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.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents.

Importance: US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms. Objective: To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group. Design, Setting, and Participants: This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit. Exposure: AHIs. Main Outcomes and Measures: Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling. Results: Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs. Conclusions and Relevance: In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.

"Pscore": A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics.

BACKGROUND: Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSE: To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue. STUDY TYPE: Retrospective cohort. POPULATION: Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project. FIELD STRENGTH/SEQUENCE: 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE. ASSESSMENT: The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTS: ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95 (%), PEV<5 (%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations. RESULTS: The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (average PEV > 95 ¯ = PEV < 5 ¯ = 5 ± 0 % $$ \overline{{\mathrm{PEV}}_{>95}}=\overline{{\mathrm{PEV}}_{<5}}=5\pm 0\% $$ [SD]). DATA CONCLUSION: The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

'Pscore' - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics.

BACKGROUND: Quantitative MRI metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSE: To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, 'Pscore' to address this issue. STUDY TYPE: Retrospective cohort. POPULATION: 961 healthy young-adults (age:22-35 years, Females:53%) from the Human Connectome Project. FIELD STRENGTH/SEQUENCE: 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE. ASSESSMENT: The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU-atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate 'Pscores'- which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTS: ROI-wise distributions were assessed using Log transformations, Zscore, and the 'Pscore' methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV<5(%),PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (n=100) using 100 iterations. RESULTS: The dMRI metric distributions were systematically non-Gaussian, including positively skewed (e.g., mean and radial distributions PEV>95≠5%,PEV<5≠5% whereas 'Pscore' distributions were symmetric and balanced PEV>95=PEV<5=5%; even for small bootstrapped samples (average PEV>95¯=PEV<5¯=5±0%SD). DATA CONCLUSION: The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed 'Pscore' method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.

Hypoplasia of cerebellar afferent networks in Down syndrome revealed by DTI-driven tensor based morphometry.

Quantitative magnetic resonance imaging (MRI) investigations of brain anatomy in children and young adults with Down syndrome (DS) are limited, with no diffusion tensor imaging (DTI) studies covering that age range. We used DTI-driven tensor based morphometry (DTBM), a novel technique that extracts morphometric information from diffusion data, to investigate brain anatomy in 15 participants with DS and 15 age- and sex-matched typically developing (TD) controls, ages 6-24 years (mean age ~17 years). DTBM revealed marked hypoplasia of cerebellar afferent systems in DS, including fronto-pontine (middle cerebellar peduncle) and olivo-cerebellar (inferior cerebellar peduncle) connections. Prominent gray matter hypoplasia was observed in medial frontal regions, the inferior olives, and the cerebellum. Very few abnormalities were detected by classical diffusion MRI metrics, such as fractional anisotropy and mean diffusivity. Our results highlight the potential importance of cerebro-cerebellar networks in the clinical manifestations of DS and suggest a role for DTBM in the investigation of other brain disorders involving white matter hypoplasia or atrophy.

Evaluating corrections for Eddy-currents and other EPI distortions in diffusion MRI: methodology and a dataset for benchmarking.

PURPOSE: To propose a methodology for assessment of algorithms that correct distortions due to motion, eddy-currents, and echo planar imaging in diffusion weighted images (DWIs). METHODS: The proposed method evaluates correction performance by measuring variability across datasets of the same object acquired with images having distortions in different directions, thereby overcoming the unavailability of ground-truth, undistorted DWIs. A comprehensive diffusion MRI dataset, collected using a suitable experimental design, is made available to the scientific community, consisting of three DWI shells (Bmax = 5000 s/mm2 ), 30 gradient directions, a replicate set of antipodal gradient directions, four phase-encoding directions, and three different head orientations. The proposed methodology was tested using the TORTOISE diffusion MRI processing pipeline. RESULTS: The median variability of the original distorted data was 123% higher for DWIs, 100-168% higher for tensor-derived metrics and 28-111% higher for MAPMRI metrics, than in the corrected versions. EPI distortions induced substantial variability, nearly comparable to the contribution of eddy-current distortions. CONCLUSIONS: The dataset and the evaluation strategy proposed herein enable quantitative comparison of different methods for correction of distortions due to motion, eddy-currents, and other EPI distortions, and can be useful in benchmarking newly developed algorithms.

Tensor-based morphometry using scalar and directional information of diffusion tensor MRI data (DTBM): Application to hereditary spastic paraplegia.

