Frontal, temporal and cerebellar topological property alterations predispose cognitive impairment of ICU sepsis survivors: A resting-state fMRI study

Frontal, temporal and cerebellar topological property alterations predispose cognitive impairment of ICU sepsis survivors: A resting-state fMRI study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Frontal, temporal and cerebellar topological property alterations predispose cognitive impairment of ICU sepsis survivors: A resting-state fMRI study Ying Li, Jianqing Chen, Hui Wang, Lina Wang, Jingjing Li, Mengqing Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5226224/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to explore the topological alterations of the brain networks of ICU sepsis survivors and their correlation with cognitive impairment. Methods 16 sepsis survivors from ICU and 19 healthy controls from the community were recruited. Within one month after discharge, neurocognitive tests were administered to assess cognitive performance. Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired and the topological properties of brain networks were measured based on graph theory approaches. Granger causality analysis (GCA) was conducted to quantify effective connectivity (EC) between brain regions showing positive topological alterations and other regions in the brain. The correlations between topological properties and cognitive performance were analyzed. Results Sepsis survivors exhibited significant cognitive impairment. At the global level, sepsis survivors showed lower normalized clustering coefficient (γ) and small-worldness (σ). At the local level, degree centrality (DC) and nodal efficiency (NE) decreased in the right orbital part of inferior frontal gyrus (ORBinf.R), NE decreased in the left temporal pole of superior temporal gyrus (TPOsup.L)whereas DC and NE increased in the right cerebellum Crus 2 (CRBLCrus2.R). Regarding directional connection alterations, GCA revealed that EC from left cerebellum 6 (CRBL6.L) to ORBinf.R and EC from TPOsup.L to right cerebellum 1 (CRBLCrus1.R) decreased, whereas EC from right lingual gyrus (LING.R) to TPOsup.L increased. Correlation analysis demonstrated a significant relationship between cerebellar topological alterations and cognitive performance. Conclusions Frontal, temporal and cerebellar topological property alterations are involved in the mechanisms of cognitive impairment of ICU sepsis survivors and may serve as biomarkers for early diagnosis. Trial registration NCT03946839 (Registered May 10, 2019). Health sciences/Pathogenesis/Inflammation/Sepsis Health sciences/Pathogenesis/Infection Health sciences/Diseases/Infectious diseases/Bacterial infection Health sciences/Biomarkers/Diagnostic markers Health sciences/Biomarkers/Predictive markers Health sciences/Biomarkers/Prognostic markers Sepsis Cognitive impairment Functional magnetic resonance imaging Graph theory Granger causality analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [ 1 ]. According to epidemiological data of World Health Organization, 19.4 million people develop sepsis each year [ 2 ]. Although the use of antimicrobials and other appropriate management strategies has significantly reduced the incidence and mortality of sepsis, long-term sequela, including physical, cognitive, and psychological impairments, have become another pressing issue. Among these various sequela, cognitive impairment is particularly common [ 3 ], significantly decreasing the life-quality for patients and increasing the burden on caregivers [ 4 , 5 ]. However, the mechanisms underlying cognitive impairment in sepsis survivors remains unclear. Magnetic resonance imaging (MRI) is a non-invasive and non-radioactive tool widely used in the field of neurocognitive impairment. Specifically, functional MRI (fMRI) is based on the principle that the blood oxygen level-dependent (BOLD) signal fluctuations strongly reflect neuronal activity. With numerous analytical methods, rs-fMRI can be used to explore the intrinsic activity and connectivity of the brain networks of both healthy individuals and patients [ 6 ]. Graph theory provides a mathematical framework that is instrumental in analyzing rs-fMRI data. In the brain networks, brain regions are considered as nodes while the connections between these nodes are edges. This allows for the construction of the brain topological architecture and the quantification of its topological properties at both global and local levels [ 7 ]. As revealed by graph theoretical analysis, healthy human brain networks exhibit a “small-world” organization which enables efficient information processing at low energy costs [ 8 ]. Small-world topological abnormalities have been repeatedly implicated in cognitive impairment in numerous diseases [ 9 – 11 ]. In addition to functional connectivity, EC can also be applied in rs-fMRI data analysis to reveal the neural underpins of brain disorders. GCA is a classical and reliable method of calculating EC by extracting temporal dynamics from rs-fMRI signals. It identifies the intensity and direction the information flow, thus determining interaction and causal influence between brain regions [ 12 ]. To date, however, there is limited published literature combining cognitive impairment with topological property alterations in brain networks in sepsis survivors. Therefore, we employed graph theory and GCA methods to analyze rs-fMRI data, aiming to explore the topological alterations in the brain networks of ICU sepsis survivors. Using correlation analysis, the interplay between topological property alterations and cognitive impairment was also investigated. Participants and methods The study was approved by Ethics Committee of Jiangyin People's Hospital Affiliated with Southeast University and registered in the Clinical Trials.gov (NCT03946839). All participants provided written informed consent. Participants A total of 24 ICU sepsis survivors were initially recruited for this study and underwent neurocognitive testing and MRI scanning. Patients were recruited from the ICU of Jiangyin People's Hospital affiliated with Southeast University. 24 age-, gender-, and education-matched healthy volunteers were selected as the healthy controls through community postings and media advertising. All participants were right-handed Chinese Han individuals. After excluding participants with incomplete MRI scans, a history of epilepsy, abnormal MRI report or excessive head motion, 16 sepsis survivors and 19 healthy controls were included in the final analyses. Inclusion and exclusion criteria Inclusion criteria for sepsis survivors included the following: (1) sepsis or septic shock patients (diagnosed with Sepsis-3.0 [1] ) aged 18 to 79 years; (2) at least 6 years of education (able to speak, read, and write); (3) hospitalized in the ICU for more than 2 days, with survival and subsequent discharge. Exclusion criteria for the sepsis survivor group were: (1) death during hospitalization; (2) leaving against medical advice or transfer to another hospital; (3) declining to participate or withdrawing midway; (4) history of neuropsychiatric disease (e.g., cerebrovascular disease, Parkinson's disease (PD), Alzheimer's disease (AD), demyelinating disease, epilepsy, depression, schizophrenia, etc.); (5) severe brain injury; (6) sever systemic disease (e.g., hepatic encephalopathy, ketoacidosis, hyperosmolar hyperglycemic state, chronic renal failure, etc.); (7) history of drug abuse or insobriety; (8) use of psychotropic medications such as sleeping pills, selective serotonin reuptake inhibitors, etc.; (9) MRI incompatibility. Healthy controls were required to have a Mini-Mental State Examination (MMSE) score ≥ 24 [13]. Exclusion criteria for the health controls were a history of neuropsychiatric disease, head injury, alcohol and drug addiction or ferrous/electronic implants. Neurocognitive measurements All subjects underwent a comprehensive battery of neurological examinations 2 hours before MRI scanning, including the Montreal Cognitive Assessment (MoCA), MMSE, Complex Figure Test (CFT), Auditory Verbal Learning Test (AVLT), Digit Span Test (DST), Verbal F1uency Test (VFT), Clock Drawing Test (CDT), Symbol Digit Modalities Test (SDMT), and Trail Making Test (TMT). Each subject had 70-90 minutes to complete the neuropsychological examinations. MRI data acquisition MRI was performed at the Medical Imaging Center of Jiangyin People’s Hospital using a Discovery MR750w 3.0T scanner (GE, Boston, United States), equipped with a 24-channel head coil. Head motion was controlled using foam pads during the scans. All participants, wearing earplugs, were instructed to remain awake, motionless with their eyes closed, and not to think of anything in particular. Resting state BOLD images were acquired overa period of 7 minutes and 40 seconds by a gradient-recalled echo-planar imaging (GRE-EPI) sequence with repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle (FA) = 90°, field of view(FOV) = 220×220 mm, matrix size = 64×64, slice thickness = 4mm, gap = 0 mm, number of slices = 35. The slices were acquired in an interleaved order (1, 3, 5 …, 35, 2, 4, 6 …, 34). T1-weighted 3D fast spoiled gradient recalled echo (FSPGR) images were collected over 4 minutes 25 seconds with TR = 7.2 ms, TE = 3.1 ms, flip angle = 8°, FOV = 256×256 mm, matrix size = 256×256, slice thickness = 1mm, gap = -0.5 mm, number of slices = 312, voxel size = 1×1×1mm 3 . Additionally, routine axial T2-weighted images were obtained to exclude subjects with major cerebral infarction, white matter (WM) changes, or other brain lesions. fMRI image data preprocessing SPM12 (http://www.fil.ion.ucl.ac.uk/spm) and DPABI (http://rfmri.org/ dpabi) were used for preprocessing rs-fMRI data in the MATLAB environment. The first 10 images were removed to allow steady state, leaving 220 functional volumes for each subject. After slice timing correction, images were realigned to the first volume to correct for head motion. After excluding 1 healthy participant and 4 sepsis patients with head motion > 2.0 mm maximum displacement in any direction (x, y, z) or 2.0° of angular motion, no significant differences in head motion were found between the two groups (p > 0.05). Then, 3D T1-weighted images were registered to the functional images and subdivided into WM, gray matter, and cerebrospinal fluid (CSF) using the new segment and DARTEL technique, followed by spatial normalizing into the Montreal Neurological Institute EPI template (voxel size 3 × 3 × 3 mm 3 ). Other preprocessing steps included spatial smoothing with an isotropic Gaussian kernel of 6 × 6 × 6 mm, temporal bandpass filtering at 0.01-0.08 Hz, and nuisance signal regression (including WM signal, CSF signal, and Friston-24 head motion parameters). Network construction and topological analysis Preprocessed rs-fMRI data were used to construct the whole brain functional connectivity network for each subject. We used automated anatomical labeling (AAL) atlas [14] to identify 116 functional regions of interest (ROIs) throughout the brain, including the cerebrum and cerebellum. Each brain region was considered as a network node. The time series of all voxels in each ROI were extracted and subsequently averaged to obtain a representative time series. Pearson’s correlation coefficients between the mean time series of all possible pairs of the 116 regions were computed, which were considered as the edges of the network. To improve the distribution of data for group analysis, Pearson correlation coefficients (r) were standardized by Fisher’s z transformation, resulting in a 116×116 correlation weight matrix for each subject. Topological properties of networks were analyzed using GRETNA software (http://www. nitrc. org/projects/gretna/) based on graph theory. Only positive correlations were involved in the subsequent network metrics analysis to minimize potential confounding effects of global signal regression. A network sparsity (S) was applied to produce binary undirected functional networks, and a wide range of sparsity threshold was identified, ranging from 0.05 to 0.4 with an interval of 0.01. The global and local metrics of brain functional network were estimated for each brain region at each selected sparsity threshold. Global metrics included small-world parameters (clustering coefficient (Cp), characteristic path length (Lp), normalized clustering coefficient (γ), normalized characteristic path length (λ), and small-worldness (σ)) and network efficiency (global efficiency (Eg) and local efficiency (Eloc)). Specifically, σ = γ/λ，and σ＞1 was used as an indication of a small-world organization of the network. Two nodal centrality properties were employed for regional nodal network analysis: DC and NE. For further statistical comparison, area under the curve (AUC) for each network metric was calculated, providing a summarized scalar for topological parameters, avoiding the error caused by a single threshold. GCA GCA is a statistical concept of causality that based on the multiple variant auto regression (MVAR) model [12], commonly used as a reliable method in neuroscience to estimate EC, which characterizes directional functional connections among brain regions [15]. In this study, GCA was conducted based on those brain regions showing significant DC or NE changes between sepsis survivors and healthy controls. Those brain regions were selected and defined as ROIs for seeds using WFU-pick-atlas (https://www.nitrc.org/ projects/wfu_pickatlas), then resampled to 3 mm×3 mm×3 mm. Based on pre-processed data, the REST_v1.8 software package (http// www.restfmri.net) was applied to compute the causal effects of the time series x of selected seed points and the time series y of each voxel over the whole brain. In the Granger causality model, a value of 0 indicates no causal connection from x to y, a value of 1 indicates strong positive causality, and a value of -1 indicates strong negative causality. The causality analysis was performed twice on each ROI: from the seed point to whole-brain voxels (x to y) and from whole-brain voxels to the seed point (y to x). The obtained effective EC graph was transformed by Fisher’s z to improve distribution normality, resulting in a Z-map of GCA. Statistical analysis 1. Demographic and neurocognitive data: statistical analyses were performed using SPSS software (version 27.0.1, IBM SPSS Inc., Chicago, IL, USA). Categorical variables were compared between groups using the Chi-squared test or Fisher’s exact test, as appropriate. Continuous variables, including demographic data and cognitive scores, were analyzed using independent t-tests. A p-value less than 0.05 was considered statistically significant. 