Ona Wu1, Eric S. Rosenthal2, Gaston Cudemus-Deseda3, Brian L. Edlow2, Marjorie Villien1, Brittany B. Mills2, Joseph T. Giacino4, James L. Januzzi5, Ming Ming Ning2, W. Taylor Kimberly2, William A. Copen6, Pamela W. Schaefer6, and David M. Greer7
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2Department of Neurology, Massachusetts General Hospital, Boston, MA, United States, 3Department of Cardiac Anesthesiology and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States, 4Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, MA, United States, 5Department of Medicine, Cardiology Division, Massachusetts General Hospital, Boston, MA, United States, 6Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, 7Department of Neurology, Yale School of Medicine, New Haven, CT, United States
Synopsis
Cardiac arrest patients in coma after
restoration of spontaneous circulation were prospectively studied to determine
whether variability in structural connectivity can discriminate patients likely
to recover consciousness from those who will not. Compared to healthy controls,
cardiac arrest patients overall had significantly lower values in the following structural
connectivity parameters: global efficiency, clustering coefficient, and degree.
Cardiac arrest patients who failed to recover alertness had a significantly lower
global clustering coefficient compared to patients who woke up. Alterations in
structural connectivity may play an important role in predicting recovery and guiding
patient management decisions in comatose cardiac arrest patients.
Introduction
For
cardiac arrest (CA) survivors initially comatose after restoration of
spontaneous circulation (ROSC), the extent of brain injury and expected
neurologic outcome are crucial for patient management decisions.
1 Critical
knowledge gaps persist in neuroprognostication of comatose post-CA survivors.
2 Early
prognostication remains difficult except in extreme cases: patients rapidly
awakening do well, and those with minimal brain function do poorly. Most CA
patients, however, fall between these extremes. Because hypoxic-ischemic injury
is typically diffuse, damage to a network of brain regions is likely involved
in the patient’s disorder of consciousness.
To quantify these complex brain network changes, we applied graph
theoretical methods
3 that are increasingly
being used to understand human brain connectivity in both health and disease.
4 Specifically, we investigated global
efficiency, degree, and clustering coefficient as a function of arousal recovery (defined
here as eye-opening either spontaneously or in response to stimulation).
Methods
Cardiac
arrest patients who remained comatose after ROSC were prospectively enrolled. Coma
was defined as Glasgow Coma Scale (GCS) <=8. Five healthy controls were also enrolled. All
subjects underwent 3T MRI scans. High-spatial resolution 3D T1-weighted anatomical
images were acquired for registration purposes with FOV=256x256 mm2,
acquisition matrix=256x256, 176 sagittal slices (thickness 1 mm). Multiple
shell diffusion imaging was acquired using 30 directions with b-values=1000
s/mm2, and 2000 s/mm2 (3x3x3 mm3), and 10
b-value=0 s/mm2 images acquired using blipped simultaneous
multi-slice5
echo planar imaging (EPI). Structural probabilistic connectivity maps were calculated
using a modification of the FSL probabilistic fiber-tracking algorithm.6, 7 The output was modified such that
each voxel represented the probability of diffusion streamlines projecting from
any voxel in the seed region-of-interest (ROI) to the voxel of interest rather
than number of streamlines. Structural connectivity with respect to each ROI
was measured using the average probability of streamlines emanating from the
source to target ROIs. Network nodes were defined using the Automated Anatomical
Labels atlas.8
Adjacency matrices were computed with a threshold of 10%. Network topology measures
were calculated using the Brain Connectivity Toolbox (BCT)9 and
compared (one-way analysis of variance, followed by post-hoc Student t-tests). To
minimize potential confounding effects from early withdrawal of life
sustaining therapy (WLST), we excluded subjects who had WLST before 10 days
without arousal recovery.
Results
Ten patients (46±26
years old, 40% male) and five controls (37±19 years old, 40% male) were
analyzed. Seven patients exhibited arousal recovery (AR), three did
not (No AR). Four were alive at
discharge, one died from brain death and five died due to WLST. GCS
(median [IQR]) was 3 [3-3.75] at admission, and 6.5 [4.5-9] at the time of
the research MRI, performed 5.5 [4-8.75] days after initial arrest. Global efficiency, degree, and clustering
coefficient (mean±SD) among healthy controls were 0.44±0.02, 12.7±1.16 and
0.15±0.00, respectively, and were lower among patients (AR: 0.36±0.06, 9.1±2.3,
0.13±0.02; No AR: 0.32±0.03, 6.6±1.06, 0.11±0.02). The Figure shows box-plots
of the different metrics across the 3 groups. One-way ANOVA found significant
differences across the 3 groups for global efficiency (P=0.0061), degree
(P=0.0015), and clustering coefficient (P=0.0059). Post-hoc analysis demonstrated
the patient group had significantly lower metrics compared to healthy controls
(P<0.05), suggesting loss of brain complexity. Compared to patients with AR,
found No AR patients demonstrated statistically significantly lower clustering
coefficients (P=0.033) and non-statistically significantly lower efficiency
(P=0.15) and degree (P=0.066) values.
Discussion
Patients who failed to exhibit
arousal recovery demonstrated greater disturbances in structural connectivity
compared to patients who recovered arousal. These findings suggest that structural
connectivity network measures may have utility in identifying patients who may achieve
good outcomes, despite presenting with poor initical GCS scores. Differences in
timing of MRI acquisition, potential bias from unblinding of clinical MRI
sequences to treating clinicians, and small sample sizes are diagnostic limitations
of our findings. Although connectivity findings were not available to the
clinical team, unblinding of clinical MRI sequences to clinicians was mitigated
by excluding patients with early WLST. Future prospective studies will benefit
from enrolling subjects whose legally authorized representatives plan to defer
WLST decisions for at least two weeks post-arrest in order to more accurately
characterize these patients’ likelihood for recovery.
Conclusion
Alterations in structural connectivity measured with diffusion MRI show
promise in predicting recovery and guiding patient management decisions in
comatose cardiac arrest patients.
Acknowledgements
We thank Drs. Himanshu
Bhat, Dylan Tisdall, Andre van der Kouwe, Kawin Setsompop and Steven Cauley for providing pulse sequences that
were used in this study.References
1. Dragancea
I, Rundgren M, Englund E, Friberg H, Cronberg T. The influence of induced
hypothermia and delayed prognostication on the mode of death after cardiac
arrest. Resuscitation.
2013;84:337-342
2. Neumar RW, Shuster M, Callaway CW, Gent LM, Atkins DL, Bhanji
F, et al. Part 1: Executive summary: 2015 American Heart Association guidelines
update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 2015;132:S315-367
3. Albert R, Barabasi AL. Statistical mechanics of complex
networks. Rev Mod Phys. 2002;74:47-97
4. Bullmore E, Sporns O. Complex brain networks: Graph
theoretical analysis of structural and functional systems. Nature reviews Neuroscience. 2009;10:186-198
5. Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ,
Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous
multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med. 2012;67:1210-1224
6. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW.
Probabilistic diffusion tractography with multiple fibre orientations: What can
we gain? Neuroimage. 2007;34:144-155
7. Jbabdi S, Sotiropoulos SN, Savio AM, Grana M, Behrens TE.
Model-based analysis of multishell diffusion MR data for tractography: How to
get over fitting problems. Magn Reson Med.
2012;68:1846-1855
8. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F,
Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM
using a macroscopic anatomical parcellation of the MNI MRI single-subject
brain. Neuroimage. 2002;15:273-289
9. Rubinov M, Sporns O. Complex network measures of brain
connectivity: Uses and interpretations. Neuroimage.
2010;52:1059-1069