A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center &

Slides:

Advertisements

Similar presentations

Corpus Callosum Damage Predicts Disability Progression and Cognitive Dysfunction in Primary-Progressive MS After Five Years.

Advertisements

National Alliance for Medical Image Computing Slide 1 NAMIC at UNC DTI, Shape and Longitudinal registration Closely linked with Utah.

National Alliance for Medical Image Computing Core DBP Dartmouth Data NA-MIC National Alliance for Medical Image Computing

Quality Control of Diffusion Weighted Images

H Jeremy Bockholt Ronald Pierson Vincent Magnotta Nancy C Andreasen The BRAINS2 Morphometry pipeline in action BRAINS2/Slicer Workshop.

The Microstructural Basis of Abnormal Connectivity in Autism Janet Lainhart, MD Associate Professor of Psychiatry, Pediatrics, Psychology, and Faculty.

Assessing Early Brain Development in Neonates by Segmentation of High-Resolution 3T MRI 1,2 G Gerig, 2 M Prastawa, 3 W Lin, 1 John Gilmore Departments.

New quantitative analysis of high-field 3T MRI/DTI to assess neonatal brain development 1,2 G Gerig, 2 Pierre Fillard, 2 M Prastawa, 3 W Lin, 1 John Gilmore,

Sponsor: Prof. Sidney Spector Computational anatomy to assess growth pattern of early brain development in healthy and disease populations Guido Gerig.

The structural organization of the Brain Gray matter: nerve cell bodies (neurons), glial cells, capillaries, and short nerve cell extensions (axons and.

Preliminary Results Longitudinal Change Conclusions Acknowledgements The most striking result of the longitudinal growth analysis between 2 and 4 years.

NAMIC: UNC – PNL collaboration- 1 - October 7, 2005 Quantitative Analysis of Diffusion Tensor Measurements along White Matter Tracts Postdoctoral fellow,

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics Hongtu Zhu, Ph.D. Department of Biostatistics.

IST8A Fall 2008 Introduction to the Brain. Outline of Topics 1.Imaging: postmortem and MRI 2.Brain Macro anatomy – lobes, tissues, cortex, hippocampus,

MNTP Trainee: Georgina Vinyes Junque, Chi Hun Kim Prof. James T. Becker Cyrus Raji, Leonid Teverovskiy, and Robert Tamburo.

Global and Regional Brain Morphology in Subjects with Huntington’s Disease Prior to Diagnosis Peg C. Nopoulos 1,2,4, Hans J. Johnson 1, Vincent A. Magnotta.

Automatic Brain Segmentation in Rhesus Monkeys February 2006, SPIE Medical Imaging 2006 Funding provided by UNC Neurodevelopmental Disorders Research Center.

NA-MIC National Alliance for Medical Image Computing UNC Medical Image Analysis Group.

Sackler Institute for Developmental Psychobiology Weill Medical College of Cornell University.

NA-MIC National Alliance for Medical Image Computing Cortical Thickness Analysis Delphine Ribes (Internship UNC 2005/2006) Guido Gerig.

NA-MIC National Alliance for Medical Image Computing Cortical Thickness Analysis with Slicer Martin Styner UNC - Departments of Computer.

NA-MIC National Alliance for Medical Image Computing Core 1 & Core 3 Projects.

Regions of the Brain  Cerebral hemispheres  Diencephalon  Brain stem  Cerebellum.

Thank you: NIMH, NINDS and NICHD Simons Foundation Autism Speaks Cure Autism Now NICHD Brain Bank ATP/Harvard Brain Bank Taran Sierra.

A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center &

Early Stimulant Exposure Impact on Later Substance Abuse: A Longitudinal Study F. Xavier Castellanos Rachel Klein Sal Mannuzza John Moulton NYU Child Study.

Early Brain Development in Normal and High Risk Children John H. Gilmore, MD Department of Psychiatry The University of North Carolina.

NA-MIC National Alliance for Medical Image Computing ABC: Atlas-Based Classification Marcel Prastawa and Guido Gerig Scientific Computing.

NCBC EAB, January 2010 NA-MIC Highlights: A Core 1 Perspective Ross Whitaker University of Utah National Alliance for Biomedical Image Computing.

NA-MIC National Alliance for Medical Image Computing Competitive Evaluation & Validation of Segmentation Methods Martin Styner, UNC NA-MIC.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

Our group recently reported that cerebral cortical development, specifically within aspects of vmPFC, is related to anxious/depressed problems in typically.

Sackler Institute for Developmental Psychobiology Weill Medical College of Cornell University.

