Formation mechanism of hairpin vortices in the wake of a truncated square cylinder in a duct

We investigate the laminar shedding of hairpin vortices in the wake of a truncated square cylinder placed in a duct, for Reynolds numbers around the critical threshold of the onset of vortex shedding. We single out the formation mechanism of the hairpin vortices by means of a detailed analysis of the flow patterns in the steady regime. We show that unlike in previous studies of similar structures, the dynamics of the hairpin vortices is entwined with that of the counter-rotating pair of streamwise vortices, which we found to be generated in the bottom part of the near wake (these are usually referred to as base vortices). In particular, once the hairpin structure is released, the base vortices attach to it, forming its legs, so these are streamwise, and not spanwise as previously observed in unconfined wakes or behind cylinders of lower aspect ratios. We also single out a trail of Omega-shaped vortices, generated between successive hairpin vortices through a mechanism that is analogous to that active in near-wall turbulence. Finally, we show how the dynamics of the structures we identified determine the evolution of the drag coefficients and Strouhal numbers when the Reynolds number varies.Comment: 17 pages, accepted for publication in the Journal of Fluid Mechanic

Differential aquaporin 4 expression during edema build-up and resolution phases of brain inflammation

<p>Abstract</p> <p>Background</p> <p>Vasogenic edema dynamically accumulates in many brain disorders associated with brain inflammation, with the critical step of edema exacerbation feared in patient care. Water entrance through blood-brain barrier (BBB) opening is thought to have a role in edema formation. Nevertheless, the mechanisms of edema resolution remain poorly understood. Because the water channel aquaporin 4 (AQP4) provides an important route for vasogenic edema resolution, we studied the time course of AQP4 expression to better understand its potential effect in countering the exacerbation of vasogenic edema.</p> <p>Methods</p> <p>Focal inflammation was induced in the rat brain by a lysolecithin injection and was evaluated at 1, 3, 7, 14 and 20 days using a combination of in vivo MRI with apparent diffusion coefficient (ADC) measurements used as a marker of water content, and molecular and histological approaches for the quantification of AQP4 expression. Markers of active inflammation (macrophages, BBB permeability, and interleukin-1β) and markers of scarring (gliosis) were also quantified.</p> <p>Results</p> <p>This animal model of brain inflammation demonstrated two phases of edema development: an initial edema build-up phase during active inflammation that peaked after 3 days (ADC increase) was followed by an edema resolution phase that lasted from 7 to 20 days post injection (ADC decrease) and was accompanied by glial scar formation. A moderate upregulation in AQP4 was observed during the build-up phase, but a much stronger transcriptional and translational level of AQP4 expression was observed during the secondary edema resolution phase.</p> <p>Conclusions</p> <p>We conclude that a time lag in AQP4 expression occurs such that the more significant upregulation was achieved only after a delay period. This change in AQP4 expression appears to act as an important determinant in the exacerbation of edema, considering that AQP4 expression is insufficient to counter the water influx during the build-up phase, while the second more pronounced but delayed upregulation is involved in the resolution phase. A better pathophysiological understanding of edema exacerbation, which is observed in many clinical situations, is crucial in pursuing new therapeutic strategies.</p

Mult Scler

Background: Investigating the degeneration of specific thalamic nuclei in multiple sclerosis (MS) remains challenging. Methods: White-matter-nulled (WMn) MPRAGE, MP-FLAIR, and standard T1-weighted magnetic resonance imaging (MRI) were performed on MS patients (n = 15) and matched controls (n = 12). Thalamic lesions were counted in individual sequences and lesion contrast-to-noise ratio (CNR) was measured. Volumes of 12 thalamic nuclei were measured using an automatic segmentation pipeline specifically developed for WMn-MPRAGE. Results: WMn-MPRAGE showed more thalamic MS lesions (n = 35 in 9 out of 15 patients) than MP-FLAIR (n = 25) and standard T1 (n = 23), which was associated with significant improvement of CNR (p < 0.0001). MS patients had whole thalamus atrophy (p = 0.003) with lower volumes found for the anteroventral (p < 0.001), the pulvinar (p < 0.0001), and the habenular (p = 0.004) nuclei. Conclusion: WMn-MPRAGE and automatic thalamic segmentation can highlight thalamic MS lesions and measure patterns of focal thalamic atrophy. © The Author(s), 2019.Translational Research and Advanced Imaging LaboratoryBordeaux Region Aquitaine Initiative for Neuroscienc

