Reproducibility of quantitative structural and physiological MRI measurements

Shape-constrained deformable brain segmentation: Methods and quantitative validation

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

Reduced grey matter volume in adolescents with conduct disorder: a region-of-interest analysis using multivariate generalized linear modeling

BACKGROUND

Conduct disorder (CD) involves a group of behavioral and emotional problems that usually begins during childhood or adolescence. Structural brain alterations have been observed in CD, including the amygdala, insula, ventrolateral and medial prefrontal cortex, anterior cingulate cortex, and fusiform gyrus. The current study developed a multivariate generalized linear model (GLM) to differentiate adolescents with CD from typically developing (TD) adolescents in terms of grey matter volume (GMV).

METHODS

The whole-brain structural MRI data were collected from 96 adolescents with CD (mean age = [Formula: see text] years; mean IQ = [Formula: see text]; 63 males) and 90 TD individuals (mean age = [Formula: see text] years; mean IQ = [Formula: see text]; 59 males) matched on age, IQ, and sex. Region-wise GMV was extracted following whole-brain parcellation into 68 cortical and 14 subcortical regions for each participant. A multivariate GLM was developed to predict the GMV of the pre-hypothesized regions-of-interest (ROIs) based on CD diagnosis, with intracranial volume, age, sex, and IQ serving as the covariate.

RESULTS

A diagnosis of CD was a significant predictor for GMV in the right pars orbitalis, right insula, right superior temporal gyrus, left fusiform gyrus, and left amygdala (F(1, 180) = 5.460-10.317, p < 0.05, partial eta squared = 0.029-0.054). The CD participants had smaller GMV in these regions than the TD participants (MCD-MTD = [- 614.898] mm3-[- 53.461] mm3).

CONCLUSIONS

Altered GMV within specific regions may serve as a biomarker for the development of CD in adolescents. Clinical work can potentially target these biomarkers to treat adolescents with CD.

Reduced Grey Matter Volume in Adolescents with Conduct Disorder: A Region-of-Interest Analysis Using Multivariate Generalized Linear Modeling

Background

Conduct disorder (CD) involves a group of behavioral and emotional problems that usually begins during childhood or adolescence. Structural brain alterations have been observed in CD, including the amygdala, insula, ventrolateral and medial prefrontal cortex, anterior cingulate cortex, and fusiform gyrus. The current study developed a multivariate generalized linear model (GLM) to differentiate adolescents with CD from typically developing (TD) adolescents in terms of grey matter volume (GMV).

Methods

The whole-brain structural MRI data were collected from 96 adolescents with CD (mean age = years; mean IQ = ; 63 males) and 90 TD individuals (mean age = years; mean IQ = ; 59 males) matched on age, IQ, and sex. Region-wise GMV was extracted following whole-brain parcellation into 68 cortical and 14 subcortical regions for each participant. A multivariate GLM was developed to predict the GMV of the pre-hypothesized regions-of-interest (ROIs) based on CD diagnosis, with intracranial volume, age, sex, and IQ serving as the covariate.

Results

A diagnosis of CD was a significant predictor for GMV in the right pars orbitalis, right insula, right superior temporal gyrus, left fusiform gyrus, and left amygdala (F(1, 180) = 5.460 - 10.317, p < 0.05, partial eta squared = 0.029 - 0.054). The CD participants had smaller GMV in these regions than the TD participants (MCD - MTD = [-614.898] mm3 - [-53.461] mm3).

Conclusions

Altered GMV within specific regions may serve as a biomarker for the development of CD in adolescents. Clinical work can potentially target these biomarkers to treat adolescents with CD.

