NeuroImage最新文献

{"title":"Cardiovascular risk in childhood and young adulthood is associated with the hemodynamic response function in midlife: The Bogalusa Heart Study","authors":"Kai-Cheng Chuang , Maryam Naseri , Sreekrishna Ramakrishnapillai , Kaitlyn Madden , Julia St Amant , Kevin McKlveen , Kathryn Gwizdala , Ramasudhakar Dhullipudi , Lydia Bazzano , Owen Carmichael","doi":"10.1016/j.neuroimage.2025.121338","DOIUrl":"10.1016/j.neuroimage.2025.121338","url":null,"abstract":"<div><h3>Background</h3><div>In functional MRI, a hemodynamic response function (HRF) describes how neural events are translated into a blood oxygenation response detected through imaging. The HRF has the potential to quantify neurovascular mechanisms by which cardiovascular risks modify brain health, but relationships among HRF characteristics, brain health, and cardiovascular modifiers of brain health have not been well studied to date.</div></div><div><h3>Methods</h3><div>One hundred and thirty-seven middle-aged participants (mean age: 53.6 ± 4.7, female (62 %), 78 % White American participants and 22 % African American participants) in the exploratory analysis from Bogalusa Heart Study completed clinical evaluations from childhood to midlife and an adaptive Stroop task during fMRI in midlife. The HRFs of each participant within seventeen brain regions of interest (ROIs) previously identified as activated by this task were calculated using a convolutional neural network approach. Faster and more efficient neurovascular functioning was characterized in terms of five HRF characteristics: faster time to peak (TTP), shorter full width at half maximum (FWHM), smaller peak magnitude (PM), smaller trough magnitude (TM), and smaller area under the HRF curve (AUHRF). The composite HRF summary characteristics over all ROIs were calculated for multivariable and simple linear regression analyses.</div></div><div><h3>Results</h3><div>In multivariable models, faster and more efficient HRF characteristic was found in non-smoker compared to smokers (AUHRF, <em>p</em> = 0.029). Faster and more efficient HRF characteristics were associated with lower systolic and diastolic blood pressures (FWHM, TM, and AUHRF, <em>p</em> = 0.030, 0.042, and 0.032) and cerebral amyloid burden (FWHM, p-value = 0.027) in midlife; as well as greater response rate on the Stroop task (FWHM, <em>p</em> = 0.042) in midlife. In simple linear regression models, faster and more efficient HRF characteristics were found in women compared to men (TM, <em>p</em> = 0.019); in White American participants compared to African American participants (AUHRF, <em>p</em> = 0.044); and in non-smokers compared to smokers (TTP and AUHRF, <em>p</em> = 0.019 and 0.010). Faster and more efficient HRF characteristics were associated with lower systolic and diastolic blood pressures (FWHM and TM, <em>p</em> = 0.019 and 0.029), and lower BMI (FWHM and AUHRF, <em>p</em> = 0.025 and 0.017), in childhood and adolescence; and lower BMI (TTP, <em>p</em> = 0.049), cerebral amyloid burden (FWHM, <em>p</em> = 0.002), and white matter hyperintensity burden (FWHM, <em>p</em> = 0.046) in midlife; as well as greater accuracy on the Stroop task (AUHRF, <em>p</em> = 0.037) in midlife.</div></div><div><h3>Conclusions</h3><div>In a diverse middle-aged community sample, HRF-based indicators of faster and more efficient neurovascular functioning were associated with better brain health and cognitive function, as well as better ","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121338"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural connector hub properties of the thalamus in large-scale brain networks: white matter structure as an anatomical basis","authors":"Tatsuya Jitsuishi,&nbsp;Atsushi Yamaguchi","doi":"10.1016/j.neuroimage.2025.121333","DOIUrl":"10.1016/j.neuroimage.2025.121333","url":null,"abstract":"<div><div>Connector hubs are critical to maintain the modular architecture of large-scale brain networks. This study aimed to explore the structural connector hub properties of the thalamus via the white matter pathways as an anatomical substrate. First, whole-brain tractography was performed to examine the thalamocortical structural connectivity (SC) based on the canonical seven resting-state networks (7-RSNs). It identified multiple overlapping clusters in the thalamus, which are highly connected to the canonical 7-RSNs. Graph theoretical analysis indicated that these clusters have higher participation coefficient (PC) and within-module degree Z-score (WMD) values, suggesting nodal centrality between separate modules. Further, thalamic nuclei with structurally defined connector hub properties showed high functional connectivity (FC) with canonical RSNs in multiple task-fMRI analyses. Collectively, these findings suggest that the thalamus harbors intrinsic structural connector hub properties in large-scale networks as an anatomical basis.