Clinical, Biomarker, and Research Tests Among US Government Personnel and Their Family Members Involved in Anomalous Health Incidents.

Importance: Since 2015, US government and related personnel have reported dizziness, pain, visual problems, and cognitive dysfunction after experiencing intrusive sounds and head pressure. The US government has labeled these anomalous health incidents (AHIs). Objective: To assess whether participants with AHIs differ significantly from US government control participants with respect to clinical, research, and biomarker assessments. Design, Setting, and Participants: Exploratory study conducted between June 2018 and July 2022 at the National Institutes of Health Clinical Center, involving 86 US government staff and family members with AHIs from Cuba, Austria, China, and other locations as well as 30 US government control participants. Exposures: AHIs. Main Outcomes and Measures: Participants were assessed with extensive clinical, auditory, vestibular, balance, visual, neuropsychological, and blood biomarkers (glial fibrillary acidic protein and neurofilament light) testing. The patients were analyzed based on the risk characteristics of the AHI identifying concerning cases as well as geographic location. Results: Eighty-six participants with AHIs (42 women and 44 men; mean [SD] age, 42.1 [9.1] years) and 30 vocationally matched government control participants (11 women and 19 men; mean [SD] age, 43.8 [10.1] years) were included in the analyses. Participants with AHIs were evaluated a median of 76 days (IQR, 30-537) from the most recent incident. In general, there were no significant differences between participants with AHIs and control participants in most tests of auditory, vestibular, cognitive, or visual function as well as levels of the blood biomarkers. Participants with AHIs had significantly increased fatigue, depression, posttraumatic stress, imbalance, and neurobehavioral symptoms compared with the control participants. There were no differences in these findings based on the risk characteristics of the incident or geographic location of the AHIs. Twenty-four patients (28%) with AHI presented with functional neurological disorders. Conclusions and Relevance: In this exploratory study, there were no significant differences between individuals reporting AHIs and matched control participants with respect to most clinical, research, and biomarker measures, except for objective and self-reported measures of imbalance and symptoms of fatigue, posttraumatic stress, and depression. This study did not replicate the findings of previous studies, although differences in the populations included and the timing of assessments limit direct comparisons.

Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI.

BACKGROUND: Achieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. PURPOSE: Magnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. STUDY TYPE: Prospective observational study. SUBJECTS: A spherical isotropic diffusion phantom, and a single human control volunteer. FIELD STRENGTH/SEQUENCE: 3 T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm2 with 12, 32, and 384 diffusion gradient directions per shell. ASSESSMENT: We compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. STATISTICAL TESTS: Wilcoxon rank-sum test between uncorrected and corrected data. RESULTS: In phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p < 0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p < 0.05). CONCLUSION: Our method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters.

Consideration of Cerebrospinal Fluid Intensity Variation in Diffusion Weighted MRI.

Diffusion weighted MRI (DW-MRI) depends on accurate quantification signal intensities that reflect directional apparent diffusion coefficients (ADC). Signal drift and fluctuations during imaging can cause systematic non-linearities that manifest as ADC changes if not corrected. Here, we present a case study on a large longitudinal dataset of typical diffusion tensor imaging. We investigate observed variation in the cerebral spinal fluid (CSF) regions of the brain, which should represent compartments with isotropic diffusivity. The study contains 3949 DW-MRI acquisitions of the human brain with 918 subjects and 542 with repeated scan sessions. We provide an analysis of the inter-scan, inter-session, and intra-session variation and an analysis of the associations with the applied diffusion gradient directions. We investigate a hypothesis that CSF models could be used in lieu of an interspersed minimally diffusion-weighted image (b0) correction. Variation in CSF signal is not largely attributable to within-scan dynamic anatomical changes (3.6%), but rather has substantial variation across scan sessions (10.6%) and increased variation across individuals (26.6%). Unfortunately, CSF intensity is not solely explained by a main drift model or a gradient model, but rather has statistically significant associations with both possible explanations. Further exploration is necessary for CSF drift to be used as an effective harmonization technique.

Characterization and correlation of signal drift in diffusion weighted MRI.