Imaging telomerase reverse transcriptase expression in oligodendrogliomas using hyperpolarized δ-[1-13C]-gluconolactone

Background

Telomere maintenance by telomerase reverse transcriptase (TERT) is essential for immortality in most cancers, including oligodendrogliomas. Agents that disrupt telomere maintenance such as the telomere uncapping agent 6-thio-2'-deoxyguanosine (6-thio-dG) are in clinical trials. We previously showed that TERT expression in oligodendrogliomas is associated with upregulation of glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the pentose phosphate pathway (PPP). We also showed that hyperpolarized δ-[1-13C]-gluconolactone metabolism to 6-phosphogluconate (6-PG) can be used to probe the PPP in glioblastomas. The goal of this study was to determine whether hyperpolarized 13C imaging using δ-[1-13C]-gluconolactone can monitor TERT expression and response to 6-thio-dG in oligodendrogliomas.

Methods

We examined patient-derived oligodendroglioma cells and orthotopic tumors to assess the link between TERT and hyperpolarized δ-[1-13C]-gluconolactone metabolism. We performed in vivo imaging to assess the ability of hyperpolarized δ-[1-13C]-gluconolactone to report on TERT and response to 6-thio-dG in rats bearing orthotopic oligodendrogliomas in vivo.

Results

Doxycycline-inducible TERT silencing abrogated 6-PG production from hyperpolarized δ-[1-13C]-gluconolactone in oligodendroglioma cells, consistent with the loss of G6PD activity. Rescuing TERT expression by doxycycline removal restored G6PD activity and, concomitantly, 6-PG production. 6-PG production from hyperpolarized δ-[1-13C]-gluconolactone demarcated TERT-expressing tumor from surrounding TERT-negative normal brain in vivo. Importantly, 6-thio-dG abrogated 6-PG production at an early timepoint preceding MRI-detectable alterations in rats bearing orthotopic oligodendrogliomas in vivo.

Conclusions

These results indicate that hyperpolarized δ-[1-13C]-gluconolactone reports on TERT expression and early response to therapy in oligodendrogliomas. Our studies identify a novel agent for imaging tumor proliferation and treatment response in oligodendroglioma patients.

Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort

Background

The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data.

Methods

We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions.

Results

Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%.

Conclusions

Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings.

Visualising spatial heterogeneity in glioblastoma using imaging habitats

Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.

Reliability of structural MRI measurements: The effects of scan session, head tilt, inter-scan interval, acquisition sequence, FreeSurfer version and processing stream

BACKGROUND

Large-scale longitudinal and multi-centre studies are used to explore neuroimaging markers of normal ageing, and neurodegenerative and mental health disorders. Longitudinal changes in brain structure are typically small, therefore the reliability of automated techniques is crucial. Determining the effects of different factors on reliability allows investigators to control those adversely affecting reliability, calculate statistical power, or even avoid particular brain measures with low reliability. This study examined the impact of several image acquisition and processing factors and documented the test-retest reliability of structural MRI measurements.

METHODS

In Phase I, 20 healthy adults (11 females; aged 20-30 years) were scanned on two occasions three weeks apart on the same scanner using the ADNI-3 protocol. On each occasion, individuals were scanned twice (repetition), after re-entering the scanner (reposition) and after tilting their head forward. At one year follow-up, nine returning individuals and 11 new volunteers were recruited for Phase II (11 females; aged 22-31 years). Scans were acquired on two different scanners using the ADNI-2 and ADNI-3 protocols. Structural images were processed using FreeSurfer (v5.3.0, 6.0.0 and 7.1.0) to provide subcortical and cortical volume, cortical surface area and thickness measurements. Intra-class correlation coefficients (ICC) were calculated to estimate test-retest reliability. We examined the effect of repetition, reposition, head tilt, time between scans, MRI sequence and scanner on reliability of structural brain measurements. Mean percentage differences were also calculated in supplementary analyses.

RESULTS

Using the FreeSurfer v7.1.0 longitudinal pipeline, we observed high reliability for subcortical and cortical volumes, and cortical surface areas at repetition, reposition, three weeks and one year (mean ICCs>0.97). Cortical thickness reliability was lower (mean ICCs>0.82). Head tilt had the greatest adverse impact on ICC estimates, for example reducing mean right cortical thickness to ICC=0.74. In contrast, changes in ADNI sequence or MRI scanner had a minimal effect. We observed an increase in reliability for updated FreeSurfer versions, with the longitudinal pipeline consistently having a higher reliability than the cross-sectional pipeline.

