MRI-Based Classification Techniques of Autistic vs. Typically Developing Brain

Transcription

1 MRI-Based Classification Techniques of Autistic vs. Typically Developing Brain Presented by: Rachid Fahmi 1 2 Collaborators: Ayman Elbaz, Aly A. Farag 1, Hossam Hassan 1, and Manuel F. Casanova3 1Computer Vision & Image Processing Lab (CVIP Lab.)/ ECE Dept. 2Bioengineering Department 3 Dept. of Psychiatry and Behavioral Sciences

2 Research Motivation Autism is neuro-developmental disorder Impairments in social interaction, communication Unusual behaviors and interests According to CDC, 1/150 American kids are autistic (4:1 ratio of boys to girls) Challenges: No definitive medical test for diagnosis No reliable cause is identified No cure. BUT: Therapies for specific symptoms

3 Autistic Brain Brain Size Enlarged Brain weight & Head Circumference (first noticed by Kanner in 1943). Normal brain size at birth and rapid expansion by age 2 By age 4 autistic children have brain the size of a typical 13-yrs old The limbic system (Center of emotions) Increased cell packing density and reduced cell size in the following brain regions: hippocampus Subiculum Amygdala Diminished width for pyramidal cell arrays in the neocortex (Casanova et al. 02, 04, 06).

4 Autistic Brain Brainstem Smaller areas of brainstem sub-regions in mentally and non-mentally retarded juvenile young adults individuals with autism relative to controls. Cerebellum (Sensory perceptions integration) Decreased number of Purkinje cell in cerebella hemisphere, vermis, and cerebellum. Enlarged total GM and WM volumes in cerebellum have been reported by well designed controlled MRI studies in autistic children and young adults mostly moderate-tohigh functioning.

5 Corpus Callosum Autistic Brain Increasing agreement from structural imaging studies on the deficits in the size of the Corpus Callosum and its sub-regions in patients with autism. White Matter Several imaging studies (MRI, DTI) reported abnormal anatomy of the WM. Excessive WM in many brain areas. Asymmetry/Increase in the outer radiate compartment of WM (Herbert et al. 04). Reduced Fractional Anisotropy (Keller et al. 06). White Matter Interface

6 Proposed Classification Approaches Taking advantage of the abnormalities of some brain regions in order to devise new techniques that allow the classification of autistic subjects vs. typically developing ones by analyzing their respective brain MR images. Focus on analyzing the white matter (WM) and the corpus callosum (CC). Increasing agreement from structural imaging studies on the abnormal anatomy of the WM in autistic brains (e.g., asymmetry, volume, fractional anisotropy, ). The deficits in the size of the corpus callosum and its sub-regions in patients with autism relative to controls is well established. Two types of MRI data are used: Post-mortem Proton density MRI Pre-mortem T1- weighted MRI Data set size: 256x256x124 Data Resolution: x0.9375x1.5

7 White Matter Analysis Fixed data Moving data Data Alignment Moving data

8 Preprocessing of postmortem data sets Due to distortions, post-mortem data sets are segmented slice by slice using our level set based approach. Stack of Images Whole 3D Head Model WM Visualization 2D Slice Extracted WM (Red)/GM (Blue) Binary Representation of WM

9 Preprocessing of Pre-mortem Data Sets Each data set is smoothed, skull stripped and then segmented into WM, GM, and CSF. Original slice Smoothed Skull Stripped Segmentation into WM, GM, and CSF

10 Segmented WM for Control and Autistic brains Autistic Control

11 Distance Map inside the WM We used the Fast Marching Level Sets to compute the 3D distance map inside the WM

12 Example Of Generated Distance Maps

13 Training Samples Cumulative distributions of the distance map inside the WM of a training set from each group are computed, normalized, and averaged.

14 Classification Procedure Given a test subject (green curves): Compute and normalize the DM inside its WM. Compute corresponding CDF and compare it to the two averaged CDF s using the Levy distance.

15 Total Classification Results Accuracy of classification based on WM shape analysis Confidence rate Autistic Control Post-mortem Pre-mortem Post-mortem Pre-mortem 85% 14 /14 13/15 12/12 30/30 90% 13/14 12/15 12/12 30/30 95% 13/14 11/15 11/12 28/30

16 New Non-rigid Registration method (EMBC 06) New feature descriptors are built as voxel signatures using scale space theory. The global motion of the imaged object is modeled by matching these descriptors. Iso-surface evolved in target image to match those of source image in four steps Generate the distance map inside of the imaged organ Use this distance map to generate iso-surfaces Number to be set by user Not necessarily the same for both volumes Find Correspondences between iso-surfaces Evolve iso-surfaces in volume A to match those in volume B

17 Example: 3D Registration of brain MRI s Before Registration Rigid Alignment Non-rigid Alignment

18 Corpus Callosum Analysis Only the pre-mortem data is considered. Corpus callosum is manually segmented from the segmented brain date sets. Illustration of the proposed non-rigid registration approach.

19 Training Sample For each group: Choose a reference within a training set. Register the rest to the reference. Compute CDF of the corresponding deformation fields. Average the deformation fields.

20 The CC-registration based classification approach

21 Classification Performance Accuracy of classification based on CC analysis Confidence level Autistic Control 85 % 10/15 29/30 90 % 9/15 29/30 95 % 7/15 25/30 Combination of the two classification approaches Confidence level Autistic Control 85 % 14/15 30/30 90 % 14/15 30/30 95 % 13/15 30/30

22 Conclusions & Future Directions Two new neuroimaging-based classification techniques have been proposed to discriminate between autistic and typically developing brains. Our methods are based on shape & geometrical models and then overcome the shortcomings of the traditional volumetric approaches. Methods tested on post and pre-mortem MRI s and show great performances. More brain structures and other image modalities will be considered in future work. Part of this work is supported by the NSF This work is part of a joint R01 submitted to the NIH with Dr. M. F. Casanova and other collaborators.

23 Some References L. Kanner, Autistic disturbances of affective contact. Nervous Child, vol. 2, pp , R. Courchesne, R. Carper, and N. Akshoomoff, Evidence of brain overgrowth in the first year of life in autism. JAMA, vol. 290, pp , E. Courchesne et al. Unusual brain growth patterns in early life in patients with autistic disorder: an mri study. Neurology, vol. 57, no. 2, pp , C. Vidal et al. Mapping corpus callosum deficits in autism: An index of aberrant cortical connectivity. Biol. Psychiatry., vol. 60, no. 3, pp , A. A. Farag and H. S. Hassan, Adaptive segmentation of multi-modal 3d data using robust level set techniques. in (MICCAI 04), Saint-Malo, France, pp R. Fahmi et al. New deformable registration technique using scale space and curve evolution theory and a finite element based validation framework. IEEE/EMBC 06. M. F. Casanova et al. Minicolumnar abnormalities in autism. Acta Neuropathological, 2006 in press (Available online). J.E. Lainhart et al. The brain during life in autism: advances in neuroimaging research. In Casanova, MF editor. Recent Advances in Autism Research, New York: NOVA Biomedical, p , M. Herbert et al. Localization of white matter volume increase in autism and developmental language disorder. Ann. Neurol., vol. 55, pp , 2004.

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