Structural Connectomics dMRI

STRUCTURAL CONNECTIVITY IN LEAD
CONNECTOME
LEAD-DBS WORKSHOP BRISBANE 02/2020 | ANDREAS HORN

„Is your tracking algorithm deterministic
or probabilistic?“
andreas.horn@charite.deLead-DBS Workshop
IMAGING DATA WITHIN LEAD-DBS

‣ DTI Concepts
‣ Fibre-Tracking
‣ Applied Example: Structural Network Analysis
‣ Advanced Concepts
‣ Example: Global Tracking
‣ Applied Example: Human Connectomics
‣ Overview: Software Packages
IMAGING DATA WITHIN LEAD-DBS

IF WE COULD MEASURE DIFFUSION
Courtesy Alexander Lehmans

TRACTOGRAPHY

Isotropic Diffusion Tensor: Bucket of Water (or cerebrospinal fluid)
TRACTOGRAPHY

Anisotropic Diffusion Tensor: Asparagus (or white matter)
TRACTOGRAPHY

THE SITUATION IN THE BRAIN

Hagmann, P. et al., 2006. Understanding diffusion MR imaging
techniques: from scalar diffusion-weighted imaging to diffusion
tensor imaging and beyond. Radiographics : a review publication
of the Radiological Society of North America, Inc, 26 Suppl 1,
pp.S205–23.
THE SITUATION IN THE BRAIN

Stejskal, E.O. & Tanner, J.E., 1965. Spin diffusion measurements: Spin echoes in the presence of a time‐dependent field gradient. The Journal of Chemical Physics.
DIFFUSION MRI

CHANGING GRADIENT DIRECTION

Anisotropic DiffusionIsotropic
DIFFUSION MRI

Le Bihan, D. et al., 2001. Diffusion tensor imaging: concepts
and applications. Journal of magnetic resonance imaging :
JMRI, 13(4), pp.534–546.
DIFFUSION MRI

Diffusion Imaging
Diffusion Tensor Imaging
DIFFUSION MRI

(e1, √λ1)
(e2, √λ2)
(e3, √λ3) D =
( )
Dxx Dyx Dxz
Dxy Dyy Dyz
Dxz Dyz Dzz
eigenvalue
decomposition
D = E • Λ • E-1
Λ =
( )
λ1 0 0
0 λ2 0
0 0 λ3
E =
( )
e1x e2x e3x
e1y e2y e3y
e1z e2z e3z
DIFFUSION MRI

T1-weighted image
DIFFUSION MRI

Diffusion tensor ‘image’
DIFFUSION MRI

DIFFUSION MRI

histology ‘in vivo’
DIFFUSION MRI

deterministic probabilistic
local global
streamlines
probability-
maps
Algorithm
Location
Data-Format
Tracking Concepts
CLASSIFICATION OF TRACKING ALGORITHMS

‣ crossings?
‣ length bias
‣ seed-bias
‣ cross-sectional
area invariance
‣ error summation
‣ …
GENERAL PROBLEMS IN TRACTOGRAPHY

A solution to some of the problems?
‣ reconstruction of
the whole fiber-set
without definition
of seed regions
‣ bayesian
approach
simulating MR-
signal based on
particle simulation
Reisert, M. et al., 2011. Global fiber
reconstruction becomes practical.
NeuroImage, 54(2), pp.955–962.
Structural Brain Connectivity Measures
andreas.horn@mpib-
Global Fibre-Tracking

3% of calculated fibers displayed
‣ quantitative
measurements
possible?
andreas.horn@mpib-

Gibbs‘ Tracking
‣ possible to
normalize fibre-
sets
‣ possible to
establish group-
connectomes
andreas.horn@mpib-

Gibbs‘ Tracking
‣ „tracking“
possible after
reconstruction
‣ online-“tracking“
possible
andreas.horn@mpib-

Cylinders aligning while the temperature is slowly decreased. – (Kreher, et al.
2008).
Cylinders and their elements: x:= particle midpoints, α:={+,–}, ι:= particle length,
n:= particle orientation. Sum of Squares of red line: „Internal Energy“ – (Reisert, et
al. 2011).
THE CONCEPT

‣ left: GT-Toolbox
‣ right: FACT-
tracking (Mori &
Barker 1999)
Reisert, M. et al., 2011. Global fiber
COMPARISON

‣ low length parameter: ‘kissing fibres’ arise
‣ higher length parameter: crossing
resolved
FIBER CROSSINGS

‣ similar to FA-maps
‣ different in crossing regions (where FA is
low) Reisert, M. et al., 2011. Global fiber
FIBER DENSITIES

‣ FSL
‣ DTI Toolkit & Trackvis
‣ MRTrix
‣ DTI&Fibre-Tools for SPM
‣ Explore DTI
‣ DTI-Studio
‣ MITK Diffusion
‣ DSI STUDIO
Image Sciences Institute
University Medical Center Utrecht
Alexander Leemans / Derek Jones
Nuffield Department of Clinical Neurosciences
Oxford Centre for Functional MRI of the Brain
Tim Behrens / Steve Smith
Martinos Center for Biomedical Imaging
Massachusetts General Hospital
Ruopeng Wang / Van J. Wedeen
Brain Research Institute, Melbourne, Australia
Jacques-Donald Tournier / Fernando Calamante
University Clinic Freiburg
Department for MRI-Physics
Marco Reisert / Valerij Kiselev / Jürgen Hennig
F. M. Kirby Research Center for Functional Brain
Imaging, Kennedy Krieger Institute
Susumu Mori
AN OVERVIEW OF POPULAR DIFFUSION SOFTWARE
University Hospital Pittsburgh
Department for Neurosurgery
Fang-Cheng Yeh

‘CLUSTER FAILURE’ IN dMRI
4x more false
positive tracts than
true positive tracts!

‘CLUSTER FAILURE’ IN dMRI
More Than Just Pretty Pictures
The deterministic fiber tracking method in DSI Studio has achieved
the highest 92% valid connection (ID#3) among 96 methods
submitted from 20 different research groups, examined by an open
competition (Nature communications, 8(1), 1349, 2017). The average
accuracy is 54%.

OPTIONS IN LEAD-DBS
Winner in
Neurocup 2009
(Fillard 2011)
-> Successor
Winner in
Maier-Hein 2017
“Oldschool
method”
Overview in Horn & Li et al.
2018 NeuroImage

HANDS ON
– Hands on Session –
Reconstructing a
whole-brain
connectome using
Lead Connectome
GQI Method

THANK YOU FOR YOUR ATTENTION

Structural Connectomics dMRI

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