©AP Diffusion Tensor Imaging and Fiber Tractography: Analysis Options in Pediatric Neuroimaging Research Avner Meoded, Thierry A.G.M. Huisman, Andrea Poretti

©AP

Diffusion Tensor Imaging and Fiber Tractography: Analysis Options in Pediatric Neuroimaging Research Avner Meoded, Thierry A.G.M. Huisman,

Andrea Poretti Section of Pediatric Neuroradiology, Division of Pediatric Radiology, Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD

ASNR 53rd Annual Meeting, Chicago, April 25-30, 2015

eEdE-181

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Disclosure• We have nothing to disclose • No relevant financial relations interfering with

our presentation

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Learning objectives1. To identify the principles of Diffusion Tensor

Imaging (DTI) and Fiber Tractography (FT)

2. To recognize the significance of the DTI scalars including fractional anisotropy (FA), mean (MD), axial (AD) and radial (RD) diffusivity and their changes in different conditions

3. To describe different analytic approaches of DTI data and their advantages and disadvantages

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Outline1. Basic knowledge about DTI + FT2. Qualitative DTI/FT analysis3. Quantitative analysis:

i. Region of interest (ROI) based analysisii. Atlas-based analysisiii. Voxel-based analysisiv. Tract-based spatial statistics

4. Structural connectome

• Principles• Examples

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Diffusion tensor imaging (DTI)

• Diffusion = in all directions: isotropic

• Diffusion ≠ in all directions: anisotropic

Characterization of 3D shape of water diffusion

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Characterization of diffusion

1. v1, v2, v3 = eigenvectors describes the orientation of the principal coordinate axes

2. λ1, λ2, λ3 = eigenvalues define the shape of the ellipsoid

z

x

y

Measure diffusion alongvarious directions (≥ 6)

λ1

λ2

λ3

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Fractional anisotropy (FA)• FA = degree of anisotropic diffusion• Rotationally invariant scalar that measures the

fraction of the tensor that can be assigned to anisotropic diffusion

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Fractional anisotropy (FA)

FA

Isotropic diffusion = low FA = dark; anisotropic diffusion = high FA = bright

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Directionally encoded color map

Color-coding = information about the principal direction of diffusion:1.red = predominant left-right anisotropic diffusion2.green = predominant anterior-posterior anisotropic diffusion3.blue = predominant superior-inferior anisotropic diffusion

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Fractional anisotropy (FA)

Axon

Fiber mixture

Myelination

FA increaseFA decrease LESS

MORE

MORE

LESS

LESS

MORE

Mori S and Zhang J, Neuron, 2006

anisotropic diffusion

isotropic diffusion

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Axial and radial diffusivity

Axial diffusivity (AD)

• Rate of diffusion in the direction that is parallel to the main direction of diffusion

• AD = λ1

Radial diffusivity (RD)

• Rate of diffusion in the direction that is perpendicular to the main direction of diffusion

• RD = (λ2 + λ3)/2

White matter characteristics Axial diffusivity Radial diffusivity

Increased myelination High Low

Dense axonal packing Unaffected Low

Large axonal diameter High Low

Axonal degeneration Low High

Demyelination Unaffected High

Feldman HM et al, J Dev Behav Pediatr, 2010

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Deterministic fiber tractography (FT) • Post-processing technique reconstruction of the

course of major fibers in the brain (several algorithms)• Assumption: each voxel contains a single, coherently

oriented bundle of axons

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Deterministic FT: Limitations1. Crossing, kissing, merging, or diverging fibers

2. High dependency on FA and angle thresholds

Tracking of pathways that do not exist (false positive) or ineffective tracking of existing pathways (false negative)

Interpretation of FT results requires knowledge of brain anatomy

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Crossing fibers

1. In regions with crossing, kissing, merging, or diverging fibers = more than one population of fibers

2. New techniques needed with a higher number of diffusion directions, and higher b-values (HARDI, DSI) longer acquisition time

Jbabdi S and Johansen-Berg H, Brain Connect, 2011

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Threshold dependency

FA>20 + Angle>30⁰ FA>20 + Angle>45⁰ FA>20 + Angle>70⁰

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Qualitative analysis• Morphological information • Based on directionally encoded color maps and FT• Knowledge of normal DTI images is mandatory• Application: e.g. brain malformations to better

understand the pathogenesis

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Joubert syndrome

Molar tooth sign Normal anatomyPoretti A et al, AJNR, 2011

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DTI in Joubert syndromeJoubert syndrome Control

