nActually it is a quite early step of study so far. What we did is fairly simple, so we are planning to examine further aspects of the networks in future.\n\nAlso I&#x2019;d like to mention that this dataset has concurrent DTI measures. So it is a good chance to directly compare the thickness-based network to the DTI-based network.\n
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