DISCUSSION

Longitudinal studies should monitor or control head tilt to maximise reliability. We provided the ICC estimates and mean percentage differences for all FreeSurfer brain regions, which may inform power analyses for clinical studies and have implications for the design of future longitudinal studies.

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.

Role of White Matter Microstructure in Impulsive Behavior

OBJECTIVE

Increased impulsivity is a hallmark trait of some neuropsychiatric illnesses, including addiction, traumatic brain injury, and externalizing disorders. The authors hypothesized that altered cerebral white matter microstructure may also underwrite normal individual variability in impulsive behaviors and tested this among healthy individuals.

METHODS

Impulsivity and diffusion tensor imaging (DTI) data were collected from 74 healthy adults (32 women; mean age=36.6 years [SD=13.6]). Impulsivity was evaluated using the Barratt Impulsiveness Scale-11, which provides a total score and scores for three subdomains: attentional, motor, and nonplanning impulsiveness. DTI was processed using the Enhancing Neuro Imaging Genetics Through Meta Analysis-DTI analysis pipeline to measure whole-brain and regional white matter fractional anisotropy (FA) values in 24 tracts.

RESULTS

Whole-brain total average FA was inversely correlated with motor impulsiveness (r=-0.32, p=0.007) and positively correlated with nonplanning impulsiveness (r=0.29, p=0.02); these correlations were significant after correction for multiple comparisons. Additional significant correlations were observed for motor impulsiveness and regional FA values for the corticospinal tract (r=-0.29, p=0.01) and for nonplanning impulsiveness and regional FA values for the superior fronto-occipital fasciculus (r=0.32, p=0.008).

CONCLUSIONS

These results provide initial evidence that the motor and nonplanning subdomains of impulsive behavior are linked to specific white matter microstructural connectivity, supporting the notion that impulsivity is in part a network-based construct involving white matter microstructural integrity among otherwise healthy populations.

White matter in prolonged glucocorticoid response to psychological stress in schizophrenia

Stress is implicated in psychosis etiology and exacerbation, but pathogenesis toward brain network alterations in schizophrenia remain unclear. White matter connects limbic and prefrontal regions responsible for stress response regulation, and white matter tissues are also vulnerable to glucocorticoid aberrancies. Using a novel psychological stressor task, we studied cortisol stress responses over time and white matter microstructural deficits in schizophrenia spectrum disorder (SSD). Cortisol was measured at baseline, 0-, 20-, and 40-min after distress induction by a psychological stressor task in 121 SSD patients and 117 healthy controls (HC). White matter microstructural integrity was measured by 64-direction diffusion tensor imaging. Fractional anisotropy (FA) in white matter tracts were related to cortisol responses and then compared to general patterns of white matter tract deficits in SSD identified by mega-analysis. Differences between 40-min post-stress and baseline, but not acute reactivity post-stress, was significantly elevated in SSD vs HC, time × diagnosis interaction F_2.3,499.9 = 4.1, p = 0.013. All SSD white matter tracts were negatively associated with prolonged cortisol reactivity but all tracts were positively associated with prolonged cortisol reactivity in HC. Individual tracts most strongly associated with prolonged cortisol reactivity were also most impacted in schizophrenia in general as established by the largest schizophrenia white matter study (r = -0.56, p = 0.006). Challenged with psychological stress, SSD and HC mount similar cortisol responses, and impairments arise in the resolution timeframe. Prolonged cortisol elevations are associated with the white matter deficits in SSD, in a pattern previously associated with schizophrenia in general.

Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help?

Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of multiple sclerosis pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair and neurodegeneration. Here, we aim to unify recent findings of grey matter circuits dynamics in multiple sclerosis within the framework of molecular and pathophysiological hallmarks combined with disease-related network reorganization, while highlighting advances from animal models (in vivo and ex vivo) and human clinical data (imaging and histological). We propose that MRI-based brain networks characterization is essential for better delineating ongoing pathology and elaboration of particular mechanisms that may serve for accurate modelling and prediction of disease courses throughout disease stages.

