Bedside tools are needed to alert clinicians to the onset of ischemic stroke. Resting-state hemodynamics with optical intrinsic signal imaging (OIS) were assessed in mice before and after middle cerebral artery (MCA) stroke. OIS analysis included resting-state functional connectivity (FC), low frequency power (LFP, local brain activity), and temporal-shift delay (impaired perfusion). Immediately after stroke, there is a decrease in homotopic connectivity and an absence of LFP in stroke-affected hemisphere; perfusion deficits were more localized. Over 24 hours, LFP and delayed perfusion localized in the core MCA territory (matching TTC staining). Such biomarker development could translate to optical bedside technologies.
SignificanceStatistical inference in functional neuroimaging is complicated by the multiple testing problem and spatial autocorrelation. Common methods in functional magnetic resonance imaging to control the familywise error rate (FWER) include random field theory (RFT) and permutation testing. The ability of these methods to control the FWER in optical neuroimaging has not been evaluated.AimWe attempt to control the FWER in optical intrinsic signal imaging resting-state functional connectivity using both RFT and permutation inference at a nominal value of 0.05. The FWER was derived using a mass empirical analysis of real data in which the null is known to be true.ApproachData from normal mice were repeatedly divided into two groups, and differences between functional connectivity maps were calculated with pixel-wise t-tests. As the null hypothesis was always true, all positives were false positives.ResultsGaussian RFT resulted in a higher than expected FWER with either cluster-based (0.15) or pixel-based (0.62) methods. t-distribution RFT could achieve FWERs of 0.05 (cluster-based or pixel-based). Permutation inference always controlled the FWER.ConclusionsRFT can lead to highly inflated FWERs. Although t-distribution RFT can be accurate, it is sensitive to statistical assumptions. Permutation inference is robust to statistical errors and accurately controls the FWER.
Significance: Resting-state functional connectivity imaging in mice with optical intrinsic signal (OIS) imaging could provide a powerful translational tool for developing imaging biomarkers in preclinical disease models. However, statistical interpretation of correlation coefficients is hampered by autocorrelations in the data.
Aim: We sought to better understand temporal and spatial autocorrelations in optical resting-state data. We then adapted statistical methods from functional magnetic resonance imaging to improve statistical inference.
Approach: Resting-state data were obtained from mice using a custom-built OSI system. The autocorrelation time was calculated at each pixel, and z scores for correlation coefficients were calculated using Fisher transforms and variance derived from either Bartlett’s method or xDF. The significance of each correlation coefficient was determined through control of the false discovery rate (FDR).
Results: Autocorrelation was generally even across the cortex and parcellation reduced variance. Correcting variance with Bartlett’s method resulted in a uniform reduction in z scores, with xDF preserving high z scores for highly correlated data. Control of the FDR resulted in reasonable thresholding of the correlation coefficient matrices. The use of Bartlett’s method compared with xDF results in more conservative thresholding and fewer false positives under null hypothesis conditions.
Conclusions: We developed streamlined methods for control of autocorrelation in OIS functional connectivity data in mice, and Bartlett’s method is a reasonable compromise and simplification that allows for accurate autocorrelation correction. These results improve the rigor and reproducibility of functional neuroimaging in mice.
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