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.
Pediatric spinal cord morphometry has been relatively understudied because of non-optimal image quality due to the difficulty of spine imaging, rarity of post-mortem analysis, motion artifacts, and pediatric MR imaging research focus on understanding spinal injury or pathology. The pediatric brain has been comparatively well-studied with white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) differences observed with age and gender. Therefore, a greater understanding of pediatric cervical and thoracic spinal cord morphometry would be beneficial for developing clinically relevant cord growth models. We focused on retrospectively characterizing cervical and thoracic spinal cord growth and morphometry changes in a healthy pediatric population. High resolution multi-echo gradient echo (mFFE) images were acquired from pediatric spinal cord scans from 63 patients (mean: 9.24 years, range: 0.83-17.67 years). The mFFE scans were then registered to the template space for uniform viewing and analysis by using a customized semi-automatic processing pipeline involving Spinal Cord Toolbox (SCT). Jacobian control determinants were calculated, and subsequent WM, GM, dorsal column, lateral funiculi, and ventral funiculi scalar averaging was conducted. Random effects models were used to model age-related Jacobian scalar differences. Observing the growth of cord matter by patient age and vertebral level suggests that the upper cervical spinal cord, specifically C2-C3, and mid-thoracic spinal cord, T3-T8, grow faster than other cervical levels and thoracic levels, respectively. This knowledge will facilitate clinical decision making when considering spine interventions and conducting radiological analysis in children with cervical and thoracic spine abnormalities.
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.
KEYWORDS: Magnetic resonance imaging, Image fusion, Image segmentation, Detection and tracking algorithms, Medical imaging, Image processing algorithms and systems
In this work, we extend the joint-intensity fusion (JIF) algorithm to use normalized cross-correlation (NCC) as the weighting metric for use in the context of cross-modality MRI synthesis, rather than sum squared intensity differences (SSD). We evaluate our method using the Kirby dataset, by synthesizing a representative FLAIR from a target subject’s T1w image. The accuracy of the synthetic FLAIR can be confirmed using the FLAIR taken for the target subject during the imaging session. For each subject in the dataset, using NCC with JIF results in a 51% lower mean absolute error than using SSD.
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