Academic Journal
Neural connectivity patterns associated with high-altitude residence duration: An exploratory cross-validated multimodal MRI study.
| Τίτλος: | Neural connectivity patterns associated with high-altitude residence duration: An exploratory cross-validated multimodal MRI study. |
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| Συγγραφείς: | Guan F; School of Psychology, Third Military Medical University, Chongqing, China., Xie W; Department of Respiratory Disease, Xinqiao Hospital, Third Military Medical University, Chongqing, China., Wang F; Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China., Ma H; Plateau Brain Science Research Centre, Xizang University, Lhasa, China., Li Y; Department of Pediatrics, Fukang Hospital, Xizang University, Lhasa, China., Zhang Y; Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China. Electronic address: zhang22@tsinghua.org.cn., Peng K; Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China. Electronic address: pengkp@mail.tsinghua.edu.cn. |
| Πηγή: | Brain research bulletin [Brain Res Bull] 2026 Oct 15; Vol. 245, pp. 112127. Date of Electronic Publication: 2026 Sep 23. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Country of Publication: United States NLM ID: 7605818 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2747 (Electronic) Linking ISSN: 03619230 NLM ISO Abbreviation: Brain Res Bull Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York Ny : Elsevier Science Original Publication: Phoenix, N. Y. ANKHO International Inc. |
| Ιατρικοί όροι (MeSH): | Brain*/physiology , Brain*/diagnostic imaging , Nerve Net*/physiology , Nerve Net*/diagnostic imaging , Altitude*, Magnetic Resonance Imaging/methods ; Multimodal Imaging/methods ; Brain Mapping/methods ; Neural Pathways/physiology ; Humans ; Male ; Female ; Adult ; Machine Learning |
| Περίληψη: | Living at high altitudes (HA) poses challenges to the brain due to hypobaric hypoxia. However, most neuroimaging studies have focused on group-level comparisons, leaving the brain features associated with individual differences in HA residence duration incompletely characterized. We therefore used a data-driven, individualized, multimodal machine learning framework for exploratory cross-validated association analysis to estimate individuals' HA residence duration after adjustment for age and gender. The analysis included 217 healthy migrants at HA and incorporated whole-brain anatomical morphometry, structural connectivity (SC), and functional connectivity (FC). Resting-state FC showed the highest estimate-target correlation among the unimodal models (r = 0.34, permutation p = 0.01, out-of-sample predictive R² = -0.15, MAE = 41.04 months, RMSE = 52.39 months). Combining FC and SC produced a numerically higher r and lower MAE and RMSE (r = 0.36, permutation p = 0.01, out-of-sample predictive R² = 0.00, MAE = 38.37 months, RMSE = 48.86 months) than FC alone, but the difference in correlation was not statistically significant. The FC model thus showed a statistically significant but modest cross-validated association with the residualized target, but its accuracy was insufficient to support clinical prediction or monitoring of HA residence duration. Meanwhile, FC within the somatomotor network (SMN) showed a positive contribution to model estimation, while FC between the SMN and the default mode network (DMN) showed a negative contribution. Positive contributions were observed for SC between the DMN and the frontoparietal network and for FC between the DMN and the dorsal attention network. SC between the thalamus and the posterior superior temporal sulcus (pSTS) showed a positive contribution to model estimation. Whether these residence-duration-associated connectivity patterns reflect within-person changes over time requires longitudinal investigation. As no behavioral validation was performed in this study, the functional significance of these connectivity patterns remains to be determined. (Copyright © 2026. Published by Elsevier Inc.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: High altitude; Machine learning; Multimodal analyses; Neural connectivity patterns |
| Entry Date(s): | Date Created: 20260923 Date Completed: 20261006 Latest Revision: 20261006 |
| Update Code: | 20261007 |
| DOI: | 10.1016/j.brainresbull.2026.112127 |
| PMID: | 42778069 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-2747 |
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| DOI: | 10.1016/j.brainresbull.2026.112127 |