Neuroimage-Clinical最新文献

{"title":"Predicting language outcome after stroke using machine learning: in search of the big data benefit.","authors":"Margarita Saranti, Douglas Neville, Adam White, Pia Rotshtein, Thomas M H Hope, Cathy J Price, Howard Bowman","doi":"10.1016/j.nicl.2025.103858","DOIUrl":"10.1016/j.nicl.2025.103858","url":null,"abstract":"<p><p>Accurate prediction of post-stroke language outcomes using machine learning offers the potential to enhance clinical treatment and rehabilitation for aphasic patients. This study of 758 English speaking stroke patients from the PLORAS project explores the impact of sample size on the performance of logistic regression and a deep learning (ResNet-18) model in predicting language outcomes from neuroimaging and impairment-relevant tabular data. We assessed the performance of both models on two key language tasks from the Comprehensive Aphasia Test: Spoken Picture Description and Naming, using a learning curve approach. Contrary to expectations, the simpler logistic regression model performed comparably or better than the deep learning model (with overlapping confidence intervals), with both models showing an accuracy plateau around 80% for sample sizes larger than 300 patients. Principal Component Analysis revealed that the dimensionality of the neuroimaging data could be reduced to as few as 20 (or even 2) dominant components without significant loss in accuracy, suggesting that classification may be driven by simple patterns such as lesion size. The study highlights both the potential limitations of current dataset size in achieving further accuracy gains and the need for larger datasets to capture more complex patterns, as some of our results indicate that we might not have reached an absolute classification performance ceiling. Overall, these findings provide insights into the practical use of machine learning for predicting aphasia outcomes and the potential benefits of much larger datasets in enhancing model performance.</p>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"48 ","pages":"103858"},"PeriodicalIF":3.6,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800936","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":"Effects of Parkinson's disease on mechanical and microstructural properties of the brain.","authors":"Christoffer Olsson, Mikael Skorpil, Per Svenningsson, Rodrigo Moreno","doi":"10.1016/j.nicl.2025.103857","DOIUrl":"10.1016/j.nicl.2025.103857","url":null,"abstract":"<p><p>Magnetic Resonance Elastography (MRE) is a novel technique to study the brain by measuring its mechanical properties, such as stiffness and viscosity. These properties may provide insights into how the microstructure of the brain changes due to a pathology, however the connection between these microstructural mechanisms and the measured biomechanical properties are still largely unknown. For this reason, the present exploratory study utilizes multidimensional diffusion magnetic resonance imaging (MD-dMRI), apart from MRE, to extract microstructural parameters of the whole brain tissue for a small cohort of 12 Parkinson disease (PD) patients and 17 healthy controls. A combination of these methods provides valuable insights into subtle changes due to PD as it probes variables such as microscopic fractional anisotropy (μFA) combined with measures of shear stiffness. MRE and MD-dMRI quantities across the brain are compared between the two groups and analyzed. It was found that there were significant softening effects in the temporal and occipital lobes due to PD, associated with an increase in the mean diffusivity in those regions, whereas other microstructural properties remained largely unchanged. The mesencephalon, on the other hand, displays changes in the MD-dMRI parameters consistent with neuronal atrophy, however no softening of this region was detected. In most regions, stiffness is significantly reduced due to age, which is correlated with a decrease in μFA and increase in MD. We hypothesize that age effects can mostly explain neuronal atrophy, whereas softening due to PD effects involve additional mechanisms.</p>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"48 ","pages":"103857"},"PeriodicalIF":3.