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 (averagePEV > 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 P E V > 95 ≠ 5 % , P E V < 5 ≠ 5 % whereas 'Pscore' distributions were symmetric and balanced P E V > 95 = P E V < 5 = 5 % ; even for small bootstrapped samples (average P E V > 95 ¯ = P E V < 5 ¯ = 5 ± 0 % S D ).

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.

Enhancing the network specific individual characteristics in rs-fMRI functional connectivity by dictionary learning

Most fMRI inferences are based on analyzing the scans of a cohort. Thus, the individual variability of a subject is often overlooked in these studies. Recently, there has been a growing interest in individual differences in brain connectivity also known as individual connectome. Various studies have demonstrated the individual specific component of functional connectivity (FC), which has enormous potential to identify participants across consecutive testing sessions. Many machine learning and dictionary learning-based approaches have been used to extract these subject-specific components either from the blood oxygen level dependent (BOLD) signal or from the FC. In addition, several studies have reported that some resting-state networks have more individual-specific information than others. This study compares four different dictionary-learning algorithms that compute the individual variability from the network-specific FC computed from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data having 10 scans per subject. The study also compares the effect of two FC normalization techniques, namely, Fisher Z normalization and degree normalization on the extracted subject-specific components. To quantitatively evaluate the extracted subject-specific component, a metric named Overlap $$ Overlap $$ is proposed, and it is used in combination with the existing differential identifiability I diff $$ \left({I}_{diff}\right) $$ metric. It is based on the hypothesis that the subject-specific FC vectors should be similar within the same subject and different across different subjects. Results indicate that Fisher Z transformed subject-specific fronto-parietal and default mode network extracted using Common Orthogonal Basis Extraction (COBE) dictionary learning have the best features to identify a participant.

ALFF response interaction with learning during feedback in individuals with multiple sclerosis

Amplitude of low-frequency fluctuations (ALFF) is defined as changes of BOLD signal during resting state (RS) brain activity. Previous studies identified differences in RS activation between healthy and multiple sclerosis (MS) participants. However, no research has investigated the relationship between ALFF and learning in MS. We thus examine this here. Twenty-five MS and nineteen healthy participants performed a paired-associate word learning task where participants were presented with extrinsic or intrinsic performance feedback. Compared to healthy participants, MS participants showed higher local brain activation in the right thalamus. We also observed a positive correlation in the MS group between ALFF and extrinsic feedback within the left inferior frontal gyrus, and within the left superior temporal gyrus in association with intrinsic feedback. Healthy participants showed a positive correlation in the right fusiform gyrus between ALFF and extrinsic feedback. Findings suggest that while MS participants do not show a feedback learning impairment compared to the healthy participants, ALFF differences might suggest a general maladaptive pattern of task unrelated thalamic activation and adaptive activation in frontal and temporal regions. Results indicate that ALFF can be successfully used at capturing pathophysiological changes in local brain activation in MS in association with learning through feedback.

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearman's ρ = -0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders.

Higher limbic and basal ganglia volumes in surviving COVID-negative patients and the relations to fatigue

Background

Among systemic abnormalities caused by the novel coronavirus, little is known about the critical attack on the central nervous system (CNS). Few studies have shown cerebrovascular pathologies that indicate CNS involvement in acute patients. However, replication studies are necessary to verify if these effects persist in COVID-19 survivors more conclusively. Furthermore, recent studies indicate fatigue is highly prevalent among 'long-COVID' patients. How morphometry in each group relate to work-related fatigue need to be investigated.

Method

COVID survivors were MRI scanned two weeks after hospital discharge. We hypothesized, these survivors will demonstrate altered gray matter volume (GMV) and experience higher fatigue levels when compared to healthy controls, leading to stronger correlation of GMV with fatigue. Voxel-based morphometry was performed on T1-weighted MRI images between 46 survivors and 30 controls. Unpaired two-sample t-test and multiple linear regression were performed to observe group differences and correlation of fatigue with GMV.

Results

The COVID group experienced significantly higher fatigue levels and GMV of this group was significantly higher within the Limbic System and Basal Ganglia when compared to healthy controls. Moreover, while a significant positive correlation was observed across the whole group between GMV and self-reported fatigue, COVID subjects showed stronger effects within the Posterior Cingulate, Precuneus and Superior Parietal Lobule.