Regional hippocampal vulnerability in early multiple sclerosis: a dynamic pathological spreading from dentate gyrus to CA1

"This is the peer reviewed version of the following article: Planche, V., Koubiyr, I., Romero, J. E., Manjon, J. V., Coupé, P., Deloire, M., ... & Tourdias, T. (2018). Regional hippocampal vulnerability in early multiple sclerosis: Dynamic pathological spreading from dentate gyrus to CA 1. Human brain mapping, 39(4), 1814-1824., which has been published in final form at https://doi.org/10.1002/hbm.23970. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Background: Whether hippocampal subfields are differentially vulnerable at the earliest stages of multiple sclerosis (MS) and how this impacts memory performance is a current topic of debate. Method: We prospectively included 56 persons with clinically isolated syndrome (CIS) suggestive of MS in a 1-year longitudinal study, together with 55 matched healthy controls at baseline. Participants were tested for memory performance and scanned with 3T MRI to assess the volume of 5 distinct hippocampal subfields using automatic segmentation techniques. Results: At baseline, CA4/dentate gyrus was the only hippocampal subfield with a volume significantly smaller than controls (p < .01). After one year, CA4/dentate gyrus atrophy worsened (-6.4%, p < .0001) and significant CA1 atrophy appeared (both in the stratum-pyramidale and the stratum radiatum-lacunosum-moleculare, -5.6%, p < .001 and -6.2%, p < .01, respectively). CA4/dentate gyrus volume at baseline predicted CA1 volume one year after CIS (R-2 = 0.44 to 0.47, p < .001, with age, T2 lesion-load, and global brain atrophy as covariates). The volume of CA4/dentate gyrus at baseline was associated with MS diagnosis during follow-up, independently of T2-lesion load and demographic variables (p < .05). Whereas CA4/dentate gyrus volume was not correlated with memory scores at baseline, CA1 atrophy was an independent correlate of episodic verbal memory performance one year after CIS (beta = 0.87, p < .05). Conclusion: The hippocampal degenerative process spread from dentate gyrus to CA1 at the earliest stage of MS. This dynamic vulnerability is associated with MS diagnosis after CIS and will ultimately impact hippocampal-dependent memory performance.ARSEP Foundation; Bordeaux University Hospital; TEVA Laboratories; French Agence Nationale de la Recherche, Grant/Award Numbers: ANR-10-LABX-57, ANR-10-LABX-43, ANR-10-IDEX-03-02, ANR-10-COHO-002; UPV, Grant/Award Numbers: UPV2016-0099, TIN2013-43457-R; Ministerio de Economia y competitividadPlanche, V.; Koubiyr, I.; Romero Gómez, JE.; Manjón Herrera, JV.; Coupe, P.; Deloire, M.; Dousset, V.... (2018). Regional hippocampal vulnerability in early multiple sclerosis: a dynamic pathological spreading from dentate gyrus to CA1. Human Brain Mapping. 39(4):1814-1824. https://doi.org/10.1002/hbm.23970S18141824394Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033-2044. doi:10.1016/j.neuroimage.2010.09.025Bakker, A., Kirwan, C. B., Miller, M., & Stark, C. E. L. (2008). Pattern Separation in the Human Hippocampal CA3 and Dentate Gyrus. Science, 319(5870), 1640-1642. doi:10.1126/science.1152882Coupé, P., Manjón, J. V., Chamberland, M., Descoteaux, M., & Hiba, B. (2013). Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage, 83, 245-261. doi:10.1016/j.neuroimage.2013.06.030De Stefano, N., Airas, L., Grigoriadis, N., Mattle, H. P., O’Riordan, J., Oreja-Guevara, C., … Kieseier, B. C. (2014). Clinical Relevance of Brain Volume Measures in Multiple Sclerosis. CNS Drugs, 28(2), 147-156. doi:10.1007/s40263-014-0140-zDu, A. T., Schuff, N., Kramer, J. H., Ganzer, S., Zhu, X. P., Jagust, W. J., … Weiner, M. W. (2004). Higher atrophy rate of entorhinal cortex than hippocampus in AD. Neurology, 62(3), 422-427. doi:10.1212/01.wnl.0000106462.72282.90Dutta, R., Chang, A., Doud, M. K., Kidd, G. J., Ribaudo, M. V., Young, E. A., … Trapp, B. D. (2011). Demyelination causes synaptic alterations in hippocampi from multiple sclerosis patients. Annals of Neurology, 69(3), 445-454. doi:10.1002/ana.22337De Flores, R., La Joie, R., & Chételat, G. (2015). Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience, 309, 29-50. doi:10.1016/j.neuroscience.2015.08.033Fraser, M. A., Shaw, M. E., & Cherbuin, N. (2015). A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. NeuroImage, 112, 364-374. doi:10.1016/j.neuroimage.2015.03.035Frisoni, G. B., Ganzola, R., Canu, E., Rub, U., Pizzini, F. B., Alessandrini, F., … Thompson, P. M. (2008). Mapping local hippocampal changes in Alzheimer’s disease and normal ageing with MRI at 3 Tesla. Brain, 131(12), 3266-3276. doi:10.1093/brain/awn280Gold, S. M., Kern, K. C., O’Connor, M.-F., Montag, M. J., Kim, A., Yoo, Y. S., … Sicotte, N. L. (2010). Smaller Cornu Ammonis 2–3/Dentate Gyrus Volumes and Elevated Cortisol in Multiple Sclerosis Patients with Depressive Symptoms. Biological Psychiatry, 68(6), 553-559. doi:10.1016/j.biopsych.2010.04.025Habbas, S., Santello, M., Becker, D., Stubbe, H., Zappia, G., Liaudet, N., … Volterra, A. (2015). Neuroinflammatory TNFα Impairs Memory via Astrocyte Signaling. Cell, 163(7), 1730-1741. doi:10.1016/j.cell.2015.11.023Hulst, H. E., Schoonheim, M. M., Van Geest, Q., Uitdehaag, B. M., Barkhof, F., & Geurts, J. J. (2015). Memory impairment in multiple sclerosis: Relevance of hippocampal activation and hippocampal connectivity. Multiple Sclerosis Journal, 21(13), 1705-1712. doi:10.1177/1352458514567727Jack, C. R., Petersen, R. C., Xu, Y., O’Brien, P. C., Smith, G. E., Ivnik, R. J., … Kokmen, E. (2000). Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology, 55(4), 484-490. doi:10.1212/wnl.55.4.484Jack, C. R., Barkhof, F., Bernstein, M. A., Cantillon, M., Cole, P. E., DeCarli, C., … Foster, N. L. (2011). Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimer’s & Dementia, 7(4), 474-485.e4. doi:10.1016/j.jalz.2011.04.007Kerchner, G. A., Bernstein, J. D., Fenesy, M. C., Deutsch, G. K., Saranathan, M., Zeineh, M. M., & Rutt, B. K. (2013). Shared Vulnerability of Two Synaptically-Connected Medial Temporal Lobe Areas to Age and Cognitive Decline: A Seven Tesla Magnetic Resonance Imaging Study. Journal of Neuroscience, 33(42), 16666-16672. doi:10.1523/jneurosci.1915-13.2013La Joie, R., Fouquet, M., Mézenge, F., Landeau, B., Villain, N., Mevel, K., … Chételat, G. (2010). Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence. NeuroImage, 53(2), 506-514. doi:10.1016/j.neuroimage.2010.06.024Longoni, G., Rocca, M. A., Pagani, E., Riccitelli, G. C., Colombo, B., Rodegher, M., … Filippi, M. (2013). Deficits in memory and visuospatial learning correlate with regional hippocampal atrophy in MS. Brain Structure and Function, 220(1), 435-444. doi:10.1007/s00429-013-0665-9Manjón, J. V., & Coupé, P. (2016). volBrain: An Online MRI Brain Volumetry System. Frontiers in Neuroinformatics, 10. doi:10.3389/fninf.2016.00030Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Manjón, J. V., Eskildsen, S. F., Coupé, P., Romero, J. E., Collins, D. L., & Robles, M. (2014). Nonlocal Intracranial Cavity Extraction. International Journal of Biomedical Imaging, 2014, 1-11. doi:10.1155/2014/820205Maruszak, A., & Thuret, S. (2014). Why looking at the whole hippocampus is not enough—a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer’s disease diagnosis. Frontiers in Cellular Neuroscience, 8. doi:10.3389/fncel.2014.00095Miller, D. H., Chard, D. T., & Ciccarelli, O. (2012). Clinically isolated syndromes. The Lancet Neurology, 11(2), 157-169. doi:10.1016/s1474-4422(11)70274-5Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Avedissian, C., … Madsen, S. K. (2009). Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Human Brain Mapping, 30(9), 2766-2788. doi:10.1002/hbm.20708Ny�l, L. G., & Udupa, J. K. (1999). On standardizing the MR image intensity scale. Magnetic Resonance in Medicine, 42(6), 1072-1081. doi:10.1002/(sici)1522-2594(199912)42:63.0.co;2-mPapadopoulos, D., Dukes, S., Patel, R., Nicholas, R., Vora, A., & Reynolds, R. (2009). Substantial Archaeocortical Atrophy and Neuronal Loss in Multiple Sclerosis. Brain Pathology, 19(2), 238-253. doi:10.1111/j.1750-3639.2008.00177.xPérez-Miralles, F., Sastre-Garriga, J., Tintoré, M., Arrambide, G., Nos, C., Perkal, H., … Montalban, X. (2013). Clinical impact of early brain atrophy in clinically isolated syndromes. Multiple Sclerosis Journal, 19(14), 1878-1886. doi:10.1177/1352458513488231Planche, V., Ruet, A., Coupé, P., Lamargue-Hamel, D., Deloire, M., Pereira, B., … Tourdias, T. (2016). Hippocampal microstructural damage correlates with memory impairment in clinically isolated syndrome suggestive of multiple sclerosis. Multiple Sclerosis Journal, 23(9), 1214-1224. doi:10.1177/1352458516675750Planche, V., Panatier, A., Hiba, B., Ducourneau, E.-G., Raffard, G., Dubourdieu, N., … Tourdias, T. (2017). Selective dentate gyrus disruption causes memory impairment at the early stage of experimental multiple sclerosis. Brain, Behavior, and Immunity, 60, 240-254. doi:10.1016/j.bbi.2016.11.010Planche, V., Ruet, A., Charré-Morin, J., Deloire, M., Brochet, B., & Tourdias, T. (2017). Pattern separation performance is decreased in patients with early multiple sclerosis. Brain and Behavior, 7(8), e00739. doi:10.1002/brb3.739Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., … Wolinsky, J. S. (2011). Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Annals of Neurology, 69(2), 292-302. doi:10.1002/ana.22366Rocca, M. A., Longoni, G., Pagani, E., Boffa, G., Colombo, B., Rodegher, M., … Filippi, M. (2015). In vivo evidence of hippocampal dentate gyrus expansion in multiple sclerosis. Human Brain Mapping, 36(11), 4702-4713. doi:10.1002/hbm.22946Romero, J. E., Coupe, P., & Manjón, J. V. (2016). High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion. Lecture Notes in Computer Science, 117-124. doi:10.1007/978-3-319-47118-1_15Romero, J. E., Coupé, P., & Manjón, J. V. (2017). HIPS: A new hippocampus subfield segmentation method. NeuroImage, 163, 286-295. doi:10.1016/j.neuroimage.2017.09.049Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., … Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59(4), 3774-3783. doi:10.1016/j.neuroimage.2011.11.032Sicotte, N. L., Kern, K. C., Giesser, B. S., Arshanapalli, A., Schultz, A., Montag, M., … Bookheimer, S. Y. (2008). Regional hippocampal atrophy in multiple sclerosis. Brain, 131(4), 1134-1141. doi:10.1093/brain/awn030Small, S. A. (2014). Isolating Pathogenic Mechanisms Embedded within the Hippocampal Circuit through Regional Vulnerability. Neuron, 84(1), 32-39. doi:10.1016/j.neuron.2014.08.030Stark, S. M., Yassa, M. A., Lacy, J. W., & Stark, C. E. L. (2013). A task to assess behavioral pattern separation (BPS) in humans: Data from healthy aging and mild cognitive impairment. Neuropsychologia, 51(12), 2442-2449. doi:10.1016/j.neuropsychologia.2012.12.014Thompson, P. M., Hayashi, K. M., de Zubicaray, G. I., Janke, A. L., Rose, S. E., Semple, J., … Toga, A. W. (2004). Mapping hippocampal and ventricular change in Alzheimer disease. NeuroImage, 22(4), 1754-1766. doi:10.1016/j.neuroimage.2004.03.040Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908Wang, L., Swank, J. S., Glick, I. E., Gado, M. H., Miller, M. I., Morris, J. C., & Csernansky, J. G. (2003). Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging☆. NeuroImage, 20(2), 667-682. doi:10.1016/s1053-8119(03)00361-6West, M. ., Coleman, P. ., Flood, D. ., & Troncoso, J. . (1994). Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. The Lancet, 344(8925), 769-772. doi:10.1016/s0140-6736(94)92338-8Winterburn, J. L., Pruessner, J. C., Chavez, S., Schira, M. M., Lobaugh, N. J., Voineskos, A. N., & Chakravarty, M. M. (2013). A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging. NeuroImage, 74, 254-265. doi:10.1016/j.neuroimage.2013.02.003Wisse, L. E. M., Daugherty, A. M., Olsen, R. K., Berron, D., Carr, V. A., … Stark, C. E. L. (2016). A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals? Hippocampus, 27(1), 3-11. doi:10.1002/hipo.22671Yushkevich, P. A., Amaral, R. S. C., Augustinack, J. C., Bender, A. R., Bernstein, J. D., Boccardi, M., … Zeineh, M. M. (2015). Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: Towards a harmonized segmentation protocol. NeuroImage, 111, 526-541. doi:10.1016/j.neuroimage.2015.01.00