2. rs-fMRI data: The topological properties of brain functional networks were analyzed using GRETNA software. Between-group comparisons of the AUC for these topological properties were conducted using one-way ANOVA in R software (version 4.3.2; https://www.r-project.org/), with age, gender, and years of education included as covariates. Multiple comparisons were corrected using the false discovery rate (FDR) method. 3. GCA: Granger causal influence measures (Z-EC values) derived from healthy control subjects and sepsis survivors were compared using two-sample t-tests, with age, gender, and years of education as covariates. Statistical significance was determined using Alphasim correction with a voxel-level threshold of p < 0.01 and a cluster-level threshold of p < 0.01 (two-tailed). 4. Correlation analysis: Pearson correlation analyses were performed to assess the relationships between cognitive test scores and topological properties, controlling for age, gender, and years of education. These analyses were conducted using SPSS software, and a p-value less than 0.05 was considered statistically significant. Results Demographic and neuropsychological data A total of 16 ICU sepsis survivors and 19 healthy controls were included in the final analysis (Supplementary Fig. 1). There were no statistical differences in gender, age, education years, diabetes and hypertension between sepsis survivors and healthy controls (Table 1 ). Sepsis survivors had a significant lower body mass index (Table 1 , t = -2.914, p = 0.006) likely due to weight loss during their ICU stay. In terms of cognitive function, sepsis survivors scored significant lower on the MoCA, MMSE, CFT-I, CFT-D, AVLT-N4, AVLT-N5, DST, VFT and SDMT. Conversely, sepsis survivors had higher completion times on the TMT-A and TMT-B tests compared to healthy controls (Fig. 1 ). Table 1 Demographic and clinical data of sepsis survivors and healthy controls. Characteristics SS (n = 16) HC (n = 19) χ 2 /t p -value Sex, female/male 8/8 8/11 0.218 0.640 Age, years 62.00 ± 9.42 63.32 ± 8.85 -0.426 0.673 Education, years 9.25 ± 3.98 9.11 ± 3.76 0.111 0.913 BMI, kg/m 2 21.27 ± 2.82 23.75 ± 2.23 -2.914 0.006* Diabetes, yes/no 3/13 0/0 NA 0.086 Hypertension, yes/no 7/9 6/13 0.551 0.458 SS, sepsis survivor; HC, healthy control; CRP, C-reactive protein (CRP); PCT, procalcitonin; NA, not available; BMI, body mass index. * The difference is statistically significant (P<0.05). Data are presented as mean ± SD. Comparison of topological properties of functional network Global network properties Seven topological small-world parameters (Fig. 2 ) were analyzed across a sparsity range of 0.05 to 0.40 with an interval of 0.01. Cp, γ, λ, σ and Lp were negatively whereas Eg and Eloc were positively correlated with sparsity (Fig. 2 ). The σ values for both sepsis survivors and healthy controls were greater than 1 (data not shown), indicating that both groups exhibited small-world organization pattern. However, the γ value of sepsis survivors was significantly lower than that of healthy controls (Fig. 2 , F = 7.807, p = 0.009, FDR). Meanwhile, the σ value for sepsis survivors was significantly lower than that of healthy controls (Fig. 2 , F = 7.494, p = 0.010, FDR). No differences of λ、Cp、Lp、Eg、Eloc were observed between the two groups (Fig. 2 ). Local network properties At the local level, significant differences between groups were found in a temporal, a frontal and a cerebellar region. In detail, in a frontal region ORBinf.R, sepsis survivors had significantly decreased DC (p = 0.003, FDR, Table 2 , Fig. 3 A) and NE (p = 0.011, FDR, Table 2 , Fig. 3 B). In a temporal region TPOsup.L, sepsis survivors also had significantly decreased NE (p = 0.039, FDR, Table 2 , Fig. 3 B). Conversely, in a cerebellar region CRBLCrus2.R, sepsis survivors had significantly increased DC (p<0.001, FDR, Table 2 , Fig. 3 A) and NE (p = 0.011, FDR, Table 2 , Fig. 3 B). Table 2 Brain regions with significant nodal properties differences between sepsis survivors and healthy controls. Brain Region Lobe AAL Nodal Parameter SS (n = 16) HC (n = 19) F P (FDR) ORBinf.R Right frontal gyrus 16 DC 6.407 ± 3.543 12.093 ± 3.323 21.866 0.003 ORBinf.R Right frontal gyrus 16 NE 0.168 ± 0.030 0.208 ± 0.019± 19.165 0.011 TPOsup.L Left temporal gyrus 83 NE 0.172 ± 0.034 0.205 ± 0.020 12.492 0.039 CRBLCrus2.R Right cerebellar lobule VII 94 DC 14.953 ± 2.512 10.312 ± 2.586 31.422 <0.001 CRBLCrus2.R Right cerebellar lobule VII 94 NE 0.223 ± 0.013 0.200 ± 0.019 17.986 0.011 DC, degree centrality; NE: nodal efficiency; SS, sepsis survivor; HC, healthy control; AAL, automated anatomical labeling; ORBinf.R, right orbital part of inferior frontal gyrus; CRBLCrus2.R: right cerebellum Crus 2; TPOsup.L: left temporal pole of superior temporal gyrus; FDR, False discovery rate. GCA of brain networks Based on the above results, we then chose ORBinf.R, TPOsup.L and CRBLCrus2.R as seed regions for further analysis of EC between these three regions and all the other regions in the network. In sepsis survivor group, EC from CRBL6.L (Fig. 4 , A1) to ORBinf.R evidently decreased (Fig. 4 , A2). The EC from TPOsup.L (Fig. 4 , B1) to CRBLCrus1.R also decreased (Fig. 4 , B2). The EC from (Fig. 4 , C1) to TPOsup.L increased (Fig. 4 , C2). The EC between CRBLCrus2.R and other regions generated no significant results. Correlation analysis between cognitive performance and network properties. No significant correlations were observed between global network properties and cognitive performance. At the local level, DC in CRBLCrus2.R was negatively correlated with both MMSE (r=-0.572, p = 0.041, Fig. 5 A) and MoCA scores (r=-0.629, p = 0.021, Fig. 5 B). NE in CRBLCrus2.R was negatively correlated MoCA scores (r=-0.633, p = 0.020, Fig. 5 C). No significant correlations were found between EC and cognitive performance. Discussion Sepsis survivors often experience long-term disabilities, including cognitive impairment, which significantly impact their quality of life and ability to return to everyday activities [ 16 ]. In the present study, we first demonstrated that sepsis survivors experienced significant impairment in various cognitive facets within one month after ICU discharge. Additionally, we observed that, at the global level, sepsis survivors had degraded small-worldness. Local properties decreased in frontal and temporal regions but increased in the cerebellum, which are involved in the cognitive impairment of sepsis survivors. Furthermore, we identified alterations in connections between the cerebrum and the cerebellum, which might also contributed to cognitive impairment in sepsis survivors. Finally, correlation analysis revealed an interplay between cognitive impairment and topological alterations in brain networks. The majority of sepsis survivors in our study experienced cognitive impairment. Specifically, 15 and 12 out of the 16 (93.8% and 75.0%) sepsis survivors obtained MoCA and MMSE scores below the cut-off for normal performance, respectively. In a Japanese multicenter observational study [ 24 ], the prevalence of cognitive impairment was 37.5% in ICU survivors after 6 months. A recent study of severe COVID-19 patients who survived ICU found that 53.4% scored below the cut-off for normal performance on the MoCA, and 19% scored below the threshold for mild cognitive impairment 6 months after ICU discharge [ 25 ]. Cognitive impairment is thus common following sepsis, with its severity related to the recency of septic shock. In our study, the majority of the survivors suffered from severe abdominal infections, which are common causes of sepsis in the ICU [ 26 ]. It has also been suggested that severe abdominal infections undergoing surgical treatment tended to have poor long-term outcomes [ 27 ]. A recent meta-analysis found a significant association between severe sepsis and an increased risk of cognitive impairment, indicating that the specific cause of sepsis also influences the severity of cognitive impairment [ 28 ]. At the global level, we found that the small-worldness of sepsis survivors’ brain networks remained but decreased significantly compared to healthy controls. Among the global parameters, γ represents the local characteristics of the network, quantifying the local segregation function of the brain network. λ reflects the capability of global information integration and transmission. The ratio of γ to λ, denoted as σ, represents small-worldness. Compared to a random network, a small-world network has a smaller λ and a larger γ, and a σ greater than 1. In our study, the σ values of both sepsis survivors and healthy controls were greater than 1, indicating that sepsis survivors retained a small-world organization pattern. However, the lower σ value in sepsis survivors suggested loss of small-worldness of the brain networks. Specifically, the γ and σ values of sepsis survivors were statistically lower than those of healthy controls, while the λ values were similar between the two groups. According to the definition of σ, it can be inferred that the decrease in σ was primarily due to the decrease in γ. Additionally, the similar λ values between the two groups demonstrated that the long-distance information transmission in the brain networks of sepsis survivors remained intact. On the other hand, the decreased γ demonstrated a reduced capacity for local information processing reflecting degraded local connections and grouping of neural units in the brain networks. It is also noteworthy that the differences in γ and σ between the groups were more pronounced when the sparsity threshold was smaller. Therefore, a small threshold would be recommended when using these small-world properties to predict network disruption. In summary, sepsis survivors exhibited degraded small-worldness, primarily due to a decreased capacity for local information processing. We then demonstrated local information processing capability decreased primarily in a frontal and a temporal region but increased in a cerebellar region. Specifically, both DC and NE decreased in ORBinf.R, NE was decreased in TPOsup.L, and both DC and NE increased in CRBLCrus2.R. DC measures how many edges a node possesses within the entire network, with a higher DC representing a node’s central hub role for information communication [ 17 ]. A decrease in DC indicates a reduction in the number of brain regions connected to this region. NE quantifies how efficiently a node can exchange information within the network, so a decrease in NE reflects attenuated information transmission efficiency in that region. Both ORBinf.R and TPOsup.L are implicated in cognitive function. ORBinf.R, also known as the right pars orbitalis, refers to the most rostral portion of the inferior frontal gyrus in the frontal lobe. The pars orbitalis is involved primarily in semantic processing in the dominant hemisphere. However, in the non-dominant hemisphere, it plays a role in behavioral and motor inhibition [ 18 ] and emotional regulation [ 19 ]. ORBinf.R volume loss has been significant in PD patients [ 20 ]. Furthermore, the volume of this region is associated with the diagnosis of conduct disorder, which involves various behavioral and emotional problems [ 21 ]. Besides, cortical thickness of this region has shown significant correlation with digit span test scores in PD patients [ 22 ], animacy scores in frontotemporal dementia and AD patients [ 23 ] and the ability to inhibit ad lib smoking during the smoking relapse analog task in individuals with nicotine dependence [ 24 ]. Interestingly, the thickness of this region could also be a predictor of suicide attempt in young major depressive disorder patients [ 25 ]. TPOsup.L, which refers to the left anterior end of temporal