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121333"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reweighting of visuomotor areas during motor processing subsequent to somatosensory cortical damage","authors":"Yuqi Liu ,&nbsp;Elizabeth J. Halfen ,&nbsp;Jeffrey M. Yau ,&nbsp;Simon Fischer-Baum ,&nbsp;Peter J. Kohler ,&nbsp;Olufunsho Faseyitan ,&nbsp;H. Branch Coslett ,&nbsp;Jared Medina","doi":"10.1016/j.neuroimage.2025.121336","DOIUrl":"10.1016/j.neuroimage.2025.121336","url":null,"abstract":"<div><div>Somatosensory inputs are critical to motor control. Animal studies have shown that primary somatosensory lesions cause sensorimotor deficits along with disrupted organization in primary motor cortex (M1). How does damage in primary somatosensory cortex (S1) influence motor networks in humans? Using fMRI, we examined two individuals, LS and RF, who had extensive damage to left somatosensory cortex, but primarily intact motor cortex and preserved motor abilities. Given left S1 damage, tactile detection and localization were impaired for the contralesional hand in both individuals. When moving the contralesional hand, LS, with near complete damage to S1 hand area, showed increased activation in ipsilesional putamen and deactivation in contralesional cerebellum relative to age-matched controls. These findings demonstrate influences of S1 damage to subcortical sensorimotor areas that are distant from the lesion site, and a potential reweighting of the motor network with increased action selection in putamen and inhibition of sensory prediction in cerebellum in the face of sensory loss. In contrast, RF, who had a small island of spared S1 in the hand area, showed greater activation in contralesional S1 for movement versus rest. This same region was also activated by pure somatosensory stimulation in a second experiment, suggesting that the spared S1 area in RF still subserves sensorimotor processing. Finally, the right middle occipital gyrus was more strongly activated in both individuals compared with controls, suggesting the potential reliance on visual imagery in the face of degraded sensory feedback.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121336"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atrial fibrillation reduces CSF flow dynamics. A multimodal MRI study","authors":"Sabine Hofer ,&nbsp;Marlena Schnieder ,&nbsp;Leonie Polster ,&nbsp;Peter Dechent ,&nbsp;Mathias Bähr","doi":"10.1016/j.neuroimage.2025.121337","DOIUrl":"10.1016/j.neuroimage.2025.121337","url":null,"abstract":"<div><h3>Background</h3><div>Atrial fibrillation (AF), the most common cardiac arrhythmia, is linked to cognitive impairment and dementia but the mechanisms behind are not understood. In the brain, the glymphatic system (GS) is crucial for clearing waste from the brain through rhythmic flow of CSF. In order to ensure optimal GS function a synchrony between the dynamics of blood flow and cerebrospinal fluid (CSF) flow is necessary. Thus, our aim was to examine GS function in AF using a fast multimodal imaging protocol in a single MRI session.</div></div><div><h3>Methods</h3><div>We measured 13 healthy volunteers and 13 patients with AF, using a 3T MRI system. To capture CSF and blood flow, real-time phase-contrast flow MRI was employed in the aqueduct, internal carotid artery, and jugular vein. T1-weighted imaging segmented brain tissue and ventricular size, while fast T1Flash examined tissue anatomy. EPI diffusion assessed fluid motion along the perivascular space, and artefact-free STEAM diffusion described whole-brain CSF dynamics.</div></div><div><h3>Results</h3><div>The results showed that AF patients had reduced and aperiodic CSF flow compared to healthy controls, with lower flow volume and less periodic flow patterns. Anatomical parameters such as brain volume, ventricular size, white matter integrity, and perivascular fluid flow showed no significant differences between the groups.</div></div><div><h3>Conclusions</h3><div>These findings suggest that AF changes CSF dynamics and disrupts its rhythmicity, likely impairing GS function.