Lumbar spinal muscles and spinal canal study by MRI three-dimensional reconstruction in adult lumbar spinal stenosis

Background: Lumbar spinal stenosis is degenerative disc disease most common manifestation. If stenosisdegree seems poorly related to symptom severity, lumbar muscles role is recognized. Many studiesreport imaging methods, to analyze muscle volumes and fat infiltration (FI), but remain limited due tothe difficulty to represent entire muscle volume variability. Recently a 3D muscle reconstruction protocol(using the deformation of a parametric specific object method (DPSO) and three-point Dixon images) wasreported. It offers the ability to evaluate, muscles volumes and muscle FI.Purpose: To describe, in a lumbar spinal stenosis population, muscle volumes, muscle FI and lumbarspinal canal volume with 3D MRI images reconstructions.Materials and methods: Ten adults presenting L4–L5 lumbar stenosis, were included. After specific MRIprotocol, three-dimensional, muscle and spinal canal, reconstructions were performed. Muscle (psoasand paraspinal muscles) volumes and fat infiltration (FI), the spinal canal volume, age, and height werecorrelated one to each other with Spearman correlation factor. An ANOVA was performed to evaluate theintervertebral level influence (P ≤ 0.05).Results: Muscle volumes correlated with height (r = 0.68 for psoas). Muscles FI correlated with age (r = 0.66for psoas) and lumbar spinal canal volume (r = 0.91). Psoas and paraspinal volumes were maximum atL3–L4 level whereas FI increased from L1–L2 to L5–S1 level.Discussion: These first results illustrate the importance to consider muscles entirely and report correla-tions between muscles FI, lumbar spinal canal volume and age; and between muscle volumes and patientsheight. Muscle degeneration seems more related to muscle FI than muscle volume.Level of evidence: 3.The authors declare that they have no competing interest

AJNR Am J Neuroradiol

Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.Translational Research and Advanced Imaging Laborator