lobe, belongs to the anterior default mode network and is associated with semantic memory and other cognitive functions [ 26 , 27 ]. In a study of patients with white matter lesions (WMLs), the NE value of TPOsup.L showed significant differences across normal people, WMLs with non-dementia vascular cognitive impairment and WMLs with vascular dementia showed significant difference, indicating the role of the network properties of this region in cognitive function [ 28 ]. Thus, we speculate that the local information processing disruptions in frontal and temporal lobes may contribute to the cognitive impairment seen in sepsis survivors. In contrast, we observed that local properties increased in the cerebellum. The cerebellum has long been recognized as an crucial component involved in various cognitive functions [ 29 ]. CRBLCrus2.R belongs to lobule VII of the cerebellum. It is critical for language [ 30 , 31 ], visual memory [ 32 , 33 ], working memory [ 34 , 35 ] and spatial memory processing [ 36 ]. Importantly, the cerebrocerebellar circuit serves as an efficient pathway for information exchange between the cerebral cortex to the cerebellum. Therefore, we speculated that the increase in local properties in the cerebellum might compensate for the decrease in local properties in the frontal and temporal regions. Interestingly, Zhao and colleagues [ 37 ] demonstrated that sepsis enhanced the intrinsic excitability and synaptic transmission of cerebellar Purkinje cells, which might be associated with the increase of local properties in the cerebellum. Although we did not observe significant correlations between DC or NE in ORBinf.R or TPOsup.L and cognitive performance of sepsis survivors, we did find that DC or NE in CRBLCrus2.R negatively correlated with MMSE or MoCA scores. We concluded that disruptions in frontal, temporal and cerebellar regions are involved in the cognitive impairment observed in sepsis survivors. Using GCA, we further examined the alterations in directional connections between above mentioned regions mentioned above (ORBinf.R, TPOsup.L, and CRBLCrus2.R) and other brain regions. We found that EC from CRBL6.L to ORBinf.R and from TPOsup.L to CRBLCrus1.R decreased. EC from LING.R to TPOsup.L increased. A previous imaging study showed that gray matter atrophy in both CRBL6.L and ORBinf.R were significantly implicated in AD pathology [ 38 ]. Maesawa and colleagues [ 39 ] investigated the connection between resting-state networks and cognitive performance of healthy individuals. Their results indicated that CRBL6.L, within the higher visual networks, exhibited within-network functional connectivity that was negatively correlated with age. Another study identified that the correlated transfer function connections between CRBLCrus1.R and left insula were the most significant markers for discriminating AD patients from healthy controls [ 40 ]. Therefore, the decrease in EC from CRBL6.L to ORBinf.R and from TPOsup.L to CRBLCrus1.R represents critical network alterations in sepsis survivors and might be related to cognitive impairment. The lingual gyrus, part of the occipital lobe, is mainly involved in processing vision, playing a role in logical analysis and encoding visual memories. Specifically, the right lingual gyrus is responsible for the perception and recognition of familiar landmarks and scenes as well as the identification of faces. Anatomically, the lingual gyrus and the temporal pole are connected by inferior longitudinal fasciculus fibers [ 41 ]. Given that our results suggested an increased EC from LING.R to TPOsup.L, it can be inferred that LING.R and TPOsup.L functionally interact in the brain network of sepsis survivors. In summary, directional connections particularly between the cerebrum and the cerebellum were disrupted and are implicated in cognitive impairment in sepsis survivors. Limitations Firstly, the small sample size of our study is a disadvantage for controlling interindividual heterogeneity, which could increase potential bias. This might explain why we did not observe significant correlations between decreased temporal and frontal topological properties and cognitive impairment. Secondly, our study only interviewed patients within one month after ICU discharge, a longitudinal follow-up will provide deeper insight into the interplay between topological alterations and cognitive function. Conclusions Collectively, sepsis survivors suffer from cognitive impairment, which correlates with topological property alterations of their brain networks. These topological alterations may serve as biomarkers for early diagnosis of cognitive impairment in sepsis survivors. Abbreviations ICU intensive care unit rs-fMRI Resting-state functional magnetic resonance imaging GCA Granger causality analysis EC effective connectivity λ normalized characteristic path length σ small-worldness DC degree centrality NE nodal efficiency ORBinf.R right orbital part of inferior frontal gyrus TPOsup.L left temporal pole of superior temporal gyrus CRBLCrus2.R right cerebellum Crus 2 CRBL6.L left cerebellum 6 CRBLCrus1.R right cerebellum 1 LING.R right lingual gyrus MRI Magnetic resonance imaging fMRI functional MRI BOLD blood oxygen level-dependent PD Parkinson's disease AD Alzheimer's disease MoCA Montreal Cognitive Assessment MMSE Mini-Mental State Examination CFT Complex Figure Test AVLT Auditory Verbal Learning Test DST Digit Span Test VFT Verbal F1uency Test CDT Clock Drawing Test SDMT Symbol Digit Modalities Test TMT Trail Making Test WM white matter CSF cerebrospinal fluid AAL automated anatomical labeling ROIs regions of interest Cp clustering coefficient Lp characteristic path length γ normalized clustering coefficient Eg global efficiency Eloc local efficiency AUC area under the curve WMLs white matter lesions Declarations Ethics approval and consent to participate The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Jiangyin People’s Hospital (No. 2019ER(021)). Informed consent (participation and publication) was obtained from all subjects. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests All authors declare no conflict of interest. Funding This research was supported by the National Natural Science Foundation of China (No. 82372182, 82172131, and U23A20421) and Scientific Research Program of Wuxi Municipal Health Commission (Q202153). Authors' contributions YL performed the majority of the study, collected and analyzed the data and wrote the manuscript. JQC contributed to study conception. HW and LNW contributed to data analysis and result interpretation. JJL and MQL contributed to recruitment and clinical data collection. HTY contributed to manuscript revision. WL contributed to data analysis. MHJ and JJY contributed to study design, acquisition of funding and manuscript revision. All authors read and approved the final manuscript. Acknowledgements The authors thank staff of the Intensive Care Unit and Department of Radiology of Jiangyin People’s Hospital. Thanks to all participants who contributed to the present study. Authors' information (optional) 1 School of Medicine, Southeast University, Nanjing, China. 2 Department of Anesthesiology, Jiangyin Hospital, Affiliated to Southeast University Medical School, Jiangyin, China. 3 Department of Interventional Neurology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China. 4 Department of Neurology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, China. 5 Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 6 Department of Anesthesiology, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, China. References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM et al : The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) . Jama 2016, 315 (8):801-810. Fleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, Angus DC, Reinhart K: Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations . American journal of respiratory and critical care medicine 2016, 193 (3):259-272. Iwashyna TJ, Ely EW, Smith DM, Langa KM: Long-term cognitive impairment and functional disability among survivors of severe sepsis . Jama 2010, 304 (16):1787-1794. Lei S, Li X, Zhao H, Feng Z, Chun L, Xie Y, Li J: Risk of Dementia or Cognitive Impairment in Sepsis Survivals: A Systematic Review and Meta-Analysis . Frontiers in aging neuroscience 2022, 14 :839472. Burgdorf JG, Chase JD, Whitehouse C, Bowles KH: Unmet Caregiving Needs Among Sepsis Survivors Receiving Home Health Care: The Need for Caregiver Training . Journal of applied gerontology : the official journal of the Southern Gerontological Society 2022, 41 (10):2180-2186. Lv H, Wang Z, Tong E, Williams LM, Zaharchuk G, Zeineh M, Goldstein-Piekarski AN, Ball TM, Liao C, Wintermark M: Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know . AJNR American journal of neuroradiology 2018, 39 (8):1390-1399. Sporns O: Graph theory methods: applications in brain networks . Dialogues in clinical neuroscience 2018, 20 (2):111-121. Liao X, Vasilakos AV, He Y: Small-world human brain networks: Perspectives and challenges . Neuroscience and biobehavioral reviews 2017, 77 :286-300. Chen X, Liu M, Wu Z, Cheng H: Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson's Disease Patients With Mild Cognitive Impairment . Frontiers in neuroscience 2020, 14 :616872. Fathian A, Jamali Y, Raoufy MR: The trend of disruption in the functional brain network topology of Alzheimer's disease . Scientific reports 2022, 12 (1):14998. Wang M, Xu B, Hou X, Shi Q, Zhao H, Gui Q, Wu G, Dong X, Xu Q, Shen M et al : Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment . Frontiers in aging neuroscience 2023, 15 :1153496. Gao Q, Luo N, Liang M, Zhou W, Li Y, Li R, Hu X, Zou T, Wang X, Yu J et al : A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network . IEEE transactions on neural networks and learning systems 2024, 35 (4):4974-4984. He C, Gong L, Yin Y, Yuan Y, Zhang H, Lv L, Zhang X, Soares JC, Zhang H, Xie C et al : Amygdala connectivity mediates the association between anxiety and depression in patients with major depressive disorder . Brain imaging and behavior 2019, 13 (4):1146-1159. Rolls ET, Huang CC, Lin CP, Feng J, Joliot M: Automated anatomical labelling atlas 3 . NeuroImage 2020, 206 :116189. Seth AK, Barrett AB, Barnett L: Granger causality analysis in neuroscience and neuroimaging . The Journal of neuroscience : the official journal of the Society for Neuroscience 2015, 35 (8):3293-3297. Li Y, Ji M, Yang J: Current Understanding of Long-Term Cognitive Impairment After Sepsis . Frontiers in immunology 2022, 13 :855006. Sun Y, Shi Q, Ye M, Miao A: Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation . Frontiers in neuroscience 2023, 17 :1282232. Boen R, Raud L, Huster RJ: Inhibitory Control and the Structural Parcelation of the Right Inferior Frontal Gyrus . (1662-5161 (Print)). Hou W, Sahakian BJ, Langley C, Yang Y, Bethlehem RAI, Luo Q: Emotion dysregulation and right pars orbitalis constitute a neuropsychological pathway to attention deficit hyperactivity disorder . Nature Mental Health 2024:1-13. Wu C, Wu H, Zhou C, Guo T, Guan X, Cao Z, Wu J, Liu X, Chen J, Wen J et al : The effect of dopamine replacement therapy on cortical structure in Parkinson's disease . CNS neuroscience & therapeutics 2023. Zhang R, Blair RJR, Blair KS, Dobbertin M, Elowsky J, Bashford-Largo J, Dominguez AJ, Hatch M, Bajaj S: Reduced grey matter volume in adolescents with conduct disorder: a region-of-interest analysis using multivariate generalized linear modeling . Discover mental health 2023, 3 (1):25. Deng X, Tang CY, Zhang J, Zhu L, Xie ZC, Gong HH, Xiao XZ, Xu RS: The cortical thickness correlates of clinical manifestations in the mid-stage sporadic Parkinson's disease . Neuroscience letters 2016, 633 :279-289. Fong SS, Paholpak P, Daianu M, Deutsch MB, Riedel BC, Carr AR, Jimenez EE, Mather MM, Thompson PM, Mendez MF: The attribution of animacy and agency in frontotemporal dementia versus Alzheimer's disease . Cortex; a journal devoted to the study of the nervous system and behavior 2017, 92 :81-94. Brown AA, Upton S, Craig S, Froeliger B: Associations between right inferior frontal gyrus morphometry and inhibitory control in individuals with nicotine dependence . Drug and alcohol dependence 2023, 244 :109766. Hong S, Liu YS, Cao B, Cao J, Ai M, Chen J, Greenshaw A, Kuang L: Identification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach . Journal of affective disorders 2021, 280 (Pt A):72-76. Snowden JS, Harris JM, Thompson JC, Kobylecki C, Jones M, Richardson AM, Neary D: Semantic dementia and the left and right temporal lobes . Cortex; a journal devoted to the study of the nervous system and behavior 2018, 107 :188-203. Herlin B, Navarro V, Dupont S: The temporal pole: From anatomy to function-A literature appraisal . Journal of chemical neuroanatomy 2021, 113 :101925. Wang J, Chen Y, Liang H, Niedermayer G, Chen H, Li Y, Wu M, Wang Y, Zhang Y: The Role of Disturbed Small-World Networks in Patients with White Matter Lesions and Cognitive Impairment Revealed by Resting State Function Magnetic Resonance Images (rs-fMRI) . Medical science monitor : international medical journal of experimental and clinical research 2019, 25 :341-356. Carey