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John G. Csernansky, M.D. Washington University School of Medicine

Computational Anatomy and Neuropsychiatric Disease Probabilistic Assessment of Variation and Statistical Inference of Group Difference, Hemispheric Asymmetry, and Time-Dependent Change. John G. Csernansky, M.D. Washington University School of Medicine.

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John G. Csernansky, M.D. Washington University School of Medicine

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  1. Computational Anatomy and Neuropsychiatric DiseaseProbabilistic Assessment of Variation and Statistical Inference of Group Difference, Hemispheric Asymmetry, and Time-Dependent Change John G. Csernansky, M.D. Washington University School of Medicine

  2. Rationale for Assessing Neuroanatomy as a Disease Biomarker • Neuroanatomical changes are characteristic of neuropsychiatric diseases and may be discoverable before clinical symptoms occur (preclinical diagnosis) • Ongoing changes in neuroanatomy may occur during the disease process and may be modified by treatment (monitoring of treatment response)

  3. Challenges in Assessing Neuroanatomy as a Disease Biomarkers • Small sample sizes • Normative variability (age, gender, etc.) • Disease heterogeneity • Abnormalities may be specific a particular stages of illness

  4. Approaches to Hypothesis Testing: Using a ROI Approach • Group comparisons of individual structures - volumes and shapes • Group comparisons of the relationship between structures - hemispheric asymmetries • Group comparisons of the rate of change in the volume and shape of structures over time

  5. Rationale for Using a ROI Approach • Problems encountered in structural analysis may be region specific • Different regions may have different tissue characteristics and be susceptible to different sources of measurement error • Hypothesis generation versus hypothesis testing - taking advantage of prior knowledge about a disease

  6. Dementia of the Alzheimer Type (DAT) Presymptomatic Clinical Dementia CDR 0.5 CDR 1 CDR 2 CDR 3 NeuropsychologicalFunctional Status Threshold for Clinical Detection Progression AD Disease Process Time (years) Adapted from: Daffner & Scinto, 2000

  7. Distribution of Neuropathology in Alzheimer Disease is Not Uniform From: Arnold SE, et al. (1991) Cerebral Cortex 1:103-116.

  8. Structure/Function Relationships in DAT Subjects In patients with very mild DAT (MMSE = 25, N = 8), glucose metabolism (18F-FDG uptake) is reduced in the lateral medial cerebral cortex. From: Minoshima, et al (1997) Ann Neurol 42:85-94.

  9. Group Comparisons of Individual Structures in DAT Subjects • Hippocampus (subcortical gray matter structure - volume enclosed by a single surface) • Cingulate gyrus (cortical mantle structure - subregion of gray matter layered between CSF and white matter)

  10. The Circuit of Papez (Limbic Lobe) AC PC 23 24 Picture of limbic lobe here 32 F H AT PHG M S EC • Cingulate efferents (from 32 and 23) project to the entorhinal cortex and subiculum • Hippocampal efferents project to the anterior thalamic nucleus and mammillary body • Afferents from the anterior thalamic nucleus project throughout the cingulate gyrus • From: Nieuwenhuys, Voogd and Huijzen (1998) The Human Central Nervous System, Springer-Verlag

  11. Conventional Neuromorphometry: Manual Segmentation • Labor intensive • Difficult to maintain reliability • Difficult to share neuroanatomical knowledge across sites • Overemphasis on simple measures (volumes) R L

  12. Large Deformation High Dimensional Brain Mapping Template Coarse Registration Patient Landmark-based Low Dimensional Transformation High Dimensional Large Deformation Transformation Miller, et al.