Conclusion

Brain regions with GMV alterations in our analysis align with both single case acute patient reports and current group level neuroimaging findings. We also newly report a stronger positive correlation of GMV with fatigue among COVID survivors within brain regions associated with fatigue, indicating a link between structural abnormality and brain function in this cohort.

Higher Limbic and Basal Ganglia volumes in surviving COVID-negative patients and the relations to fatigue

BACKGROUND

Among systemic abnormalities caused by the novel coronavirus, little is known about the critical attack on the central nervous system (CNS). Few studies have shown cerebrovascular pathologies that indicate CNS involvement in acute patients. However, replication studies are necessary to verify if these effects persist in COVID-19 survivors more conclusively. Furthermore, recent studies indicate fatigue is highly prevalent among 'long-COVID' patients. How morphometry in each group relate to work-related fatigue need to be investigated.

METHOD

COVID survivors were MRI scanned two weeks after hospital discharge. We hypothesized, these survivors will demonstrate altered gray matter volume (GMV) and experience higher fatigue levels when compared to healthy controls, leading to stronger correlation of GMV with fatigue. Voxel-based morphometry was performed on T1-weighted MRI images between 46 survivors and 30 controls. Unpaired two-sample t-test and multiple linear regression were performed to observe group differences and correlation of fatigue with GMV.

RESULTS

The COVID group experienced significantly higher fatigue levels and GMV of this group was significantly higher within the Limbic System and Basal Ganglia when compared to healthy controls. Moreover, while a significant positive correlation was observed across the whole group between GMV and self-reported fatigue, COVID subjects showed stronger effects within the Posterior Cingulate, Precuneus and Superior Parietal Lobule .

CONCLUSION

Brain regions with GMV alterations in our analysis align with both single case acute patient reports and current group level neuroimaging findings. We also newly report a stronger positive correlation of GMV with fatigue among COVID survivors within brain regions associated with fatigue, indicating a link between structural abnormality and brain function in this cohort.

Higher Limbic and Basal Ganglia volumes in surviving COVID-negative patients and the relations to fatigue.. [PMID: 34845462 PMCID: PMC8629206 DOI: 10.1101/2021.11.23.21266761]

Background:

Among systemic abnormalities caused by the novel coronavirus, little is known about the critical attack on the central nervous system (CNS). Few studies have shown cerebrovascular pathologies that indicate CNS involvement in acute patients. However, replication studies are necessary to verify if these effects persist in COVID-19 survivors more conclusively. Furthermore, recent studies indicate fatigue is highly prevalent among ‘long-COVID’ patients. How morphometry in each group relate to work-related fatigue need to be investigated.

Method:

COVID survivors were MRI scanned two weeks after hospital discharge. We hypothesized, these survivors will demonstrate altered gray matter volume (GMV) and experience higher fatigue levels when compared to healthy controls, leading to stronger correlation of GMV with fatigue. Voxel-based morphometry was performed on T1-weighted MRI images between 46 survivors and 30 controls. Unpaired two-sample t-test and multiple linear regression were performed to observe group differences and correlation of fatigue with GMV.

Results:

The COVID group experienced significantly higher fatigue levels and GMV of this group was significantly higher within the Limbic System and Basal Ganglia when compared to healthy controls. Moreover, while a significant positive correlation was observed across the whole group between GMV and self-reported fatigue, COVID subjects showed stronger effects within the Posterior Cingulate, Precuneus and Superior Parietal Lobule.

Conclusion:

Brain regions with GMV alterations in our analysis align with both single case acute patient reports and current group level neuroimaging findings. We also newly report a stronger positive correlation of GMV with fatigue among COVID survivors within brain regions associated with fatigue, indicating a link between structural abnormality and brain function in this cohort.

Striatal and prefrontal D2R and SERT distributions contrastingly correlate with default-mode connectivity