MR: The cerebellum . Current biology : CB 2024, 34 (1):R7-r11. Stoodley CJ, MacMore JP, Makris N, Sherman JC, Schmahmann JD: Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke . NeuroImage Clinical 2016, 12 :765-775. Gao Q, Tao Z, Cheng L, Leng J, Wang J, Yu C, Chen H: Language lateralization during the Chinese semantic task relates to the contralateral cerebra-cerebellar interactions at rest . Scientific reports 2017, 7 (1):14056. Zhao Y, Lin J, Qi X, Cao D, Zhu F, Chen L, Tan Z, Mo T, Zeng H: To explore the potential mechanisms of cognitive impairment in children with MRI-negative pharmacoresistant epilepsy due to focal cortical dysplasia: A pilot study from gray matter structure view . Heliyon 2024, 10 (4):e26609. Liu ZX, Shen K, Olsen RK, Ryan JD: Visual Sampling Predicts Hippocampal Activity . The Journal of neuroscience : the official journal of the Society for Neuroscience 2017, 37 (3):599-609. Ni S, Gao S, Ling C, Jiang J, Wu F, Peng T, Sun J, Zhang N, Xu X: Altered brain regional homogeneity is associated with cognitive dysfunction in first-episode drug-naive major depressive disorder: A resting-state fMRI study . Journal of affective disorders 2023, 343 :102-108. Wang Y, Lu Y, Du M, Hussein NM, Li L, Wang Y, Mao C, Chen T, Chen F, Liu X et al : Altered Spontaneous Brain Activity in Left-Behind Children: A Resting-State Functional MRI Study . Frontiers in neurology 2022, 13 :834458. Shcherbinin S, Schwarz AJ, Joshi A, Navitsky M, Flitter M, Shankle WR, Devous MD, Sr., Mintun MA: Kinetics of the Tau PET Tracer 18F-AV-1451 (T807) in Subjects with Normal Cognitive Function, Mild Cognitive Impairment, and Alzheimer Disease . Journal of nuclear medicine : official publication, Society of Nuclear Medicine 2016, 57 (10):1535-1542. Zhao Y, Jiang Y, Shen Y, Su LD: Sepsis Impairs Purkinje Cell Functions and Motor Behaviors Through Microglia Activation . Cerebellum (London, England) 2024, 23 (2):329-339. Zhou TD, Zhang Z, Balachandrasekaran A, Raji CA, Becker JT, Kuller LH, Ge Y, Lopez OL, Dai W, Gach HM: Prospective Longitudinal Perfusion in Probable Alzheimer's Disease Correlated with Atrophy in Temporal Lobe . Aging and disease 2023. Maesawa S, Mizuno S, Bagarinao E, Watanabe H, Kawabata K, Hara K, Ohdake R, Ogura A, Mori D, Nakatsubo D et al : Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging . In: Frontiers in human neuroscience. vol. 15; 2021: 753836. Mousa D, Zayed N, Yassine IA: Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging . PloS one 2022, 17 (4):e0264710. Palejwala AH, Dadario NB, Young IM, O'Connor K, Briggs RG, Conner AK, O'Donoghue DL, Sughrue ME: Anatomy and White Matter Connections of the Lingual Gyrus and Cuneus . World neurosurgery 2021, 151 :e426-e437. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1withLegend.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5226224","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":370396107,"identity":"32b50661-0404-47db-9f73-4e61211f002f","order_by":0,"name":"Ying Li","email":"","orcid":"","institution":"School of Medicine, Southeast University, Nanjing, China","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":370396108,"identity":"74238359-88c4-4d09-a8c8-0a601dea923b","order_by":1,"name":"Jianqing Chen","email":"","orcid":"","institution":"Department of Anesthesiology, Jiangyin Hospital, Affiliated to Southeast University Medical School, Jiangyin, China","correspondingAuthor":false,"prefix":"","firstName":"Jianqing","middleName":"","lastName":"Chen","suffix":""},{"id":370396109,"identity":"fd6d6099-7578-401f-9f4a-5300b2205edd","order_by":2,"name":"Hui Wang","email":"","orcid":"","institution":"Department of Interventional Neurology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":370396110,"identity":"2538df33-1d64-4a7e-867b-9a5bf92f2285","order_by":3,"name":"Lina Wang","email":"","orcid":"","institution":"Department of Neurology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, China","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Wang","suffix":""},{"id":370396111,"identity":"261000fa-a64d-4895-af49-955bb6733442","order_by":4,"name":"Jingjing Li","email":"","orcid":"","institution":"Department of Anesthesiology, Jiangyin Hospital, Affiliated to Southeast University Medical School, Jiangyin, China","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Li","suffix":""},{"id":370396112,"identity":"02e91a53-2a40-4c42-a392-ecb0fc5b9e1b","order_by":5,"name":"Mengqing Li","email":"","orcid":"","institution":"Department of Anesthesiology, Jiangyin Hospital, Affiliated to Southeast University Medical School, Jiangyin, China","correspondingAuthor":false,"prefix":"","firstName":"Mengqing","middleName":"","lastName":"Li","suffix":""},{"id":370396113,"identity":"cd91e9b0-7c06-4b0a-b111-18b90ba5522e","order_by":6,"name":"Haotian Ye","email":"","orcid":"","institution":"Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Haotian","middleName":"","lastName":"Ye","suffix":""},{"id":370396114,"identity":"98eceee0-41a5-4755-b7a2-a87dc33c0d7c","order_by":7,"name":"Wen Liu","email":"","orcid":"","institution":"Department of Anesthesiology, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, China","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Liu","suffix":""},{"id":370396115,"identity":"6c0a5d72-6181-43e5-8bfc-c8f89098d6d4","order_by":8,"name":"Muhuo Ji","email":"","orcid":"","institution":"Department of Anesthesiology, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, China","correspondingAuthor":false,"prefix":"","firstName":"Muhuo","middleName":"","lastName":"Ji","suffix":""},{"id":370396116,"identity":"b3a4e543-2113-4e48-8c68-d17a39c5dfe7","order_by":9,"name":"Jianjun Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFCCAyCCjZmfmfngA9K0SLazJRuQZpnBeR4zAeJUHjxj+LngFx+78WEGMwaGGptowloOnDGWntnHxmx2mCHtAcOxtNwGIrQYSPP2gLUcN2BsOEyUFuPfIC3GzYxtEsRqMZPm+cHGbMDMzEacFskDx8qseRvYmCUOA7UlEOMXvhuHN9/m+XMsmb///McHH2psCGtRuHHCgIGx7VgymJdASDkIyPe3P2Bg+FNjR4ziUTAKRsEoGKEAALr5QPg3WfeqAAAAAElFTkSuQmCC","orcid":"","institution":"School of Medicine, Southeast University, Nanjing, China","correspondingAuthor":true,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-10-08 14:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5226224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5226224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67634486,"identity":"d36aa3bb-16c5-41fb-b0a9-453cce3f09be","added_by":"auto","created_at":"2024-10-28 09:17:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical representation of the comparisons of cognitive performance between groups.\u003c/strong\u003e SS, sepsis survivor; HC, healthy control; MoCA, Montreal Cognitive Assessment; MMSE, Mini-Mental State Examination; CFT-I, Complex Figure Test-immediate; CFT-I, Complex Figure Test-delay; AVLT-N4, Auditory Verbal Learning Test; AVLT-N5, Auditory Verbal Learning Test; DST, Digit Span Test; VFT, Verbal F1uency Test; CDT, Clock Drawing Test; SDMT, Symbol Digit Modalities Test; TMT-A, Trail Making Test-part A; TMT-B, Trail Making Test-part B.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5226224/v1/ad81d57e2ecaa3b9cac2d475.png"},{"id":67634491,"identity":"a7d00327-70e7-4752-8d48-8fb78092f305","added_by":"auto","created_at":"2024-10-28 09:17:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges of global properties of the functional brain networks with sparsity and the comparison of AUC of global properties.\u003c/strong\u003e SS, sepsis survivor (red); HC, healthy control (blue); AUC, area under the curve; γ, normalized clustering coefficient; λ, normalized characteristic path length; σ, small-worldness; Cp, clustering coefficient; Lp, shortest path length; Eg, global efficiency; Eloc: local efficiency. The left column stood for the sparsity range (0.05–0.4). The right column is the AUC. * The difference is statistically significant (P＜0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5226224/v1/5fb5dd517512cd9d9897a1a8.png"},{"id":67634488,"identity":"6adac261-a44a-408e-9f24-72306cdfaa69","added_by":"auto","created_at":"2024-10-28 09:17:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":192243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain regions with positive DC and NE alterations. \u003c/strong\u003eBlue or red node represents a decreased or increased DC/NE value respectively. ORBinf.R, right orbital inferior frontal gyrus; CRBLCrus2.R, right cerebellum Crus 2; TPOsup.L, left temporal pole of superior temporal gyrus. (A) Compared to healthy controls, sepsis survivors had decreased DC in ORBinf.R and increased DC in CRBLCrus2.R; (B) sepsis survivors had decreased NE in ORBinf.R and TPOsup.L and increased NE in CRBLCrus2.R\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5226224/v1/35acd33d155adfb8edaeb898.png"},{"id":67636078,"identity":"e006af69-703f-4fa1-80a7-5d25dca00c36","added_by":"auto","created_at":"2024-10-28 09:25:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":334127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGCA of EC between regions with abnormal local properties and other regions in the whole brain.\u003c/strong\u003e \u0026nbsp;ORBinf, orbital inferior frontal gyrus; CRBL6, cerebellum 6; TPOsup, temporal pole of superior temporal gyrus; CRBLCrus1: right cerebellum Crus 1; LING, lingual gyrus. Pink dots indicated ROIs, i.e. regions with abnormal local properties. Yellow dots indicated correlated regions between which and ROI, the EC had significant alterations. Blue and red curved arrows indicated positive and negative causality respectively. The direction of the arrows indicated the EC directions. (A1) Compared to healthy controls, the EC between CRBL6.L and ORBinf.R in sepsis survivors significantly changed; (A2) The EC from CRBL6.L to ORBinf.R in sepsis survivors decreased. (B1) Compared to healthy controls, the EC between TPOsup.L and CRBLCrus1.R in sepsis survivors significantly changed; (B2) The EC from TPOsup.L to CRBLCrus1.R in sepsis survivors significantly decreased; (C1) The EC between LING.R to TPOsup.L in sepsis survivors significantly changed; (C2) The EC from LING.R to TPOsup.L in sepsis survivors significantly increased.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5226224/v1/77358544036113e2f5c5922d.png"},{"id":67636077,"identity":"64b8af9b-174f-44be-ad7d-418e7aa6de33","added_by":"auto","created_at":"2024-10-28 09:25:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":133216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between cognitive performance and topological properties.\u003c/strong\u003e (A) DC value of CRBLCrus2.R negatively correlated with MMSE score; (B) DC value of CRBLCrus2.R negatively correlated with MoCA scores; (C) NE value of CRBLCrus2.R negatively correlated with MoCA scores.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5226224/v1/6160437d068fc18009e83388.png"},{"id":71494093,"identity":"c34ec934-3898-4c4a-a5ef-0870fea3d539","added_by":"auto","created_at":"2024-12-16 08:02:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2627820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5226224/v1/c7300192-92db-4bd3-9765-48bb50bcb43d.pdf"},{"id":67634487,"identity":"888c20d1-d441-443d-ac35-5db67d762c17","added_by":"auto","created_at":"2024-10-28 09:17:22","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":132316,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1withLegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-5226224/v1/10a1434059eec0cea211b221.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFrontal, temporal and cerebellar topological property alterations predispose cognitive impairment of ICU sepsis survivors: A resting-state fMRI study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to epidemiological data of World Health Organization, 19.4\u0026nbsp;million people develop sepsis each year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the use of antimicrobials and other appropriate management strategies has significantly reduced the incidence and mortality of sepsis, long-term sequela, including physical, cognitive, and psychological impairments, have become another pressing issue. Among these various sequela, cognitive impairment is particularly common [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], significantly decreasing the life-quality for patients and increasing the burden on caregivers [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the mechanisms underlying cognitive impairment in sepsis survivors remains unclear.