Distinct neurologic state in patients with traumatic brain injury and hemorrhagic stroke during the stage of acute disorders of consciousness and the correlation with the neurological prognosis: A multi-modal PET/rs-fMRI study.

Distinct neurologic state in patients with traumatic brain injury and hemorrhagic stroke during the stage of acute disorders of consciousness and the correlation with the neurological prognosis: A multi-modal PET/rs-fMRI study - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice Neuroimage Clin . 2026 Apr 7;50:103990. doi: 10.1016/j.nicl.2026.103990 Search in PMC Search in PubMed View in NLM Catalog Add to search Distinct neurologic state in patients with traumatic brain injury and hemorrhagic stroke during the stage of acute disorders of consciousness and the correlation with the neurological prognosis: A multi-modal PET/rs-fMRI study Danjing Yu Danjing Yu a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Danjing Yu a, 1 , Kemeng Gao Kemeng Gao b Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Kemeng Gao b, 1 , Xiefeng Wang Xiefeng Wang a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Xiefeng Wang a, 1 , Lin Zhao Lin Zhao a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Lin Zhao a, 1 , Yi Sun Yi Sun a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Yi Sun a , Zhiyan Shen Zhiyan Shen a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Zhiyan Shen a , Yu Wang Yu Wang a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Yu Wang a , Ying Wang Ying Wang a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Ying Wang a , Wei Ding Wei Ding b Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Wei Ding b , Lin Yang Lin Yang a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Lin Yang a , Ying Duan Ying Duan b Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Ying Duan b , Daniel Cui Daniel Cui c University of Wisconsin, Madison, USA Find articles by Daniel Cui c , Zekai Qiang Zekai Qiang d University of Sheffield, Sheffield, United Kingdom Find articles by Zekai Qiang d , Federico Capelli Federico Capelli e Department of Neuroscience, Neurosurgery Clinic, Psychology, Pharmacology and Child Health (NEUROFARBA), Careggi University Hospital and University of Florence, Italy Find articles by Federico Capelli e , Yuxi Gong Yuxi Gong a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Yuxi Gong a , Ailiang Zeng Ailiang Zeng f Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, NY, USA Find articles by Ailiang Zeng f, ⁎⁎ , Xi Wang Xi Wang a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Xi Wang a, ⁎ , Wei Yan Wei Yan a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China Find articles by Wei Yan a, ⁎ Author information Article notes Copyright and License information a Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China b Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China c University of Wisconsin, Madison, USA d University of Sheffield, Sheffield, United Kingdom e Department of Neuroscience, Neurosurgery Clinic, Psychology, Pharmacology and Child Health (NEUROFARBA), Careggi University Hospital and University of Florence, Italy f Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, NY, USA ⁎ Corresponding authors at: Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road. Nanjing 210000, China. [email protected] [email protected] ⁎⁎ Corresponding author at: Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA. [email protected] 1 These authors contributed equally to this work and joint first authors. Received 2025 Oct 20; Revised 2026 Mar 13; Accepted 2026 Mar 30; Collection date 2026. © 2026 The Author(s) This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). PMC Copyright notice PMCID: PMC13091778  PMID: 41965151 Graphical abstract Open in a new tab Keywords: Traumatic brain injury, Hemorrhagic stroke, PET/rs-fMRI, Neurologic state, Acute disorders of consciousness Highlights • TBI and HS patients with presented different neurological states in the aDoC phase. • Analysis of simultaneous PET/MRI scans tested both neural activity and metabolism. • Noise reduction measures were taken for abnormal signals during PET scans. • Brain network analysis showed deeper neurobiological distinctions between TBI and HS. Abstract Purpose The exact mechanisms underlying the distinct neurological outcomes between Traumatic Brain Injury (TBI) and Hemorrhagic Stroke (HS) remain unclear. Our objective is to assess distinct features of neurologic state between comatose patients with TBI and HS during the stage of acute disorder of consciousness (aDoC) and to identify the correlation of neurologic features with prognosis. Methods Data were analyzed from TBI and HS patients examined by positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) simultaneously. Primary clinical outcomes consisted of the state of consciousness and neurological prognosis. The regional neural activity was assessed by the amplitude of fractional low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) on rs-fMRI scans. The standardized uptake value (SUV) on PET scans quantified neural metabolism. Functional connectivity (FC) and graph theoretic approach (GTA) were employed to compare the FC patterns between TBI and HS. Correlations of PET/rs-fMRI indicators with the prognosis of HS and TBI were identified. Results Muti-modal PET/rs-fMRI analysis showed more active local neurological state in TBI patients than HS patients, specifically in the right precentral gyrus (PreCG.R), right postcentral gyrus (PoCG.R), right superior temporal gyrus (STG.R) and right middle temporal gyrus (MTG.R). TBI patients demonstrated significantly higher clustering coefficient and nodal efficiency of the sensorimotor network (SMN) along with lower connectivity and network efficiency in the default network (DMN) compared to HS patients. PET/rs-fMRI indicators significantly correlated with the neurological prognosis of TBI and HS. Conclusions This study elucidated the underlying mechanisms contributing to the distinct neurologic prognosis between comatose TBI and HS patients, and may contribute to the development of early targeted intervention strategies for specific diseases. 