Protocol for magnetic resonance imaging acquisition, quality assurance, and quality check for the Accelerator program for Discovery in Brain disorders using Stem cells

OBJECTIVE

The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies.

METHODS

We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test-retest reliability of volumetric and diffusion measures.

RESULTS

Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test-retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole-brain, regional, and voxel-level.

CONCLUSION

The described acquisition and QA-QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal neuroimaging dataset will, upon its release, serve the scientific community well and pave the way for interesting discoveries.

Interaction of Sleep and Cortical Structural Maintenance From an Individual Person Microlongitudinal Perspective and Implications for Precision Medicine Research

Sleep and maintenance of brain structure are essential for the continuity of a person's cognitive/mental health. Interestingly, whether normal structural maintenance of the brain and sleep continuously interact in some way over day-week-month times has never been assessed at an individual-person level. This study used unconventional microlongitudinal sampling, structural magnetic resonance imaging, and n-of-1 analyses to assess normal interactions between fluctuations in the structural maintenance of cerebral cortical thickness and sleep duration for day, week, and multi-week intervals over a 6-month period in a healthy adult man. Correlation and time series analyses provided indications of "if-then," i.e., "if" this preceded "then" this followed, sleep-to-thickness maintenance and thickness maintenance-to-sleep bidirectional inverse interactions. Inverse interaction patterns were characterized by concepts of graded influences across nights, bilaterally positive relationships, continuity across successive weeks, and longer delayed/prolonged effects in the thickness maintenance-to-sleep than sleep-to-thickness maintenance direction. These interactions are proposed to involve normal circadian/allostatic/homeostatic mechanisms that continuously influence, and are influenced by, cortical substrate remodeling/turnover and sleep/wake cycle. Understanding interactions of individual person "-omics" is becoming a central interest in precision medicine research. The present n-of-1 findings contribute to this interest and have implications for precision medicine research use of a person's cortical structural and sleep "-omics" to optimize the continuous maintenance of that individual's cortical structure, sleep, and cognitive/mental health.

Neurovascular and cortical responses to hyperoxia: enhanced cognition and electroencephalographic activity despite reduced perfusion

KEY POINTS

Extreme aviation is accompanied by ever-present risks of hypobaric hypoxia and decompression sickness. Neuroprotection against those hazards is conferred through fractional inspired oxygen ( F I , O 2 ) concentrations of 60-100% (hyperoxia). Hyperoxia reduces global cerebral perfusion (gCBF), increases reactive oxygen species within the brain and leads to cell death within the hippocampus. However, an understanding of hyperoxia's effect on cortical activity and concomitant levels of cognitive performance is lacking. This limits our understanding of whether hyperoxia could lower the brain's threshold of tolerance to physiological stressors inherent to extreme aviation, such as high gravitational forces. This study aimed to quantify the impact of hyperoxia upon global cerebral perfusion (gCBF), cognitive performance and cortical electroencephalography (EEG). Hyperoxia evoked a rapid reduction in gCBF, yet cognitive performance and vigilance were enhanced. EEG measurements revealed enhanced alpha power, suggesting less desynchrony, within the cortical temporal regions. Collectively, this work suggests hyperoxia-induced brain hypoperfusion is accompanied by enhanced cognitive processing and cortical arousal.

