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

doi:10.21203/rs.3.rs-5226224/v1

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

https://doi.org/10.21203/rs.3.rs-5226224/v1

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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

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.

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 = 1×1×1mm³. 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 mm³). 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.

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)	χ²/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/m²	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. 3A) and NE (p = 0.011, FDR, Table 2, Fig. 3B). In a temporal region TPOsup.L, sepsis survivors also had significantly decreased NE (p = 0.039, FDR, Table 2, Fig. 3B). Conversely, in a cerebellar region CRBLCrus2.R, sepsis survivors had significantly increased DC (p<0.001, FDR, Table 2, Fig. 3A) and NE (p = 0.011, FDR, Table 2, Fig. 3B).

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. 5A) and MoCA scores (r=-0.629, p = 0.021, Fig. 5B). NE in CRBLCrus2.R was negatively correlated MoCA scores (r=-0.633, p = 0.020, Fig. 5C). No significant correlations were found between EC and cognitive performance.

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.

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.

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

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.

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

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Background

Participants and methods

Results

Demographic and neuropsychological data

Comparison of topological properties of functional network

Global network properties

Local network properties

GCA of brain networks

Discussion

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