(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

rh

rh

lh

lh

lh

lh

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)