Human Brain Mapping最新文献

{"title":"Morphological Brain Analysis Using Ultra Low-Field MRI","authors":"Peter Hsu,&nbsp;Elisa Marchetto,&nbsp;Daniel K. Sodickson,&nbsp;Patricia M. Johnson,&nbsp;Jelle Veraart","doi":"10.1002/hbm.70232","DOIUrl":"https://doi.org/10.1002/hbm.70232","url":null,"abstract":"<p>Ultra low-field (ULF) MRI is an accessible neuroimaging modality that can bridge healthcare disparities and advance population-level brain health research. However, the inherently low signal-to-noise ratio of ULF-MRI often necessitates reductions in spatial resolution and, combined with the field-dependency of MRI contrast, challenges the accurate extraction of clinically relevant brain morphology. We evaluate the current state of ULF-MRI brain volumetry utilizing techniques for enhancing spatial resolution and leveraging recent advancements in brain segmentation. This is based on the agreement between ULF and corresponding high-field (HF) MRI brain volumes, and test–retest repeatability for multiple ULF scans. In this study, we find that accurate brain volumes can be measured from ULF-MRIs when combining orthogonal imaging directions for T₂-weighted images to form a higher resolution image volume. We also demonstrate that not all orthogonal imaging directions contribute equally to volumetric accuracy and provide a recommended scan protocol given the constraints of the current technology.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individualized Functional and Structural Language Lateralities in Temporal Lobe Epilepsy and Their Impact on Memory","authors":"Ankeeta Ankeeta,&nbsp;Qirui Zhang,&nbsp;Sam Sharifzadeh Javidi,&nbsp;Shilpi Modi,&nbsp;Michael R. Sperling,&nbsp;Joseph I. Tracy","doi":"10.1002/hbm.70250","DOIUrl":"https://doi.org/10.1002/hbm.70250","url":null,"abstract":"<p>The basis and impact of functional asymmetries in the brain, particularly language lateralization, are not fully understood, and the relationship between functional and structural asymmetries remains largely untested. This study investigated the degree to which asymmetries in hemispheric language laterality are concordant with asymmetries in gray matter (GM) structure and whether the hemispheric organization of memory is influenced by functional language asymmetries. Structural and functional MR data was acquired from 261 individuals, including those with unilateral temporal lobe epilepsy (LTLE = 96, RTLE = 69) and matched with healthy participants (HPs = 96). Functional language laterality indices (LIs) were calculated using two methods: (1) standard LIs from the frontal, temporal, and parietal lobes and (2) targeted LIs (T-LIs) from individually defined activation peaks. Structural LIs (ST-LIs) were derived from the GM volumes underlying these functional LIs. We observed significant shifts in language laterality in LTLE compared to HPs in 8 out of 12 brain regions. Strong correlations were observed between functional LIs and their structural counterparts. Discriminant analyses demonstrated that targeted LIs and ST-LIs more effectively distinguished TLE patients from HPs, with ST-LIs being the most powerful discriminator. Partial least squares analyses showed verbal and visual memory have a direct dependence on targeted LIs in HPs and LTLE, with this effect more pronounced in HPs. In RTLE, verbal memory showed a similar dependency. These findings underscored the importance of individualized, region-specific measures for understanding language laterality, its relation to structural underpinnings, and its impact on the organization of other cognitive functions such as memory.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Functional Connectivity Between Amygdala and Cognitive Control Network Predicts Delay Discounting in Older Adolescents","authors":"Attakias T. Mertens,&nbsp;Callum Goldsmith,&nbsp;Derek J. Pavelka,&nbsp;Jacob J. Oleson,&nbsp;Gaelle E. Doucet","doi":"10.1002/hbm.70270","DOIUrl":"https://doi.org/10.1002/hbm.70270","url":null,"abstract":"<p>There is a spike in reward sensitivity during adolescence. Delay discounting involves subjective valuation of a reward in context of when it is gained. This study sought to assess if dynamic functional connectivity between the cognitive control network and the amygdala could predict delay discounting in adolescents and young adults. 448 participants were separated into three age groups, including younger and older adolescents and young adults. A sliding-window approach was used to calculate dynamic functional connectivity between the left/right amygdala and the cognitive control network. Hierarchical models were run for each age group to determine if dynamic functional connectivity could predict delay discounting behavior in two delayed amount conditions above and beyond age and sex. Results showed that dynamic functional connectivity between the left, but not the right, amygdala and cognitive control network significantly predicted delay discounting in the larger amount condition for older adolescents. No significant results were found for the smaller amount condition in older adolescents or either condition in younger adolescents or adults. Stronger dynamic functional connectivity indicated a preference for immediate rewards during a larger amount condition in older adolescents only.