Although brain research has taken important strides in recent decades, the interaction and coupling of its different physiological levels is still not elucidated. Specifically, the molecular substrates of resting-state functional connectivity (rs-FC) remain poorly understood. The aim of this study was elucidating interactions between dopamine D2 receptors (D2R) and serotonin transporter (SERT) availabilities in the striatum (CPu) and medial prefrontal cortex (mPFC), two of the main dopaminergic and serotonergic projection areas, and the default-mode network. Additionally, we delineated its interaction with two other prominent resting-state networks (RSNs), the salience network (SN) and the sensorimotor network (SMN). To this extent, we performed simultaneous PET/fMRI scans in a total of 59 healthy rats using [¹¹C]raclopride and [¹¹C]DASB, two tracers used to image quantify D2R and SERT respectively. Edge, node and network-level rs-FC metrics were calculated for each subject and potential correlations with binding potentials (BP_ND) in the CPu and mPFC were evaluated. We found widespread negative associations between CPu D2R availability and all the RSNs investigated, consistent with the postulated role of the indirect basal ganglia pathway. Correlations between D2Rs in the mPFC were weaker and largely restricted to DMN connectivity. Strikingly, medial prefrontal SERT correlated both positively with anterior DMN rs-FC and negatively with rs-FC between and within the SN, SMN and the posterior DMN, underlining the complex role of serotonergic neurotransmission in this region. Here we show direct relationships between rs-FC and molecular properties of the brain as assessed by simultaneous PET/fMRI in healthy rodents. The findings in the present study contribute to the basic understanding of rs-FC by revealing associations between inter-subject variances of rs-FC and receptor and transporter availabilities. Additionally, since current therapeutic strategies typically target neurotransmitter systems with the aim of normalizing brain function, delineating associations between molecular and network-level brain properties is essential and may enhance the understanding of neuropathologies and support future drug development.

Elucidating the complementarity of resting-state networks derived from dynamic [18F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI

Functional connectivity (FC) and resting-state network (RSN) analyses using functional magnetic resonance imaging (fMRI) have evolved into a growing field of research and have provided useful biomarkers for the assessment of brain function in neurological disorders. However, the underlying mechanisms of the blood oxygen level-dependant (BOLD) signal are not fully resolved due to its inherent complexity. In contrast, [¹⁸F]fluorodeoxyglucose positron emission tomography ([¹⁸F]FDG-PET) has been shown to provide a more direct measure of local synaptic activity and may have additional value for the readout and interpretation of brain connectivity. We performed an RSN analysis from simultaneously acquired PET/fMRI data on a single-subject level to directly compare fMRI and [¹⁸F]FDG-PET-derived networks during the resting state. Simultaneous [¹⁸F]FDG-PET/fMRI scans were performed in 30 rats. Pairwise correlation analysis, as well as independent component analysis (ICA), were used to compare the readouts of both methods. We identified three RSNs with a high degree of similarity between PET and fMRI-derived readouts: the default-mode-like network (DMN), the basal ganglia network and the cerebellar-midbrain network. Overall, [¹⁸F]FDG connectivity indicated increased integration between different, often distant, brain areas compared to the results indicated by the more segregated fMRI-derived FC. Additionally, several networks exclusive to either modality were observed using ICA. These networks included mainly bilateral cortical networks of a limited spatial extent for fMRI and more spatially widespread networks for [¹⁸F]FDG-PET, often involving several subcortical areas.

This is the first study using simultaneous PET/fMRI to report RSNs subject-wise from dynamic [¹⁸F]FDG tracer delivery and BOLD fluctuations with both independent component analysis (ICA) and pairwise correlation analysis in small animals. Our findings support previous studies, which show a close link between local synaptic glucose consumption and BOLD-fMRI-derived FC. However, several brain regions were exclusively attributed to either [¹⁸F]FDG or BOLD-derived networks underlining the complementarity of this hybrid imaging approach, which may contribute to the understanding of brain functional organization and could be of interest for future clinical applications.

Widespread paralytic poliomyelitis in Pakistan: A case-control study to determine risk factors and implications for poliomyelitis eradication

Despite substantial efforts to eradicate poliomyelitis by administering oral poliovirus vaccine through routine immunization and annual national immunization days (NIDs), Pakistan reported 22% (1147) of the worldwide cases in 1997. Reasons for continued high poliomyelitis incidence include failure to vaccinate, vaccine failure, or inadequate immunization strategies. A case-control study was conducted to measure vaccination status and reasons for undervaccination among 66 poliomyelitis cases and 130 age- and neighborhood-matched controls. Cases were undervaccinated through routine immunization (matched odds ratio [MOR], 0.3; 95% confidence interval [CI], 0.1-0.5); however, NID immunization was similar for cases and controls (MOR, 0.6; 95% CI, 0.3-1.2). Reasons for undervaccination included not being informed, considering vaccination unimportant, and long distances to vaccination sites. Failure to vaccinate through routine immunization was a major risk factor for poliomyelitis in Pakistan. Successful NIDs alone will not interrupt poliovirus circulation in Pakistan, and children remain at risk unless routine immunization is strengthened or additional supplementary immunization is provided.