Diffusion Tractography as a guide for functional neurosurgery
Johansen-Berg H1, Behrens TEJ1, Stein JF2, Aziz TZ2,3
1Centre
for Functional MRI of the Brain, University of Oxford, UK; 2Dept of Physiology, University of
Oxford, UK, Dept of Neurosurgery, Radcliffe Infirmary, Oxford
Introduction
Phase 2: Define surgical targets based on connection
patterns
• Deep Brain Stimulation can be an effective
treatment for movement, pain, mood, and
anxiety disorder
Chronic Pain
• Periacquaductal-periventricular gray, and nucleus cuneiformis are popular
targets for treatment of pain disorders.
• Accurate target localisation is critical for efficacy
but challenging using conventional approaches
• We have provided the first descriptions of the anatomical connections of
these areas in the human brain using DTI [4-6]
• Effects of stimulation are probably mediated via
anatomical connections from the stimulation site
• Both structures connect strongly
to thalamus and prefrontal cortex,
providing routes by which DBS
could influence the pain matrix
• Diffusion-weighted MRI and tractography
provides a method for non-invasive visualisation
of anatomical connections
We aimed to test whether diffusion MRI and tractography could aid
targeting by identifying structures based on their anatomical
connections.
Phase
1:
Characterise
variability
reproducibility in the healthy population
and
Variability in tractography and diffusion measures
Quantitative diffusion measures, such as fractional anisotropy (FA), are
sensitive to changes in white matter microstructure. If these measures are
to be used to address clinical questions it is important to characterise their
variability. We scanned 8 subjects on 3 different days to quantify between
subject and between session variation in tractography results using a range
of methods
Overlap across subjects
Overlap across sessions
Movement Disorders
• The pedunculopontine and subthalamic nuclei are important targets for
treatment of movement disorders
• We have characterised the connections of these regions in the human brain
using tractography [7]
• This includes precise descriptions
of the topography of connections
with different cortical and subcortical areas as well as somatotopic
mapping to different body part
representations in M1.
Depression
a)
c)
d)
e)
f)
b)
Steps involved in blind parcellation. Probabilistic tractography is run from every voxel in a seed mask (a) and the probability of connection
to all other voxels in the brain (b) is recorded in a connectivity matrix (c). The cross-correlation matrix (d) of the connectivity matrix is
found and re-ordered (e) to bring voxels with similar connection patterns close to each other. Clusters in the re-ordered matrix represent
regions with distinct connection patterns. This approach reliably differentiates SMA/pre-SMA based on connectivity (f, top), and these
regions correspond closely to those defined based on function using FMRI (f, bottom). From ref [2]
Day 1
Spectral
reordering
Day 4 - 3T
Ideal segmentation
Day 3
Day 2
Individual anatomy
k-means clus
t ering
Using the data from 8 subjects scanned 3
times at 1.5T and once at 3T we tested the
reproducibility of the approach:
• Overlap between days 89.3%
• Overlap between scanners 87.3%
• Overlap with FMRI-based parcellations 80%
• Overlap with cytoarchitectonic maps 93%
See ref [3]
A. Location of seed points in STN on coronal slice . B. Schematic of
STN, showing seed voxels. C. Topography of cortical connections. D.
Zoomed version of topography. Consistent with animal data, cortical
motor areas (M1, SMA, PMC) connect to dorso-lateral portions of STN
whereas connections to associative subcortical areas are found in
more ventral portions
• DBS of the white matter underlying the subgenual cingulate results in
dramatic remission of symptoms in some previously drug-resistant patients
• We have used blind cortical
parcellation
to
define
subregions within subgenual
cingulate and have defined the
connections of these regions
(right)
For measures of mean fractional anisotropy (FA) along tracts, co-efficients
of variation (CV) were below 5% between sessions and below 10% between
subjects. These variability measures were used to perform power
calculations to determine numbers of subjects required to detect differences
of a given size between groups of over time [1].
Variability in ‘Blind’ Cortical Parcellation
We previously proposed a method for parcellation of grey matter areas
based on their pattern of anatomical connections [2]. This approach could
aid targeting but first it is important to establish its reliability and validity.
Tracts from PVG/PAG
Top: Location of electrode contact points (red=responders;
blue=non-responders) in relation to anterior (red-yellow) and
posterior (blue-turquoise) clusters. Bottom: Paths from
effective electrode contacts consistently travel to amygdala,
nucleus accumbens, hypothalamus, cingulum bundle, medial
frontal cortex and orbitofrontal cortex.
After re-ordering of x-correlation
matrices, each matrix was
manually
divided
into
two
clusters, which are mapped back
onto the brain. Data for 16
healthy subjects are shown.
Group average paths from posterior
cluster (top, blue) and anterior
cluster (bottom, red-yellow)
• In collaboration with Helen Mayberg
(Emory, USA), we have projected
electrode locations onto DTI data to
define the network of connections that
potentially mediates therapeutic effects
[8]
Discussion
• We have characterised the anatomical connections from surgical
targets of interest in movement, pain, and mood disorders.
• An outstanding question is the relationship between electrode
placement and outcome. Specifically, we aim to show that the
pattern of anatomical connections of a target site will partly
determine surgical efficacy.
• We are therefore also acquiring pre-operative DTI data in patients
themselves so that we can test the connections from electrode
locations using the individual’s own brain anatomy and relate this
information to clinical outcome
References: 1. Heiervang, Behrens, Mackay, Robson, Johansen-Berg. Between session reproducibility and between subject variability of diffusion MR and
tractography measures. NeuroImage. In press. 2. Johansen-Berg, Behrens, Robson, Drobnjak, Rushworth, Brady, Smith and Matthews. Changes in
connectivity profiles define functionally distinct regions in human medial frontal cortex. Proceedings of the National Academy of Sciences USA. 101. 1333540. 2004. 3. Klein, Behrens, Robson, Mackay, Higham and Johansen-Berg. Connectivity-based parcellation of human cortex using diffusion MRI: establishing
reproducibility, validity and observer-independence in BA 44/45 and SMA/pre-SMA. NeuroImage. In press. 4. Sillery, Bittar, Robson, Behrens, Aziz, Stein and
Johansen-Berg (2005) Connectivity of the human periventricular gray. Journal of Neurosurgery. 103(6):1030-4. 5. Owen, Green, Davies, Stein, Aziz,
Behrens, Voets, Johansen-Berg. Connectivity of an effective hypothalamic surgical target for cluster headache. Journal of Clinical Neuroscience. In press. 6.
Hadjipavlou, Dunckley, Behrens and Tracey. Determining anatomical connectivities between cortical and brainstem pain processing regions in humans: a
diffusion tensor imaging study in healthy controls. Pain. 123. 169-178. 7. Aravamuthan, Arasu, Johansen-Berg, Voets, Liu, Stein, Aziz. Human
pedunculopontine nucleus connections traced using probabilistic diffusion tractography. Society for Neuroscience Abstracts 2006. 8. Johansen-Berg, Behrens,
Matthews, Katz, Metwalli, Lozano and Mayberg. Connectivtiy of a subgenual cingulate target for treatment resistant depression. OHBM Meeting , 2006