Robust spatial extent inference with a semiparametric bootstrap joint inference procedure

Evaluation of resampling-based inference for topological features of neuroimages

Many recent studies have demonstrated the inflated type 1 error rate of the original Gaussian random field (GRF) methods for inference of neuroimages and identified resampling (permutation and bootstrapping) methods that have better performance. There has been no evaluation of resampling procedures when using robust (sandwich) statistical images with different topological features (TF) used for neuroimaging inference. Here, we consider estimation of distributions TFs of a statistical image and evaluate resampling procedures that can be used when exchangeability is violated. We compare the methods using realistic simulations and study sex differences in life-span age-related changes in gray matter volume in the Nathan Kline Institute Rockland sample. We find that our proposed wild bootstrap and the commonly used permutation procedure perform well in sample sizes above 50 under realistic simulations with heteroskedasticity. The Rademacher wild bootstrap has fewer assumptions than the permutation and performs similarly in samples of 100 or more, so is valid in a broader range of conditions. We also evaluate the GRF-based pTFCE method and show that it has inflated error rates in samples less than 200. Our R package, pbj , is available on Github and allows the user to reproducibly implement various resampling-based group level neuroimage analyses.

Permutation-based inference for spatially localized signals in longitudinal MRI data

Alzheimer's disease is a neurodegenerative disease in which the degree of cortical atrophy in specific structures of the brain serves as a useful imaging biomarker. Recent approaches using linear mixed effects (LME) models in longitudinal neuroimaging have been powerful and flexible in investigating the temporal trajectories of cortical thickness. However, massive-univariate analysis, a simplified approach that obtains a summary statistic (e.g., a p-value) for every vertex along the cortex, is insufficient to model cortical atrophy because it does not account for spatial similarities of the signals in neighboring locations. In this article, we develop a permutation-based inference procedure to detect spatial clusters of vertices showing statistically significant differences in the rates of cortical atrophy. The proposed method, called SpLoc, uses spatial information to combine the signals adaptively across neighboring vertices, yielding high statistical power while controlling family-wise error rate (FWER) accurately. When we reject the global null hypothesis, we use a cluster selection algorithm to detect the spatial clusters of significant vertices. We validate our method using simulation studies and apply it to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data to show its superior performance over existing methods. An R package for implementing SpLoc is publicly available.

Improving the replicability of neuroimaging findings by thresholding effect sizes instead of p-values

The classical approach for testing statistical images using spatial extent inference (SEI) thresholds the statistical image based on the p‐value. This approach has an unfortunate consequence on the replicability of neuroimaging findings because the targeted brain regions are affected by the sample size—larger studies have more power to detect smaller effects. Here, we use simulations based on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) to show that thresholding statistical images by effect sizes has more consistent estimates of activated regions across studies than thresholding by p‐values. Using a constant effect size threshold means that the p‐value threshold naturally scales with the sample size to ensure that the target set is similar across repetitions of the study that use different sample sizes. As a consequence of thresholding by the effect size, the type 1 and type 2 error rates go to zero as the sample size gets larger. We use a newly proposed robust effect size index that is defined for an arbitrary statistical image so that effect size thresholding can be used regardless of the test statistic or model.

Hyperactivity and Reduced Activation of Anterior Hippocampus in Early Psychosis

OBJECTIVE

In schizophrenia, the anterior hippocampus is hyperactive and shows reduced task-related recruitment, but the relationship between these two findings is unclear. The authors tested the hypothesis that hyperactivity impairs recruitment of the anterior hippocampus during scene processing.

METHODS

Functional MRI data from 45 early-psychosis patients and 35 demographically matched healthy control subjects were analyzed using a block-design 1-back scene-processing task. Hippocampal activation in response to scenes and faces compared with scrambled images was measured. In a subset of 20 early-psychosis patients and 31 healthy control subjects, baseline hippocampal activity using cerebral blood volume (CBV) mapping was measured. Correlation analyses were used to examine the association between baseline hippocampal activity and task-related hippocampal activation.

RESULTS

Activation of the anterior hippocampus was significantly reduced and CBV in the anterior hippocampus was significantly increased in the early stages of psychosis. Increased CBV in early-psychosis patients was inversely correlated with task-related activation during scene processing in the anterior hippocampus.

CONCLUSIONS

Anterior hippocampal hyperactivity in early-psychosis patients appears to limit effective recruitment of this region during task performance. These findings provide novel support for the anterior hippocampus as a therapeutic target in the treatment of cognitive deficits in psychosis.