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI) is a non-invasive and non-radioactive tool widely used in the field of neurocognitive impairment. Specifically, functional MRI (fMRI) is based on the principle that the blood oxygen level-dependent (BOLD) signal fluctuations strongly reflect neuronal activity. With numerous analytical methods, rs-fMRI can be used to explore the intrinsic activity and connectivity of the brain networks of both healthy individuals and patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Graph theory provides a mathematical framework that is instrumental in analyzing rs-fMRI data. In the brain networks, brain regions are considered as nodes while the connections between these nodes are edges. This allows for the construction of the brain topological architecture and the quantification of its topological properties at both global and local levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As revealed by graph theoretical analysis, healthy human brain networks exhibit a \u0026ldquo;small-world\u0026rdquo; organization which enables efficient information processing at low energy costs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Small-world topological abnormalities have been repeatedly implicated in cognitive impairment in numerous diseases [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition to functional connectivity, EC can also be applied in rs-fMRI data analysis to reveal the neural underpins of brain disorders. GCA is a classical and reliable method of calculating EC by extracting temporal dynamics from rs-fMRI signals. It identifies the intensity and direction the information flow, thus determining interaction and causal influence between brain regions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, however, there is limited published literature combining cognitive impairment with topological property alterations in brain networks in sepsis survivors. Therefore, we employed graph theory and GCA methods to analyze rs-fMRI data, aiming to explore the topological alterations in the brain networks of ICU sepsis survivors. Using correlation analysis, the interplay between topological property alterations and cognitive impairment was also investigated.\u003c/p\u003e"},{"header":"Participants and methods","content":"\u003cp\u003eThe study was approved by Ethics Committee of Jiangyin People's Hospital Affiliated with Southeast University and registered in the Clinical Trials.gov (NCT03946839). All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 24 ICU sepsis survivors were initially recruited for this study and underwent neurocognitive testing and MRI scanning. Patients were recruited from the ICU of Jiangyin People's Hospital affiliated with Southeast University. 24 age-, gender-, and education-matched healthy volunteers were selected as the healthy controls through community postings and media advertising. All participants were right-handed Chinese Han individuals. After excluding participants with incomplete MRI scans,\u0026nbsp;a history of epilepsy,\u0026nbsp;abnormal MRI report or excessive head motion, 16 sepsis survivors and 19 healthy controls were included in the final analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and exclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInclusion criteria for sepsis survivors included the following: (1) sepsis or septic shock patients (diagnosed with Sepsis-3.0\u0026nbsp;[1]\u0026nbsp;) aged 18 to 79 years; (2) at least 6 years of education (able to speak, read, and write); (3) hospitalized in the ICU for more than 2 days, with survival and subsequent discharge. Exclusion criteria for the sepsis survivor group were: (1) death during hospitalization; (2) leaving against medical advice or transfer to another hospital; (3) declining to participate or withdrawing midway; (4) history of neuropsychiatric disease (e.g., cerebrovascular disease, Parkinson's disease (PD), Alzheimer's disease (AD), demyelinating disease, epilepsy, depression, schizophrenia, etc.); (5) severe brain injury; (6) sever systemic disease (e.g., hepatic encephalopathy, ketoacidosis, hyperosmolar hyperglycemic state, chronic renal failure, etc.); (7) history of drug abuse or insobriety; (8) use of psychotropic medications such as sleeping pills, selective serotonin reuptake inhibitors, etc.; (9) MRI incompatibility. Healthy controls were required to have a Mini-Mental State Examination (MMSE) score ≥ 24\u0026nbsp;[13]. Exclusion criteria for the health controls were a history of neuropsychiatric disease, head injury, alcohol and drug addiction or ferrous/electronic implants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeurocognitive measurements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects underwent a comprehensive battery of neurological examinations 2 hours before MRI scanning, including the Montreal Cognitive Assessment (MoCA), MMSE, Complex Figure Test (CFT), Auditory Verbal Learning Test (AVLT), Digit Span Test (DST), Verbal F1uency Test (VFT), Clock Drawing Test (CDT), Symbol Digit Modalities Test (SDMT), and Trail Making Test (TMT). Each subject had 70-90 minutes to complete the neuropsychological examinations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI data acquisition\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRI was performed at the Medical Imaging Center of Jiangyin People’s Hospital using a Discovery MR750w 3.0T scanner (GE, Boston, United States), equipped with a 24-channel head coil. Head motion was controlled using foam pads during the scans. All participants, wearing earplugs, were instructed to remain awake, motionless with their eyes closed, and not to think of anything in particular. Resting state BOLD images were acquired overa period of 7 minutes and 40 seconds by a gradient-recalled echo-planar imaging (GRE-EPI) sequence with repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle (FA) = 90°, field of view(FOV) = 220×220 mm, matrix size = 64×64, slice thickness = 4mm, gap = 0 mm, number of slices = 35. The slices were acquired in an interleaved order (1, 3, 5 …, 35, 2, 4, 6 …, 34). T1-weighted 3D fast spoiled gradient recalled echo (FSPGR) images were collected over 4 minutes 25 seconds with TR = 7.2 ms, TE = 3.1 ms, flip angle = 8°, FOV = 256×256 mm, matrix size = 256×256, slice thickness = 1mm, gap = -0.5 mm, number of slices = 312, voxel size = 1×1×1mm\u003csup\u003e3\u003c/sup\u003e. Additionally, routine axial T2-weighted images were obtained to exclude subjects with major cerebral infarction, white matter (WM) changes, or other brain lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003efMRI image data preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPM12 (http://www.fil.ion.ucl.ac.uk/spm) and DPABI (http://rfmri.org/ dpabi) were used for preprocessing rs-fMRI data in the MATLAB environment. The first 10 images were removed to allow steady state, leaving 220 functional volumes for each subject. After slice timing correction, images were realigned to the first volume to correct for head motion. After excluding 1 healthy participant and 4 sepsis patients with head motion \u0026gt; 2.0 mm maximum displacement in any direction (x, y, z) or 2.0° of angular motion, no significant differences in head motion were found between the two groups (p \u0026gt; 0.05). Then, 3D T1-weighted images were registered to the functional images and subdivided into WM, gray matter, and cerebrospinal fluid (CSF) using the new segment and DARTEL technique, followed by spatial normalizing into the Montreal Neurological Institute EPI template (voxel size 3 × 3 × 3 mm\u003csup\u003e3\u003c/sup\u003e). Other preprocessing steps included spatial smoothing with an isotropic Gaussian kernel of 6 × 6 × 6 mm, temporal bandpass filtering at 0.01-0.08 Hz, and nuisance signal regression (including WM signal, CSF signal, and Friston-24 head motion parameters).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork construction and topological analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreprocessed rs-fMRI data were used to construct the whole brain functional connectivity network for each subject. We used automated anatomical labeling (AAL)\u0026nbsp;atlas\u0026nbsp;[14]\u0026nbsp;to identify 116 functional regions of interest (ROIs) throughout the brain, including the cerebrum and cerebellum. Each brain region was considered as a network node. The time series of all voxels in each ROI were extracted and subsequently averaged to obtain a representative time series. Pearson’s correlation coefficients between the mean time series of all possible pairs of the 116 regions were computed, which were considered as the edges of the network. To improve the distribution of data for group analysis, Pearson correlation coefficients (r) were standardized by Fisher’s z transformation, resulting in a 116×116 correlation weight matrix for each subject.\u003c/p\u003e\n\u003cp\u003eTopological properties of networks were analyzed using GRETNA software (http://www. nitrc. org/projects/gretna/) based on graph theory. Only positive correlations were involved in the subsequent network metrics analysis to minimize potential confounding effects of global signal regression. A network sparsity (S) was applied to produce binary undirected functional networks, and a wide range of sparsity threshold was identified, ranging from 0.05 to 0.4 with an interval of 0.01. The global and local\u0026nbsp;metrics\u0026nbsp;of brain functional network were estimated for each brain region at each selected sparsity threshold. Global metrics included small-world parameters (clustering coefficient (Cp), characteristic path length (Lp), normalized clustering coefficient (γ), normalized characteristic path length (λ), and small-worldness (σ)) and network efficiency (global efficiency (Eg) and local efficiency (Eloc)). Specifically, σ = γ/λ，and σ＞1 was used as an indication of a small-world organization of the network. Two nodal centrality properties were employed for regional nodal network analysis: DC and NE. For further statistical comparison, area under the curve (AUC) for each network metric was calculated, providing a summarized scalar for topological parameters, avoiding the error caused by a single threshold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGCA is a statistical concept of causality that based on the multiple variant auto regression (MVAR) model\u0026nbsp;[12], commonly used as a reliable method in neuroscience to estimate EC, which characterizes directional functional connections among brain regions\u0026nbsp;[15]. In this study, GCA was conducted based on those brain regions showing significant DC or NE changes between sepsis survivors and healthy controls. Those brain regions were selected and defined as ROIs for seeds using WFU-pick-atlas (https://www.nitrc.org/ projects/wfu_pickatlas), then resampled to 3 mm×3 mm×3 mm. Based on pre-processed data, the REST_v1.8 software package (http// www.restfmri.net)\u0026nbsp;was applied to compute the causal effects of the time series x of selected seed points and the time series y of each voxel over the whole brain. In the Granger causality model, a value of 0 indicates no causal connection from x to y, a value of 1 indicates strong positive causality, and a value of -1 indicates strong negative causality. The causality analysis was performed twice on each ROI: from the seed point to whole-brain voxels (x to y) and from whole-brain voxels to the seed point (y to x). The obtained effective EC graph was transformed by Fisher’s z to improve distribution normality, resulting in a Z-map of GCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Demographic and neurocognitive data: statistical analyses were performed using SPSS software (version 27.0.1, IBM SPSS Inc., Chicago, IL, USA). Categorical variables were compared between groups using the Chi-squared test or Fisher’s exact test, as appropriate. Continuous variables, including demographic data and cognitive scores, were analyzed using independent t-tests. A p-value less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e2. rs-fMRI data: The topological properties of brain functional networks were analyzed using GRETNA software. Between-group comparisons of the AUC for these topological properties were conducted using one-way ANOVA in R software (version 4.3.2; https://www.r-project.org/), with age, gender, and years of education included as covariates. Multiple comparisons were corrected using the false discovery rate (FDR) method.\u003c/p\u003e\n\u003cp\u003e3. GCA: Granger causal influence measures (Z-EC values) derived from healthy control subjects and sepsis survivors were compared using two-sample t-tests, with age, gender, and years of education as covariates. Statistical significance was determined using Alphasim correction with a voxel-level threshold of p \u0026lt; 0.01 and a cluster-level threshold of p \u0026lt; 0.01 (two-tailed).\u003c/p\u003e\n\u003cp\u003e4. Correlation analysis: Pearson correlation analyses were performed to assess the relationships between cognitive test scores and topological properties, controlling for age, gender, and years of education. These analyses were conducted using SPSS software, and a p-value less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and neuropsychological data\u003c/h2\u003e \u003cp\u003eA total of 16 ICU sepsis survivors and 19 healthy controls were included in the final analysis (Supplementary Fig.