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphometric Evidence of a U-Shaped Relationship Between Loss Aversion and Posterior Insular/Somatosensory Cortical Features","authors":"Maria Arioli,&nbsp;Letizia Richelli,&nbsp;Giulia Mattavelli,&nbsp;Zaira Cattaneo,&nbsp;Paolo Poggi,&nbsp;Nicola Canessa","doi":"10.1002/hbm.70274","DOIUrl":"https://doi.org/10.1002/hbm.70274","url":null,"abstract":"<p>Neuroeconomic findings show that interoceptive sensitivity contributes to the typical overweighting of prospective losses over gains known as “loss aversion.” Whether the latter is related to the morphometric properties of the insula—a key node for interoception—remains, however, debated, due to previous conflicting evidence of both positive and negative correlations between their respective metrics. We combined a well-established behavioral modeling approach with a comprehensive morphometric protocol to explore both a linear and quadratic relationship between loss aversion and distinct voxel-based and surface-based cortical features in a sample of 208 healthy young individuals. Both univariate and multivariate analyses highlighted a positive quadratic (i.e., U-shaped) relationship between loss aversion and distinct morphometric features of the posterior insula and somatosensory-parietal cortex. These results first suggest that previous inconsistent findings might reflect methodological differences across studies, facilitating the detection of either the descending or ascending sectors of a U-shaped relationship between loss aversion and structural features. Moreover, they provide novel insights into the interoceptive modulation of choice-related evaluations guiding decision-making towards or away from loss avoidance, thus paving the way to studies investigating alterations of this mechanism in neuro-psychiatric conditions and its susceptibility to different types of intervention including neuromodulation.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuropathological Correlates of Volume and Shape of Deep Gray Matter Structures in Community-Based Older Adults","authors":"Khalid Saifullah,&nbsp;Nazanin Makkinejad,&nbsp;Md Tahmid Yasar,&nbsp;Arnold M. Evia,&nbsp;Sue E. Leurgans,&nbsp;David A. Bennett,&nbsp;Julie A. Schneider,&nbsp;Konstantinos Arfanakis","doi":"10.1002/hbm.70273","DOIUrl":"https://doi.org/10.1002/hbm.70273","url":null,"abstract":"<p>Age-related neurodegenerative and cerebrovascular neuropathologies often coexist in the brain of older adults and contribute to brain abnormalities, cognitive decline and dementia. While deep gray matter structures are implicated early and/or strongly in these processes, the independent effects of various age-related neuropathologies on these structures remain poorly understood. The goal of this study was to investigate the independent association of various age-related neuropathologies with the volume and shape of deep gray matter structures in a large number of community-based older adults that came to autopsy. Cerebral hemispheres from 842 participants of four community studies at the Rush Alzheimer's Disease Center were imaged with MRI ex vivo and underwent detailed neuropathologic examination. Linear regression was used to study the association of various neuropathologies with the volume and shape of six deep gray matter structures (hippocampus, amygdala, caudate, thalamus, nucleus accumbens, putamen) controlling for age at death, sex, years of education, scanner, and postmortem intervals. Both the volumetric and shape analyses showed independent associations of tangles with structural abnormalities in all deep brain structures, of limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) with hippocampus and amygdala, of atherosclerosis with hippocampus, and of gross infarcts with caudate (all <i>p</i> &lt; 0.05 corrected for multiple comparisons). Shape analysis revealed the corresponding independent spatial patterns of inward deformation and also showed additional associations of neuropathologies with deep brain structures (<i>p</i> &lt; 0.05 corrected for multiple comparisons). When analyses were repeated in left and right hemispheres separately, the results were mostly similar in both hemispheres. Mixed pathologies are very common in the older adult brain and the present comprehensive study disentangles their independent effects on multiple deep gray matter structures. These neuropathologic signatures may potentially be used in combination with other features toward in vivo prediction of neuropathologies which could have important implications in future clinical trials and the development of prevention and