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800934","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":"Multi-Center 3D CNN for Parkinson's disease diagnosis and prognosis using clinical and T1-weighted MRI data.","authors":"Silvia Basaia, Elisabetta Sarasso, Francesco Sciancalepore, Roberta Balestrino, Simona Musicco, Stefano Pisano, Iva Stankovic, Aleksandra Tomic, Rosita De Micco, Alessandro Tessitore, Massimo Salvi, Kristen M Meiburger, Vladimir S Kostic, Filippo Molinari, Federica Agosta, Massimo Filippi","doi":"10.1016/j.nicl.2025.103859","DOIUrl":"10.1016/j.nicl.2025.103859","url":null,"abstract":"<p><strong>Objective: </strong>Parkinson's disease (PD) presents challenges in early diagnosis and progression prediction. Recent advancements in machine learning, particularly convolutional-neural-networks (CNNs), show promise in enhancing diagnostic accuracy and prognostic capabilities using neuroimaging data. The aims of this study were: (i) develop a 3D-CNN based on MRI to distinguish controls and PD patients and (ii) employ CNN to predict the progression of PD.</p><p><strong>Methods: </strong>Three cohorts were selected: 86 mild, 62 moderate-to-severe PD patients, and 60 controls; 14 mild-PD patients and 14 controls from Parkinson's Progression Markers Initiative database, and 38 de novo mild-PD patients and 38 controls. All participants underwent MRI scans and clinical evaluation at baseline and over 2-years. PD subjects were classified in two clusters of different progression using k-means clustering based on baseline and follow-up UDPRS-III scores. A 3D-CNN was built and tested on PD patients and controls, with binary classifications: controls vs moderate-to-severe PD, controls vs mild-PD, and two clusters of PD progression. The effect of transfer learning was also tested.</p><p><strong>Results: </strong>CNN effectively differentiated moderate-to-severe PD from controls (74% accuracy) using MRI data alone. Transfer learning significantly improved performance in distinguishing mild-PD from controls (64% accuracy). For predicting disease progression, the model achieved over 70% accuracy by combining MRI and clinical data. Brain regions most influential in the CNN's decisions were visualized.</p><p><strong>Conclusions: </strong>CNN, integrating multimodal data and transfer learning, provides encouraging results toward early-stage classification and progression monitoring in PD. Its explainability through activation maps offers potential for clinical application in early diagnosis and personalized monitoring.</p>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"48 ","pages":"103859"},"PeriodicalIF":3.6,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800935","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":"Neuroimaging and kinematic biomarkers of post-stroke upper limb motor impairment.","authors":"Joyce L Chen, Timothy K Lam, Melanie C Baniña, Daniele Piscitelli, Mindy F Levin","doi":"10.1016/j.nicl.2025.103854","DOIUrl":"10.1016/j.nicl.2025.103854","url":null,"abstract":"<p><p>Structural and functional biomarkers derived from magnetic resonance imaging explain some variance in post-stroke motor impairment. The understanding of the nature of impairment and the discrimination between true behavioural motor recovery/restitution and motor compensation may be improved by the addition of kinematic information. The aim of the study was to determine the influence of neuroimaging combined with kinematic biomarkers in explaining the variance in motor impairment of the upper limb. People living with late sub-acute to chronic stroke (n = 25) underwent the Fugl Meyer Assessment - Upper Limb (FMA-UL), magnetic resonance imaging, and completed a reaching task where upper limb and trunk kinematics were recorded. Regression analyses were performed to determine the amount of variability in FMA-UL explained by the following biomarkers: the amount of corticospinal tract impacted by the stroke lesion (CST involvement), interhemispheric and ipsilesional resting state connectivity, and the Trunk-based Index of Performance (IPt) that measures skilled reaching ability while accounting for trunk compensation. CST involvement, interhemispheric connectivity, and the IPt, together explained ∼49 % of the variance in the FMA-UL (F(3,21) = 8.694, p = 0.001, R²_adj = 0.49). The IPt explained an additional 14 % of the variance in the FMA-UL compared to CST involvement alone (p = 0.02). The IPt is a relevant kinematic biomarker of post-stroke upper limb motor impairment. Our findings suggest the importance of using multiple categories of biomarkers to better understand the level of post-stroke motor impairment.