  13. Transformation Vector Fields and Shape Change C C A B B A Transformation Template Transformed Transformation Template Transformed

  14. Eigenvectors Derived from Vector Fields Using Singular Value Decomposition • Latent variables representing dimensions of shape variation within a population • Use first n eigenvectors and MANOVA to test basic “shape” hypothesis • Logistic regression is used to select most informative eigenvectors, and a leave-one-out analysis to test power of classification

  15. Selecting Brain Regions to Look for Early Changes in Alzheimer Disease • Hippocampus (CA1 and subiculum) • Cingulate gyrus (posterior > anterior)

  16. Hippocampal Volume Changes in Early AD From: Csernansky, et al (2000) Neurology 55:1636-1643.

  17. Comparison of CDR 0.5, CDR 0 and Young Controls: Hippocampal Volume and Shape 4000 3500 3000 2500 2000 1500 1000 Hippocampus volume (mm3) L R L R L R Young CDR 0 CDR 0.5 VOLUME Group Effect: F = 20.0, df = 2,48, p = .0001 Between Groups F p CDR 0/CDR 0.5 19.4 .0001 Young/CDR 0.5 37.1 .0001 Young/CDR 0 3.6 .065 SHAPE MANOVA (first five EVs) F = 40.8, df = 10,88, p < .0001 SHAPE + VOLUME MANOVA (vols + first 5 EVs) F = 28.6, df = 14,84, p < .0001 From: Csernansky, et al (2000) Neurology 55:1636-1643.

  18. Shape and Volume: CDR 0 vs CDR 0.5 Outward, p < 0.05 p > 0.05 Inward, p < 0.05 Log-likelihood ratio Log-likelihood ratio CDR 0 CDR 0.5 CDR 0 CDR 0.5 CDR 0 CDR 0.5 Rank-order test [ev1 and ev5] Outward, 1.8mm R L R L Inward, 1.8mm Shape Alone, Logistic Regression: EVs 1 and 5 CDR 0.5 12/18 CDR 0 14/18 Shape + Volume, Logistic Regression: Left and Right volumes + EV 5 CDR 0.5 15/18 CDR 0 14/18

  19. Shape and Volume: CDR 0 vs Young Outward, p < 0.05 p > 0.05 Inward, p < 0.05 Log-likelihood ratio Log-likelihood ratio CDR 0 Young CDR 0 Young Young CDR 0 Rank-order test [ev1 and ev2] Outward, 1.8mm R L R L Inward, 1.8mm Shape Alone, Logistic Regression: EVs 1 and 2 CDR 0 18/18 Young 15/15 Shape + Volume, Logistic Regression: Left and Right volumes + EVs 1 and 2 CDR 0 18/18 Young 15/15

  20. Top View Bottom View Tail Shape Change May Reflect Changes in Internal Structure of the Hippocampus Henri M. Duvernoy (1988) The Human Hippocampus: An Atlas of Applied Anatomy, Springer-Verlag, New York.

  21. Group Comparison of Rate of Change in Hippocampal Volume and Shape From: Wang, et al (2003) NeuroImage 20:667-682.

  22. Progression of Hippocampal Volume Loss in Early AD (CDR 0.5) Groups Change in Hippocampal Volume (~ two years) CDR 0.5 Left 8.7 % Right 9.8 % Group Effect CDR 0 Left 3.9 % Right 5.5 % F = 7.81, p = .0078 From: Wang, et al (2003) NeuroImage 20:667-682.

  23. Pattern of Surface Deformation Over Time Distinguishes Groups In, p < .05 p > .05 Out, p < .05 -1 0mm 1 Baseline to Follow-up * * * * CDR 0.5 CDR 0 15/18 22/26 ev 1 2, 4, 11 From: Wang, et al (2003) NeuroImage 20:667-682.

  24. Spreading Deformation of the Hippocampal Surface in Early AD In, p < .05 p > .05 Out, p < .05 -1 0mm 1 38% Follow-up Baseline CDR 0.5 vs CDR 0 CDR 0.5 vs CDR 0 rank order test 47% From: Wang, et al (2003) NeuroImage 20:667-682.