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121337"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The diagnostic potential of resting state functional MRI: Statistical concerns","authors":"Evan D. Doubovikov ,&nbsp;Daniil P. Aksenov","doi":"10.1016/j.neuroimage.2025.121334","DOIUrl":"10.1016/j.neuroimage.2025.121334","url":null,"abstract":"<div><div>Blood oxygen level-dependent functional magnetic resonance imaging (fMRI) is a widely used, non-invasive method to assess brain hemodynamics. Resting-state fMRI (rsfMRI) estimates functional connectivity (FC) by measuring correlations between the time courses of different brain regions. However, the reliability of rsfMRI FC is fundamentally compromised by statistical artifacts arising from signal cyclicity, autocorrelation, and preprocessing-induced distortions.</div><div>We discuss how standard rsfMRI preprocessing —particularly the widely used band-pass filters such as 0.009–0.08 Hz and 0.01–0.10 Hz— introduce biases that increase correlation estimates between independent time series. Additionally, filtering without appropriate downsampling further distorts correlation coefficients, inflating statistical significance and increasing the risk of false positives. Under these conditions, commonly used multiple comparison corrections fail to fully control Type I errors, with up to 50–60 % of detected correlations in white noise signals remaining significant after correction depending on the sampling rate, filter and duration.</div><div>To mitigate these biases, we recommend adjusting sampling rates to align with the analyzed frequency band and employing surrogate data methods that better account for the statistical properties of rsfMRI signals and reduce autocorrelation-driven false positives. Additionally, we show that structured brain states—such as epilepsy and anesthesia-induced burst suppression—impose low-frequency neural activity that further amplifies these biases, distorting FC estimates.</div><div>These findings indicate that accepted rsfMRI preprocessing pipelines systematically amplify spurious correlations and call for an improved statistical framework. This framework must explicitly account for autocorrelation, cyclicity, and multiple comparison biases, while excluding or correcting for structured neural activity that further distorts connectivity estimates.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121334"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain signatures of predictive and reactive strategies in a simple delayed reaction time task: an EEG study","authors":"Arianna Brancaccio,&nbsp;Marco Tagliaferri,&nbsp;Carlo Miniussi,&nbsp;Luigi Cattaneo","doi":"10.1016/j.neuroimage.2025.121335","DOIUrl":"10.1016/j.neuroimage.2025.121335","url":null,"abstract":"<div><div>In a simple pre-cued sensorimotor task, two behavioral patterns emerge spontaneously on a trial-by-trial basis, characterized by a bimodal distribution. The early and the late patterns are likely the product of two distinct mutually exclusive strategies: predictive and reactive. Predictive behavior is driven by an internally generated, top-down mechanism, allowing participants to estimate the timing of the target stimulus. In contrast, reactive behavior relies on an externally-driven, bottom-up mechanism, where participants wait for the target stimulus before responding. In this exploratory study, we aimed to further validate the existence of these two strategies by showing they are distinguishable based on EEG patterns, analyzed in both temporal and frequency domains using different metrics, including event-related potentials (ERP), time-frequency representations, modulation index, inter-trial phase coherence, and connectivity. Early behavioral responses showed an augmented ERP, named contingent negative variation, in comparison to late behavioral responses. This validate the hypothesis of a top-down, predictive mechanism, based on temporal estimations. In addition, we showed that EEG dynamics differentiated the two conditions in the SET period. This result further corroborates the hypothesis that the commitment to one strategy does not occur before trial onset but rather builds up during the SET period. When analyzing the electrical activity after the GO-signal, we observed that early and late responses are associated with distinct EEG features, with early behavior displaying feature typical of top-down processes. In our experiment, the two behaviors occurred naturally, without external manipulations that could introduce confounding cognitive demands and obscure genuine differences in EEG patterns between strategy-dependent conditions.