Differences in Physical Characteristics between Children with Autism and Children with Other Developmental Disorders Bhavan A. Modi, Dr. Sarah O’Kelley,

National Alliance for Medical Image Computing Core What We Need from Cores 1 & 2 NA-MIC National Alliance for Medical Image Computing.

NA-MIC National Alliance for Medical Image Computing UNC Core 1: What did we do for NA-MIC and/or what did NA-MIC do for us Guido Gerig,

NA-MIC National Alliance for Medical Image Computing Mental Illness and Neuroscience Discovery (MIND) Consortium Data Randy L. Gollub,

NA-MIC National Alliance for Medical Image Computing NA-MIC UNC Guido Gerig, Martin Styner, Isabelle Corouge

INTRODUCTION ADULT AGE DIFFERENCES IN THE HEMODYNAMIC RESPONSE DURING VISUAL TARGET DETECTION MEASURED BY FUNCTIONAL MRI David J. Madden 1, Scott A. Huettel.

The many facets of brain aging as assessed by mri (and Freesurfer)

PSY4320 Research methods in cognitive neuroscience Preliminary results Lars T. Westlye, PhD Research Fellow Center for the Study of Human Cognition Department.

Automatic pipeline for quantitative brain tissue segmentation and parcellation: Experience with a large longitudinal schizophrenia MRI study 1,2 G Gerig,

Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skill Heidi Johansen-Berg 1, Valeria Della-Maggiore 3, Steve Smith.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

Statistical Shape Analysis of Multi-object Complexes June 2007, CVPR 2007 Funding provided by NIH NIBIB grant P01EB and NIH Conte Center MH

Corpus Callosum Probabilistic Subdivision based on Inter-Hemispheric Connectivity May 2005, UNC Radiology Symposium Original brain images for the corpus.

Joan Carles Soliva Vila Cognitive Neuroscience Research Unit (URNC) Dept. of Psychiatry. Autonomous University of Barcelona (UAB)

Department of Psychiatry, Department of Computer Science, 3 Carolina Institute for Developmental Disabilities 1 Department of Psychiatry, 2 Department.

By Suzanne Goh, Ravi Bansal, Dongrong Xu, Xuejun Hao, Jun Liu, Bradley S. Peterson Anchor Study.

National Alliance for Medical Image Computing Volumetric Studies.

NA-MIC National Alliance for Medical Image Computing Analysis and Results of Brockton VA study: Controls vs Schizophrenics Personality Disorder Martin.

Asymmetric Bias in User Guided Segmentations of Subcortical Brain Structures May 2007, UNC/BRIC Radiology 2007 Funding provided by UNC Neurodevelopmental.

NA-MIC National Alliance for Medical Image Computing NAMIC Core 3.1 Overview: Harvard/BWH and Dartmouth Structural and Functional Connectivity.

Naama Barnea-Goraly, MD; Linda J. Lotspeich, MD; Allan L. Reiss, MD 認知所碩一陳彥州 1.

Corpus Callosum Probabilistic Subdivision based on Inter-Hemispheric Connectivity Martin Styner1,2, Ipek Oguz1, Rachel Gimpel Smith2, Carissa Cascio2,

Mapping Early Brain Development in Autism

RESULTS AND DISCUSSION

Voxel-based Morphometric Analysis

A longitudinal study of brain development in autism

A longitudinal study of brain development in autism

Moo K. Chung1,3, Kim M. Dalton3, Richard J. Davidson2,3

Voxel-based Morphometric Analysis

Detecting Gray Matter Maturation via Tensor-based Surface Morphometry

Mapping Early Brain Development in Autism

Neuroimaging Schizophrenia and Related Disorders

Voxel-based Morphometric Analysis

Jay N. Giedd, Judith L. Rapoport Neuron

Voxel-based Morphometric Analysis

Presentation transcript:

A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center & UNC-CH Dept of Psychiatry NA-MIC AHM Salt Lake City, UTJan 11, 2007

Overview Summary of structural imaging studies of autism Findings from our longitudinal autism study Challenges & benefits to imaging across development Future projects & goals for NA-MIC

Structural Imaging in Autism

MRI Studies of Brain Volume in Autism StudyBrain FindingSubject Age Piven et al. (1992) mid-sagittal area yrs Piven et al (1995) total brain volume14 – 29 yrs Courchesne et al (2001)cerebral. gray and white2 – 4 yrs only Sparks et al (2002)total cerebral yrs Aylward et al (2002)TBV (HFA) under 12 yrs Lotspeich et al (2004)cerebral gray (N=52) 7 – 18 yr Herbert et al (2004)cerebral white5 – 11 yrs Hazlett at al (2005)gray matter volume yrs Palmen et al (2005)TBV, cerebral gray (N=21)7 – 15 yrs Limitations: no developmental studies, heterogeneity of samples