ABSTRACT

Extreme aviators continually inspire hyperoxic gas to mitigate risk of hypoxia and decompression injury. This neuroprotection carries a physiological cost: reduced cerebral perfusion (CBF). As reduced CBF may increase vulnerability to ever-present physiological challenges during extreme aviation, we defined the magnitude and duration of hyperoxia-induced changes in CBF, cortical electrical activity and cognition in 30 healthy males and females. Magnetic resonance imaging with pulsed arterial spin labelling provided serial measurements of global CBF (gCBF), first during exposure to 21% inspired oxygen ( F I , O 2 ) followed by a 30-min exposure to 100% F I , O 2 . High-density EEG facilitated characterization of cortical activity during assessment of cognitive performance, also measured during exposure to 21% and 100% F I , O 2 . Acid-base physiology was measured with arterial blood gases. We found that exposure to 100% F I , O 2 reduced gCBF to 63% of baseline values across all participants. Cognitive performance testing at 21% F I , O 2 was accompanied by increased theta and beta power with decreased alpha power across multiple cortical areas. During cognitive testing at 100% F I , O 2 , alpha activity was less desynchronized within the temporal regions than at 21% F I , O 2 . The collective hyperoxia-induced changes in gCBF, cognitive performance and EEG were similar across observed partial pressures of arterial oxygen ( P a O 2 ), which ranged between 276-548 mmHg, and partial pressures of arterial carbon dioxide ( P aC O 2 ), which ranged between 34-50 mmHg. Sex did not influence gCBF response to 100% F I , O 2 . Our findings suggest hyperoxia-induced reductions in gCBF evoke enhanced levels of cortical arousal and cognitive processing, similar to those occurring during a perceived threat.

A phantom system for assessing the effects of membrane lipids on water proton relaxation

Quantitative MRI (qMRI) is a method for the non-invasive study of brain-structure-associated changes expressed with measurable units. The qMRI-derived parameters have been shown to reflect brain tissue composition such as myelin content. Nevertheless, it remains a major challenge to identify and quantify the contributions of specific molecular components to the MRI signal. Here, we describe a phantom system that can be used to evaluate the contribution of membrane lipids to qMRI-derived parameters. We used a hydration-dehydration dry film technique to formulate liposomes that can be used as a model of the bilayer lipid membrane. The liposomes were comprised of the most abundant types of lipid found in the human brain. We then applied clinically available qMRI techniques with adjusted bias corrections in order to test the ability of the phantom system to estimate multiple qMRI parameters such as proton density (PD), T₁ , T₂ , T₂ * and magnetization transfer. In addition, we accurately measured the phantom sample water fraction (normalized PD). A similar protocol was also applied to the human brain in vivo. The phantom system allows for a reliable estimation of qMRI parameters for phantoms composed of various lipid types using a clinical MRI scanner. We also found a comparable reproducibility between the phantom and in vivo human brain qMRI estimations. To conclude, we have successfully created a biologically relevant liposome phantom system whose lipid composition can be fully controlled. Our lipid system and analysis can be used to measure the contributions to qMRI parameters of membrane lipids found in the human brain under scanning conditions that are relevant to in vivo human brain scans. Such a model system can be used to test the contributions of lipidomic changes in normal and pathological brain states.

White matter and hypoxic hypobaria in humans

Occupational exposure to hypobaria (low atmospheric pressure) is a risk factor for reduced white matter integrity, increased white matter hyperintensive burden, and decline in cognitive function. We tested the hypothesis that a discrete hypobaric exposure will have a transient impact on cerebral physiology. Cerebral blood flow, fractional anisotropy of water diffusion in cerebral white matter, white matter hyperintensity volume, and concentrations of neurochemicals were measured at baseline and 24 hr and 72 hr postexposure in N = 64 healthy aircrew undergoing standard US Air Force altitude chamber training and compared to N = 60 controls not exposed to hypobaria. We observed that hypobaric exposure led to a significant rise in white matter cerebral blood flow (CBF) 24 hr postexposure that remained elevated, albeit not significantly, at 72 hr. No significant changes were observed in structural measurements or gray matter CBF. Subjects with higher baseline concentrations of neurochemicals associated with neuroprotection and maintenance of normal white matter physiology (glutathione, N-acetylaspartate, glutamate/glutamine) showed proportionally less white matter CBF changes. Our findings suggest that discrete hypobaric exposure may provide a model to study white matter injury associated with occupational hypobaric exposure.

Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data

Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.