Stroke

Background and Purpose-The aim of the present study was to evaluate the relationship between normal-appearing white matter (NAWM) integrity and postischemic stroke recovery in 4 main domains including cognition, mood, gait, and dependency. Methods-A prospective study was conducted, including patients diagnosed for an ischemic supratentorial stroke on a 3T brain MRI performed 24 to 72 hours after symptom onset. Clinical assessment 1 year after stroke included a Montreal Cognitive Assessment, an Isaacs set test, a Zazzo cancelation task, a Hospital Anxiety and Depression scale, a 10-meter walking test, and a modified Rankin Scale (mRS). Diffusion tensor imaging parameters in the NAWM were computed using FMRIB (Functional Magnetic Resonance Imaging of the Brain) Diffusion Toolbox. The relationships between mean NAWM diffusion tensor imaging parameters and the clinical scores were assessed using linear and ordinal regression analyses, including the volumes of white matter hyperintensities, gray matter, and ischemic stroke as radiological covariates. Results-Two hundred seven subjects were included (66±13 years old; 67% men; median National Institutes of Health Stroke Scale score, 3; interquartile range, 2-6). In the models including only radiological variables, NAWM fractional anisotropy was associated with the mRS and the cognitive scores. After adjusting for demographic confounders, NAWM fractional anisotropy remained a significant predictor of mRS (β=-0.24; P=0.04). Additional path analysis showed that NAWM fractional anisotropy had a direct effect on mRS (β=-0.241; P=0.001) and a less important indirect effect mediating white matter hyperintensity burden. Similar results were found with mean diffusivity, axial diffusivity, and radial diffusivity. In further subgroup analyses, a relationship between NAWM integrity in widespread white matter tracts, mRS, and Isaacs set test was found in right hemispheric strokes. Conclusions-NAWM diffusion tensor imaging parameters measured early after an ischemic stroke are independent predictors of functional outcome and may be additional markers to include in studies evaluating poststroke recovery. © 2020 Lippincott Williams and Wilkins. All rights reserved.Translational Research and Advanced Imaging Laborator

Radiology

Background: A target mismatch profile can identify good clinical response to recanalization after acute ischemic stroke, but does not consider region specificities. Purpose: To test whether location-weighted infarction core and mismatch, determined from diffusion and perfusion MRI performed in patients with acute stroke, could improve prediction of good clinical response to mechanical thrombectomy compared with a target mismatch profile. Materials and Methods: In this secondary analysis, two prospectively collected independent stroke data sets (2012–2015 and 2017–2019) were analyzed. From the brain before stroke (BBS) study data (data set 1), an eloquent map was computed through voxel-wise associations between the infarction core (based on diffusion MRI on days 1–3 following stroke) and National Institutes of Health Stroke Scale (NIHSS) score. The French acute multimodal imaging to select patients for mechanical thrombectomy (FRAME) data (data set 2) consisted of large vessel occlusion–related acute ischemic stroke successfully recanalized. From acute MRI studies (performed on arrival, prior to thrombectomy) in data set 2, target mismatch and eloquent (vs noneloquent) infarction core and mismatch were computed from the intersection of diffusion- and perfusion-detected lesions with the coregistered eloquent map. Associations of these imaging metrics with early neurologic improvement were tested in multivariable regression models, and areas under the receiver operating characteristic curve (AUCs) were compared. Results: Data sets 1 and 2 included 321 (median age, 69 years [IQR, 58–80 years]; 207 men) and 173 (median age, 74 years [IQR, 65–82 years]; 90 women) patients, respectively. Eloquent mismatch was positively and independently associated with good clinical response (odds ratio [OR], 1.14; 95% CI: 1.02, 1.27; P =.02) and eloquent infarction core was negatively associated with good response (OR, 0.85; 95% CI: 0.77, 0.95; P =.004), while noneloquent mismatch was not associated with good response (OR, 1.03; 95% CI: 0.98, 1.07; P =.20). Moreover, adding eloquent metrics improved the prediction accuracy (AUC, 0.73; 95% CI: 0.65, 0.81) compared with clinical variables alone (AUC, 0.65; 95% CI: 0.56, 0.73; P =.01) or a target mismatch profile (AUC, 0.67; 95% CI: 0.59, 0.76; P =.03). Conclusion: Location-weighted infarction core and mismatch on diffusion and perfusion MRI scans improved the identification of patients with acute stroke who would benefit from mechanical thrombectomy compared with the volume-based target mismatch profile. © RSNA, 2022.Translational Research and Advanced Imaging Laborator

Genetic variation and exercise-induced muscle damage: implications for athletic performance, injury and ageing.

