and its applications - NUDZ
Transcript of and its applications - NUDZ
(Cortical) Surface-based morphometry
and its applications
Number of Project: CZ.1.05/2.1.00/03.0078Title: National Institute of Mental Health (NIMH)
ESO konference, Klecany 2017-06-29
Antonín ŠkochVP3
Brain morphometry
Investigation of morphological/structural changes of brain on macroscopic scale- volume, shape, area, signal intensity
Voxel-based morphometry (VBM)
- voxel-by-voxel comparison of image intensity (tissue concentration or volume)- older, more traditional, methodologically simpler
Surface-based morphometry (SBM) a.k.a. surface-based (morphometric) analysis
- estimation of shape of cortical surfaces, surface-based metrics- more sophisticated, higher demands on data processing and manual intervention- more specific metrics: cortical thickness, pial surface area, cortical curvature
Voxel-based (brain) morphometry (VBM)
T1 images
Subj1
Subj2
Subj3
Tissue probability maps
Map of:Gray matter density(volume)
Statistical map
Study-specific(average)template
Registration Segmentation SmoothingModulation (optional)
Voxel-wiseHypothesis testing
Generallinearmodel
Hypothesisencoding
Voxel-based morphometry pros and cons
Pros: Much more simple – less steps, less computationally intensive,less manual intervention (not even possible)Maybe more sensitive (but less specific)
Cons:Problematic interpretation of results:
The signal intensity change – consequence of change in cortical thickness?or surface area?or local cortical gyrification?or signal intensity – change in myelinization?….
Results highly dependent on preprocessing parameters(DOF in registration, amount of smoothing)
Surface-based morphometrySurfaces
Pial surface (GM surface) White surface (GM/WM border)
Surface represented by vertices, faces(triangles)
Cortical sheet as 2D structure
Estimated for each individual from structural MR imageMany complex processing stepsRequires inspection and manual editing (time consuming)
Average cortical thicknessSurface areaAverage curvatureLocal gyrification indexGray matter volume
Etc.
Surface-based metrics
Well defined and (mostly) well interpretable(in contrast to VBM)
Surface-based registration(„spatial normalization“)
Central Sulcus
Gyri
Sulci
Central Sulcus
Height or depth of vertex - encodes folding pattern
Used as metric in registration - aligning sulci and gyri
Function largely follows folding pattern - aligning patterns aligns function
Height
Depth
Surface inflation
1. Mild inflation to reveal sulci – good for visualization
2. Complete inflation to sphere – internally for implementation of Inter-subject (subject to template) registration
- transformation defined on sphere is convenient (simple definition of coordinate system)
original „inflated“ - with encoded sulci
„sphere“ - with encoded sulci
inflation
inflation
Function follows surface
GM areas close in volume are notnecessarily functionally related!
In volume:Averaging of GM/WM/CSFAveraging of unrelated GM areas
On surface:Averaging only functionally(cytoarchitectionally) adjacent GM
Surface-based smoothing
2D
3D
Subj1
Subj2 ….
Estimation of:GM/WM borderPial surfaceGyral folds mapSurface-based metrics
Surface Inflation
Subj3 ...
Surface-based template
Surface-based (brain) morphometry (SBM)
Statistical Map
MN
Hypothesisencoding
Subj1
Subj3
Vertex-wiseHypothesis testing
Generallinearmodel
T1 images
Surface-based registration
Cortical thickness maps(or other metric)
Subj2
Smoothing
Using probabilistic atlas to assign ROI to each vertex on surface
Stats for each ROI:
Surface areaAverage cortical thickness...
Surface-based (brain) morphometry (ROI-wise)
Precentral Gyrus Postcentral Gyrus
Superior Temporal Gyrus
Implementation: FreeSurfer suite
Suite of tools for surface-based analysis
Cortical surface modelsCortical parcellationSubcortical segmentationHighly precise inter-subject registrationLongitudinal analysis (improved and unbiased estimation in repeated measurements)
Automated tractographySurface-based fMRI analysis
Linux and OS X - basedCompletely free!
Over 20 years of development
Example 1: Vertex-wise SBM in ESO
cortical thickness
controls vs. first episode patients
Controls n = 87Patients n = 177
Visit 1
Matched for age, sex
Response variable: cortical thickness
Covariates: age, education
Vertex-wise general linear model, cluster extent inference, one sided hypothesis, cluster-forming threshold 0.01
Important to disentangle age effect – very prominent
Vertex-wise SBM in ESOleft hemisphere
Cingulum, superior frontal gyrus
Orbitofrontal cortex
Inferior parietal/occipital
Cluster-wise p-map on Inflated surface template
Vertex-wise SBM in ESOright hemisphere
Inferior frontal gyrus
Insula (non significant)
Heteromodal association cortical areas – integration of multisensory inputs
Part of network hypothesized to be involved in pathogenesis of schizophrenia – supports our hypothesis of cascading-network-failure (CANEF)
Comparison with literature findings
Schultz CC, Schizophrenia Research 116 (2010) 204–209.
Our data
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Relativelygood agreement!
Example 2: Surface-based analysis in HCENAT project
Parcellation of entorhinal and perirhinal cortex
ROIs used for quantification of structural connectivity
Entorhinal cortex
Perirhinal cortex
Hippocampus
Rhinal cortex ROI
Gray matter mapWith overlaid stramlines
HCENAT – structural connectivity
SBM in ESOPerspective
Done:
ESO dataset (IKEM data, total 526 visits) - surface models including inspection and manual edits already done
Todo:
Re-process and check the IKEM data with new version of FreeSurfer - Improves accuracy and precision
Process and check the NUDZ data - much higher quality data, ready for Human connectome pipelines
- more complex analysis of cortical models- myelin mapping, much more detailed cortical atlas
Longitudinal reconstruction of all data
- suite for improved precision and reducing bias when analysing data of the same subject
- study of trajectory of development of spatiotemporal progression
- pursuing hypothesis of Cascading network failure (CANEF)
ConclusionWhy surface-based analysis?
- more precise and better interpretable cortical morphometry
- precise intersubject registration, cortical parcellation
- more precise anatomically-informed analysis of
TractographyfMRI analysis…(other)
Successfully implemented and used for data analysis in NUDZ
But beware.. !
- computationally demanding- good quality results need manual inspection
and manual edits (time consuming in large datasetsand in case of lower-quality data)