When compared to typically developing individuals…. increased brain weight in autism macrocephaly in 20% increased brain volume on MRI enlarged tissue volumes (both WM & GM) age effects present

Longitudinal MRI study of brain development in autism

AIMS To characterize patterns of brain development longitudinally in autism cases versus controls (TYP, DD) To examine cross-sectional & longitudinal relationships between selected brain regions and behavioral characteristics associated with autism

N% male years (SD) IQ-SS (SD) * Autism 51 88% 2.7 (0.3) 54.2 (9.4) Controls 25 DD 11 55% 2.7 (0.4) 59.7 (9.4) TYP 14 64% 2.4 (0.4) (18.7) * IQ-SS = Mullen composite Standard Score UNC Longitudinal MRI Study of Autism Hazlett et al Arch Gen Psych 2005

UNC Longitudinal MRI Study of Autism autism controls autism controls mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p TBV (13.4) (16.2) cerebrum (10.5) (12.3) cerebellum (1.5)114.4 (2.2) Adjusted for Gender and Age

autism controls autism controls mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p TBV (13.4) (16.2) cerebrum (10.5) (12.3) gray (7.7)644.2 (8.8) white (3.1)246.2 (3.7) cerebellum (1.5) (2.2) UNC Longitudinal MRI Study of Autism

autism typical autism typical mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p cerebrum (10.5) (17.4) gray (7.7)652.7 (12.2) gray (7.7)652.7 (12.2) white (3.1)250.4 (5.4) white (3.1)250.4 (5.4) autism dev delayed autism dev delayed mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p cerebrum (10.5) (17.2) gray (7.7)633.5 (12.4) gray (7.7)633.5 (12.4) white (3.1)240.9 (5.1) white (3.1)240.9 (5.1) UNC Longitudinal MRI Study of Autism

Substructures of interest

Relationship between Brain Volume and Autistic Features Social Communication Atypical Behaviors

Substructures of interest Basal ganglia –Caudate –Putamen –Globus pallidus AmygdalaHippocampus

Caudate Enlargement in Autism Nage t p Nage t p Study 1 autism controls Study 2 autism15m = controls15m = 30.3 (Sears, Vest, Bailey, Ransom, Piven 1999)

Clinical Correlates of Caudate Volume ADI Domain Spearman r p social 0.19 ns communication 0.05 ns ritualistic/repetitive (Sears, Vest, Bailey, Ransom, Piven 1999)

Hollander et al. Biological Psychiatry 2005 Clinical Correlates of Caudate Volume

Descriptives %Years Cognitive* Adaptive** GroupNMaleM (SD) M (SD)M (SD) autism5287%2.7 (0.3) 54.1 (9.3)60.8 (5.9) controls3370%2.6 (0.5) 87.4 (28.6)850.4 (21.1) developmental delay1267%2.8 (0.4) 55.5 (6.7)65.8 (14.0) developmental delay1267%2.8 (0.4) 55.5 (6.7)65.8 (14.0) typically developing2171%2.4 (0.5) (16.8)98.3 (13.4) typically developing2171%2.4 (0.5) (16.8)98.3 (13.4) * Cognitive estimate from Mullen Composite Standard Score ** Adaptive behavior estimate from Vineland Adaptive Behavior Composite

Basal Ganglia Volumes in 2 Year Olds with Autism (adjusted for TBV) Aut v Total Controls Aut v TYP Aut v DD diff (SE) p % diff (SE) p % diff (SE) p % Caudate.50 (.29).094 7% 0.8 (.31) %.20 (.43).65 3% Globus Pallidus.16 (.29).09 6%.17 (.10).094 6%.16 (.12).20 6% Putamen -.16 (.20) % -.19 (.22) % -.14 (.25) % Note - all comparisons also adjusted for age and gender

Clinical Correlates of Basal Ganglia Volume in 2 year olds with Autism Caudate Globus Pallidus Putamen B (SE) p * B (SE) p B (SE) p ADI Item Minor Change-.35 (.230) (.071) (.135).001 Rituals - -- Body Mvt.413 (.150) (.049) * one-sided t-test

MRI Studies of Amygdala Volume in Autism Sparks (2002)45 ASDinc vs. TYP and DD controls (3-4 yr olds) Schumann (2004)61 ASDincreased in 7-12 year olds, not increased year olds