Prolonged unaccustomed exercise involving muscle lengthening (eccentric) actions can result in ultrastructural muscle disruption, impaired excitation-contraction coupling, inflammation and muscle protein degradation. This process is associated with delayed onset muscle soreness and is referred to as exercise-induced muscle damage. Although a certain amount of muscle damage may be necessary for adaptation to occur, excessive damage or inadequate recovery from exercise-induced muscle damage can increase injury risk, particularly in older individuals, who experience more damage and require longer to recover from muscle damaging exercise than younger adults. Furthermore, it is apparent that inter-individual variation exists in the response to exercise-induced muscle damage, and there is evidence that genetic variability may play a key role. Although this area of research is in its infancy, certain gene variations, or polymorphisms have been associated with exercise-induced muscle damage (i.e. individuals with certain genotypes experience greater muscle damage, and require longer recovery, following strenuous exercise). These polymorphisms include ACTN3 (R577X, rs1815739), TNF (-308 G>A, rs1800629), IL6 (-174 G>C, rs1800795), and IGF2 (ApaI, 17200 G>A, rs680). Knowing how someone is likely to respond to a particular type of exercise could help coaches/practitioners individualise the exercise training of their athletes/patients, thus maximising recovery and adaptation, while reducing overload-associated injury risk. The purpose of this review is to provide a critical analysis of the literature concerning gene polymorphisms associated with exercise-induced muscle damage, both in young and older individuals, and to highlight the potential mechanisms underpinning these associations, thus providing a better understanding of exercise-induced muscle damage

Assessment of Translocator Protein Density, as Marker of Neuroinflammation, in Major Depressive Disorder: A Pilot, Multicenter, Comparative, Controlled, Brain PET Study (INFLADEP Study)

Background: Major depressive disorder (MDD) is a serious public health problem with high lifetime prevalence (4.4–20%) in the general population. The monoamine hypothesis is the most widespread etiological theory of MDD. Also, recent scientific data has emphasized the importance of immuno-inflammatory pathways in the pathophysiology of MDD. The lack of data on the magnitude of brain neuroinflammation in MDD is the main limitation of this inflammatory hypothesis. Our team has previously demonstrated the relevance of [18F] DPA-714 as a neuroinflammation biomarker in humans. We formulated the following hypotheses for the current study: (i) Neuroinflammation in MDD can be measured by [18F] DPA-714; (ii) its levels are associated with clinical severity; (iii) it is accompanied by anatomical and functional alterations within the frontal-subcortical circuits; (iv) it is a marker of treatment resistance.Methods: Depressed patients will be recruited throughout 4 centers (Bordeaux, Montpellier, Tours, and Toulouse) of the French network from 13 expert centers for resistant depression. The patient population will be divided into 3 groups: (i) experimental group—patients with current MDD (n = 20), (ii) remitted depressed group—patients in remission but still being treated (n = 20); and, (iii) control group without any history of MDD (n = 20). The primary objective will be to compare PET data (i.e., distribution pattern of neuroinflammation) between the currently depressed group and the control group. Secondary objectives will be to: (i) compare neuroinflammation across groups (currently depressed group vs. remitted depressed group vs. control group); (ii) correlate neuroinflammation with clinical severity across groups; (iii) correlate neuroinflammation with MRI parameters for structural and functional integrity across groups; (iv) correlate neuroinflammation and peripheral markers of inflammation across groups.Discussion: This study will assess the effects of antidepressants on neuroinflammation as well as its role in the treatment response. It will contribute to clarify the putative relationships between neuroinflammation quantified by brain neuroimaging techniques and peripheral markers of inflammation. Lastly, it is expected to open innovative and promising therapeutic perspectives based on anti-inflammatory strategies for the management of treatment-resistant forms of MDD commonly seen in clinical practice.Clinical trial registration (reference: NCT03314155): https://www.clinicaltrials.gov/ct2/show/NCT03314155?term=neuroinflammation&amp;cond=depression&amp;cntry=FR&amp;rank=