treatment strategies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated Epigenetic Aging and Its Role in Brain Dynamics and Cognition in Young Adulthood","authors":"Tomáš Jordánek,&nbsp;Radek Mareček,&nbsp;Anna Pačínková,&nbsp;Lenka Andrýsková,&nbsp;Milan Brázdil,&nbsp;Klára Marečková","doi":"10.1002/hbm.70261","DOIUrl":"https://doi.org/10.1002/hbm.70261","url":null,"abstract":"<p>Accelerated epigenetic aging has been associated with changes in cognition. However, due to the lack of neuroimaging epigenetics studies, it is still unclear whether accelerated epigenetic. Aging in young adulthood might underlie the relationship between altered brain dynamics and cognitive functioning. We conducted neuroimaging epigenetics follow-up of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort in young adulthood and tested the possible mediatory role of accelerated epigenetic aging in the relationship between dynamic functional connectivity (DFC) and worse cognition. A total of 240 young adults (51% men; 28–30 years, all of European ancestry) participated in the neuroimaging epigenetics follow-up. Buccal swabs were collected to assess DNA methylation and calculate epigenetic aging using Horvath's epigenetic clock. Full-scale IQ was assessed using the Wechsler adult intelligence scale (WAIS). Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired using a 3T Siemens Prisma MRI scanner, and DFC was assessed using mixture factor analysis, revealing information about the coverage of different DFC states. In women (but not men), lower coverage of DFC state 4 and thus lower frequency of epochs with high connectivity within the default mode network and between default mode, fronto-parietal, and visual networks was associated with lower full-scale IQ (Adj<i>R</i>² = 0.05, std. beta = 0.245, <i>p</i> = 0.008). This relationship was mediated by accelerated epigenetic aging (ab = 7.660, SE = 4.829, 95% CI [0.473, 19.264]). In women, accelerated epigenetic aging in young adulthood mediates the relationship between altered brain dynamics and cognitive functioning. Prevention of cognitive decline should target women already in young adulthood.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 10","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big Data, Small Bias: Harmonizing Diffusion MRI-Based Structural Connectomes to Mitigate Site-Related Bias in Data Integration","authors":"Rui Sherry Shen,&nbsp;Drew Parker,&nbsp;Andrew An Chen,&nbsp;Benjamin E. Yerys,&nbsp;Birkan Tunç,&nbsp;Timothy P. L. Roberts,&nbsp;Russell T. Shinohara,&nbsp;Ragini Verma","doi":"10.1002/hbm.70256","DOIUrl":"https://doi.org/10.1002/hbm.70256","url":null,"abstract":"<p>Diffusion MRI-based structural connectomes are increasingly used to investigate brain connectivity changes associated with various disorders. However, small sample sizes in individual studies, along with highly heterogeneous disorder-related manifestations, underscore the need to pool datasets across multiple studies to be able to identify coherent and generalizable connectivity patterns linked to these disorders. Yet, combining datasets introduces site-related differences due to variations in scanner hardware or acquisition protocols. These differences highlight the necessity for statistical data harmonization to mitigate site-related effects on structural connectomes while preserving the biological information associated with participant demographics and the disorders. While several paradigms exist for harmonizing normally distributed neuroimaging measures, this paper represents the first effort to establish a harmonization framework specifically tailored for the structural connectome. We conduct a thorough investigation of various statistical harmonization methods, adapting them to accommodate the unique distributional characteristics and graph-based properties of structural connectomes. Through rigorous evaluation, we show that our MATCH algorithm, based on the gamma-distributed model, consistently outperforms existing approaches in modeling structural connectomes, enabling the effective removal of site-related biases in both edge-based and downstream graph analyses while preserving biological variability. Two real-world applications further highlight the utility of our harmonization framework in addressing challenges in multi-site structural connectome analysis. Specifically, harmonization with MATCH enhances the generalizability of connectome-based machine learning predictors to new datasets and increases statistical power for detecting group-level differences. Our work provides essential guidelines for harmonizing multi-site structural connectomes, paving the way for more robust discoveries through collaborative research in the era of team science and big data.