This prospective study was approved by the Medical Ethics Committee of the Affiliated Hospital of Qinghai University, and an ethics approval letter was obtained (Ethics approval number: P-SL-2023-483). All participants provided written informed consent.<\/p>\n
Participants<\/p>\n
From August 2023 to December 2024, 41 individuals presenting with symptoms after rapid ascent to high-altitude areas were recruited from the Affiliated Hospital of Qinghai University. AMS diagnosis was based on the 2018 Lake Louise Score(LLS), which assesses four core symptoms: headache, gastrointestinal symptoms, fatigue and\/or weakness, and dizziness\/light-headedness2. The scoring criteria were as follows: 0\u2009=\u2009no symptoms, 1\u2009=\u2009mild, 2\u2009=\u2009moderate, and 3\u2009=\u2009severe symptoms. A total score\u2009\u2265\u20093, in the presence of a headache, was considered diagnostic for AMS.<\/p>\n
Among the 41 participants, 21 were assigned to the AMS group (median age: 28.00 years; 5 males) and 20 to the non-AMS group (median age: 29.50 years; 10 males). All participants were right-handed. Exclusion criteria included contraindications to MRI, a history of head trauma, psychiatric disorders, substance abuse, or recent medication use. To reduce confounding factors related to transient hypoxia or travel fatigue, all MR scans were performed at least 6\u00a0h after high-altitude exposure.<\/p>\n
MRI data acquisition<\/p>\n
All participants underwent MR examinations using a 3.0T MR scanner (uMR 880, United Imaging Healthcare, Shanghai, China) with a 32-channel head\/neck coil. Anatomical images were acquired using a three-dimensional T1-Weighted fast spoiled gradient-echo (3D T1WI-FSP) sequence with the following parameters: echo time(TE)\u2009=\u20093.1 ms; repetition time (TR)\u2009=\u20097.7 ms; inversion time\u2009=\u2009790 ms; flip angle\u2009=\u200910\u00b0; slice thickness\u2009=\u20091\u00a0mm; field of view (FOV)\u2009=\u2009232\u2009\u00d7\u2009256 mm2; matrix\u2009=\u2009232\u2009\u00d7\u2009256; voxel size\u2009=\u20091\u2009\u00d7\u20091\u2009\u00d7\u20091 mm3.Perfusion imaging was performed using a 3D-pCASL sequence with a 3D Gradient and Spin Echo (GRASE) readout and multiple post-labeling delays (mPLDs). The parameters were: TE\u2009=\u200913.94 ms; TR\u2009=\u20095786 ms; labeling pulses with a duration of 1800 ms21<\/a>; five PLDs\u2009=\u2009500, 1000, 1500, 2000, 2500 ms; slice thickness\u2009=\u20094\u00a0mm; FOV\u2009=\u2009224\u2009\u00d7\u2009224 mm2; matrix=64\u2009\u00d7\u200964; voxel size\u2009=\u20093.5\u2009\u00d7\u20093.5\u2009\u00d7\u2009 4 mm3. For each volunteer, label and control images were acquired to compute perfusion-weighted images.<\/p>\n Diffusion tensor imaging (DTI) data were obtained using a single-shot echo planar-based diffusion-weighted imaging sequence. The phase-encoding (PE) direction for the 48- direction dataset was chosen to be \u201cPA\u201d and a separate, shorter (two b\u2009=\u20090 scans, ) in which the PE direction was reversed to \u201cAP\u201d. The reverse PE polarity data were used to estimate and correct image distortions. Field of View (FOV) =220\u2009\u00d7\u2009220 mm2, matrix\u2009=\u2009110\u2009\u00d7\u2009110, slice thickness\u2009=\u20092\u00a0mm, 40 slices, TR\/ TE\u2009=\u20094582\/64.8 ms, b\u2009=\u20090 and 1000\u00a0s\/mm2 with 48 directions.<\/p>\n Image analysis<\/p>\n All radiologists were blinded to participants\u2019 age, sex, group allocation, and clinical data during image analysis. For the DTI-ALPS index, all subject data were registered to the MNI152 standard space, and calculations were initially based on three preset region-of-interest (ROIs) with fixed coordinates. However, due to subtle individual anatomical variations, manual verification was required to ensure that the ROIs accurately covered the target structures (e.g., the regions surrounding the medullary veins) and to exclude artifacts. Therefore, the intraclass correlation coefficient (ICC) was employed to assess the reliability of this manual intervention. Two radiologists (Ya Guo and Shengbao Wen, with 6 and 18 years of neuroimaging experience, respectively) independently performed the quantification to evaluate inter-observer reliability. One radiologist (Ya Guo) repeated the measurements two weeks later to determine intra-observer reliability.<\/p>\n Image processingDTI-ALPS processing<\/p>\n DTI data were processed using MRtrix3 (https:\/\/www.mrtrix.org<\/a>) and the FMRIB Software Library (FSL version 6.0.6, https:\/\/fsl.fmrib.ox.ac.uk\/fsl\/<\/a>), in accordance with the UKB diffusion pipeline. The preprocessing initiated with Rician noise reduction through Marchenko-Pastur principal component analysis (MP-PCA) implemented in MRtrix3\u2019s dwidenoise module. Subsequent Gibbs ring artifact removal was performed using the mrdegibbs algorithm within the same software22<\/a>. To address susceptibility-induced distortions, eddy-current effect and motion correction were corrected through integrated application of FSL\u2019s topup and eddy tools. Non-brain tissues were removed with FSL\u2019s bet tool.<\/p>\n Diffusion tensor fitting was performed using FSL\u2019s dtifit, generating voxel-wise diffusivity maps (Dxx, Dxy, Dxz, Dyy, Dyz, Dzz), eigenvectors, and diffusion metrics, like fractional anisotropy (FA). Each subject\u2019s FA map was linearly registered to the standard FSL_HCP1065_FA_1mm template, and the same transformation matrix was applied to the color-coded FA map, and principal diffusivity components (Dxx, Dyy, Dzz) to ensure consistent spatial alignment across subjects.