Ebola Alters Some, But Not All, Resting-State Intrinsic Functional Connectivity Networks In The Macaque Brain
Eswar Damaraju1, Margaret Lentz2, Jeffrey David Lewine1,3, David Thomasson2, Nadia Biassou4, Anna Honko2, Vince Calhoun1, and Peter Jahrling2

1Mind Research Network, Albuquerque, NM, United States, 2Integrated Research Facility/NIAID, Frederick, MD, United States, 3Lovelace Family of Companies, Albuquerque, NM, United States, 4NIH Clinical Center, Bethesda, MD, United States

Synopsis

Ebola has the potential to cause both acute and chronic compromise of neurological status. To better understand the relevant neurobiology, a pilot MRI study of infected macaques was performed. Data indicate that Ebola exposure leads to acute disruption of some, but not all, intrinsic connectivity networks, even in the absence of intraparenchymal lesions. These studies represent the first non-invasive functional imaging studies of living, Ebola infected non-human primates.

Introductory Overview

During the acute phase of Ebola virus disease, neurological symptoms including headache, encephalitis, seizures, and cognitive difficulties are often reported, and there is growing evidence for long-lasting neurological and cognitive compromise in Ebola survivors 1-3. To better understand the relevant neurobiology, it would be valuable to have a non-human primate model amenable to serial evaluation with translational non-invasive brain imaging methods. Such studies have heretofore not been possible given the need for high-level acute containment of infected animals. NIAID has recently established the Integrated Research Facility (IRF) which provides a unique opportunity for performing non-invasive brain imaging (MRI, CT, PET, and SPECT) within a BSL-4 environment. As a first step towards the development of a NHP model to look at neurobiological deficits in Ebola survivors, we examined the acute impact of Ebola on brain resting-state functional connectivity.

Purpose

The main goal of this study was to assess the impact of Ebola on brain function during the acute phase of the disease.

Methods

Pre and post-Ebola exposure MRI data were collected from four isoflurane sedated rhesus macaques. Animals were imaged using a Philips Achieva 3 Tesla MR clinical scanner equipped with a Head SENSE Coil. Multi-slice, FFE, EPI-based resting state fMRI data were collected over the course of 7 minutes, with an in-plane resolution of 1.5 x 1.5 mm2 and a slice thickness of 1.5 mm. Other imaging parameters included a TR/TE of 2070/25 ms, NSA = 1, 100 dynamic scans, fat saturation using SPIR, EPI factor = 31, and a FOV = 96 mm x 96 mm. Data were collected a few days prior to Ebola infection and 7-10 days after exposure. Animals were infected with Ebola (Makona strain) through an intramuscular route, with a target dose of 1,000 TCID50.

Pre-processing of the resting-state fMRI data was performed using AFNI4. Following rigid body head motion correction, extracted brain data underwent spatial normalization with respect to a macaque atlas5. Subsequently, data were smoothed using 3mm FWHM Gaussian filter. Prior to performing group independent component analysis (GICA), the time course of each voxel was normalized with respect to its variance. Using the GIFT Toolbox6 a single GICA was performed to extract 20 independent spatial sources, using all of the data from both pre and post-exposures session with each of the 4 macaques. Subject-specific component maps and associated time courses were obtained using back projection. Artifact components and components of biological interest were separated by (1) evaluating the dynamic range and amplitude of low frequency power in the time course spectrum of each component7and (2) evaluation of the spatial profile of the ICA-extracted intrinsic connectivity networks (ICNs) with respect to the profiles of components reported in prior literature8,9.

Results

Six ICNs of interest were identified. These included the default-mode network, a pre-frontal network, auditory and sensorimotor networks, and two visual networks (primary and association). For three of these (default mode, pre-frontal, and primary visual), Ebola infection had minimal impact on functional connectivity. In contrast, the auditory, sensorimotor, and association visual networks showed marked, Ebola-related reductions in functional connectivity (see figure 1). Concurrent T1/T2 structural imaging did not reveal any gross lesions, although some generalized mild atrophy and ventricular dilation was present for some animals. Susceptibility weighted imaging revealed diffuse subarachnoid hemorrhage and venous congestion.

Discussion

The most likely explanation for network-specific disruptions in connectivity is a selective vulnerability in the disrupted networks with respect to (1) a general Ebola-related reduction in hemodynamic compliance [animals were generally at the time of the second scan), (2) local mass/pressure-effects from the hemorrhagic blood products leaked into sub-arachnoid regions around blood vessels, and/or (3) an Ebola triggered neuro-inflammatory response. Future studies, to include more time points and additional measures of regional blood flow and hemodynamic response, will help to clarify these mechanisms of action.

Conclusions

Functional brain imaging of Ebola infected NHP is possible, even when animals are maintained in a BSL-4 environment. Data indicate that some ICNs are more vulnerable to perturbation than others. This study sets the stage for future work to explore the neurobiological impact of Ebola on survivors.

Acknowledgements

Data collection was supported by the NIAID Division of Intramural Research and the NIAID DCR, and was performed under Battelle Memorial Institute contract (No. HHSN272200700016I) with NIAID. Data analyses were supported by internal development funds from the Mind Research Network and Lovelace Family of Companies.

References

[1] Chertow DS, Kleine C, Edwards JK, et al. (2014) Ebola virus disease in West Africa – clinical manifestations and management, N Engl J Med, 371(22) 2054-2057.

[2] Chiapelli F, Bakhordarian A, Thames AD, et al. (2015) Ebola: Translational science considerations, J Trans Med., 13:11.

[3] Clark DV, Kibuuka H, Millard M, et al. (2015) Long-term sequelae after Ebola virus disease in Bundibugyo, Uganda: a retrospective cohort study, Lancet, 15(8): 905-912.

[4] AFNI (http://afni.nimh.nih.gov/afni)

[5] McLaren DG, Kosmatka KJ, Oaker TR, et al. (2009) A population-average MRI-based atlas collection of the rhesus monkey, Neuroimage, 45(1): 52-59.

[6] GIFT (http://mialab.mrn.org/software/gift)

[7] Allen EA, Erjardt ED. Damaraju E, et al. (2011) A baseline for the multivariate comparison of resting-state networks, Front Syst Neurosci., 5(2).

[8] Hutchison RM, Leung LS, Mirsattari SM, et al. (2011) Resting- state networks in the macaque at 7 T. NeuroImage. 56, 1546-1555.

[9] Hutchison RM, Everling S. (2012) Monkey in the middle: Why nonhuman primates are needed to bridge the gap in resting-state investigations. Frontiers in Neuroanatomy. 6:29.

Figures

Figure 1: Select slices from example intrinsic connectivity networks without and with Ebola-related changes.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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