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 9","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction of Physiological Artifacts in Multi-Echo fMRI Data—Evaluation of Possible RETROICOR Implementations","authors":"Anežka Kovářová,&nbsp;Michal Mikl","doi":"10.1002/hbm.70264","DOIUrl":"https://doi.org/10.1002/hbm.70264","url":null,"abstract":"<p>The study evaluates the efficacy of RETROICOR (Retrospective Image Correction) in mitigating physiological artifacts within multi-echo (ME) fMRI data. Two RETROICOR implementations were compared: applying corrections to individual echoes (RTC_ind) versus composite multi-echo data (RTC_comp). Data from 50 healthy participants were collected using diverse acquisition parameters, including multiband acceleration factors and varying flip angles, on a Siemens Prisma 3T scanner. Key metrics such as temporal signal-to-noise ratio (tSNR), signal fluctuation sensitivity (SFS), and variance of residuals demonstrated improved data quality in both RETROICOR models, particularly in moderately accelerated runs (multiband factors 4 and 6) with lower flip angles (45°). Differences between RTC_ind and RTC_comp were minimal, suggesting both methods are viable for practical applications. While the highest acceleration (multiband factor 8) degraded data quality, RETROICOR's compatibility with faster acquisition sequences was confirmed. These findings underscore the importance of optimizing acquisition parameters and noise correction techniques for reliable fMRI investigations.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 9","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Modality Image Translation of 3 Tesla Magnetic Resonance Imaging to 7 Tesla Using Generative Adversarial Networks","authors":"Eduardo Diniz,&nbsp;Tales Santini,&nbsp;Helmet Karim,&nbsp;Howard J. Aizenstein,&nbsp;Tamer S. Ibrahim","doi":"10.1002/hbm.70246","DOIUrl":"https://doi.org/10.1002/hbm.70246","url":null,"abstract":"<p>The rapid advancements in magnetic resonance imaging (MRI) technology have precipitated a new paradigm wherein cross-modality data translation across diverse imaging platforms, field strengths, and different sites is increasingly challenging. This issue is particularly accentuated when transitioning from 3 Tesla (3T) to 7 Tesla (7T) MRI systems. This study proposes a novel solution to these challenges using generative adversarial networks (GANs)—specifically, the CycleGAN architecture—to create synthetic 7T images from 3T data. Employing a dataset of 1112 and 490 unpaired 3T and 7T MR images, respectively, we trained a 2-dimensional (2D) CycleGAN model, evaluating its performance on a paired dataset of 22 participants scanned at 3T and 7T. Independent testing on 22 distinct participants affirmed the model's proficiency in accurately predicting various tissue types, encompassing cerebral spinal fluid, gray matter, and white matter. Our approach provides a reliable and efficient methodology for synthesizing 7T images, achieving a median Dice coefficient of 83.62% for cerebral spinal fluid (CSF), 81.42% for gray matter (GM), and 89.75% for White Matter (WM), while the corresponding median Percentual Area Differences (PAD) were 6.82%, 7.63%, and 4.85% for CSF, GM, and WM, respectively, in the testing dataset, thereby aiding in harmonizing heterogeneous datasets. Furthermore, it delineates the potential of GANs in amplifying the contrast-to-noise ratio (CNR) from 3T, potentially enhancing the diagnostic capability of the images. While acknowledging the risk of model overfitting, our research underscores a promising progression toward harnessing the benefits of 7T MR systems in research investigations while preserving compatibility with existing 3T MR data. This work was previously presented at the ISMRM 2021 conference.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 9","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonization of Structural Brain Connectivity Through Distribution Matching","authors":"Zhen Zhou,&nbsp;Bruce Fischl,&nbsp;Iman Aganj,&nbsp;for the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/hbm.70257","DOIUrl":"https://doi.org/10.1002/hbm.70257","url":null,"abstract":"<p>The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power to investigate brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods for dMRI data harmonization exist, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from three distinct datasets (OASIS-3, ADNI-2, and PREVENT-AD), comparing its performance to the widely used ComBat method and the more recent CovBat approach. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique effectively harmonizes structural brain connectivity while maintaining non-negativity of the connectivity values and produces correlation strengths and significance levels competitive with alternative approaches. Qualitative assessments illustrate the desired distributional alignment across datasets, while quantitative evaluations confirm competitive performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 9","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70257","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}