Long-term alterations in brain and behavior after postnatal Zika virus infection in infant macaques

Zika virus (ZIKV) infection has a profound impact on the fetal nervous system. The postnatal period is also a time of rapid brain growth, and it is important to understand the potential neurobehavioral consequences of ZIKV infection during infancy. Here we show that postnatal ZIKV infection in a rhesus macaque model resulted in long-term behavioral, motor, and cognitive changes, including increased emotional reactivity, decreased social contact, loss of balance, and deficits in visual recognition memory at one year of age. Structural and functional MRI showed that ZIKV-infected infant rhesus macaques had persistent enlargement of lateral ventricles, smaller volumes and altered functional connectivity between brain areas important for socioemotional behavior, cognitive, and motor function (e.g. amygdala, hippocampus, cerebellum). Neuropathological changes corresponded with neuroimaging results and were consistent with the behavioral and memory deficits. Overall, this study demonstrates that postnatal ZIKV infection in this model may have long-lasting neurodevelopmental consequences.


Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable:  Table 2), and rs-fMRI (Supplemental Table 3).

nature research | reporting summary
October 2018 For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design
All studies must disclose on these points even when the disclosure is negative. No data were excluded from the study. Data tables can be found in supplementary materials.
To date, no replication study has been performed. This study is an exploratory, proof-of-principle experiment.
Infants were pseudo-randomly assigned to either the experimental or control group based on birth date and sex.
For social interactions, the experimenter was not blind to the animals group, therefore videos were coded without identifying information about the subject, which was revealed after the coding was complete. GFAP (Agilent Technologies, M0761): From the website (https://www.agilent.com/en/product/immunohistochemistry/ antibodies-controls/primary-antibodies/glial-fibrillary-acidic-protein-(concentrate)-76524#productdetails Clone 6F2, Labels astrocytes and some CNS ependymal cells. The antibody is a useful aid for classification of tumors of uncertain origin. Immunogen GFAP from human brain, Isotype: IgG1, kappa. Monoclonal mouse antibody provided in liquid form as cell culture supernatant dialysed against 0.05 mol/L Tris-HCl, pH 7.2, and containing 15 mmol/L NaN3. Species : Mouse Anti-Human, Specificity : As demonstrated by immunocytochemistry, the antibody labels GFAP in astrocytes and cells of astrocytic origin. This antibody in non-human primates: Mavigner et al (2018)  Caspase 3 (Cell Signaling, 9662) From the website (https://www.cellsignal.com/products/primary-antibodies/caspase-3antibody/9662) Caspase-3 (CPP-32, Apoptain, Yama, SCA-1) is a critical executioner of apoptosis, as it is either partially or totally responsible for the proteolytic cleavage of many key proteins, such as the nuclear enzyme poly (ADP-ribose) polymerase (PARP) (1). Activation of caspase-3 requires proteolytic processing of its inactive zymogen into activated p17 and p12 fragments. No wild animals were used in this study.
No field-collected samples were used in this study. Macaques born at the Yerkes National Primate Research Center and were housed indoors on a 12hour light-dark cycle (7am-7pm). Macaques were socially housed in pairs with visual and auditory contact with another pair of the same age. all were fed a diet of Purina Primate Chow, Old World Monkey formulation, supplemented with daily fruits and vegetables. Water was provided ad libitum.
This study was conducted in strict accordance with U.S. Department of Agriculture regulations and the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health, approved by the Emory University Institutional Animal Care and Use Committee, and conducted in an AAALAC accredited facility.
Structural and resting-state functional (rs-fMRI) MRI scans 8 T1 images, 3 T2 images, 2x15 min rs-fMRI EPI sequences with an additional short reverse-phase encoding scan were collected No behavioral task was conducted in the MRI. MRI scans were performed under anesthesia and only resting-state fMRI was analyzed.

Whole brain
sMRI: AutoSeg uses the ANTS registration tool (Grossman et al. 2008;Wang et al. 2014) to register each atlas/template image to the subjects' brain image. In order to do this, ANTS uses a cross-correlation similarity metric and a symmetric diffeomorphic deformation model to preserve the geometric properties of the subject image even if large distortions are needed for the registration. rs-fMRI: After the EPI functional time series were concatenated and rigid-body co-registered to the subject's averaged T1-weighted structural image, the T1-weighted structural images were transformed to conform to the age-specific sMRI: Image processing steps included averaging the T1 and the T2 images to improve signal-to-noise ratio, and intensity inhomogeneity correction, using N4-ITK bias field correction. rs-fMRI: As part of the noise and artifact reducing procedures, after file conversion, functional imaging series were: 1) unwarped using a reverse phase-encoding distortion correction method (TOPUP correction, Anderson et al. 2003), 2) slice-time corrected (for the even vs. odd slice intensity differences due to interleaved acquisition), 3) motion-corrected (rigid body motion correction within-run, linear registration from EPI to T1, and nonlinear registration from T1 to template applied all in one resampling step), and 4) normalized (signal normalization to a whole brain mode value gradient of 1000 was done to scale the BOLD values across subjects at an acceptable range to perform the rest of preprocessing steps). Additional pre-processing steps included: 1) functional signal detrending; 2) nuisance regressor removing (i.e. regression of rigid body head motion parameters in 6 directions, regression of the global whole-brain signal, regression of the ventricular and white matter functional signal (averaged from a ventricle-and a white matter mask, respectively), and regression of the first-order derivatives for the whole brain, ventricular and white matter signals); and 3) temporal low-pass filtering ( Proof-of-Principle study with small sample size, thus direct statistical analyses were not appropriate.
Proof-of-Principle study with small sample size, thus direct statistical analyses were not appropriate.
ROIs were defined based on the combined Lewis and Van Essen (2000)  Eklund et al, 2016 does not apply because this study was did not conduct voxelwise analyses MRI analyses were primarily descriptive due to the small sample size, thus did not require multiple comparisons correction