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121335"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of diffusion analysis along the perivascular space (DTI-ALPS) index during childhood and adolescence: Evidence from two longitudinal cohorts","authors":"Shiwei Lin ,&nbsp;Qunjun Liang ,&nbsp;Ying Li ,&nbsp;Caixue Cheng ,&nbsp;Tingting Gong ,&nbsp;Yingwei Qiu","doi":"10.1016/j.neuroimage.2025.121331","DOIUrl":"10.1016/j.neuroimage.2025.121331","url":null,"abstract":"<div><div>Brain glymphatic activity, as indicated by diffusion analysis along the perivascular space (DTI-ALPS) index, has been discovered to participate in the pathogenesis of atypical development. However, little is known about the physiological developmental trajectory of the DTI-ALPS index during childhood and adolescence. Here, we evaluated DTI-ALPS index developmental characteristics and investigated its potential association with sex in 627 participants from two longitudinal cohorts. The global DTI-ALPS (gDTI-ALPS) index showed a positive cross-sectional relationship with age in both cohorts even after adjusting for sex. The annual net increase in the gDTI-ALPS index in both cohorts ranged from 0.003 to 0.005. Increases in the gDTI-ALPS index with age were also observed in the longitudinal analysis. Moreover, regional DTI-ALPS index analysis revealed the increment of DTI-ALPS mainly involved in the anterior brain regions, with the peak value of the regional DTI-ALPS index moving forward with age. Sex had no significant moderating effect on DTI-ALPS index. Our results suggest DTI-ALPS index development is a dynamic process during childhood and adolescence, particularly within the anterior brain regions, which may represent glymphatic system maturation during this critical period. The global and regional DTI-ALPS index could serve as a sensitive biomarker for monitoring this process.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121331"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aphasia severity prediction using a multi-modal machine learning approach","authors":"Xinyi Hu ,&nbsp;Maria Varkanitsa ,&nbsp;Emerson Kropp ,&nbsp;Margrit Betke ,&nbsp;Prakash Ishwar ,&nbsp;Swathi Kiran","doi":"10.1016/j.neuroimage.2025.121300","DOIUrl":"10.1016/j.neuroimage.2025.121300","url":null,"abstract":"<div><div>The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in 76 individuals with post-stroke aphasia. We employed Support Vector Regression (SVR) and Random Forest (RF) models with supervised feature selection and a stacked feature prediction approach. The SVR model outperformed RF, achieving an average root mean square error (RMSE) of <span><math><mrow><mn>16</mn><mo>.</mo><mn>38</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>5</mn><mo>.</mo><mn>57</mn></mrow></math></span>, Pearson’s correlation coefficient (<span><math><mi>r</mi></math></span>) of <span><math><mrow><mn>0</mn><mo>.</mo><mn>70</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>13</mn></mrow></math></span>, and mean absolute error (MAE) of <span><math><mrow><mn>12</mn><mo>.</mo><mn>67</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>3</mn><mo>.</mo><mn>27</mn></mrow></math></span>, compared to RF’s RMSE of <span><math><mrow><mn>18</mn><mo>.</mo><mn>41</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>4</mn><mo>.</mo><mn>34</mn></mrow></math></span>, <span><math><mi>r</mi></math></span> of <span><math><mrow><mn>0</mn><mo>.</mo><mn>66</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>15</mn></mrow></math></span>, and MAE of <span><math><mrow><mn>14</mn><mo>.</mo><mn>64</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>3</mn><mo>.</mo><mn>04</mn></mrow></math></span>. Resting-state neural activity and structural integrity emerged as crucial predictors of aphasia severity, appearing in the top 20% of predictor combinations for both SVR and RF. Finally, the feature selection method revealed that functional connectivity in both hemispheres and between homologous language areas is critical for predicting language outcomes in patients with aphasia. The statistically significant difference in performance between the model using only single modality and the optimal multi-modal SVR/RF model (which included both resting-state connectivity and structural information) underscores that aphasia severity is influenced by factors beyond lesion location and volume. These findings suggest that integrating multiple neuroimaging modalities enhances the prediction of language outcomes in aphasia beyond lesion characteristics alone, offering insights that could inform personalized rehabilitation strategies.