1. Introduction Traumatic brain injury (TBI) and hemorrhagic stroke (HS), as common causes of acute disorders of consciousness (aDoC) ( Edlow et al., 2021 ), are characterized by high disability rate, poor prognosis, and heavy societal and family burdens ( van Asch et al., 2010 , Capizzi et al., 2020 ). However, long-term outcomes between TBI and HS can vary given a similar degree of consciousness impairment in the early stage ( Smania et al., 2013 , Seo and Oh, 2009 , Egawa et al., 2024 ). Previous evidence showed that TBI patients score better in the Glasgow Outcome Scale (GOS) and Coma Recovery Scale Revised (CRS-R) at 6 months and at 1 year post-injury compared to non-TBI patients, including those with HS ( Lanzillo et al., 2019 , Bagnato et al., 2017 ). A long-term follow-up study of adolescents with brain lesions demonstrated that patients with a traumatic etiology had a higher probability of being partly or fully productive than those with other etiologies ( Strazzer et al., 2023 ). So far, the exact mechanisms underlying the distinct neurological outcomes between TBI and HS remain unclear ( Egawa et al., 2024 ). While some studies believe that age and comorbid conditions attribute to the prognostic differences ( Seo and Oh, 2009 , Siddique et al., 2002 ), others found worse prognosis of HS than TBI after aligning baseline characteristics such as age, medical history, and clinical manifestations ( [11] , Katz et al., 2009 ). Different neural activity patterns during the chronic stage of disorders of consciousness (DoC) are strongly correlated with functional recovery ( Stender et al., 2014 ), and evaluating neural activity during the stage of aDoC ( Schnakers et al., 2009 ) faces immense challenges. Our previous work not only detected distinct neural activity profiles among aDoC patients with comparable clinical consciousness levels in the neurosurgical intensive care unit (NICU), but also unmasked their correlation with long-term outcomes ( Wang et al., 2024 ). This implies that similar clinical manifestations of DoC may mask substantial differences in the central nervous system (CNS) activity. Building on the well-documented disparities in the neurological recovery between TBI and HS, we hypothesized that neurodynamic profiles and brain network connectivity are the cornerstones underlying these distinct outcomes. Local neurological state and brain network connectivity during the acute phase in TBI versus HS patients have not been extensively analyzed. In the present study, a non-invasive, simultaneous, multi-modal neuroimaging approach, namely positron emission tomography (PET)/resting-state functional MRI (PET/rs-fMRI) was performed in a novel manner to assess TBI and HS patients during the stage of aDoC in the NICU. This measure mapped the entire brain and captured both neural functional activity and metabolic profiles simultaneously, thereby landscaping an accurate and holistic view of CNS dynamics ( Stender et al., 2014 , Duclos et al., 2021 , Practice guideline update recommendations summary, 2019 ). Currently, PET/rs-fMRI has been widely validated for probing brain network alterations in Alzheimer’s disease ( Bateman et al., 2012 ), Parkinson’s disease ( Basaia et al., 2024 ), and depression ( Goldfarb et al., 2020 ), serving as a useful tool to unveil the pathological mechanism and to predict prognosis ( Zhang and Raichle, 2010 ). The data utilized in this study were obtained from our retrospective cohort study of aDoC patients assessed by PET/fMRI (Clinical Trial Registries: ChiCTR2500107962), comparing the neural activity, metabolic patterns, and brain network connectivity between TBI and HS patients during the stage of aDoC via the multi-modal PET/rs-fMRI, aiming to elucidate the neurophysiological basis for their recovery trajectories. 2. Materials and methods 2.1. Participants Adults with neurological injury and aDoC at onset were enrolled. All patients, not relying on ventilation and possessing stable vital signs, were examined by PET/rs-fMRI within 28 days of onset. Caregivers of patients agreed to participate in the study with the intervention of standardized rehabilitation therapy for more than 1  month and follow-up after leaving the NICU. Excluded were patients with a history of TBI, stoke, brain tumor, and hydrocephalus; persistent epileptic state, uncontrolled intracranial infection, or secondary hydrocephalus without a timely shunt surgery; the Glasgow Coma Scale (GCS) score exceeded 12 points or death prior to PET/rs-fMRI; CNS or spinal cord stimulation during hospitalization or follow-up; and metal or electronic implants in the head or facial area. Elimination criteria: (1) changing physical conditions during PET/rs-fMRI; (2) advanced age over 80; (3) inconsistent with the diagnostic criteria for TBI or HS; (4) death during 3 months of onset; (5) loss of follow-up; (6) GCS score > 8 at onset; (7) abnormal PET/rs-fMRI sequences or poor image quality. 2.2. Registration This study was registered in the Chinese Clinical Trial Registry ( https://www.chictr.org.cn ) with the registration number of ChiCTR2500107962. 2.3. Ethics approval This study was performed in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2025-SR-598). 2.4. Collection of baseline characteristics Baseline characteristics of eligible participants were categorized into four dimensions: demographic data, scale scores, lesion characteristics, and comorbidities. Demographic data included the age, gender, and medical history of hypertension, diabetes, and hyperlipidemia. Scaled scores included the GCS and the Full Outline of Unresponsiveness Scale (FOUR) on admission and the day of PET/rs-fMRI. Lesion characteristics encompassed the impacted brain regions and the distribution of lesions. Associated neurological conditions included brain herniation, central nervous system infections, epilepsy, and secondary cerebral infarction. Detailed data sources and check mechanisms can be found in the supplementary materials . 2.5. Assessment of neurological prognosis All participants were followed up at 3 months after onset for the state of consciousness and 6  months after onset for both the state of consciousness and neurological prognosis through in-person or telephone interviews. The state of consciousness, scored using the CRS-R, was assessed for at least 5 times within 10  days of the follow-up by trained raters in the presence of family members. The highest score of CRS-R was recorded to reflect the state of consciousness. According to the assessed states of consciousness recovery, participants were divided into vegetative state (VS), minimally consciousness state minus (MCS-), minimally consciousness state plus (MCS + ), and emergence from minimally conscious state (eMCS). The neurological prognosis, scored using the Glasgow Outcome Scale-Extended (GOSE) at 6 months of onset, was assessed via telephone or video follow-ups by trained raters. Following conventions, the GOSE scores categorized neurological outcomes into two ranges based on the presence of severe disability: 1–4 indicating severe disability and 5–8 indicating recovery from severe disability. Within this framework, we modified the scoring categorization into 1–5 indicating disability of life roles and 6–8 indicating capability of life roles according to the capacity to participate in one or more life roles. 