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

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)

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

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

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

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

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

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

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)

The Circuit of Papez (Limbic Lobe) Picture of limbic lobe here AC PC H PHG AT EC S M F 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

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

Large Deformation High Dimensional Brain Mapping High Dimensional Large Deformation Transformation Coarse RegistrationPatientTemplate Landmark-based Low Dimensional Transformation Miller, et al.

Transformation Vector Fields and Shape Change Transformation Template AB C Transformed A B C TemplateTransformed Transformation

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

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

Hippocampal Volume Changes in Early AD From: Csernansky, et al (2000) Neurology 55:

Comparison of CDR 0.5, CDR 0 and Young Controls: Hippocampal Volume and Shape Hippocampus volume (mm 3 ) YoungCDR 0CDR 0.5 LRLRLR VOLUME Group Effect: F = 20.0, df = 2,48, p =.0001 Between Groups F p CDR 0/CDR Young/CDR Young/CDR 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:

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

Shape and Volume: CDR 0 vs Young 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 Log-likelihood ratio CDR 0Young Log-likelihood ratio CDR 0 Young RL Outward, p < 0.05 Inward, p < 0.05 p > 0.05 Rank-order test RL Inward, 1.8mm Outward, 1.8mm YoungCDR 0 [ev1 and ev2]

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. Top View Bottom View Tail

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

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

Pattern of Surface Deformation Over Time Distinguishes Groups In, p <.05Out, p <.05p > mm1 CDR 0.5 CDR 0 From: Wang, et al (2003) NeuroImage 20: ev 1 2, 4, 11 * * * * 15/18 22/26 Baseline to Follow-up

Spreading Deformation of the Hippocampal Surface in Early AD From: Wang, et al (2003) NeuroImage 20: In, p <.05Out, p <.05p > mm1 Follow-up Baseline 38% 47% CDR 0.5 vs CDR 0CDR 0.5 vs CDR 0 rank order test

Progressive Deformation of CA1 and Subiculum in Alzheimer Disease CA1CA2CA3CA4Gyrus DentausSubiculum Baseline Follow-up

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

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)

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:

AC 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 B From: Miller, et al (2003) Proc Natl Acad Sci USA 100: Labeled Cortical Depth Mapping: Automated Tissue Segmentation

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. G CSF W From: Miller, et al (2003) Proc Natl Acad Sci USA 100: Cingulate Surface Labeled Cortical Depth Mapping (LCDM)

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

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

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

Left Posterior Right Posterior Stochastic Ordering AnteriorPosterior LeftRightLeftRight Young Subjects vs CDR 0.5 CDR CDR 0vs CDR 0.5 CDR Cingulate Depths in CDR 1, CDR 0.5, CDR 0 and Young Controls CDF 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

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?)

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

Subcortical Neuroanatomical Abnormalities in Schizophrenia From: Roberts (1990) TINS 13:

Hippocampal Deformities in Schizophrenia Variables (mean +/- SEM [range]) Schizophrenia SubjectsHealthy Controls N5265 Age38.0 (1.74 [20-63])40.0 (1.78 [20-67]) Gender (M/F)30/2233/32 Race (Cau/Afr-Amer/Other)22/30/234/18/0 Parental SES 4.1 (0.12 [2-5])3.6 (0.13 [1.5-5]) Age of Illness Onset22.8 (1.18 [13-54])----- Total SAPS Score19.7 (2.41 [0-67])----- Total SANS Score19.7 (1.76 [0-52])----- From: Csernansky, et al (2002) Am J Psychiatry 159:

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) From: Csernansky, et al (2002) Am J Psychiatry 159: Log-Likelihood Plot 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 F = 2.7, df = 15,101, p =.002 (first fifteen EV) Logistic regression - EV 1, 5, 14 (70.9% classified)

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

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:

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

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) 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. Thalamic Volume (mm 3 ) Schizophrenia Controls Log-likelihood Ratio Values SchizophreniaControls

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

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

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

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.

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

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.