</p>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"48 ","pages":"103854"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812678","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":"Characterising grey-white matter relationships in recent-onset psychosis and its association with cognitive function","authors":"Yoshito Saito ,&nbsp;Christos Pantelis ,&nbsp;Vanessa Cropley ,&nbsp;Liliana Laskaris ,&nbsp;Cassandra M.J. Wannan ,&nbsp;Warda T. Syeda","doi":"10.1016/j.nicl.2025.103824","DOIUrl":"10.1016/j.nicl.2025.103824","url":null,"abstract":"<div><div>Individuals with recent-onset psychosis (ROP) present widespread grey matter (GM) reductions and white matter (WM) abnormalities. While prior studies used univariate approaches, understanding how multiple GM regions relate to WM tracts is important, as psychosis involves network-level brain dysfunction. Understanding characteristic GM-WM patterns may also clarify the basis of cognitive impairments, which are potentially linked to network dysfunction in psychosis. Using multivariate analysis, we examined whole-brain GM-WM relationships and their association with cognitive abilities in ROP.</div><div>We used T1 and diffusion-weighted images from 71 non-affective ROP individuals (age 22.09 ± 3.08) and 71 matched controls (age 22.05 ± 3.21). We performed multiblock partial least squares correlation (MB-PLS-C) to identify GM-WM patterns based on GM thickness or surface area and WM fractional anisotropy (FA), and examined their associations with cognitive abilities.</div><div>MB-PLS-C identified a ‘GM thickness’–‘WM FA’ pattern representing group differences, explaining 12.38 % of the variance and associated with frontal and temporal GM regions and seven WM tracts around subcortical structures. MB-PLS-C also identified a ‘GM surface area’–‘WM FA’ pattern showing group differences, explaining 18.92 % and related with cingulate, frontal, temporal, and parietal GM regions and 15 WM tracts, including the inferior cerebellar peduncle and corona radiata. The ‘GM thickness’–‘WM FA’ pattern describing group differences was significantly correlated with processing speed in ROP.</div><div>MB-PLS-C identified differential whole-brain GM-WM relationships, indicating a potential signature of brain alterations in ROP. Our findings of a relationship between processing speed and GM-WM patterns for GM thickness have implications for our understanding of brain-behaviour relationships in psychosis.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"47 ","pages":"Article 103824"},"PeriodicalIF":3.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291304","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":"Adults with down syndrome exhibit altered somatosensory cortical inhibition","authors":"Jiraros Meejang ,&nbsp;Morgan T. Busboom ,&nbsp;Sarah E. Baker ,&nbsp;Yasra Arif ,&nbsp;Olyvia Kastner ,&nbsp;Tony W. Wilson ,&nbsp;Max J. Kurz","doi":"10.1016/j.nicl.2025.103797","DOIUrl":"10.1016/j.nicl.2025.103797","url":null,"abstract":"<div><div>Down syndrome (DS) is a developmental genetic disorder that is associated with an accelerated aging profile and high probability of early incidence Alzheimer’s disease like symptoms. It is well established that there are morphological differences in the brains of adults with DS, but the net impact of the genetic disruption on cortical function remains poorly understood. To address this knowledge gap, we used magnetoencephalographic (MEG) brain imaging to assess the somatosensory cortical activity elicited by a paired-pulse electrical stimulation of the right median nerve of adults with DS (N = 19; Age = 28.05 ± 7.9 yrs.) and neurotypical controls (NT) (N = 21; Age = 30.81 ± 8.2 yrs.). sLORETA was used to image neural responses to the somatosensory stimulation, which were centered on the left central sulcus posterior to the motor hand knob region. Our results revealed that adults with DS had weaker somatosensory cortical activity after the second electrical stimulation in the paired-pulse paradigm (DS = 594.1 ± 194.22 AU; NT = 750.48 ± 256.6; P = 0.038) and a pronounced hyper-gating response (DS = 78.9 ± 6.8 %; NT = 87.4 ± 9.9 %; P = 0.003). Together, these results suggest that adults with DS may have an imbalance in the excitatory/inhibitory ratio. These novel data enhance our understanding of the neurophysiological aberrations associated with DS and may hold promise in understanding the origins of Alzheimer’s disease like symptoms in this population. Future studies should examine whether these inhibitory alterations are restricted to the sensorimotor cortices or extend across the brain.