2.6. Acquisiton of PET/rs-fMRI data A 55-min PET/rs-fMRI was performed using a 3.0 T GE SIGNA PET/MR scanner (GE Healthcare, Waukesha, Wisconsin, USA). After confirming a baseline serum glucose level of less than 12 mmol/L, 18 F-fluorodeoxyglucose ( 18 F-FDG) was administered intravenously according to the standard weight-based scale. PET/rs-fMRI was initiated at 60 min post-injection. The detailed parameters of the MRI scan are described in the supplementary materials . 2.7. Pre-processing and value extraction of BOLD (Blood-Oxygen-Level Dependant Signal) data Resting-state images were pre-processed using ITK-SNAP ( Yushkevich et al., 2006 ) and Data Processing and Analysis for Brain Imaging (DPABI) ( Yan et al., 2016 ). The preprocessing steps included slice timing adjustment, head motion correction, nuisance covariates regression, detrend, segment, normalization and smoothing. Regional brain activity was assessed using amplitude of fractional low-frequency fluctuation (fALFF) ( Zou et al., 2008 ) and regional homogeneity (ReHo) ( Zang et al., 2004 ) values. The detailed procedures were described in the supplementary materials . 2.8. Pre-processing and value extraction of PET data PET images were processed with Statistical Parametric Mapping(SPM) ( Zeidman et al., 2025 ). Each PET image is examined by radiologists to ensure the quality of the original images. The pre-procession including an origin correction, co-registration with MRI structural images, and partial volume effect (PVC) correction using segmented T1 structural images and zero of cerebrospinal fluid (CSF) signals. Finally, PET images were normalized into the MNI space and spatially smoothed with an 8-mm FWHM. The original standard uptake rates of 90 cerebral regions were extracted from preprocessed PET data according to the anatomical automatic labeling (AAL) template. The final standard uptake value (SUV) was obtained by intensity normalization of the metabolic rate using SNBPI to quantify signal intensity and minimize influences of individual variations (e.g., blood glucose levels, medications, age, and gender) ( Zhang et al., 2022 ). Subsequently, a general linear model (GLM) was constructed using SPM with head motion displacement parameters included as covariates for analyses. 2.9. Statistical analysis of baseline characteristics Statistical analysis was performed using SPSS 24.0. Categorical variables of baseline characteristics were expressed as number of cases (n) and percentage (%), followed by a chi-square test for statistical analysis. The normality of all continuous variables was examined by the Shapiro–Wilk test. Normally distributed continuous variables were expressed as mean ± standard deviation and analyzed with an independent samples t -test; otherwise, variables were expressed as median with interquartile boundary values (P25, P75) and examined with non-parametric tests. 2.10. Statistical analysis of zfALFF and zReHo values in BOLD data Two-sample t -test was performed to compare zfALFF and zReHo values between TBI and HS patients. Brain regions reaching significant differences in BOLD data were visualized using BrainNet Viewer for three-dimensional reconstruction ( Xia et al., 2013 ). The gaussian random field (GRF) was applied to correct data to a significant level of voxel p < 0.001 and cluster p < 0.001 ( Ashburner and Friston, 2000 ). The threshold-free cluster enhancement (TFCE) boosted data reliability when data were distributed ambiguously ( p < 0.05) ( Chen et al., 2018 ). Brain regions with differentially expressed data from between-group comparisons reached a cluster size ≥ 30 voxels, and delineated using the AAL template to clarify their anatomical locations. Subsequently, corresponding brain regions were mapped onto the Dosenbach atlas for obtaining 160 regions of interest (ROIs), which were used for subsequent analyses of functional connectivity (FC) and graph theoretical approach (GTA). 2.11. Statistical analysis of SUV values in PET data SUV values between groups were compared using independent samples t-tests and the false discovery rate (FDR) method was applied to the correction of multiple comparisons. A threshold of p < 0.05 was considered statistically significant. To mitigate the influence of signal artifacts, only brain regions with a minimum cluster size criterion (voxel count ≥ 30) were retained as final results. 2.12. Analyses of FC and GTA in BOLD data DPABI and Gretna toolbox in Matlab were used to analyze FC and GTA. ROIs of brain regions with differentially expressed zfALFF and zReHO values were used for FC analysis. Brain regions with certain connection were defined as nodes. The connection strength of FC was defined as edges, thereby obtaining the network connectivity matrix between nodes. For intergroup comparison of edges, FDR-corrected multiple comparison tests were performed with a corrected p < 0.05. Detailed procedure of FC analyses could be found in the supplementary materials . Network topological indicators in both TBI and HS patients were evaluated by the Gretna graph theoretical toolkit. The following GTA indicators were assessed, including nodal efficiency, clustering coefficient (Cp), characteristic path length (Lp), local efficiency, global efficiency (Eglob), and small-world coefficients. All network properties were compared by two-sample t-tests with a significance threshold of p < 0.05. Nodal efficiency was ultimately selected as the node-level feature and visualized using the BrainNet Viewer toolbox. 