  25. Progressive Deformation of CA1 and Subiculum in Alzheimer Disease CA1 CA2 CA3 CA4 Gyrus Dentaus Subiculum Baseline Follow-up

  26. Selecting Brain Regions to Look for Early Changes in Alzheimer Disease • Hippocampus (CA1 and subiculum) • Cingulate gyrus (posterior > anterior)

  27. Methodological Challenges in the Assessment of Cortical Structures • Segmentation of tissue subtypes (gray, white and mixed) • Definition of a reference surface (gray/CSF vs gray/white) • Definition of boundaries with neighboring cortical regions (gross anatomy, histology, function) • Definition and calculation of distinct metrics (volume, thickness, surface area)

  28. Labeled Cortical Depth Mapping: Outlining the Structure in a Template Scan Manual outlining is used as a basis for the validation of Bayesian (automated) segmentation. Ten brains were manually segmented (cingulate region) into three compartments: CSF, Gray, and White. These hand segmentations were used to determine optimal thresholds for partial volume compartments (CSF/Gray and Gray/White). From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.

  29. Labeled Cortical Depth Mapping: Automated Tissue Segmentation A B C A Original T-1 weighted, MR image of anterior cingulate gyrus (coronal view) B Tissue histogram generated by Bayesian segmentation (5 compartments) - selection of optimal G/W matter threshold guided by results of expert segmentation C Tissue segmentation overlaid on MR image From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.

  30. Labeled Cortical Depth Mapping (LCDM) Cingulate Surface CSF W G The gray-white surface is generated from the automatic tissue segmentation and then the boundaries of the desired cortical region are determined. The extent of gray matter is estimated using the conditional probabilities of the occurrence of the gray matter tissue type as a function of distance from the gray-white surface. From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.

  31. LCDM: Generating Metrics Related to Volume and Depth 1 .9x d’ 0 Distance from cortical surface Cumulative probability Depth (thickness) Volume Number of voxels Distance from cortical surface Gray matter profile From: Miller, et al (2003) Proc Natl Acad Sci USA 100:15172-15177.

  32. Validity of Cortical Depth Mapping Gray White CSF Agreement between surfaces derived from automated segmentations and hand contouring in 3 subjects: 75% of all voxels are within 0.5 mm

  33. Cingulate Volumes in CDR 1, CDR 0.5, CDR 0 and Young Controls Left Right VOLUME Anterior/Left YC ~ 0 ~ 0.5 > 1 Anterior/Right YC ~ 0 > 0.5 ~ 1 Posterior/Left YC ~ 0 > 0.5 ~ 1 Posterior/Right YC ~ 0 > 0.5 ~ 1 F=1.22, df=3,33, p=.32 F=3.68, p=.02 * + Anterior F=7.10, p=.0008 F=4.92, p=.0006 * * + Between-group comparisons vs Young Controls: * p < .05 + p < .01 + Posterior

  34. Cingulate Depths in CDR 1, CDR 0.5, CDR 0 and Young Controls DEPTH Anterior/Left YC (~ 0 ~ 0.5) > 1 Anterior/Right YC ~ 0 (~ 0.5) > 1 Posterior/Left YC ~ 0 (~ 0.5) > 1 Posterior/Right YC ~ 0 (~ 0.5) > 1 CDF Left Posterior CDF Right Posterior

  35. Summary of Findings in AD • Hippocampus - Smaller volumes and patterns of shape deformation consistent with damage to the CA1 subfield are present in very mildly demented subjects and progress in parallel with the worsening of dementia. Little change with healthy aging. • Cingulate gyrus (posterior/anterior)- Smaller volumes and thinning are present in mildly demented subjects. Little change with healthy aging. Volume loss may precede thinning (shrinkage of surface area?)

  36. Analysis of Neuroanatomical Structure in Schizophrenia • Group comparisons of individual structures • Analysis of structural asymmetries • Combining information from more than one brain structure

  37. Subcortical Neuroanatomical Abnormalities in Schizophrenia From: Roberts (1990) TINS 13:207-211

  38. Hippocampal Deformities in Schizophrenia Variables (mean +/- SEM [range]) Schizophrenia SubjectsHealthy Controls N 52 65 Age 38.0 (1.74 [20-63]) 40.0 (1.78 [20-67]) Gender (M/F) 30/22 33/32 Race (Cau/Afr-Amer/Other) 22/30/2 34/18/0 Parental SES 4.1 (0.12 [2-5]) 3.6 (0.13 [1.5-5]) Age of Illness Onset 22.8 (1.18 [13-54]) ----- Total SAPS Score 19.7 (2.41 [0-67]) ----- Total SANS Score 19.7 (1.76 [0-52]) ----- From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006