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"46 ","pages":"Article 103797"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928964","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":"Probing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry","authors":"Linn B. Norbom ,&nbsp;Bilal Syed ,&nbsp;Rikka Kjelkenes ,&nbsp;Jaroslav Rokicki ,&nbsp;Antoine Beauchamp ,&nbsp;Stener Nerland ,&nbsp;Azadeh Kushki ,&nbsp;Evdokia Anagnostou ,&nbsp;Paul Arnold ,&nbsp;Jennifer Crosbie ,&nbsp;Elizabeth Kelley ,&nbsp;Robert Nicolson ,&nbsp;Russell Schachar ,&nbsp;Margot J. Taylor ,&nbsp;Lars T. Westlye ,&nbsp;Christian K. Tamnes ,&nbsp;Jason P. Lerch","doi":"10.1016/j.nicl.2025.103736","DOIUrl":"10.1016/j.nicl.2025.103736","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be valuable for predicting clinical outcomes and tailoring treatment strategies. Cortical myelination is a prominent developmental process, and its disruption is a candidate mechanism for both disorders. Yet, no studies have attempted to identify subtypes using T1w/T2w-ratio, a magnetic resonance imaging (MRI) based proxy for intracortical myelin. Moreover, cortical variability arises from numerous biological pathways, and multimodal approaches can integrate cortical metrics into a single network. We analyzed data from 310 individuals aged 2.6–23.6 years, obtained from the Province of Ontario Neurodevelopmental (POND) Network consisting of individuals diagnosed with ASD (n = 136), ADHD (n = 100), and typically developing (TD) individuals (n = 74). We first tested for differences in T1w/T2w-ratio between diagnostic categories and controls. We then performed unimodal (T1w/T2w-ratio) and multimodal (T1w/T2w-ratio, cortical thickness, and surface area) spectral clustering to identify diagnostic-blind subgroups. Linear models revealed no statistically significant case-control differences in T1w/T2w-ratio. Unimodal clustering mostly isolated single individual- or minority clusters, driven by image quality and intensity outliers. Multimodal clustering suggested three distinct subgroups, which transcended diagnostic boundaries, showing separate cortical patterns but similar clinical and cognitive profiles. T1w/T2w-ratio features were the most relevant for demarcation, followed by surface area. While our analysis revealed no significant case-control differences, multimodal clustering incorporating the T1w/T2w-ratio among cortical features holds promise for identifying biologically similar subsets of individuals with neurodevelopmental conditions.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"45 ","pages":"Article 103736"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016495","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":"Neurotransmitter imbalance, glutathione depletion and concomitant susceptibility increase in Parkinson’s disease","authors":"Su Yan ,&nbsp;Bingfang Duan ,&nbsp;Yuanhao Li ,&nbsp;Hongquan Zhu ,&nbsp;Zhaoqi Shi ,&nbsp;Xiaoxiao Zhang ,&nbsp;Yuanyuan Qin ,&nbsp;Wenzhen Zhu","doi":"10.1016/j.nicl.2025.103740","DOIUrl":"10.1016/j.nicl.2025.103740","url":null,"abstract":"<div><h3>Background</h3><div>Emerging insights into the pathophysiology of Parkinson’s disease (PD) underscore the involvement of dysregulated neurotransmission, iron accumulation and oxidative stress. Nonetheless, the excitatory and inhibitory neurometabolites, the antioxidant glutathione (GSH), and magnetic susceptibility are seldom studied together in the clinical PD literature.</div></div><div><h3>Methods</h3><div>We acquired MEGA-PRESS and multi-echo gradient echo sequences from 60 PD patients and 47 healthy controls (HCs). Magnetic resonance spectroscopy voxels were respectively positioned in the midbrain to quantify neurotransmitter including γ-aminobutyric acid (GABA) and glutamate plus glutamine, and in the left striatum to estimate GSH levels. Group differences in metabolite levels normalized to total creatine (Cr) and their clinical relevance were determined. Furthermore, relationships among GSH levels, neurotransmitter estimates and susceptibility values were explored in both PD patients and HCs.</div></div><div><h3>Results</h3><div>PD patients exhibited reduced midbrain GABA levels (P = 0.034, P_FDR = 0.136), diminished GSH in the left striatum (P = 0.032, P_FDR = 0.096), and increased susceptibility values in the substantia nigra (P_FDR &lt; 0.001). Mesencephalic choline levels were correlated with the severity of rapid eye movement sleep behavior disorders symptoms, whereas striatal N-acetylaspartate levels were linked to Hoehn-Yahr stage and motor symptom severity. Notably, the disruption of associations between striatal GSH levels and susceptibility values in globus pallidus, as well as midbrain GABA levels, were evident in PD.</div></div><div><h3>Conclusions</h3><div>These findings offer compelling evidence for metabolic dysregulation in PD, characterized by a concomitant reduction in GABA and GSH levels, alongside iron deposition.