2.13. Correlation analysis between neuroimaging indicators and neurological prognosis Linear correlation identified the association between neuroimaging indicators (e.g., zfALFF, SUV, GTA indicators) and neurological prognosis. Original SUV values were obtained directly from postprocessed PET scans and GTA indicators were represented by the normalized area under the curve (AUC) to account for all scenarios under varying sparsity thresholds. Statistical analysis was conducted using Prismchs software with correlation coefficients denoted by r-values and statistical significance determined by p-values. Positive r-values indicated direct correlations while negative values denoted inverse relationships. P < 0.05 considered as statistically significant. 3. Results 3.1. Differences in baseline characteristics and prognostic outcomes between TBI and HS patients Initially, 79 aDoC stage patients with neurological injury in the NICU, examined by the PET/rs-fMRI were from a registered clinical trial (ChiCTR2500107962). After further excluding one case of a terminated scan, one case of advanced age at the time of PET/rs-fMRI (>80 years), one case of ischemic stroke, two cases of deaths within three months of onset, one patient lost to follow up, five cases with a GCS score greater than 8 at disease onset, and nine cases with poor image quality, a total of 59 eligible patients were enrolled in our study, involving 31 comatose TBI patients and 28 comatose HS patients ( Fig. 1 ). Fig. 1. Open in a new tab A flow chart of participant enrollment. Significant differences were seen in the incidence of hypertension (22.58% vs. 89.29%), bilateral brain injury (38.71% vs. 7.14%), brainstem involvement (6.45% vs. 39.29%), thalamic involvement (6.45% vs. 42.86%), and subarachnoid hemorrhage (67.74% vs. 32.14%) between TBI and HS patients (all p < 0.05, Table 1 ). The remaining baseline characteristics were comparable between groups ( p > 0.05). Table 1. Baseline characteristics (n = 59). Variables TBI patients (n = 31) HS patients (n = 28) Statistic value P -value Age (mean ± SD) 58.13 ± 12.00 60.50 ± 12.45 t = 0.743 0.461 Male sex (n, %) 18 (58.06) 13 (46.43) χ 2 = 0.799 0.371 History of comorbidities (n, %) Hypertension 7 (22.58) 25 (89.29) χ 2 = 26.374 <0.001* Diabetes 8 (25.81) 8 (28.57) χ 2 = 0.571 0.811 Hyperlipidemia 6 (19.35) 5 (17.86) χ 2 = 0.224 0.883 Scale scores (median, P25, P75) GCS at onset 5.0 (4,8) 5.0 (4,7) u = 421.5 0.847 FOUR at onset 7.0 (6,10) 7.5 (5,9) u = 403.0 0.635 GCS before PET/rs-fMRI 7.0 (5,8) 6.5 (5,8) u = 433.5 0.994 FOUR before PET/rs-fMRI 11.0 (9,13) 11.0 (9,12) u = 427.0 0.915 Lesion distribution (n, %) Bilateral sides 12 (38.71) 2 (7.14) χ 2 = 8.100 0.004* Side of unilateral lesion (n, %) Left side 9 (29.03) 14 (50.00) χ 2 = 0.184 0.668 Right side 10 (32.26) 12 (42.86) Affected brain regions (n, %) Temporal lobe 21 (67.74) 13 (46.43) χ 2 = 2.737 0.098 Frontal lobe 17 (54.84) 10 (35.71) χ 2 = 2.168 0.141 Parietal lobe 7 (22.58) 5 (17.86) χ 2 = 0.203 0.653 Occipital lobe 6 (19.35) 3 (10.71) χ 2 = 0.850 0.357 Brain stem 2 (6.45) 11 (39.29) χ 2 = 9.233 0.002* Thalamus 2 (6.45) 12 (42.86) χ 2 = 10.774 0.001* Subarachnoid hemorrhage 21 (67.74) 9 (32.14) χ 2 = 7.460 0.006* Complications (n, %) Cerebral herniation 13 (41.94) 8 (28.57) χ 2 = 1.146 0.284 CNS infection 5 (16.13) 4 (14.29) χ 2 = 0.039 0.844 Epilepsy 2 (6.45) 2 (7.14) χ 2 = 0.011 0.916 Secondary cerebral infarction 14 (45.16) 9 (32.14) χ 2 = 1.048 0.306 Open in a new tab TBI, traumatic brain injury; HS, hemorrhagic stroke; SD, standard deviation; GCS, Glasgow Coma Scale; FOUR, Full Outline of Unresponsiveness Scale; PET, positron emission tomography; rs-fMRI, resting-state functional magnetic resonance imaging; CNS, central nervous system. At 3 months and 6 months after onset, the distribution of states of consciousness was similar between groups ( Table 2 and Fig. 2 A, B). Using GOSE assessment, the neurological prognosis between TBI and HS patients was significantly different at 6 months ( Table 2 ). Stratified by the GOSE scoring of 1–4 and 5–8 points, a significantly higher proportion of TBI patients benefited from a better neurological prognosis than that of HS patients (38.71% vs. 10.71%, P = 0.014). Assessed by the cut-off values of 1–5 and 6–8 points of the GOSE ( Wilson et al., 2021 ), TBI patients also demonstrated a better neurological prognosis than HS patients (25.81% vs. 2.57%, P = 0.044) ( Fig. 2 C). Overall, TBI patients were more likely to recover from severe disability and achieve functional recovery compatible with one or more life roles than HS patients. It is noteworthy that there was a statistically significant difference in the number of brainstem and thalamic injuries between the TBI and HS patient groups. To exclude the potential impact of thalamic and brainstem injuries on prognosis and neural activity outcomes, a supplementary analysis of patients who did not have thalamic or brainstem involvement (including 15 HS and 29 TBI patients) were conducted. We repeated the baseline data analysis and neuroimaging analysis, obtaining results similar to the aforementioned results ( Supplementary Table 1, 5 ; Supplementary Fig. 1, 3 ). Table 2. Comparison of prognosis between the TBI and HS groups. TBI patients (n = 31) HS patients (n = 28) Statistic value P -value State of consciousness after 3 mouths (n, %) VS 6(19.35) 4(14.29) u = 369.5 0.304 MCS- 8(25.81) 7(25.00) MCS+ 7(22.58) 3(10.71) eMCS 10(32.26) 14(50.00) pDOC 21(67.74) 14(50.00) χ 2 = 1.919 0.166 eMCS 10(32.26) 14(50.00) State of consciousness after 6 mouths (n, %) Dead 2(6.45) 2(7.14) — — Number of patients included 29 26 VS 4(13.79) 1(3.85) u = 276.5 0.140 MCS- 7(24.14) 5(19.23) MCS+ 5(17.24) 3(11.54) eMCS 13(44.83) 17(65.38) pDOC 16(55.17) 9(34.62) χ 2 = 2.337 0.126 eMCS 13(44.83) 17(65.38) Patients in various GOSE score ranges after 6 mouths (n, %) 1 ∼ 4 19(61.29) 25(89.29) χ 2 = 6.081 0.014* 5 ∼ 8 12(38.71) 3(10.71) 1 ∼ 5 23(74.19) 27(96.43) χ 2 = 4.038 0.044* 6 ∼ 8 8(25.81) 1(3.57) GOSE scores after 6 mouths (median, P25, P75) 3(3,6) 4(3,4) u = 420.5 0.838 Open in a new tab TBI, traumatic brain injury; HS, hemorrhagic stroke; VS, vegetative state; MCS-, minimally conscious state minus; MCS+, minimally conscious state minus plus; eMCS, emergence from minimally conscious state; pDOC, prolonged disorders of consciousness. Fig. 2. Open in a new tab Neurologic prognosis between TBI and HS. (A) The distribution of the state of consciousness at 3 months of TBI and HS. (B) The distribution of the state of consciousness at 6 months of TBI and HS. (C) The distribution of the GOSE categories at 6 months of TBI and HS. 3.2. Distinct neurological states between TBI and HS revealed by BOLD data analysis