Amygdala/Hippocampus Volume in 2 Year Olds with Autism Aut v Total Controls Aut v TYP Aut v DD diff (SE) p % diff (SE) p % diff (SE) p % amygdala.35 (.12) %.55 (.11) < %.16 (.17).336 3% hippocampus.03 (.11).78 1% -.03 (.14).841 0%.09 (.15).55 2% *Note – all comparisons also adjusted for age and gender (adjusted for TBV)

Imaging Development

Challenges to Developmental Studies Difficult for very young children and/or lower functioning children to remain still May need to remain motionless for long periods of time Sleep studies vary in success rates Subjects may require training and practice – this adds to expense

total cerebral To total cerebral white matter frontal grayparietal gray temporal gray occipital gray Longitudinal Studies: Brain Development During Childhood and Adolescence Age in years Peak 12 y 12 yrs 16 yrs 20 yrs 4 more sensitive for detecting growth patterns, even in the presence of large inter-individual variation and non-linear growth Longitudinal Methods time 1  time 2 Giedd et al., Nature Neuroscience, 1999

Gray matter maturation Gogtay, Giedd et al PNAS N = 13 (7 male, 6 female) typical subjects

Time Course of Critical Events in the Determination of Human Brain Morphometry Neurodevelopmental processes, cortical synapse density, and their relationship to gray and white matter volumes on MRI. Giedd et al. 1999, Sowell et al

Neonatal Brain MRI T2 T1 non- myelinated white matter early myelinated white matter gray matter

Corpus Callosum Neonate (2 wks) Adult Corpus callosum: FA along Commissural bundles Infant (1 year)

Infancy to Childhood Hermove et al., NeuroImage 2005.

Data

Data Structural MRI Diffusion Tensor Behavioral, cognitive, developmental Processed longitudinal data

Data Structural MRI TI:coronal 3D SPGR IRprep, 0.78 x 0.78 x 1.5 mm, 124 slices, 5 TE/12 TR, 20 FOV, 1 NEX, 256x192 PD/T2: coronal FSE, 0.78 x 0.78 x 3.0 mm, 128 slices, 20 FOV, 17 TE/7200 TR, 1 NEX, 256x160 DTI axial oblique 2D spin echo EPI, 0.93 x 0.97 x 3.8 mm, 30 slices, 24 FOV, 12 dir

Image Processing

Tissue segmentation –2 yr old EMS hard segmentations EMS segmentations overlaid on MRI

Automatic parcellation by template warping Manually-derived parcellation “warped” to new subjects

Substructures of interest Basal ganglia –Caudate –Putamen –Globus pallidus AmygdalaHippocampus

Data Processed datasets* Time1 (2 yr old) Time2 (4 yr old) EMS/lobes CN AMYG Autism (+2 CS) DD Typical FX *As of Nov06 Also have segmented data for: Put/GP, Hipp, CC area, Ventricles, Ant Cing

Challenges to Image Processing

Registration of images to a common atlas Inhomogeneities – bias correction Tissue contrast – myelination Brain shape changes across development Challenges to Image Processing

Future Directions

Examination of longitudinal data e.g., 2-4 years old, follow-ups at 6-8 Development & application of novel image processing methods e.g., shape, cortical thickness

Change from 2 to 4 years These frames show the evolution from 2 year old to 4 year old using high dimensional fluid warping (Joshi)

Surface growth maps age 2 4

NA-MIC Collaboration Our site can offer NAMIC collaborators: 1) Pediatric dataset of sMRI & DTI 2) Longitudinal data 3) Segmented datasets (e.g., substructures, ROIs) to be used as validation tools

NA-MIC Collaboration Goals/Projects for NAMIC collaborators: 1) Pipelines for growth-rate analysis 2) Longitudinal analysis of cortical thickness, cortical folding patterns, etc. 3) Automating DTI processing, creating more regionally defined DTI analysis (?) 4) Development of new segmentation protocols (e.g., dorsolateral prefrontal cortex) 5) Quantify shape changes over time to allow for analysis with behavioral data

Contributors Joe Piven, MD Guido Gerig, PhD Sarang Joshi, PhD Michele Poe, PhD Chad Chappell, MA Judy Morrow, PhD Nancy Garrett, BS, OTA Robin Morris, BA Rachel Smith, BA Mike Graves, MChE Sarah Peterson, BA Matthieu Jomier, MS Carissa Cascio, PhD Matt Mosconi, PhD Martin Styner, PhD Allison Ross, MD James MacFall, PhD Alan Song, PhD Valerie Jewells, MD James Provenzale, MD Greg McCarthy, Ph.D. John Gilmore, MD Allen Reiss, MD UNC Fragile X Center NDRC Research Registry Funded by the National Institutes of Health Many thanks to the families that have generously participated !