Tensor-based morphometry (TBM) performed using T1-weighted images (T1WIs) is a well-established method for analyzing local morphological changes occurring in the brain due to normal aging and disease. However, in white matter regions that appear homogeneous on T1WIs, T1W-TBM may be inadequate for detecting changes that affect specific pathways. In these regions, diffusion tensor MRI (DTI) can identify white matter pathways on the basis of their different anisotropy and orientation. In this study, we propose performing TBM using deformation fields constructed using all scalar and directional information provided by the diffusion tensor (DTBM) with the goal of increasing sensitivity in detecting morphological abnormalities of specific white matter pathways. Previously, mostly fractional anisotropy (FA) has been used to drive registration in diffusion MRI-based TBM (FA-TBM). However, FA does not have the directional information that the tensors contain, therefore, the registration based on tensors provides better alignment of brain structures and better localization of volume change. We compare our DTBM method to both T1W-TBM and FA-TBM in investigating differences in brain morphology between patients with complicated hereditary spastic paraplegia of type 11 (SPG11) and a group of healthy controls. Effect size maps of T1W-TBM of SPG11 patients showed diffuse atrophy of white matter. However, DTBM indicated that atrophy was more localized, predominantly affecting several long-range pathways. The results of our study suggest that DTBM could be a powerful tool for detecting morphological changes of specific white matter pathways in normal brain development and aging, as well as in degenerative disorders.

DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures.

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM.

The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI).

The NIH MRI Study of normal brain development sought to characterize typical brain development in a population of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity of the population of the United States. The study began in 1999 with data collection commencing in 2001 and concluding in 2007. The study was designed with the final goal of providing a controlled-access database; open to qualified researchers and clinicians, which could serve as a powerful tool for elucidating typical brain development and identifying deviations associated with brain-based disorders and diseases, and as a resource for developing computational methods and image processing tools. This paper focuses on the DTI component of the NIH MRI study of normal brain development. In this work, we describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data included in the database, along with database access requirements. For more details, visit http://www.pediatricmri.nih.gov. This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3 mm) DTI datasets from 274 unique subjects, and 193 high resolution (2.5 mm) DTI datasets from 152 unique subjects. Subjects range in age from 10 days (from date of birth) through 22 years. Additionally, a set of age-specific DTI templates are included. This forms one component of the larger NIH MRI study of normal brain development which also includes T1-, T2-, proton density-weighted, and proton magnetic resonance spectroscopy (MRS) imaging data, and demographic, clinical and behavioral data.

Investigation of vibration-induced artifact in clinical diffusion-weighted imaging of pediatric subjects.

It has been reported that mechanical vibrations of the magnetic resonance imaging scanner could produce spurious signal dropouts in diffusion-weighted images resulting in artifactual anisotropy in certain regions of the brain with red appearance in the Directionally Encoded Color maps. We performed a review of the frequency of this artifact across pediatric studies, noting differences by scanner manufacturer, acquisition protocol, as well as weight and position of the subject. We also evaluated the ability of automated and quantitative methods to detect this artifact. We found that the artifact may be present in over 50% of data in certain protocols and is not limited to one scanner manufacturer. While a specific scanner had the highest incidence, low body weight and positioning were also associated with appearance of the artifact for both scanner types evaluated, making children potentially more susceptible than adults. Visual inspection remains the best method for artifact identification. Software for automated detection showed very low sensitivity (10%). The artifact may present inconsistently in longitudinal studies. We discuss a published case report that has been widely cited and used as evidence to set policy about diagnostic criteria for determining vegetative state. That report attributed longitudinal changes in anisotropy to white matter plasticity without considering the possibility that the changes were caused by this artifact. Our study underscores the need to check for the presence of this artifact in clinical studies, analyzes circumstances for when it may be more likely to occur, and suggests simple strategies to identify and potentially avoid its effects.

Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain.

Metrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have been used in many studies of postnatal brain development. A common finding of previous studies is that these tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent inter-individual biological differences as well as experimental noise. Moreover, when comparing different studies, additional variability can be introduced by different acquisition protocols. In this study we examined scans of 61 individuals (aged 4-22 years) from the NIH MRI study of normal brain development. Two scans were collected with different protocols (low and high resolution). Our goal was to separate the contributions of biological variability and experimental noise to the overall measured variance, as well as to assess potential systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of interest. We found that biological variability for both FA and MD varies widely across brain regions; biological variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the splenium. These regions with high inter-individual biological variability are the most likely candidates for assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects, the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared with the higher resolution protocol. However, the majority of the regions did not show any age-protocol interaction, indicating similar trajectories were obtained irrespective of the protocol used.

DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions.

We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred to as blip-up blip-down acquisitions. DR-BUDDI can incorporate information from an undistorted structural MRI and also use diffusion-weighted images (DWI) to guide the registration, improving the quality of the registration in the presence of large deformations and in white matter regions. DR-BUDDI does not require the transformations for correcting blip-up and blip-down images to be the exact inverse of each other. Imposing the theoretical "blip-up blip-down distortion symmetry" may not be appropriate in the presence of common clinical scanning artifacts such as motion, ghosting, Gibbs ringing, vibrations, and low signal-to-noise. The performance of DR-BUDDI is evaluated with several data sets and compared to other existing blip-up blip-down correction approaches. The proposed method is robust and generally outperforms existing approaches. The inclusion of the DWIs in the correction process proves to be important to obtain a reliable correction of distortions in the brain stem. Methods that do not use DWIs may produce a visually appealing correction of the non-diffusion weighted images, but the directionally encoded color maps computed from the tensor reveal an abnormal anatomy of the white matter pathways.

DR-BUDDI: diffeomorphic registration for blip up-down diffusion imaging.

In this work we propose a novel method to correct echo planar imaging (EPI) distortions in diffusion MRI data acquired with reversed phase encoding directions ("blip-up blip-down" acquisitions). The transformation model is symmetric, diffeomorphic and capable of capturing large deformations. It can take advantage of a structural MRI target and include the contribution of diffusion weighted images, in addition to EPI images acquired without diffusion sensitization. The proposed correction significantly outperform existing strategies, assuring anatomically accurate characterization of the orientation, mean diffusivity, and anisotropy of white matter structures in the human brain.

A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies.

Diffusion tensor imaging (DTI) is commonly used for studies of the human brain due to its inherent sensitivity to the microstructural architecture of white matter. To increase sampling diversity, it is often desirable to perform multicenter studies. However, it is likely that the variability of acquired data will be greater in multicenter studies than in single-center studies due to the added confound of differences between sites. Therefore, careful characterization of the contributions to variance in a multicenter study is extremely important for meaningful pooling of data from multiple sites. We propose a two-step analysis framework for first identifying outlier datasets, followed by a parametric variance analysis for identification of intersite and intrasite contributions to total variance. This framework is then applied to phantom data from the NIH MRI study of normal brain development (PedsMRI). Our results suggest that initial outlier identification is extremely important for accurate assessment of intersite and intrasite variability, as well as for early identification of problems with data acquisition. We recommend the use of the presented framework at frequent intervals during the data acquisition phase of multicenter DTI studies, which will allow investigators to identify and solve problems as they occur.

Spectroscopic sensitive polarimeter for biomedical applications.

We present the design and calibration of a spectroscopic sensitive polarimeter. The polarimeter can measure the full Stokes vector in the wavelength range 550 to 750 nm with 1-nm resolution and consists of a fiber-based spectrophotometer, a white light emitting diode light source, two liquid crystal retarders, and one polarizer. Calibration of the system is achieved with a scheme that does not require knowledge of the polarizing elements' orientation or retardation. Six intensity spectra are required to calculate the full spectrum Stokes vector. Error in the polarimeter is less than 5%. We report the Stokes vectors for light transmitted through nonscattering polarizing elements as well as a measurement of the depolarizing properties of chicken muscle at several wavelengths.

Reliability, validity, and precision of an active stereophotogrammetry system for three-dimensional evaluation of the human torso.

To determine the reliability, stability, validity and precision of a stereophotogrammetry (SP) system for use in quantifying the complex three-dimensional structure of the human torso, we performed assessments of the system using images of geometric solids and a human-form mannequin. Analysis of geometric solids revealed excellent intra- and interrater reliability of the system for linear, surface area and volume measurements (r>0.99, P<0.001). Overall, no significant difference was found between SP and manual measurements (F=4.23, P>0.06). The system exhibited excellent stability in images of the mannequin over time (r>0.99). The limit of precision (error>5%) of the system to detect objects on the surface of the mannequin was estimated at an object size of 23.5cm(2) for surface area and 32mL for volume. These results demonstrate the capability of SP of the torso to be used as a reliable, stable and valid measure of torso morphology to be applied as a clinical outcome tool in studies of bony and soft tissue pathologies such as scoliosis, rib deformities, obesity or edema.