Significant differences in zfALFF values with GRF correction between TBI and HS patients were mainly located in the right precentral gyrus (PreCG.R), right postcentral gyrus (PoCG.R), right superior temporal gyrus (STG.R), right middle temporal gyrus (MTG.R), right supramarginal gyrus (SMG.R), right fusiform gyrus (FFG.R), right lingual gyrus (LING.R), left precuneus (PCUN.L), and left post cingulate gyrus (PCG.L) ( Fig. 3 A, C). These fALFF values were significantly higher in TBI patients than HS patients. Additionally, TFCE tests showed significantly higher zfALFF values of the right middle frontal gyrus (MFG.R), right precuneus (PCUN.R), PreCG.R, PoCG.R, MTG.R, STG.R, and SMG.R in TBI patients than HS patients ( Fig. 3 B, D). The overlapping results of the two examination methods showed distinct neurological states between TBI and HS patients within the PreCG.R, PoCG.R, MTG.R, STG.R, and SMG.R. Six ROIs with a radius of 5 mm were selected on the Dosenbach atlas for subsequent FC and network analyses. These nodes were primarily located within the sensorimotor network (SMN) or the dorsal attention network (DAN). The detailed differential brain regions and nodal coordinates are provided in supplementary Table 2 . Fig. 3. Open in a new tab Differences in the fALFF between TBI and HS patients. Axial brain maps show brain regions with differentially expressed ALFF between TBI and HS through the GRF correction (A) and TFCE correction (B). 3D reconstruction images show ALFF analysis after GRF correction (C) and TFCE correction (D). Red color represents the higher ALFF in the TBI group than the HS group. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Analysis of zReHo values with GRF correction showed significantly better regional homogeneity in TBI patients than HS patients within the following brain regions: the right putamen (PUT.R), right pallidum (PAL.R), right insula (INS.R), left middle frontal gyrus (MFG.L), left superior frontal gyrus (SFG.L), left precentral gyrus (PreCG.L), left putamen (PUT.L), left thalamus (THA.L), left pallidum (PAL.L), and left insula (INS.L) ( Supplementary figure 2 A, C). However, TFCE test only found better regional homogeneity within the MFG.L, SFG.L, and PreCG.L in TBI patients than HS patients ( Supplementary figure 2 B, D). The overlapping results showed the key brain regions of the MFG.L, SFG, and PreCG. Based on this result, we selected two ROIs for subsequent FC and network analyses. The detailed differential brain regions and nodal coordinates are provided in supplementary Table 3 . 3.3. Distinct neurological states between TBI and HS similarly revealed by denoised PET data analysis A significantly higher metabolic rate was observed in TBI patients compared to HS patients in the bilaterial PoCG, bilaterial PreCG, bilaterial Caudate, the right Putamen, MTG.R, right Superior Temporal Pole, right Parahippocampal cortex and a small potion of the right Angular (p < 0.05, FDR correction) ( Fig. 4 A, B). Their MNI space coordinates, cluster size, and peak intensity were listed in Supplementary Table 4 . The average metabolic rates in these brain regions were shown in the violin figures ( Fig. 4 C). TBI patients demonstrated better local neurologic state in the PreCG.R, PoCG.R, STG.R and MTG.R than HS patients, which is consistent with the zfALFF analysis results,. Fig. 4. Open in a new tab Metabolic rates between TBI and HS. (A) Axial brain maps show TBI patients demonstrated higher metabolic rate in the bilaterial PoCG, bilaterial PreCG, bilaterial Caudate, the right Putamen, MTG.R, right Superior Temporal Pole, right Parahippocampal cortex and a small potion of the right Angular (p < 0.05, FDR correction) compared to HS patients after eliminating the effects of abnormal signals. The colour bar represents normalized standard uptake value ranging from 0 to 6. (B) 3D reconstruction images show brain regions with differentially expressed metabolic rate between TBI and HS. Highlighted color represents the higher metabolic rates in the TBI group than the HS group. (C) Violin plots demonstrate difference in the average metabolic rates between TBI and HS patients in the bilaterial PreCG,PoCG and Caudate (using the AAL template) as well as the STG.R and MTG.R. *P < 0.05, **P < 0.005, ****P < 0.0001. 3.4. Distinct brain networks between TBI and HS revealed by FC and GTA analyses Significant differences in the FC were only detected in the precentral (MNI coordinates: 44––11 38) and parietal nodes (MNI coordinates: 46––20 45). In the TBI group, precentral node exhibited a stronger FC with the pole of the MTG.R, bilateral INS, bilateral MTG, and bilateral Pre.CG ( Fig. 5 A). Conversely, a significantly weaker FC of precentral node in TBI patients was seen within the right orbital part of inferior frontal gyrus, right triangular part of inferior frontal gyrus, right inferior parietal lobule, right angular gyrus, and right cingulate gyrus ( Fig. 5 B). For the parietal node, brain regions with differentially expressed FC were located in the left angular gyrus, right middle cingulate gyrus, small portions of the right posterior cingulate gyrus, and right precuneus. TBI patients exhibited a weaker FC in these brain regions with parietal node compared to HS patients ( Fig. 5 C). Additionally, positive connections were pronounced in TBI patients, which were primarily distributed within the SMN and DAN. In contrast, significant negative connections in the TBI group relative to the HS group were mainly observed in the default mode network (DMN) and frontoparietal network (FPN). Fig. 5. Open in a new tab FC and GTA revealed contrary active mode in the SMN and DMN between TBI and HS patients. (A) The brain regions with differentially expressed positive FC with the ROI precentral (MNI coordinates: 44––11 38) between TBI and HS. (B) The brain regions with differentially expressed negative FC with the ROI precentral (MNI coordinates: 44––11 38) between TBI and HS. (C) The brain regions with differentially expressed negative FC with the ROI parietal (MNI coordinates: 46––20 45) between TBI and HS. No positive FC of this ROI is found. (D) 3D reconstruction image shows stronger connectivity strength and higher network efficiency in TBI than HS group within the SMN. The size of nodes represents the value of nodal efficiency, and a larger node has better nodal efficiency. The colour represents the connectivity strength, and red colour indicates high connectivity strength. (E) 3D reconstruction image shows weaker connectivity strength and higher network efficiency in TBI than HS within the DMN. The node FFG.R shows better nodal efficiency in TBI than HS. The colour blue means weak connectivity strength. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) To further investigate the different brain networks between TBI and HS patients, all nodes corresponding to these four networks were computed for the strength of FC. Compared to HS patients TBI patients presented a significantly higher strength of network connectivity in the SMN ( Fig. 5 D) and a lower strength in the DMN ( Fig. 5 E). No significant differences were observed in the other two networks. The significant difference in the SMN was still valid after FDR ( P < 0.001). GTA was conducted using network matrix to calculate node efficiency and network parameters. While a significantly higher node efficiency in the SMN was found in TBI patients, it was no longer valid after FDR correction. Differential expressed nodes ( P < 0.0005 in a two-sample t -test) were identified in the bilateral PreCG, PoCG, parietal lobe, insula, and ventrolateral prefrontal cortex and visualized in Fig. 5 D. After FDR correction, TBI patients possessed a significantly higher node efficiency of FFG.R in DMN than HS patients ( Fig. 5 E). Even when applying more lenient statistical thresholds, no other nodes demonstrated significant differences. The network parameters were also calculated in GTA. The clustering coefficient (Cp) in the SMN of TBI patients was significantly higher than HS patients ( Supplementary figure 4 A and Supplementary Table 6 ), indicating a closer connection of the brain regions in the SMN of TBI patients. In the DMN, the characteristic path length (Lp) in the TBI group was significantly longer than the HS group ( Supplementary figure 4 B and Supplementary Table 6 ), suggesting an inferior information transmission efficiency of DMN in TBI patients. Although other metrics did not reach statistical significance, an overall conclusion could be drawn that the SMN in the TBI group was more active, while the DMN appeared relatively suppressed compared to the HS group. 3.5. Multi-modal PET/rs-fMRI values in specific brain regions are correlated with neurologic prognosis A linear correlation analysis was conducted to visualize the correlation of zfALFF and SUV values in the PreCG.R and PoCG.R in accordance of the AAL template, as well as all GTA indicators of SMN and DMN with the GOSE scores. zfALFF values in the PreCG.R and PoCG.R were positively correlated with neurologic outcomes ( Fig. 6 A). Similarly, SUV values in these regions also exhibited positive correlations with the prognosis ( Fig. 6 A B). Within the SMN, clustering coefficient (Cp_AUC) and global efficiency (Eglob_AUC) were positively correlated with neurologic recovery, while characteristic path length (Lp_AUC) showed a negative correlation ( Fig. 6 C). Correlations were less observed in the DMN, except for a weak correlation between the global efficiency and the prognosis ( Fig. 6 D). Fig. 6. Open in a new tab Correlation of multi-modal PET/rs-fMRI data with neurologic prognosis. (A) Correlation analysis between zALFF values in the PreCG.R and PoCG.R with the GOSE scores. (B) Correlation analysis between SUV values in the PreCG.R and PoCG.R with the GOSE scores. (C) Correlation analysis of clustering coefficient, characteristic path length, and global efficiency in the SMN with the GOSE scores. (D) Correlation analysis of global efficiency in the DMN with the GOSE scores. 4. Discussion For the first time, this study unmasked the distinct characteristics of neural activity, neuro-metabolic levels, and brain networks between comatose TBI and HS patients in the NICU during the stage of aDoC via a multi-modal imaging system of PET and rs-fMRI. Moreover, the PET/rs-fMRI data were significantly correlated with the neurologic prognosis. There are different baseline characteristics between patients with TBI and HS related to the location of the lesions. Although no statistically significant differences were observed in unilateral or bilateral injuries between the two patient groups in this study, certain trends were noted. TBI patients were more likely to have bilateral injuries, while HS patients showed a higher probability of left hemisphere involvement in this study. These trends may have influences on the analysis in this study, and further studies with larger cohorts are needed to mitigate such effects. TBI patients experienced a better neurologic function at 6 months than HS patients, although the conscious prognosis at 3 months and 6 months post-onset was comparable. Currently, neural mechanisms of how TBI and HS progress have not been clearly elucidated yet ( Smania et al., 2013 , Seo and Oh, 2009 ). Here, we innovatively employed a multi-modal PET/rsfMRI study to explore the distinct neurologic state that consists of both neural activity and neural metabolism between TBI and HS patients. Previously, great strides have been made in investigating the neurologic state of local brain regions via a single modality using the BOLD sequence. However, the BOLD sequence only captures one dimension of the local neural state by quantifying the neural activity, which is unable to provide a holistic view of neuro-metabolic processes. Moreover, the accuracy of BOLD in assessing the neural activity can be influenced by hemodynamics and age distribution ( Amemiya et al., 2012 , D'Esposito et al., 1999 ). PET examinations have been applied less onto analyzing neurological injuries during the acute phase. Previous PET data show significantly reduced metabolic rates in the frontal-parietal lobes among comatose patients with severe brain damage ( Thibaut et al., 2012 , Soddu et al., 2016 ), although those with structural deformations (e.g., TBI, HS) in the brain were excluded. A precise illustration of brain network activities is challenging but of great importance. TBI patients exhibited significantly higher neuro-metabolic levels of the PreCG.R, PoCG.R, MTG.R and STG.R compared to HS patients. The central locations and sizes of these differentially expressed brain regions were consistent with BOLD data findings. Given that a considerable number of patients in this study had temporal lobe injuries, and some hematoma evacuation surgeries adopted a temporal surgical approach, abnormal PET signals caused by