Cerebral Biochemical Effect of Pregabalin in Patients with Painful Diabetic Neuropathy: A Randomized Controlled Trial

INTRODUCTION

With the development of new neuroimaging tools it has become possible to assess neurochemical alterations in patients experiencing chronic pain and to determine how these factors change during pharmacological treatment. The goal of this study was to examine the exact neurochemical mechanism underlying pregabalin treatment, utilizing magnetic resonance spectroscopy (¹H-MRS), in a population of patients with painful diabetic polyneuropathy (PDN), with the overall aim to ultimately objectify the clinical effect of pregabalin.

METHODS

A double blind, randomized, placebo-controlled study was conducted. A total of 27 patients with PDN were enrolled in the study, of whom 13 received placebo treatment (control group) and 14 received pregabalin (intervention group). Pregabalin treatment consisted of stepwise dose escalation over the study period from 75 mg daily ultimately to 600 mg daily. ¹H-MRS was performed at 3T on four regions of interest in the brain: the rostral anterior cingulate cortex (rACC), left and right thalamus and prefrontal cortex. The absolute concentrations of N-acetyl aspartate, glutamate, glutamine, gamma-amino-butyric-acid (GABA), glucose (Glc) and myo-inositol (mINS) were determined using LCModel.

RESULTS

The concentration of most neurometabolites in the placebo and pregabalin group did not significantly differ over time, with only a small significant difference in Glc level in the left thalamus (p = 0.049). Comparison of the effects of the different doses revealed significant differences for mINS in the rACC (baseline 2.42 ± 1.21 vs. 450 mg 1.58 ± 0.94; p = 0.022) and dorsolateral prefrontal cortex (75 mg 2.38 ± 0.89 vs. 450 mg 1.59 ± 0.85; p = 0.042) and also for GABA in the rACC (75 mg 0.53 ± 0.51 vs. 225 mg 0.28 ± 0.19; p = 0.014).

CONCLUSION

No differences were found in metabolite concentrations between the placebo (control) and intervention groups, but some differences, although small, were found between the different doses.

TRIAL REGISTRATION

This study is registered at ClinicalTrials.gov (NCT01180608).

FUNDING

Lyrica Independent Investigator Research Award (LIIRA) 2010 (Pfizer) funded the study.

Reproducibility of quantitative structural and physiological MRI measurements

INTRODUCTION

Quantitative longitudinal magnetic resonance imaging and spectroscopy (MRI/S) is used to assess progress of brain disorders and treatment effects. Understanding the significance of MRI/S changes requires knowledge of the inherent technical and physiological consistency of these measurements. This longitudinal study examined the variance and reproducibility of commonly used quantitative MRI/S measurements in healthy subjects while controlling physiological and technical parameters.

METHODS

Twenty-five subjects were imaged three times over 5 days on a Siemens 3T Verio scanner equipped with a 32-channel phase array coil. Structural (T1, T2-weighted, and diffusion-weighted imaging) and physiological (pseudocontinuous arterial spin labeling, proton magnetic resonance spectroscopy) data were collected. Consistency of repeated images was evaluated with mean relative difference, mean coefficient of variation, and intraclass correlation (ICC). Finally, a "reproducibility rating" was calculated based on the number of subjects needed for a 3% and 10% difference.

RESULTS

Structural measurements generally demonstrated excellent reproducibility (ICCs 0.872-0.998) with a few exceptions. Moderate-to-low reproducibility was observed for fractional anisotropy measurements in fornix and corticospinal tracts, for cortical gray matter thickness in the entorhinal, insula, and medial orbitofrontal regions, and for the count of the periependymal hyperintensive white matter regions. The reproducibility of physiological measurements ranged from excellent for most of the magnetic resonance spectroscopy measurements to moderate for permeability-diffusivity coefficients in cingulate gray matter to low for regional blood flow in gray and white matter.

DISCUSSION

This study demonstrates a high degree of longitudinal consistency across structural and physiological measurements in healthy subjects, defining the inherent variability in these commonly used sequences. Additionally, this study identifies those areas where caution should be exercised in interpretation. Understanding this variability can serve as the basis for interpretation of MRI/S data in the assessment of neurological disorders and treatment effects.