Victoria X Wang1, Caroline Menard2, Cheuk Ying Tang3, Frances Marks1, Johnny C Ng1, Lazar Fleysher1, Zahi A Fayad1, and Scott Russo2
1Radiology, Translational and Molecular Imaging Institute at Mount Sinai, New York, NY, United States, 2Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 3Radiology & Psychiatry, Translational and Molecular Imaging Institute at Mount Sinai, New York, NY, United States
Synopsis
We studied functional and structural connectivity in a stress susceptible and resilient mouse model using rsfMRI and DTI. We also investigated the integrity of the Blood Brain Barrier using Gd-DTPA. We found hyperactivity in the susceptible mice which had also several regions in the brain with compromised BBB. We also detected increased structural connectivity in the resilient mice.Background
Major depressive disorder
(MDD) will affect one out of every five people in their lifetime. It is also
the leading cause of disabilities worldwide. Unfortunately current treatments
are ineffective in a large subset of patients and pathogenesis of MDD has yet
to be completely understood. Alterations within the peripheral immune system
and overactivation of proinflammatory cytokines have been associated with mood
disorders for decades. Monocytes may even traffic to the brain via the
bloodstream suggesting that the blood-brain barrier (BBB) is compromised.
Evidences from clinical studies such as an altered ratio of cerebrospinal fluid
to serum of various markers in depressed patients also suggest BBB dysfunction.
In this pilot study we compared BBB integrity and brain connectivity in vivo in mice following chronic social
defeat stress [1], an established rodent model of MDD.
Methods
3 groups of 5 C57Bl/6
mice were scanned in this pilot study. 10 mice were subjected to a 10-day social defeat stress paradigm (stressed mice). Stressed mice
were exposed to physical interaction with a novel larger CD-1 aggressor every
day for 10 minutes followed by sensory interaction for 24 hours. Mice were then
separated into 2 groups based on their social interaction phenotype which was conducted 24 hours after the last defeat. 5
control mice were housed in the same room but not subjected to the stress
paradigm (unstressed control mice). All mice
were scanned on a Bruker Biospec 7T/30 scanner using a 4 channel phased array
coil 2-4 days following the social interaction test.
The following protocols were obtained: T1 weighted 2D-FLASH, PGSE-DTI
(b-value=1200s/mm2, FOV=15mm, MTX=128x128, slice-thickness=0.5mm,22 slices, 30
directions, 5 b=0), resting state fMRI (GE-EPI: FOV=20mm, Matrix=64x64, slice
thickness=0.5mm, 20 slices, TE=20, TR=1s, Imaging time=10 minutes (600
volumes), Manual shimming was performed for every fMRI scan. T1 and T2 weighted
images were first acquired and the mouse was taken out and contrast agent
(Gd-DTPA) was administered through the intraocular pathway and the mouse was
then rescanned. Resting state and DTI scans were obtained on a separate date to
minimize the effects of lingering contrast agents. Anesthesia used for fMRI was
Dexmedetomidine infused through a subcutaneous catheter and infusion pump. All
other scans were performed using isoflurane.
Analysis
Regions of interests were defined over the
striatum, thalamus, hippocampus, nucleus acumbens, and various portions of the
sensory/motor cortices using in-house developed matlab based software.
Statistical differences were computed using Statistica (Statsoft Inc.). Resting
state fmri scans were coregistered to T2 anatomical scans. The brain was
identified on the T2 scans and a binary mask was generated using FSL software (www.fmrib.ox.ac.uk/fsl). This mask was
then used to brain extract the fMRI data. A study specific anatomical template
was generated from the 15 mice. All fMRI scans were then transformed and
coregistered to this template. Independent component analysis was performed
using MELODIC (FSL) with 20 networks. Dual regression analysis was performed to
compare the different groups. DTI data was processed using FSL. Fractional
anisotropy (FA) maps were computed and region of interest analysis was
performed on the following structures: corpus callosum, cingulum bundle,
external capsule, internal capsule, fimbria and the tractus retroflexus.
Statistical group comparisons and correlations were computed using statistica.
Results
Several regions in the brain showed significant
signal differences when the contrast agent was administered. This was detected
in the hippocampus, sensory/motor cortices and thalamus. The same regions were
also significantly correlated with the Social Interaction (SI) index. Resting
state fMRI showed significant increased activity in the default mode network of
the susceptible mice when compared with resilient or control. No differences
were detected between controls and resilient mice. Fractional anisotropy ROIs
did not produce any significant group differences, but when all ROIs were
pooled together to represent whole brain FA index, the resilient group has
significantly high FA values.
Discussion
In this pilot study we have
performed a battery of imaging protocols on a social defeat model in rodents.
We have shown that several regions may have compromised BBB in the susceptible
phenotype. In addition the anterior cingulate in the default mode network
showed hyperactivity in the susceptible mice. This result is consistent with
hyperactivity in PTSD patients as reported in fMRI studies [2]. The higher whole
brain fractional anisotropy in the resilient group might signify better
structure connectivity that predisposes them to be resilient. Further studies with increased number of animals will need to be conducted to confirm these results.
Acknowledgements
Part of this work was support by NIH grant RO1 MH104559References
1. Krishnan V, Han MH, Graham DL,
Berton O, Renthal W, Russo SJ, et al. (2007): Molecular adaptations underlying
susceptibility and resistance to social defeat in brain reward regions. Cell
131:391–404.
2. Thomaes, K., et al., Increased anterior cingulate cortex and
hippocampus activation in Complex PTSD during encoding of negative words.
Soc Cogn Affect Neurosci, 2013. 8(2):
p. 190-200..