hematoma and inflammation in the temporal lobe may not be accurately identified or eliminated. This could potentially affect the outcomes. Therefore, in the subsequent brain network analysis and discussion sections of the results, this study has temporarily refrained from addressing the differential findings related to the temporal lobe regions. Imaging examinations indicated that injuries to the precentral gyrus and postcentral gyrus were relatively infrequent. As of a certain degree of coupling between PET and BOLD signals ( Sundqvist et al., 2022 ), these two overlapping brain regions identified by PET possessed high credibility. Measurements of zfALFF and PET analysis exhibited superior performance from TBI patients compared to HS patients in PreCG.R, PoCG.R, and SMG.R in both neural activity and metabolism. PreCG is the primary motor cortex that strongly associates with advanced function and disease prognosis. PreCG is engaged in auditory-related functions, word recognition, and phonological processing ( Xu et al., 2019 ). Chen et al. found a worse prognosis in idiopathic tinnitus patients with a smaller gray matter volume in the PreCG.R ( Chen et al., 2021 ). The middle PreCG is a key node in the speech production network, dominating in the motor planning of speech ( Lu et al., 2021 , Silva et al., 2022 ). It is found that the beneficial effect of carotid artery stenting on the prognosis of cognitive function in patients with asymptomatic carotid stenosis is partly attributable to the increased ALFF in the PreCG.R ( Wang et al., 2017 ). A recent study investigated the relationship between rs-fMRI activity and neurological outcomes in patients with an early coma following cardiac arrest, revealing a higher neural activity in the PreCG.R among individuals with a favorable neurological prognosis ( Shao et al., 2024 ). The differentially expressed local neural activity in the PreCG.R between TBI and HS during the stage of aDoC may be attributed to the distinct neurologic functioning. In addition, both zfALFF and SUV values of the PreCG.R were positively correlated with the GOSE scores at 6 months of onset, thus suggesting the correlation of multi-modal PET/rs-fMRI with neurologic prognosis of aDoC. This correlation was stronger in the PreCG.R than the PoCG.R, warranting a further analysis. Neurologic activity increases in the PoCG following TBI. Significantly enhanced neural activity is observed in multiple brain regions of mice with mild TBI, including the primary somatosensory cortex that is corresponding to the PoCG in the human brain ( Rudolph et al., 2023 ). The network connectivity is elevated in the PreCG and PoCG of patients with acute severe TBI than health controls ( Zhang et al., 2023 ). However, changes in the neural activity of PoCG post-HS have not been extensively analyzed. The PoCG constitutes a critical component of the sensorimotor network, facilitating the integration and transmission of sensorimotor information ( Nelson and Chen, 2008 ). Its activation level is pivotal in modulating tactile processing ( de Siqueira et al., 2013 ). Empathy, emotional generation, and emotional regulation are also linked with the PoCG ( Takeuchi et al., 2019 , Seehausen et al., 2014 ). Psychological resilience in depressed patients is positively associated with resting-state activation in the PoCG.R ( Wang et al., 2024 ). As a result, the higher neural activity in the PoCG.R of TBI patients compared to HS patients partially explained a significantly higher proportion of TBI patients achieving the GOSE scores of 5–8. The SMG.R dominates time estimation and emotion regulation ( Prete et al., 2023 , Silani et al., 2013 ). In this study, the volume of the SMG.R accounted for only 3% of differentially expressed brain regions, contributing less to the distinct neurologic state between TBI and HS. Brain regions with differentially expressed ReHo values under BOLD acquisition were not exactly the same as those identified by zfALFF values. The former values reflect the coherence and centrality of adjacent brain areas, while the latter quantify the intensity of activity in local brain regions ( Lv et al., 2018 ). Although these two metrics share certain similarities, higher coherence and centrality are not necessarily equal to a higher neurologic activity ( Zang et al., 2015 ). A high brain activity is a brain’s property that provides necessary, but not sufficient support of the conscious state ( Shulman et al., 2009 ). Therefore, it is reasonable to separately examine the differences between these two measures. There is an inherent bilateral asymmetry in brain network organization ( Joshi et al., 2015 ). Higher-order functional networks, such as the dorsal attention network, exhibit right-lateralized dominance ( Vossel et al., 2014 , Corbetta and Shulman, 2011 ). Similarly, neurotrauma is also confined to unilateral cerebral hemispheres ( Ordóñez-Rubiano et al., 2024 ). Therefore, it is possible that the differential brain regions associated with neurological functional prognosis are concentrated in the right hemisphere. FC analysis based on ROIs identified through BOLD and PET showed a stronger connectivity in the PreCG with key nodes of the SMN of TBI patients. Given that the PreCG and PoCG served as key nodes of the SMN ( Yeo et al., 2011 ), we can reasonably hypothesized a higher activity in the SMN of TBI patients than HS patients, which was further confirmed by the GTA analysis. In addition to the localized neural activity in specific brain regions, we also focused on the operational efficiency and synergistic coordination of the entire network, therefore characterizing nodal attributes based on the nodal efficiency measuring the shortest average connecting path between a specific node and other nodes ( Latora and Marchiori, 2001 ). The clustering coefficient (Cp) and the characteristic path length (Lp) were utilized to quantify the efficiency and centrality of networks. The former primarily reflects the tendency of a node to connect with neighboring nodes, while the latter evaluates the global connectivity of a network. Generally, a network with a shorter Lp processes faster information transmission ( Latora and Marchiori, 2003 ). Our results indicated that in the SMN of TBI patients, most nodes exhibited higher nodal efficiency and stronger intra-network connectivity compared to HS patients. Furthermore, TBI patients demonstrated a superior Cp within the