Poretti A et al, AJNR, 2007

superior cerebellarpeduncles

Absence of decussation of the superior cerebellar peduncles (“red dot”)

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FT in Joubert syndrome (JS)Joubert syndrome Control

Poretti A et al, AJNR, 2007

Absence of decussation of the superior cerebellar peduncles (SCP) and corticospinal tracts (CST) JS = axonal guidance disorder

SCP decussation

CST decussation

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ROI-based analysis

• Manual = ROIs are manually placed• Selected anatomical regions = hypothesis driven

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ROI-based analysisAdvantages

1. Accurate especially for subjects with large anatomical changes

2. Low number of regions = higher statistical power and possibility to depict small differences

Disadvantages

1. Reproducibility multiple measurements + ICC calculation

2. Time consuming (?)3. Hard to include the entire (3D)

anatomical structure4. Not applicable without

hypothesis5. Differences missed in regions

not included in the analysis

ICC: Intra Class Coefficient

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ROI-based analysis: DTI of the brainstem in achondroplasia

• Skeletal dysplasia with narrowing of cranio-cervical junction + foramen magnum

• Goal = To study the microstructural integrity of brainstem white matter tracts in children with achondroplasia compared to age-matched controls

Bosemani T, Meoded A, Huisman TA, Poretti A et al, Dev Med Child Neurol, 2014

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ROI-based analysis: DTI in achondroplasia

• 7 ROIs; FA, MD, AD, RD• 2 analysis by first reader, 1

analysis by second reader inter-/intra-rater reliability

• Comparison patients controls

• Correlation with:– Severity of CCJ narrowing– Neurological findings


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ROI-based analysis: DTI in achondroplasia• Lower brainstem: ↓ FA + ↑ MD and RD in

patients compared to controls• ↑ MD+RD+AD in bilateral CST/MCP = white

matter injury not limited to lower brainstem • ↓ FA in lower brainstem ↑ CCJ narrowing

anatomical proximity lower brainstem CCJ• No correlation DTI clinical findings


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Atlas- + voxel-based analysis• No underlying theory about where we might find

group differences global approach1. Atlas-based analysis = automated segmentation of

the brain into 170-180 well defined anatomical areas

2. Voxel-based analysis = analysis of each voxel within the brain (>100.000)

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Atlas- + voxel-based analysis

Yoshida S et al, Pediatr Radiol, 2013

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Normalization process• Standardization of info

about location of brain structures

• Transformation to an age-matched template

• Two steps:1. Linear = low degree of

freedom transformation, good for aligning images within subject

2. Non-linear = high degree of freedom transformation (LDDMM)

Yoshida S et al, Pediatr Radiol, 2013

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LDDMM

• Large Deformation Diffeomorphic Metric Mapping• Properties of LDDMM:

– Highly elastic: important for normalization of brains with atrophy and/or ventriculomegaly

– Preservation of topology– Reciprocal transformation: allows ABA + VBA

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Atlas- + voxel-based analysisAtlas-based analysis

Advantages:1. “Limited” number of anatomical

information (170-180 regions)2. Evaluation of native images

Disadvantages:1. Information about anatomical

localization lower than VBA2. Less sensitive for very small

lesions

Voxel-based analysis

Advantages:1. Unbiased whole brain analysis2. Sensitive for small lesions3. Specific locations of significant

group differences/correlations is automatically shown

Disadvantages: 1. High amount of information

low statistical power, correction for multiple comparison needed

2. Accuracy of voxel-to-voxel alignment across subjects is low

3. Not sensitive for widespread lesions

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ABA + VBA in children after hemispherectomy

• Hemispherectomy = surgical therapy for intractable seizures arising from a single cerebral hemisphere

• Goal = to study the changes of DTI metrics in the white matter regions of the remaining hemisphere (model for brain plasticity?)

Meoded A, Huisman TA, Poretti A et al, in preparation

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• n=19– Congenital etiology (n=11)– Acquired etiology (n=8)

• ABA + VBA of the remaining cerebral hemisphere

• Measurement of FA, MD, AD and RD


VBAABA

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• FA + AD ↓ and MD + RD ↑ = secondary Wallerian degeneration

• Wallerian degeneration: commissural + association > projection

• Presurgical DTI changes + postsurgical DTI normalization: acquired > congenital reorganization, plasticity?