\u0026nbsp;1). There were no statistical differences in gender, age, education years, diabetes and hypertension between sepsis survivors and healthy controls (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sepsis survivors had a significant lower body mass index (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, t = -2.914, p\u0026thinsp;=\u0026thinsp;0.006) likely due to weight loss during their ICU stay. In terms of cognitive function, sepsis survivors scored significant lower on the MoCA, MMSE, CFT-I, CFT-D, AVLT-N4, AVLT-N5, DST, VFT and SDMT. Conversely, sepsis survivors had higher completion times on the TMT-A and TMT-B tests compared to healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical data of sepsis survivors and healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u0026nbsp;(n\u0026nbsp;=\u0026nbsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u0026nbsp;(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex,\u0026nbsp;female/male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,\u0026nbsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation,\u0026nbsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, yes/no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, yes/no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSS, sepsis survivor; HC, healthy control; CRP, C-reactive protein (CRP); PCT, procalcitonin; NA, not available; BMI, body mass index. * The difference is statistically significant (P\u0026lt;0.05). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of topological properties of functional network\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eGlobal network properties\u003c/h2\u003e \u003cp\u003eSeven topological small-world parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were analyzed across a sparsity range of 0.05 to 0.40 with an interval of 0.01. Cp, γ, λ, σ and Lp were negatively whereas Eg and Eloc were positively correlated with sparsity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The σ values for both sepsis survivors and healthy controls were greater than 1 (data not shown), indicating that both groups exhibited small-world organization pattern. However, the γ value of sepsis survivors was significantly lower than that of healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, F\u0026thinsp;=\u0026thinsp;7.807, p\u0026thinsp;=\u0026thinsp;0.009, FDR). Meanwhile, the σ value for sepsis survivors was significantly lower than that of healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, F\u0026thinsp;=\u0026thinsp;7.494, p\u0026thinsp;=\u0026thinsp;0.010, FDR). No differences of λ、Cp、Lp、Eg、Eloc were observed between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLocal network properties\u003c/h2\u003e \u003cp\u003eAt the local level, significant differences between groups were found in a temporal, a frontal and a cerebellar region. In detail, in a frontal region ORBinf.R, sepsis survivors had significantly decreased DC (p\u0026thinsp;=\u0026thinsp;0.003, FDR, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and NE (p\u0026thinsp;=\u0026thinsp;0.011, FDR, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In a temporal region TPOsup.L, sepsis survivors also had significantly decreased NE (p\u0026thinsp;=\u0026thinsp;0.039, FDR, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Conversely, in a cerebellar region CRBLCrus2.R, sepsis survivors had significantly increased DC (p\u0026lt;0.001, FDR, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and NE (p\u0026thinsp;=\u0026thinsp;0.011, FDR, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBrain regions with significant nodal properties differences between sepsis survivors and healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLobe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNodal Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e (FDR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORBinf.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight frontal gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e6.407\u0026thinsp;\u0026plusmn;\u0026thinsp;3.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e12.093\u0026thinsp;\u0026plusmn;\u0026thinsp;3.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORBinf.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight frontal gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.168\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.208\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPOsup.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft temporal gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.172\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.205\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRBLCrus2.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight cerebellar lobule VII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e14.953\u0026thinsp;\u0026plusmn;\u0026thinsp;2.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10.312\u0026thinsp;\u0026plusmn;\u0026thinsp;2.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRBLCrus2.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight cerebellar lobule VII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.223\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.200\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDC, degree centrality; NE: nodal efficiency; SS, sepsis survivor; HC, healthy control; AAL, automated anatomical labeling; ORBinf.R, right orbital part of inferior frontal gyrus; CRBLCrus2.R: right cerebellum Crus 2; TPOsup.L: left temporal pole of superior temporal gyrus; FDR, False discovery rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGCA of brain networks\u003c/h2\u003e \u003cp\u003eBased on the above results, we then chose ORBinf.R, TPOsup.L and CRBLCrus2.R as seed regions for further analysis of EC between these three regions and all the other regions in the network. In sepsis survivor group, EC from CRBL6.L (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, A1) to ORBinf.R evidently decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, A2). The EC from TPOsup.L (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, B1) to CRBLCrus1.R also decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, B2). The EC from (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, C1) to TPOsup.L increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, C2). The EC between CRBLCrus2.R and other regions generated no significant results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation analysis between cognitive performance and network properties.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNo significant correlations were observed between global network properties and cognitive performance. At the local level, DC in CRBLCrus2.R was negatively correlated with both MMSE (r=-0.572, p\u0026thinsp;=\u0026thinsp;0.041, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and MoCA scores (r=-0.629, p\u0026thinsp;=\u0026thinsp;0.021, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). NE in CRBLCrus2.R was negatively correlated MoCA scores (r=-0.633, p\u0026thinsp;=\u0026thinsp;0.020, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). No significant correlations were found between EC and cognitive performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSepsis survivors often experience long-term disabilities, including cognitive impairment, which significantly impact their quality of life and ability to return to everyday activities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the present study, we first demonstrated that sepsis survivors experienced significant impairment in various cognitive facets within one month after ICU discharge. Additionally, we observed that, at the global level, sepsis survivors had degraded small-worldness. Local properties decreased in frontal and temporal regions but increased in the cerebellum, which are involved in the cognitive impairment of sepsis survivors. Furthermore, we identified alterations in connections between the cerebrum and the cerebellum, which might also contributed to cognitive impairment in sepsis survivors. Finally, correlation analysis revealed an interplay between cognitive impairment and topological alterations in brain networks.\u003c/p\u003e \u003cp\u003eThe majority of sepsis survivors in our study experienced cognitive impairment. Specifically, 15 and 12 out of the 16 (93.8% and 75.0%) sepsis survivors obtained MoCA and MMSE scores below the cut-off for normal performance, respectively. In a Japanese multicenter observational study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the prevalence of cognitive impairment was 37.5% in ICU survivors after 6 months. A recent study of severe COVID-19 patients who survived ICU found that 53.4% scored below the cut-off for normal performance on the MoCA, and 19% scored below the threshold for mild cognitive impairment 6 months after ICU discharge [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Cognitive impairment is thus common following sepsis, with its severity related to the recency of septic shock. In our study, the majority of the survivors suffered from severe abdominal infections, which are common causes of sepsis in the ICU [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It has also been suggested that severe abdominal infections undergoing surgical treatment tended to have poor long-term outcomes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A recent meta-analysis found a significant association between severe sepsis and an increased risk of cognitive impairment, indicating that the specific cause of sepsis also influences the severity of cognitive impairment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the global level, we found that the small-worldness of sepsis survivors\u0026rsquo; brain networks remained but decreased significantly compared to healthy controls. Among the global parameters, γ represents the local characteristics of the network, quantifying the local segregation function of the brain network. λ reflects the capability of global information integration and transmission. The ratio of γ to λ, denoted as σ, represents small-worldness. Compared to a random network, a small-world network has a smaller λ and a larger γ, and a σ greater than 1. In our study, the σ values of both sepsis survivors and healthy controls were greater than 1, indicating that sepsis survivors retained a small-world organization pattern. However, the lower σ value in sepsis survivors suggested loss of small-worldness of the brain networks. Specifically, the γ and σ values of sepsis survivors were statistically lower than those of healthy controls, while the λ values were similar between the two groups. According to the definition of σ, it can be inferred that the decrease in σ was primarily due to the decrease in γ. Additionally, the similar λ values between the two groups demonstrated that the long-distance information transmission in the brain networks of sepsis survivors remained intact. On the other hand, the decreased γ demonstrated a reduced capacity for local information processing reflecting degraded local connections and grouping of neural units in the brain networks. It is also noteworthy that the differences in γ and σ between the groups were more pronounced when the sparsity threshold was smaller. Therefore, a small threshold would be recommended when using these small-world properties to predict network disruption. In summary, sepsis survivors exhibited degraded small-worldness, primarily due to a decreased capacity for local information processing.\u003c/p\u003e \u003cp\u003eWe then demonstrated local information processing capability decreased primarily in a frontal and a temporal region but increased in a cerebellar region. Specifically, both DC and NE decreased in ORBinf.R, NE was decreased in TPOsup.L, and both DC and NE increased in CRBLCrus2.R. DC measures how many edges a node possesses within the entire network, with a higher DC representing a node\u0026rsquo;s central hub role for information communication [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A decrease in DC indicates a reduction in the number of brain regions connected to this region. NE quantifies how efficiently a node can exchange information within the network, so a decrease in NE reflects attenuated information transmission efficiency in that region. Both ORBinf.R and TPOsup.L are implicated in cognitive function. ORBinf.R, also known as the right pars orbitalis, refers to the most rostral portion of the inferior frontal gyrus in the frontal lobe. The pars orbitalis is involved primarily in semantic processing in the dominant hemisphere. However, in the non-dominant hemisphere, it plays a role in behavioral and motor inhibition [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and emotional regulation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. ORBinf.R volume loss has been significant in PD patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, the volume of this region is associated with the diagnosis of conduct disorder, which involves various behavioral and emotional problems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Besides, cortical thickness of this region has shown significant correlation with digit span test scores in PD patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], animacy scores in frontotemporal dementia and AD patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and the ability to inhibit ad lib smoking during the smoking relapse analog task in individuals with nicotine dependence [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Interestingly, the thickness of this region could also be a predictor of suicide attempt