<\/p>\n On a color-coded FA map of the plane at the level of the lateral ventricle body, spherical ROIs with 5\u00a0mm diameter were manually delineated in the area of the projection fibers12<\/a>, the association fibers, and the subcortical fibers, respectively, in the bilateral cerebral hemispheres. To ensure anatomical precision, ROI placement was independently validated by trained neuroradiologists to ensure anatomical accuracy and avoid artifacts.<\/p>\n The mean diffusivities along the x-axis (Dxx), y-axis (Dyy), and z-axis (Dzz) were extracted from each ROI and recorded as Dproj, xx, Dproj, yy, Dassoc, xx, and Dassoc, zz, respectively. The DTI-ALPS index was calculated according to Taoka et al.12<\/a> using the formula:<\/p>\n $${\\text{ALPS index}} = {\\text{mean (Dxproj, Dxassoc)\/mean (Dyproj, Dzassoc)}}{\\text{.}}$$<\/p>\n mPLD processing<\/p>\n ASL data were processed using oxford_asl from the FSL toolbox (FMRIB Software Library, version 6.0.6, https:\/\/fsl.fmrib.ox.ac.uk\/fsl<\/a>), incorporating post-labeling delays of 0.5, 1, 1.5, 2, 2.5\u00a0s. The quantification process utilized the simplified kinetic model as recommended by the ISMRM consensus21<\/a>, assuming a labeling duration of 1.80\u00a0s and T\u2081 relaxation times of 1.30\u00a0s for tissue and 1.65\u00a0s for blood. Arterial transit time (ATT) maps were also quantified from the multi-PLD data. Motion correction was applied, and perfusion was quantified in absolute units (ml\/100\u00a0g\/min) using calibration with an M\u2080 image. Structural images were processed with fsl_anat, which includes bias field correction, brain extraction, tissue segmentation, and registration to MNI152 standard space. The resulting perfusion and ATT maps were then transformed into standard space for further analysis.<\/p>\n For regional CBF quantification, the predefined ROI masks in the MNI152 standard space were derived from three complementary, well-validated neuroimaging atlases to ensure anatomical accuracy and reproducibility: (1) the Harvard-Oxford Structural Atlas, which was used to delineate cerebral cortex, white matter and hippocampus ROIs; (2) the JHU White-Matter Tractography Atlas, utilized for precise localization of white matter fiber tract ROIs (e.g., corpus callosum); and (3) the MNI Structural Atlas, which was used to delineate subcortical gray matter ROIs (e.g., frontal lobe, ).The selected ROIs included the corpus callosum, frontal lobe, left cerebral cortex, left cerebral white matter, left hippocampus, right cerebral cortex, right cerebral white matter, right hippocampus, and temporal lobe. These ROIs were specifically selected based on their pathophysiological relevance to AMS. Previous studies have identified the corpus callosum, particularly the splenium, as the most sensitive site for high-altitude cerebral edema23<\/a>, while the hippocampus has been shown to possess the weakest tolerance to hypoxia compared to other brain regions24<\/a>. These ROIs were applied directly to the normalized CBF and ATT maps in standard space, ensuring accurate extraction of regional perfusion and transit time values for further statistical analysis.<\/p>\n Statistical analysis<\/p>\n All statistical analyses were conducted using SPSS 26.0. Independent-samples t-tests or Mann-Whitney U tests were employed to assess differences in continuous variables, while the chi-square test was used to analyze differences in categorical variables. A one-way analysis of covariance (ANCOVA) with sex as a covariate was performed to control for potential confounding effects in group comparisons. Paired-samples t-tests were conducted to compare differences between the left and right cerebral hemispheres. Pearson correlation analysis was performed to examine associations between variables. Categorical variables were presented as percentages, whereas continuous variables were reported as means\u2009\u00b1\u2009standard deviations (SD) or medians with interquartile ranges (IQR). A P\u2009<\u20090.05 was considered statistically significant.<\/p>\n","protected":false},"excerpt":{"rendered":"This prospective study was approved by the Medical Ethics Committee of the Affiliated Hospital of Qinghai University, and…\n","protected":false},"author":2,"featured_media":60012,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[33273,50,33276,33274,33275,24954,2844,4875,2845,1289,2123,2843],"class_list":{"0":"post-60011","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-alps","8":"tag-acute-mountain-sickness","9":"tag-alps","10":"tag-arterial-spin-labeling","11":"tag-cerebral-blood-flow","12":"tag-diffusion-tensor-imaging-along-the-perivascular-space","13":"tag-glymphatic-system","14":"tag-humanities-and-social-sciences","15":"tag-medical-research","16":"tag-multidisciplinary","17":"tag-neurology","18":"tag-neuroscience","19":"tag-science"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ch\/116518622644663087","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/posts\/60011","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/comments?post=60011"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/posts\/60011\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/media\/60012"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/media?parent=60011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/categories?post=60011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ch\/wp-json\/wp\/v2\/tags?post=60011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}