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"45 ","pages":"Article 103740"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076045","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":"Increased glymphatic system activity and thalamic vulnerability in drug-naive somatic depression: Evidenced by DTI-ALPS index","authors":"Zipeng Deng ,&nbsp;Wei Wang ,&nbsp;Zhaowen Nie ,&nbsp;Simeng Ma ,&nbsp;Enqi Zhou ,&nbsp;Xinhui Xie ,&nbsp;Qian Gong ,&nbsp;Lihua Yao ,&nbsp;Lihong Bu ,&nbsp;Lijun Kang ,&nbsp;Zhongchun Liu","doi":"10.1016/j.nicl.2025.103769","DOIUrl":"10.1016/j.nicl.2025.103769","url":null,"abstract":"<div><div>Major depressive disorder (MDD) is a significant contributor to global disease burden, with somatic symptoms frequently complicating its diagnosis and treatment. Recent advances in neuroimaging have provided insights into the neurobiological underpinnings of MDD, yet the role of the glymphatic system remains largely unexplored. This study aimed to assess glymphatic function in drug-naïve somatic depression (SMD) patients using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. A total of 272 participants, including somatic depression patients (SMD), pure depression (PMD), and healthy controls (HC), were enrolled. We collected T1-weighted (T1w) and DTI (diffusion tensor image) scans and clinical data of all participants. The DTI-ALPS indices were calculated and compared among three groups. Gray matter regions associated with the DTI-ALPS index were identified by voxel-based morphometry analysis (VBM), revealing a cluster located in the thalamus. Then, we performed partial correlation analyses to further investigate the relationships between the DTI-ALPS index, thalamic volume, and clinical data. The DTI-ALPS index was significantly higher in the MDD group compared to the HC group, particularly in the SMD group. Furthermore, a significant positive correlation was observed between the DTI-ALPS index and thalamic volume, with lower DTI-ALPS values associated with reduced thalamic volumes, especially in the SMD group. Our findings suggest heightened glymphatic activity in MDD patients, especially SMD patients, and a potential link between glymphatic function and thalamic vulnerability. Therefore, the thalamus’ vulnerability to glymphatic system function may play a role in the pathophysiology of depression, particularly somatic depression, suggesting that both the glymphatic system and the thalamus could serve as potential therapeutic or intervention targets for future treatments.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"46 ","pages":"Article 103769"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683528","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":"Altered functional connectivity of brainstem ARAS nuclei unveils the mechanisms of disorders of consciousness in sTBI: an exploratory study","authors":"Peng Zhang ,&nbsp;Yinan Zhou ,&nbsp;Haoqi Ni ,&nbsp;Zhaoneng Huang ,&nbsp;Can Tang ,&nbsp;Qichuan Zhuge ,&nbsp;Lun Dong ,&nbsp;Jun Zhang","doi":"10.1016/j.nicl.2025.103787","DOIUrl":"10.1016/j.nicl.2025.103787","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the functional connectivity (FC) characteristics of Ascending Reticular Activating System (ARAS) in patients with disorders of consciousness (DOC) following severe traumatic brain injury (sTBI), while introducing the Linear support vector machine (LSVM) to predict the recovery of consciousness.</div></div><div><h3>Methods</h3><div>Resting-state MRI was used to measure FC changes between the brainstem ARAS nuclei and whole-brain voxels. We compared the differences in FC between sTBI patients and healthy controls, as well as between the wake and DOC groups. Furthermore, the LSVM model for consciousness recovery was developed based on the Z-values of regions of interest (ROIs) and/or scale to distinguish the prognosis of sTBI patients.</div></div><div><h3>Results</h3><div>A total of 28 sTBI patients with DOC and 30 healthy controls were included, with no significant baseline differences (<em>p</em> &gt; 0.05). Using the brainstem ARAS nuclei as the ROI, we observed increased FC in the subcortical regions compared to healthy controls. The strength of FC was significantly different between patients who recovered consciousness and those who did not at 6 months post-sTBI (AlphaSim corrected, p &lt; 0.05, Cluster &gt; 154). Furthermore, the LSVM model demonstrated strong predictive performance, with an area under the receiver operating characteristic curve of 0.81–0.98.</div></div><div><h3>Conclusions</h3><div>Our study suggest that the disruption FC of ARAS from the subcortex to the cortex may be associated with DOC and prognosis in sTBI patients. Furthermore, the LSVM model shows potential value in distinguishing the recovery of consciousness.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":"46 ","pages":"Article 103787"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851943","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}