  39. Hippocampal Volume and Shape in Schizophrenia Volume Scatter Plots F = 7.9, df = 1,115, p = .006 F = 2.5, df = 1,114, p = .12 (covaried for total brain volume) Log-Likelihood Plot F = 2.7, df = 15,101, p = .002 (first fifteen EV) Logistic regression - EV 1, 5, 14 (70.9% classified) No correlations were observed between hippocampal volume or shape changes and clinical measures in the subjects with schizophrenia; hippocampal volume was correlated with general intelligence in both schizophrenia and control subjects From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006

  40. Pattern of Hippocampal Shape Deformity Outward +1.4mm -1.4mm Inward Positive +0.3 -0.3 Negative Top View R L Difference Mapped on Mean Control Reconstructed from the Eigenvector Solution From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006 Z-Scores Mapped on Mean Control

  41. Topography of Hippocampal Projections to the Frontal Cortex Summary diagram showing the relative density of labeled neurons in the hippocampal formation projecting to medial (A) and to orbital (B) prefrontal cortices. Each small symbol represents two neurons. Each large symbol represents 40 neurons. From: Barbas and Blatt (1995) Hippocampus 5:511-533

  42. Exaggerated Hippocampal Asymmetry +1.5 +1.5 0 mm 0 mm -1.5 -1.5 Point-by-Point Maps Eigenvector Maps Control Schizophrenia Group Difference From: Csernansky, et al (2002) Am J Psychiatry 159:2000-2006

  43. Thalamic Volume and Shape in Schizophrenia Volume Scatter Plots F = 6.6, df = 1,115, p = .011 F = 1.3, df = 1,114, p = .26 (covaried for total brain volume) Thalamic Volume (mm3) Log-likelihood Ratio Values Schizophrenia Controls Schizophrenia Controls Shape (log-likelihood) F = 2.8, df = 10,106, p = .004 (first ten EV) Logistic regression - EV 1, 8, 10 (66.7% classified) Correlations were observed between hippocampal volume and shape changes and a measure of visual spatial memory in the subjects with schizophrenia From: Csernansky, et al (2003) Am J Psychiatry In press.

  44. Pattern of Thalamic Shape Deformity Anterior View Posterior View S S R L L R I I Superior View 0.5 P R L 0.0 Magnitude of Displacement (mm) A -0.5 B S – superior I – inferior A – anterior P – posterior R – right L – left From: Csernansky, et al (2003) Am J Psychiatry In press.

  45. S Anterior Ventral Anterior Ventral Lateral P A Dorsal Lateral Ventral Posterior Lateral I Pulvinar S Dorsal Medial Central Medial Ventral Posterior Medial A P Lateral Geniculate Medial Geniculate I Nuclei Within the Human Thalamic Complex Lateral View Medial View

  46. Exaggerated Thalamic Asymmetry 1.5 1.5 0.0 0.0 -1.5 -1.5 S A P I Point-by-Point Maps Eigenvector Maps Left Thalamus Right Thalamus Control Schizophrenia Group Difference From: Csernansky, et al (2003) Am J Psychiatry In press.

  47. Improving Subject Classification by Combining Shape Information Combined assessment - sensitivity = 73%, specificity = 83% Evidence for neuroanatomical heterogeneity in schizophrenia ? From: Csernansky, et al (2003) Am J Psychiatry In press.

  48. Acknowledgments Collaborators Support Deanna Barch, Ph.D. MH 62130/071616 (Conte) C. Robert Cloninger, M.D. MH 56584 J. Philip Miller MH 60883 Paul A. Thompson, Ph.D. NARSAD John C. Morris, M.D. AHAF Lei Wang, Ph.D. AG 05681 (ADRC) Thomas Conturo, M.D. AG 03991 Mokhtar Gado, M.D. Michael I. Miller, Ph.D. (JHU) Tilak Ratnanather, Ph.D. (JHU) Sarang Joshi, D.Sc. (UNC)

  49. Computational Neuroanatomy Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni G, Thompson PM. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet: Neurology 2:79-88, 2003.

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