in young major depressive disorder patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. TPOsup.L, which refers to the left anterior end of temporal lobe, belongs to the anterior default mode network and is associated with semantic memory and other cognitive functions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In a study of patients with white matter lesions (WMLs), the NE value of TPOsup.L showed significant differences across normal people, WMLs with non-dementia vascular cognitive impairment and WMLs with vascular dementia showed significant difference, indicating the role of the network properties of this region in cognitive function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Thus, we speculate that the local information processing disruptions in frontal and temporal lobes may contribute to the cognitive impairment seen in sepsis survivors. In contrast, we observed that local properties increased in the cerebellum. The cerebellum has long been recognized as an crucial component involved in various cognitive functions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. CRBLCrus2.R belongs to lobule VII of the cerebellum. It is critical for language [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], visual memory [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], working memory [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and spatial memory processing [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Importantly, the cerebrocerebellar circuit serves as an efficient pathway for information exchange between the cerebral cortex to the cerebellum. Therefore, we speculated that the increase in local properties in the cerebellum might compensate for the decrease in local properties in the frontal and temporal regions. Interestingly, Zhao and colleagues [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] demonstrated that sepsis enhanced the intrinsic excitability and synaptic transmission of cerebellar Purkinje cells, which might be associated with the increase of local properties in the cerebellum. Although we did not observe significant correlations between DC or NE in ORBinf.R or TPOsup.L and cognitive performance of sepsis survivors, we did find that DC or NE in CRBLCrus2.R negatively correlated with MMSE or MoCA scores. We concluded that disruptions in frontal, temporal and cerebellar regions are involved in the cognitive impairment observed in sepsis survivors.\u003c/p\u003e \u003cp\u003eUsing GCA, we further examined the alterations in directional connections between above mentioned regions mentioned above (ORBinf.R, TPOsup.L, and CRBLCrus2.R) and other brain regions. We found that EC from CRBL6.L to ORBinf.R and from TPOsup.L to CRBLCrus1.R decreased. EC from LING.R to TPOsup.L increased. A previous imaging study showed that gray matter atrophy in both CRBL6.L and ORBinf.R were significantly implicated in AD pathology [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Maesawa and colleagues [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] investigated the connection between resting-state networks and cognitive performance of healthy individuals. Their results indicated that CRBL6.L, within the higher visual networks, exhibited within-network functional connectivity that was negatively correlated with age. Another study identified that the correlated transfer function connections between CRBLCrus1.R and left insula were the most significant markers for discriminating AD patients from healthy controls [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, the decrease in EC from CRBL6.L to ORBinf.R and from TPOsup.L to CRBLCrus1.R represents critical network alterations in sepsis survivors and might be related to cognitive impairment. The lingual gyrus, part of the occipital lobe, is mainly involved in processing vision, playing a role in logical analysis and encoding visual memories. Specifically, the right lingual gyrus is responsible for the perception and recognition of familiar landmarks and scenes as well as the identification of faces. Anatomically, the lingual gyrus and the temporal pole are connected by inferior longitudinal fasciculus fibers [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Given that our results suggested an increased EC from LING.R to TPOsup.L, it can be inferred that LING.R and TPOsup.L functionally interact in the brain network of sepsis survivors. In summary, directional connections particularly between the cerebrum and the cerebellum were disrupted and are implicated in cognitive impairment in sepsis survivors.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eFirstly, the small sample size of our study is a disadvantage for controlling interindividual heterogeneity, which could increase potential bias. This might explain why we did not observe significant correlations between decreased temporal and frontal topological properties and cognitive impairment. Secondly, our study only interviewed patients within one month after ICU discharge, a longitudinal follow-up will provide deeper insight into the interplay between topological alterations and cognitive function.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCollectively, sepsis survivors suffer from cognitive impairment, which correlates with topological property alterations of their brain networks. These topological alterations may serve as biomarkers for early diagnosis of cognitive impairment in sepsis survivors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eICU intensive care unit\u003c/p\u003e\n\u003cp\u003ers-fMRI Resting-state functional magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eGCA Granger causality analysis\u003c/p\u003e\n\u003cp\u003eEC effective connectivity\u003c/p\u003e\n\u003cp\u003eλ normalized characteristic path length\u003c/p\u003e\n\u003cp\u003eσ small-worldness\u003c/p\u003e\n\u003cp\u003eDC degree centrality\u003c/p\u003e\n\u003cp\u003eNE nodal efficiency\u003c/p\u003e\n\u003cp\u003eORBinf.R right orbital part of inferior frontal gyrus\u003c/p\u003e\n\u003cp\u003eTPOsup.L left temporal pole of superior temporal gyrus\u003c/p\u003e\n\u003cp\u003eCRBLCrus2.R right cerebellum\u0026nbsp;Crus\u0026nbsp;2\u003c/p\u003e\n\u003cp\u003eCRBL6.L left cerebellum 6\u003c/p\u003e\n\u003cp\u003eCRBLCrus1.R right cerebellum 1\u003c/p\u003e\n\u003cp\u003eLING.R right lingual gyrus\u003c/p\u003e\n\u003cp\u003eMRI Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003efMRI functional MRI\u003c/p\u003e\n\u003cp\u003eBOLD blood oxygen level-dependent\u003c/p\u003e\n\u003cp\u003ePD Parkinson's disease\u003c/p\u003e\n\u003cp\u003eAD Alzheimer's disease\u003c/p\u003e\n\u003cp\u003eMoCA Montreal Cognitive Assessment\u003c/p\u003e\n\u003cp\u003eMMSE Mini-Mental State Examination\u003c/p\u003e\n\u003cp\u003eCFT Complex Figure Test\u003c/p\u003e\n\u003cp\u003eAVLT Auditory Verbal Learning Test\u003c/p\u003e\n\u003cp\u003eDST Digit Span Test\u003c/p\u003e\n\u003cp\u003eVFT Verbal F1uency Test\u003c/p\u003e\n\u003cp\u003eCDT Clock Drawing Test\u003c/p\u003e\n\u003cp\u003eSDMT Symbol Digit Modalities Test\u003c/p\u003e\n\u003cp\u003eTMT Trail Making Test\u003c/p\u003e\n\u003cp\u003eWM white matter\u003c/p\u003e\n\u003cp\u003eCSF cerebrospinal fluid\u003c/p\u003e\n\u003cp\u003eAAL automated anatomical labeling\u003c/p\u003e\n\u003cp\u003eROIs regions of interest\u003c/p\u003e\n\u003cp\u003eCp clustering coefficient\u003c/p\u003e\n\u003cp\u003eLp characteristic path length\u003c/p\u003e\n\u003cp\u003eγ normalized clustering coefficient\u003c/p\u003e\n\u003cp\u003eEg global efficiency\u003c/p\u003e\n\u003cp\u003eEloc local efficiency\u003c/p\u003e\n\u003cp\u003eAUC area under the curve\u003c/p\u003e\n\u003cp\u003eWMLs white matter lesions\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Jiangyin People’s Hospital (No. 2019ER(021)). Informed consent (participation and publication) was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (No. 82372182, 82172131, and U23A20421) and Scientific Research Program of Wuxi Municipal Health Commission (Q202153).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL performed the majority of the study, collected and analyzed the data and wrote the manuscript. JQC contributed to study conception. HW and LNW contributed to data analysis and result interpretation. JJL and MQL contributed to recruitment and clinical data collection. HTY contributed to manuscript revision. WL contributed to data analysis. MHJ and JJY contributed to study design, acquisition of funding and manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank staff of the Intensive Care Unit and Department of Radiology of Jiangyin People’s Hospital.\u0026nbsp;Thanks to all participants who contributed to the present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e School of Medicine, Southeast University, Nanjing, China.\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e Department of Anesthesiology, Jiangyin Hospital, Affiliated to Southeast University Medical School, Jiangyin, China. \u003csup\u003e3\u003c/sup\u003e Department of Interventional Neurology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China. \u003csup\u003e4\u003c/sup\u003e Department of Neurology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, China. \u003csup\u003e5\u003c/sup\u003e Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. \u003csup\u003e6\u003c/sup\u003e Department of Anesthesiology, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, China.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e2016, \u003cstrong\u003e315\u003c/strong\u003e(8):801-810.\u003c/li\u003e\n\u003cli\u003eFleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, Angus DC, Reinhart K: \u003cstrong\u003eAssessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations\u003c/strong\u003e. \u003cem\u003eAmerican journal of respiratory and critical care medicine \u003c/em\u003e2016, \u003cstrong\u003e193\u003c/strong\u003e(3):259-272.\u003c/li\u003e\n\u003cli\u003eIwashyna TJ, Ely EW, Smith DM, Langa KM: \u003cstrong\u003eLong-term cognitive impairment and functional disability among survivors of severe sepsis\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e2010, \u003cstrong\u003e304\u003c/strong\u003e(16):1787-1794.\u003c/li\u003e\n\u003cli\u003eLei S, Li X, Zhao H, Feng Z, Chun L, Xie Y, Li J: \u003cstrong\u003eRisk of Dementia or Cognitive Impairment in Sepsis Survivals: A Systematic Review and Meta-Analysis\u003c/strong\u003e. \u003cem\u003eFrontiers in aging neuroscience \u003c/em\u003e2022, \u003cstrong\u003e14\u003c/strong\u003e:839472.\u003c/li\u003e\n\u003cli\u003eBurgdorf JG, Chase JD, Whitehouse C, Bowles KH: \u003cstrong\u003eUnmet Caregiving Needs Among Sepsis Survivors Receiving Home Health Care: The Need for Caregiver Training\u003c/strong\u003e. \u003cem\u003eJournal of applied gerontology : the official journal of the Southern Gerontological Society \u003c/em\u003e2022, \u003cstrong\u003e41\u003c/strong\u003e(10):2180-2186.\u003c/li\u003e\n\u003cli\u003eLv H, Wang Z, Tong E, Williams LM, Zaharchuk G, Zeineh M, Goldstein-Piekarski AN, Ball TM, Liao C, Wintermark M: \u003cstrong\u003eResting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know\u003c/strong\u003e. \u003cem\u003eAJNR American journal of neuroradiology \u003c/em\u003e2018, \u003cstrong\u003e39\u003c/strong\u003e(8):1390-1399.\u003c/li\u003e\n\u003cli\u003eSporns O: \u003cstrong\u003eGraph theory methods: applications in brain networks\u003c/strong\u003e. \u003cem\u003eDialogues in clinical neuroscience \u003c/em\u003e2018, \u003cstrong\u003e20\u003c/strong\u003e(2):111-121.\u003c/li\u003e\n\u003cli\u003eLiao X, Vasilakos AV, He Y: \u003cstrong\u003eSmall-world human brain networks: Perspectives and challenges\u003c/strong\u003e. \u003cem\u003eNeuroscience and biobehavioral reviews \u003c/em\u003e2017, \u003cstrong\u003e77\u003c/strong\u003e:286-300.\u003c/li\u003e\n\u003cli\u003eChen X, Liu M, Wu Z, Cheng H: \u003cstrong\u003eTopological Abnormalities of Functional Brain Network in Early-Stage Parkinson\u0026apos;s Disease Patients With Mild Cognitive Impairment\u003c/strong\u003e. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e2020, \u003cstrong\u003e14\u003c/strong\u003e:616872.\u003c/li\u003e\n\u003cli\u003eFathian A, Jamali Y, Raoufy MR: \u003cstrong\u003eThe trend of disruption in the functional brain network topology of Alzheimer\u0026apos;s disease\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2022, \u003cstrong\u003e12\u003c/strong\u003e(1):14998.\u003c/li\u003e\n\u003cli\u003eWang M, Xu B, Hou X, Shi Q, Zhao H, Gui Q, Wu G, Dong X, Xu Q, Shen M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAltered brain networks and connections in chronic heart failure patients complicated with cognitive impairment\u003c/strong\u003e. \u003cem\u003eFrontiers in aging neuroscience \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e:1153496.\u003c/li\u003e\n\u003cli\u003eGao Q, Luo N, Liang M, Zhou W, Li Y, Li R, Hu X, Zou T, Wang X, Yu J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network\u003c/strong\u003e. \u003cem\u003eIEEE transactions on neural networks and learning systems \u003c/em\u003e2024, \u003cstrong\u003e35\u003c/strong\u003e(4):4974-4984.