Diffusion weighted MRI (DWMRI) and the myriad of analysis approaches (from tensors to spherical harmonics and brain tractography to body multi-compartment models) depend on accurate quantification of the apparent diffusion coefficient (ADC). Signal drift during imaging (e.g., due to b0 drift associated with heating) can cause systematic non-linearities that manifest as ADC changes if not corrected. Herein, we present a case study on two phantoms on one scanner. Different scan protocols exhibit different degrees of drift during similar scans and may be sensitive to the order of scans within an exam. Vos et al. recently reviewed the effects of signal drift in DWMRI acquisitions and proposed a temporal model for correction. We propose a novel spatial-temporal model to correct for higher order aspects of the signal drift and derive a statistically robust variant. We evaluate the Vos model and propose a method using two phantoms that mimic the ADC of the relevant brain tissue (0.36-2.2 × 10-3 mm2/s) on a single 3 T scanner. The phantoms are (1) a spherical isotropic sphere consisting of a single concentration of polyvinylpyrrolidone (PVP) and (2) an ice-water phantom with 13 vials of varying PVP concentrations. To characterize the impact of interspersed minimally weighted volumes ("b0's"), image volumes with b-value equal to 0.1 s/mm2 are interspersed every 8, 16, 32, 48, and 96 diffusion weighted volumes in different trials. Signal drift is found to have spatially varying effects that are not accounted for with temporal-only models. The novel model captures drift more accurately (i.e., reduces the overall change per-voxel over the course of a scan) and results in more consistent ADC metrics.

Tocotrienol vitamin E protects against preclinical canine ischemic stroke by inducing arteriogenesis.

Vitamin E consists of tocopherols and tocotrienols, in which α-tocotrienol is the most potent neuroprotective form that is also effective in protecting against stroke in rodents. As neuroprotective agents alone are insufficient to protect against stroke, we sought to test the effects of tocotrienol on the cerebrovascular circulation during ischemic stroke using a preclinical model that enables fluoroscopy-guided angiography. Mongrel canines (mean weight=26.3±3.2 kg) were supplemented with tocotrienol-enriched (TE) supplement (200 mg b.i.d, n=11) or vehicle placebo (n=9) for 10 weeks before inducing transient middle cerebral artery (MCA) occlusion. Magnetic resonance imaging was performed 1 hour and 24 hours post reperfusion to assess stroke-induced lesion volume. Tocotrienol-enriched supplementation significantly attenuated ischemic stroke-induced lesion volume (P<0.005). Furthermore, TE prevented loss of white matter fiber tract connectivity after stroke as evident by probabilistic tractography. Post hoc analysis of cerebral angiograms during MCA occlusion revealed that TE-supplemented canines had improved cerebrovascular collateral circulation to the ischemic MCA territory (P<0.05). Tocotrienol-enriched supplementation induced arteriogenic tissue inhibitor of metalloprotease 1 and subsequently attenuated the activity of matrix metalloproteinase-2. Outcomes of the current preclinical trial set the stage for a clinical trial testing the effects of TE in patients who have suffered from transient ischemic attack and are therefore at a high risk for stroke.

Tensor classification of N-point correlation function features for histology tissue segmentation.

In this paper, we utilize the N-point correlation functions (N-pcfs) to construct an appropriate feature space for achieving tissue segmentation in histology-stained microscopic images. The N-pcfs estimate microstructural constituent packing densities and their spatial distribution in a tissue sample. We represent the multi-phase properties estimated by the N-pcfs in a tensor structure. Using a variant of higher-order singular value decomposition (HOSVD) algorithm, we realize a robust classifier that provides a multi-linear description of the tensor feature space. Validated results of the segmentation are presented in a case-study that focuses on understanding the genetic phenotyping differences in mouse placentae.

An imaging workflow for characterizing phenotypical change in large histological mouse model datasets.

MOTIVATION: This paper presents a workflow designed to quantitatively characterize the 3D structural attributes of macroscopic tissue specimens acquired at a micron level resolution using light microscopy. The specific application is a study of the morphological change in a mouse placenta induced by knocking out the retinoblastoma gene. RESULT: This workflow includes four major components: (i) serial section image acquisition, (ii) image preprocessing, (iii) image analysis involving 2D pair-wise registration, 2D segmentation and 3D reconstruction, and (iv) visualization and quantification of phenotyping parameters. Several new algorithms have been developed within each workflow component. The results confirm the hypotheses that (i) the volume of labyrinth tissue decreases in mutant mice with the retinoblastoma (Rb) gene knockout and (ii) there is more interdigitation at the surface between the labyrinth and spongiotrophoblast tissues in mutant placenta. Additional confidence stem from agreement in the 3D visualization and the quantitative results generated. AVAILABILITY: The source code is available upon request.