SMN relative to HS. Overall, we concluded that SMN in TBI patients displayed both greater activation and higher functional efficiency compared to HS patients. Cp and Eglob of the SMN were significantly positively correlated with the neurologic prognosis, whereas Lp showed a negative correlation with clinical outcome. In the DMN, TBI patients exhibited an opposing pattern of a weaker network connection strength and a longer characteristic shortest path than HS patients. However, FFG.R demonstrated a significantly higher nodal efficiency in TBI patients than HS patients, despite possessing a relative suppression of the DMN in the former population. FFG is responsible for advanced functions related to visual processing, naming, and memory ( Rossion et al., 2024 ), which is potentially preserved well in TBI patients. The SMN is commonly recognized as the neural network responsible for processing sensory inputs and executing motor commands, where the PreCG and PoCG serve as its core regions ( Yeo et al., 2011 ). A growing number of studies have revealed the critical role of SMN in sensory perception, motor functions, and cognitive functions ( Qin et al., 2021 , Dagnino et al., 2024 ). Significantly weakened connectivity in the SMN and enhanced DMN activity are detected in depressive patients, indicating a transition from higher-order sensory functions to lower-order processing ( Zhang et al., 2024 ). Multiple sclerosis patients with cognitive impairment have a decreased centrality in the SMN, but an increased centrality in the DMN ( Eijlers et al., 2017 ). The observed enhanced SMN connectivity and weakened DMN activity in TBI patients may partly explain their superior neurological prognosis compared to HS patients, enabling them to engage more effectively in social life. However, numerous factors can influence network activity. The heightened SMN activity observed in TBI patients might be attributed to compensatory activation following damage in specific brain regions, or alternatively, it could result from suppressed SMN activity in HS patients due to impaired neural pathways. Furthermore, as an executive network, increased SMN activity might be an epiphenomenon reflecting the functioning of deeper decision-making networks. Currently, there is no direct evidence confirming that SMN activity directly promotes neurological recovery. The specific mechanisms through which the SMN influences neurological functional prognosis warrant further investigation. In summary, our multi-modal PET/rs-fMRI analysis revealed higher neural activity and metabolic levels in the PreCG.R, PoCG.R, and SMG.R of TBI patients compared to HS patients. These brain regions are associated with advanced functions such as motor control, language processing, and cognition. TBI patients also demonstrated stronger connectivity strength and superior network efficiency within the SMN, which is linked to sensory, motor, and higher-order cognitive functions. Conversely, TBI patients showed significantly lower connectivity in the DMN compared to HS patients. Correlation analyses revealed that both the neural activity and metabolic levels in the PreCG.R and PoCG.R were positively correlated with neurologic prognosis. Furthermore, the connectivity metrics of SMN demonstrated significant correlations with clinical outcomes. These neuroimaging findings may elucidate the underlying mechanisms contributing to the distinct neurologic prognosis between comatose TBI and HS patients. Our study had several limitations, such as a relatively small sample size, reliance on single-center data, and lack of longer-term follow-up observations. We presented mechanistic insights that informs brain region target selection and parameter optimization for early neuromodulatory interventions on TBI and HS patients during the stage of aDoC. For instance, when conducting non-invasive neural stimulation on the PreCG.R region of patients with brain injuries in the acute phase, more intensive stimulation parameters are suitable for HS. 5. Consent to participate Informed consent was obtained from all family members of individual participants included in the study. CRediT authorship contribution statement Danjing Yu: Writing – original draft, Visualization, Methodology, Formal analysis, Conceptualization. Kemeng Gao: Writing – review & editing, Visualization, Validation. Xiefeng Wang: Writing – review & editing, Visualization, Validation. Lin Zhao: Writing – review & editing, Validation, Project administration. Yi Sun: Formal analysis, Data curation. Zhiyan Shen: Formal analysis, Data curation. Yu Wang: Formal analysis, Data curation. Ying Wang: Formal analysis, Data curation. Wei Ding: Formal analysis, Data curation. Lin Yang: Formal analysis, Data curation. Ying Duan: Formal analysis, Data curation. Daniel Cui: Writing – review & editing. Zekai Qiang: Writing – review & editing. Federico Capelli: Writing – review & editing. Yuxi Gong: Formal analysis, Data curation. Ailiang Zeng: Writing – review & editing, Supervision, Project administration, Methodology, Conceptualization. Xi Wang: Writing – review & editing, Visualization, Validation. Wei Yan: Writing – review & editing, Supervision, Project administration, Methodology, Conceptualization. Funding The study was funded by the National Natural Science Foundation of China (Grant No. 82402969), Jiangsu Province Hospital (the First Affiliated Hospital with Nanjing Medical University) Clinical Capacity Enhancement Project (Grant No. JSPH-MC-2023–14, JSPH-MB-2023–14, JSPH-MB-2023–19). 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. Acknowledgment We appreciate all the participants in the study and the Nuclear Medicine Department of the First Affiliated Hospital of Nanjing Medical University for offering a streamlined process for PET/rs-fMRI examination. Footnotes Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2026.103990 . Contributor Information Ailiang Zeng, Email: [email protected]. Xi Wang, Email: [email protected]. Wei Yan, Email: [email protected]. Appendix A. Supplementary data The following are the Supplementary data to this article: Supplementary Data 1 mmc1.docx (2.1MB, docx) Data availability Data will be made available on request. References Edlow B.L., Claassen J., Schiff N.D., Greer D.M. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat. Rev. Neurol. 2021;17:135–156. doi: 10.1038/s41582-020-00428-x. 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