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Tract-Based Spatial Statistics (TBSS)• Variation of whole-brain

analysis• FA images from multiple

subjects are aligned to a common space

• Tract representation including only voxels at the center of tracts common to all

• The resulting skeleton data for all subjects are analyzed

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Tract-Based Spatial Statistics (TBSS)Advantages

1. All locations across the brain are tested without a priori hypotheses

2. Powerful statistical module

Disadvantages

1. Skeletons are derived for the entire white matter region and no distinction between white matter structures is made

2. Variations in the periphery of white matter tracts may be missed (not included)

3. Possibile effect of brain atrophy

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TBSS in Niemann Pick disease type C

• Niemann Pick disease type C (NPC) = rare neurometabolic disease

• Goal = study the microstructural changes in patients with NPC compared to age-matched controls

30-year-old man with NPC

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TBSS in Niemann Pick disease type C

• n=8• Measurement of FA, MD, AD

and RD• Diffuse reduction in FA and

increase in MD, AD and RD in patients compared to age-matched controls

Poretti A, Meoded A, Huisman TA et al, in preparation

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The human structural connectome

• Structural connectome = map of the brain's structural connections, rendered as a network

• Network = set of nodes + edges• Nodes: discrete subunits cortical

regions• Edges: structural connection between

any pairs of gray mater regions

Rubinov M and Sporns O, Neuroimage, 2010; Sporns O, Neuroimage, 2013

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Structural connectivity1. Topology analysis:• Network metrics global/regional network

organization• Information: segregation, integration, small worldness,

centrality (Hubs)

2. Network based statistics: • Strength of connectivity between brain regions

at a pairwise level• Subnetworks

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Topology analysis• Segregation = ability for

processing to occur within densely interconnected groups

• Clustering coefficient = fraction of connections that connect the neighbors of a given node

• Integration = ease for brain regions to communicate based on route of information flow

• Small world = well-designed network combining high clustering and short path

• Centrality = measurement of the influence of a node compared to the rest of the network

• Betweenness centrality = fraction of short paths between nodes of the network that pass through a given node

• Hub = region with high degree of connections (high centrality)

• Assortativity = correlation between the degrees of connected node pairs

Rubinov M and Sporns O, Neuroimage, 2010

©APStructural connectivity matrix

Probabilistic tractography : network mode between all ROI

AAL template: parcellation of gray matter in 108 structures

Connectome reconstruction

DTI in native space T1 MPRAGE

Linear registration

FLIRT

Non linear registration

NIRT

Data registered to MNI space

Connectome analysisNetwork based

statistics

Topology analysis

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Structural connectome in agenesis of the corpus callosum (AgCC)

Meoded A, Huisman TA, Poretti A et al, Eur Radiol, 2014

↑ Cluster coefficient (p=0.005)

↓ Small worldness (p=0.005)

↑ Transitivity(p=0.003)

↓ Assortativity (p=0.03)

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Hubs in patients

Network hubs = nodes with high betweenness Hubs in right insula, precuneus, and lingual gyrus and left

insula and precuneusMeoded A, Huisman TA, Poretti A et al, Eur Radiol, 2014

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Network based statistics

1. Intrahemispheric left fronto-parietal (p=0.013)2. Interhemispheric fronto-cerebellar (p=0.021)3. Intrahemispheric left temporo-occipital (p=0.048)


Children with AgCC virtual callosotomy controls:

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Structural connectome in AgCC1. AgCC = more segregated + less integrated brain connectome

2. No hub in the cerebellum in patients = global connectivity ↓

3. Highly inter-connected interhemispheric fronto-cerebellar network + two insular hubs = brain plasticity ↑ interhemispheric flow of information through alternative pathway (anterior commissure?)

4. Lower modularity = connectome reorganization to reduce connection costs at the expense of decreasing integrative capacity?


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Conclusions

1. DTI/FT = ideal neuroimaging tool to study the pediatric brain ultrastructure and networking

2. Application to several pediatric neurology diseases

3. Qualitative and quantitative analysis

4. Different methods for quantitative analysis

5. Method depends on research question

Documents

©AP Diffusion Tensor Imaging and Fiber Tractography: Analysis Options in Pediatric Neuroimaging Research Avner Meoded, Thierry A.G.M. Huisman, Andrea Poretti