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121300"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HPC-DMN integration at neural event boundary affects across-boundary BOLD representations and memory recollection","authors":"Qiuyi Liu ,&nbsp;Siyang Li ,&nbsp;Lili Sun ,&nbsp;Zhipeng Li ,&nbsp;Peng Ren ,&nbsp;Wencai Ding ,&nbsp;Hongbo Bao ,&nbsp;Xia Liang","doi":"10.1016/j.neuroimage.2025.121328","DOIUrl":"10.1016/j.neuroimage.2025.121328","url":null,"abstract":"<div><div>Humans naturally divide their continuous experiences into discrete events, with event boundaries playing a critical role in this segmentation process. These boundaries are marked by significant shifts in brain activity, particularly in the hippocampal complex (HPC) and default mode network (DMN), which are key to learning and memory formation. We hypothesized that neural event boundaries in specific HPC-DMN regions contribute to episode recollection through across-boundary BOLD pattern shifts and neural event network formation, which may be influenced by two factors: network integration and boundary alignment. To test this, we used the widely recognized Sherlock fMRI dataset, which involves 22 human participants (17 subjects were used in this study). One key finding was that across-boundary BOLD pattern dissimilarity in the inferior parietal lobule (IPL) was greater for successfully recalled events compared to unsuccessful ones, specifically at boundaries with a high participation coefficient (indicative of high integration) or those aligned with the anterior superior temporal gyrus (aSTG) or the ventrolateral prefrontal cortex (vlPFC). Moreover, the entorhinal cortex played a crucial role in linking events into a network to facilitate subsequent recollection, particularly at boundaries with low participation coefficients or those aligned with the parahippocampus (PHC). These findings highlight the vital role of neural event boundaries in aiding comprehension and memory in naturalistic contexts through interactions with other brain regions.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121328"},"PeriodicalIF":4.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new multimodal neuroprognostic model for chronic disorders of consciousness: Integrating behavioral, hormonal, and imaging features","authors":"Hang Wu ,&nbsp;Xiyan Huang ,&nbsp;Dongtian Lin ,&nbsp;Ziqin Liao ,&nbsp;Zerong Chen ,&nbsp;Haili Zhong ,&nbsp;Chengwei Xu ,&nbsp;Liubei Jiang ,&nbsp;Nihui Xu ,&nbsp;LongYu Yang ,&nbsp;Pengmin Qin ,&nbsp;Qiuyou Xie","doi":"10.1016/j.neuroimage.2025.121329","DOIUrl":"10.1016/j.neuroimage.2025.121329","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Previous studies have suggested that endocrine abnormalities following brain injury may influence the long-term recovery of patients with chronic disorders of consciousness (DOC). However, it remains unclear whether combining endocrine measurements with established behavioral and imaging metrics can further enhance DOC prognostication. To address this, we aim to develop a precise neuroprognostic model by integrating hormonal, behavioral, and resting-state fMRI (rs-fMRI) assessments.</div></div><div><h3>Methods</h3><div>In this retrospective observational study, 43 patients with DOC were enrolled, each of whom was assessed using the Coma Recovery Scale-Revised (CRS-R), pituitary-related hormone levels, and rs-fMRI. Based on the Glasgow Outcome Scale (GOS), patients were classified into a favorable prognosis subgroup (GOS ≥ 3, <em>n</em> = 19) and a poor prognosis subgroup (GOS &lt; 3, <em>n</em> = 24). We calculated two rs-fMRI features for each brain region: static functional connectivity and dynamic temporal stability. A Support Vector Machine classifier was then applied using these multimodal feature subsets to predict patient prognosis.</div></div><div><h3>Results</h3><div>Our multimodal model achieved a prediction accuracy of 0.91 (sensitivity = 0.84, specificity = 0.96) for DOC prognosis, outperforming control models that used fewer feature subsets, which had accuracy ranging from 0.58 to 0.84. Additionally, brain regions primarily from the frontoparietal networks contribute most to the prediction, along with motor function scores of the CRS-R and free triiodothyronine hormone levels.</div></div><div><h3>Conclusion</h3><div>Our preliminary findings suggest that integrating multiple domains enhances the accuracy of DOC prognosis predictions. Our model shows promise as an accurate and convenient tool to aid clinical decision-making regarding DOC prognosis, though further external validation is needed.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"317 ","pages":"Article 121329"},"PeriodicalIF":4.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}