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      Spaceflight-Associated Brain White Matter Microstructural Changes and Intracranial Fluid Redistribution

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          Abstract

          How is spaceflight associated with human brain white matter and extracellular fluid distribution? In this longitudinal analysis, increased extracellular fluid was observed in widespread areas at the base of the cerebrum and decreases along the posterior aspect of the vertex following spaceflight. After adjusting for extracellular fluid, there was altered white matter microstructure in areas encompassing the superior and inferior longitudinal fasciculi, the inferior fronto-occipital fasciculus, and the corticospinal tract following spaceflight. Spaceflight is associated with redistribution of brain extracellular fluids; white matter changes occur throughout the brain and, in some cases, are significantly associated with mission duration and postflight declines in balance. Spaceflight results in transient balance declines and brain morphologic changes; to our knowledge, the effect on brain white matter as measured by diffusion magnetic resonance imaging (dMRI), after correcting for extracellular fluid shifts, has not been examined. To map spaceflight-induced intracranial extracellular free water (FW) shifts and to evaluate changes in brain white matter diffusion measures in astronauts. We performed retrospective, longitudinal analyses on dMRI data collected between 2010 and 2015. Of the 26 astronauts’ dMRI scans released by the National Aeronautics and Space Administration Lifetime Surveillance of Astronaut Health, 15 had both preflight and postflight dMRI scans and were included in the final analyses. Data were analyzed between 2015 and 2018. Seven astronauts completed a space shuttle mission (≤30 days) and 8 completed a long-duration International Space Station mission (≤200 days). The dMRI scans were acquired for clinical monitoring; in this retrospective analysis, we analyzed brain FW and white matter diffusion metrics corrected for FW. We also obtained scores from computerized dynamic posturography tests of balance to assess brain-behavior associations. Of the 15 astronauts included, the median (SD) age was 47.2 (1.5) years; 12 were men, and 3 were women. We found a significant, widespread increase in FW volume in the frontal, temporal, and occipital lobes from before spaceflight to after spaceflight. There was also a significant decrease in FW in the posterior aspect of the vertex. All FW changes were significant and ranged from approximately 2.5% to 4.0% across brain regions. We observed white matter changes in the right superior and inferior longitudinal fasciculi, the corticospinal tract, and cerebellar peduncles. All white matter changes were significant and ranged from approximately 0.75% to 1.25%. Spaceflight mission duration was associated with cerebellar white matter change, and white matter changes in the superior longitudinal fasciculus were associated with the balance changes seen in the astronauts from before spaceflight to after spaceflight. Free water redistribution with spaceflight likely reflects headward fluid shifts occurring in microgravity as well as an upward shift of the brain within the skull. White matter changes were of a greater magnitude than those typically seen during the same period with healthy aging. Future, prospective assessments are required to better understand the recovery time and behavioral consequences of these brain changes. This study maps spaceflight-induced intracranial extracellular free water (FW) shifts and evaluates changes in brain white matter diffusion measures in astronauts following spaceflight.

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          Most cited references34

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          Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies

          Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/motion), language (written/spoken word recognition, spoken/no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.
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            Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry.

            Magnetic resonance imaging volumetry studies report inverted U-patterns with increasing white-matter (WM) volume into middle age suggesting protracted WM maturation compared with the cortical gray matter. Diffusion tensor imaging (DTI) is sensitive to degree and direction of water permeability in biological tissues, providing in vivo indices of WM microstructure. The aim of this cross-sectional study was to delineate age trajectories of WM volume and DTI indices in 430 healthy subjects ranging 8-85 years of age. We used automated regional brain volume segmentation and tract-based statistics of fractional anisotropy, mean, and radial diffusivity as markers of WM integrity. Nonparametric regressions were used to fit the age trajectories and to estimate the timing of maximum development and deterioration in aging. Although the volumetric data supported protracted growth into the sixth decade, DTI indices plateaued early in the fourth decade across all tested regions and then declined slowly into late adulthood followed by an accelerating decrease in senescence. Tractwise and voxel-based analyses yielded regional differences in development and aging but did not provide ample evidence in support of a simple last-in-first-out hypothesis of life-span changes.
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              Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture.

              This study investigates water diffusion changes in Wallerian degeneration. We measured indices derived from the diffusion tensor (DT) and T2-weighted signal intensities in the descending motor pathways of patients with small chronic lacunar infarcts of the posterior limb of the internal capsule on one side. We compared these measurements in the healthy and lesioned sides at different levels in the brainstem caudal to the primary lesion. We found that secondary white matter degeneration is revealed by a large reduction in diffusion anisotropy only in regions where fibers are arranged in isolated bundles of parallel fibers, such as in the cerebral peduncle. In regions where the degenerated pathway crosses other tracts, such as in the rostral pons, paradoxically there is almost no change in diffusion anisotropy, but a significant change in the measured orientation of fibers. The trace of the diffusion tensor is moderately increased in all affected regions. This allows one to differentiate secondary and primary fiber loss where the increase in trace is considerably higher. We show that DT-MRI is more sensitive than T2-weighted MRI in detecting Wallerian degeneration. Significant diffusion abnormalities are observed over the entire trajectory of the affected pathway in each patient. This finding suggests that mapping degenerated pathways noninvasively with DT-MRI is feasible. However, the interpretation of water diffusion data is complex and requires a priori information about anatomy and architecture of the pathway under investigation. In particular, our study shows that in regions where fibers cross, existing DT-MRI-based fiber tractography algorithms may lead to erroneous conclusion about brain connectivity.
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                Author and article information

                Journal
                JAMA Neurology
                JAMA Neurol
                American Medical Association (AMA)
                2168-6149
                January 23 2019
                Affiliations
                [1 ]Department of Applied Physiology and Kinesiology, College of Health and Human Performance, University of Florida, Gainesville
                [2 ]Department of Psychiatry, University of Utah, Salt Lake City
                [3 ]Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center, Houston
                [4 ]Deparments of Psychiatry and Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
                [5 ]KBRwyle, Houston, Texas
                [6 ]National Aeronautics and Space Administration Johnson Space Center, Houston, Texas
                [7 ]Department of Neurology, University of Florida, Gainesville
                Article
                10.1001/jamaneurol.2018.4882
                6459132
                30673793
                89ff8a4e-602f-49ec-8b24-cb338a209952
                © 2019
                History

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