\u003c/li\u003e\n\u003cli\u003eHe C, Gong L, Yin Y, Yuan Y, Zhang H, Lv L, Zhang X, Soares JC, Zhang H, Xie C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAmygdala connectivity mediates the association between anxiety and depression in patients with major depressive disorder\u003c/strong\u003e. \u003cem\u003eBrain imaging and behavior \u003c/em\u003e2019, \u003cstrong\u003e13\u003c/strong\u003e(4):1146-1159.\u003c/li\u003e\n\u003cli\u003eRolls ET, Huang CC, Lin CP, Feng J, Joliot M: \u003cstrong\u003eAutomated anatomical labelling atlas 3\u003c/strong\u003e. \u003cem\u003eNeuroImage \u003c/em\u003e2020, \u003cstrong\u003e206\u003c/strong\u003e:116189.\u003c/li\u003e\n\u003cli\u003eSeth AK, Barrett AB, Barnett L: \u003cstrong\u003eGranger causality analysis in neuroscience and neuroimaging\u003c/strong\u003e. \u003cem\u003eThe Journal of neuroscience : the official journal of the Society for Neuroscience \u003c/em\u003e2015, \u003cstrong\u003e35\u003c/strong\u003e(8):3293-3297.\u003c/li\u003e\n\u003cli\u003eLi Y, Ji M, Yang J: \u003cstrong\u003eCurrent Understanding of Long-Term Cognitive Impairment After Sepsis\u003c/strong\u003e. \u003cem\u003eFrontiers in immunology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:855006.\u003c/li\u003e\n\u003cli\u003eSun Y, Shi Q, Ye M, Miao A: \u003cstrong\u003eTopological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation\u003c/strong\u003e. \u003cem\u003eFrontiers in neuroscience \u003c/em\u003e2023, \u003cstrong\u003e17\u003c/strong\u003e:1282232.\u003c/li\u003e\n\u003cli\u003eBoen R, Raud L, Huster RJ: \u003cstrong\u003eInhibitory Control and the Structural Parcelation of the Right Inferior Frontal Gyrus\u003c/strong\u003e. (1662-5161 (Print)).\u003c/li\u003e\n\u003cli\u003eHou W, Sahakian BJ, Langley C, Yang Y, Bethlehem RAI, Luo Q: \u003cstrong\u003eEmotion dysregulation and right pars orbitalis constitute a neuropsychological pathway to attention deficit hyperactivity disorder\u003c/strong\u003e. \u003cem\u003eNature Mental Health \u003c/em\u003e2024:1-13.\u003c/li\u003e\n\u003cli\u003eWu C, Wu H, Zhou C, Guo T, Guan X, Cao Z, Wu J, Liu X, Chen J, Wen J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe effect of dopamine replacement therapy on cortical structure in Parkinson\u0026apos;s disease\u003c/strong\u003e. \u003cem\u003eCNS neuroscience \u0026amp; therapeutics \u003c/em\u003e2023.\u003c/li\u003e\n\u003cli\u003eZhang R, Blair RJR, Blair KS, Dobbertin M, Elowsky J, Bashford-Largo J, Dominguez AJ, Hatch M, Bajaj S: \u003cstrong\u003eReduced grey matter volume in adolescents with conduct disorder: a region-of-interest analysis using multivariate generalized linear modeling\u003c/strong\u003e. \u003cem\u003eDiscover mental health \u003c/em\u003e2023, \u003cstrong\u003e3\u003c/strong\u003e(1):25.\u003c/li\u003e\n\u003cli\u003eDeng X, Tang CY, Zhang J, Zhu L, Xie ZC, Gong HH, Xiao XZ, Xu RS: \u003cstrong\u003eThe cortical thickness correlates of clinical manifestations in the mid-stage sporadic Parkinson\u0026apos;s disease\u003c/strong\u003e. \u003cem\u003eNeuroscience letters \u003c/em\u003e2016, \u003cstrong\u003e633\u003c/strong\u003e:279-289.\u003c/li\u003e\n\u003cli\u003eFong SS, Paholpak P, Daianu M, Deutsch MB, Riedel BC, Carr AR, Jimenez EE, Mather MM, Thompson PM, Mendez MF: \u003cstrong\u003eThe attribution of animacy and agency in frontotemporal dementia versus Alzheimer\u0026apos;s disease\u003c/strong\u003e. \u003cem\u003eCortex; a journal devoted to the study of the nervous system and behavior \u003c/em\u003e2017, \u003cstrong\u003e92\u003c/strong\u003e:81-94.\u003c/li\u003e\n\u003cli\u003eBrown AA, Upton S, Craig S, Froeliger B: \u003cstrong\u003eAssociations between right inferior frontal gyrus morphometry and inhibitory control in individuals with nicotine dependence\u003c/strong\u003e. \u003cem\u003eDrug and alcohol dependence \u003c/em\u003e2023, \u003cstrong\u003e244\u003c/strong\u003e:109766.\u003c/li\u003e\n\u003cli\u003eHong S, Liu YS, Cao B, Cao J, Ai M, Chen J, Greenshaw A, Kuang L: \u003cstrong\u003eIdentification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach\u003c/strong\u003e. \u003cem\u003eJournal of affective disorders \u003c/em\u003e2021, \u003cstrong\u003e280\u003c/strong\u003e(Pt A):72-76.\u003c/li\u003e\n\u003cli\u003eSnowden JS, Harris JM, Thompson JC, Kobylecki C, Jones M, Richardson AM, Neary D: \u003cstrong\u003eSemantic dementia and the left and right temporal lobes\u003c/strong\u003e. \u003cem\u003eCortex; a journal devoted to the study of the nervous system and behavior \u003c/em\u003e2018, \u003cstrong\u003e107\u003c/strong\u003e:188-203.\u003c/li\u003e\n\u003cli\u003eHerlin B, Navarro V, Dupont S: \u003cstrong\u003eThe temporal pole: From anatomy to function-A literature appraisal\u003c/strong\u003e. \u003cem\u003eJournal of chemical neuroanatomy \u003c/em\u003e2021, \u003cstrong\u003e113\u003c/strong\u003e:101925.\u003c/li\u003e\n\u003cli\u003eWang J, Chen Y, Liang H, Niedermayer G, Chen H, Li Y, Wu M, Wang Y, Zhang Y: \u003cstrong\u003eThe Role of Disturbed Small-World Networks in Patients with White Matter Lesions and Cognitive Impairment Revealed by Resting State Function Magnetic Resonance Images (rs-fMRI)\u003c/strong\u003e. \u003cem\u003eMedical science monitor : international medical journal of experimental and clinical research \u003c/em\u003e2019, \u003cstrong\u003e25\u003c/strong\u003e:341-356.\u003c/li\u003e\n\u003cli\u003eCarey MR: \u003cstrong\u003eThe cerebellum\u003c/strong\u003e. \u003cem\u003eCurrent biology : CB \u003c/em\u003e2024, \u003cstrong\u003e34\u003c/strong\u003e(1):R7-r11.\u003c/li\u003e\n\u003cli\u003eStoodley CJ, MacMore JP, Makris N, Sherman JC, Schmahmann JD: \u003cstrong\u003eLocation of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke\u003c/strong\u003e. \u003cem\u003eNeuroImage Clinical \u003c/em\u003e2016, \u003cstrong\u003e12\u003c/strong\u003e:765-775.\u003c/li\u003e\n\u003cli\u003eGao Q, Tao Z, Cheng L, Leng J, Wang J, Yu C, Chen H: \u003cstrong\u003eLanguage lateralization during the Chinese semantic task relates to the contralateral cerebra-cerebellar interactions at rest\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2017, \u003cstrong\u003e7\u003c/strong\u003e(1):14056.\u003c/li\u003e\n\u003cli\u003eZhao Y, Lin J, Qi X, Cao D, Zhu F, Chen L, Tan Z, Mo T, Zeng H: \u003cstrong\u003eTo explore the potential mechanisms of cognitive impairment in children with MRI-negative pharmacoresistant epilepsy due to focal cortical dysplasia: A pilot study from gray matter structure view\u003c/strong\u003e. \u003cem\u003eHeliyon \u003c/em\u003e2024, \u003cstrong\u003e10\u003c/strong\u003e(4):e26609.\u003c/li\u003e\n\u003cli\u003eLiu ZX, Shen K, Olsen RK, Ryan JD: \u003cstrong\u003eVisual Sampling Predicts Hippocampal Activity\u003c/strong\u003e. \u003cem\u003eThe Journal of neuroscience : the official journal of the Society for Neuroscience \u003c/em\u003e2017, \u003cstrong\u003e37\u003c/strong\u003e(3):599-609.\u003c/li\u003e\n\u003cli\u003eNi S, Gao S, Ling C, Jiang J, Wu F, Peng T, Sun J, Zhang N, Xu X: \u003cstrong\u003eAltered brain regional homogeneity is associated with cognitive dysfunction in first-episode drug-naive major depressive disorder: A resting-state fMRI study\u003c/strong\u003e. \u003cem\u003eJournal of affective disorders \u003c/em\u003e2023, \u003cstrong\u003e343\u003c/strong\u003e:102-108.\u003c/li\u003e\n\u003cli\u003eWang Y, Lu Y, Du M, Hussein NM, Li L, Wang Y, Mao C, Chen T, Chen F, Liu X\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAltered Spontaneous Brain Activity in Left-Behind Children: A Resting-State Functional MRI Study\u003c/strong\u003e. \u003cem\u003eFrontiers in neurology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:834458.\u003c/li\u003e\n\u003cli\u003eShcherbinin S, Schwarz AJ, Joshi A, Navitsky M, Flitter M, Shankle WR, Devous MD, Sr., Mintun MA: \u003cstrong\u003eKinetics of the Tau PET Tracer 18F-AV-1451 (T807) in Subjects with Normal Cognitive Function, Mild Cognitive Impairment, and Alzheimer Disease\u003c/strong\u003e. \u003cem\u003eJournal of nuclear medicine : official publication, Society of Nuclear Medicine \u003c/em\u003e2016, \u003cstrong\u003e57\u003c/strong\u003e(10):1535-1542.\u003c/li\u003e\n\u003cli\u003eZhao Y, Jiang Y, Shen Y, Su LD: \u003cstrong\u003eSepsis Impairs Purkinje Cell Functions and Motor Behaviors Through Microglia Activation\u003c/strong\u003e. \u003cem\u003eCerebellum (London, England) \u003c/em\u003e2024, \u003cstrong\u003e23\u003c/strong\u003e(2):329-339.\u003c/li\u003e\n\u003cli\u003eZhou TD, Zhang Z, Balachandrasekaran A, Raji CA, Becker JT, Kuller LH, Ge Y, Lopez OL, Dai W, Gach HM: \u003cstrong\u003eProspective Longitudinal Perfusion in Probable Alzheimer\u0026apos;s Disease Correlated with Atrophy in Temporal Lobe\u003c/strong\u003e. \u003cem\u003eAging and disease \u003c/em\u003e2023.\u003c/li\u003e\n\u003cli\u003eMaesawa S, Mizuno S, Bagarinao E, Watanabe H, Kawabata K, Hara K, Ohdake R, Ogura A, Mori D, Nakatsubo D\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eResting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging\u003c/strong\u003e. In: \u003cem\u003eFrontiers in human neuroscience.\u003c/em\u003e vol. 15; 2021: 753836.\u003c/li\u003e\n\u003cli\u003eMousa D, Zayed N, Yassine IA: \u003cstrong\u003eAlzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging\u003c/strong\u003e. \u003cem\u003ePloS one \u003c/em\u003e2022, \u003cstrong\u003e17\u003c/strong\u003e(4):e0264710.\u003c/li\u003e\n\u003cli\u003ePalejwala AH, Dadario NB, Young IM, O\u0026apos;Connor K, Briggs RG, Conner AK, O\u0026apos;Donoghue DL, Sughrue ME: \u003cstrong\u003eAnatomy and White Matter Connections of the Lingual Gyrus and Cuneus\u003c/strong\u003e. \u003cem\u003eWorld neurosurgery \u003c/em\u003e2021, \u003cstrong\u003e151\u003c/strong\u003e:e426-e437.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, Cognitive impairment, Functional magnetic resonance imaging, Graph theory, Granger causality analysis","lastPublishedDoi":"10.21203/rs.3.rs-5226224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5226224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to explore the topological alterations of the brain networks of ICU sepsis survivors and their correlation with cognitive impairment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e16 sepsis survivors from ICU and 19 healthy controls from the community were recruited. Within one month after discharge, neurocognitive tests were administered to assess cognitive performance. Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired and the topological properties of brain networks were measured based on graph theory approaches. Granger causality analysis (GCA) was conducted to quantify effective connectivity (EC) between brain regions showing positive topological alterations and other regions in the brain. The correlations between topological properties and cognitive performance were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSepsis survivors exhibited significant cognitive impairment. At the global level, sepsis survivors showed lower normalized clustering coefficient (γ) and small-worldness (σ). At the local level, degree centrality (DC) and nodal efficiency (NE) decreased in the right orbital part of inferior frontal gyrus (ORBinf.R), NE decreased in the left temporal pole of superior temporal gyrus (TPOsup.L)whereas DC and NE increased in the right cerebellum Crus 2 (CRBLCrus2.R). Regarding directional connection alterations, GCA revealed that EC from left cerebellum 6 (CRBL6.L) to ORBinf.R and EC from TPOsup.L to right cerebellum 1 (CRBLCrus1.R) decreased, whereas EC from right lingual gyrus (LING.R) to TPOsup.L increased. Correlation analysis demonstrated a significant relationship between cerebellar topological alterations and cognitive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrontal, temporal and cerebellar topological property alterations are involved in the mechanisms of cognitive impairment of ICU sepsis survivors and may serve as biomarkers for early diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNCT03946839 (Registered May 10, 2019).\u003c/p\u003e","manuscriptTitle":"Frontal, temporal and cerebellar topological property alterations predispose cognitive impairment of ICU sepsis survivors: A resting-state fMRI study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 09:17:17","doi":"10.21203/rs.3.rs-5226224/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"583718ff-98a0-486c-91b3-ad39d32a8b3c","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39488395,"name":"Health sciences/Pathogenesis/Inflammation/Sepsis"},{"id":39488396,"name":"Health sciences/Pathogenesis/Infection"},{"id":39488397,"name":"Health sciences/Diseases/Infectious diseases/Bacterial infection"},{"id":39488398,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":39488399,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":39488400,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2025-01-24T02:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 09:17:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5226224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5226224","identity":"rs-5226224","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}