Preoperative Cholinergic Signatures Drive Segregated Brain Architecture in Postoperative Delirium

Preoperative Cholinergic Signatures Drive Segregated Brain Architecture in Postoperative Delirium | 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 Preoperative Cholinergic Signatures Drive Segregated Brain Architecture in Postoperative Delirium Natasha Taylor, Brandon Munn, Klevest Gjini, Isabella Orlando, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7881643/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Delirium affects up to 50% of hospitalised, older patients and is linked to increased risk of death and long-term cognitive decline. Age-related changes in the ascending arousal system (AAS), including cholinergic and noradrenergic nuclei, may contribute to delirium vulnerability. Static and dynamic functional connectivity, across cortical and subcortical regions, was extracted from preoperative resting-state fMRI from 120 older adults (aged > 65 years old). Participants who developed delirium showed more segregated brain networks, cholinergic hyperconnectivity and noradrenergic hypoconnectivity. Dynamic patterns from these systems separated groups in low-dimensional space, suggesting altered temporal network dynamics. Normative maps of cholinergic gene expression density were associated with increased network segregation. These results suggest that aging-related AAS alterations—particularly compensatory cholinergic overactivity—may drive network changes that increase delirium risk. This work provides new insights into the neural mechanisms linking aging, arousal system dysfunction, and brain network disruption in delirium, mandating re-appraisal of leading delirium theories. Health sciences/Neurology/Neurological disorders/Neurodegeneration Health sciences/Neurology/Neurological disorders/Dementia Delirium Resting-state fMRI Cholinergic Segregation Gene expression Ascending Arousal Systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Delirium is an acute state of cognitive impairment, attention, and/or arousal that has significant impact on overall long-term cognitive outcomes and is associated with an increased risk of dementia 1 . It disproportionality impacts the aging population with up to 56% of elderly hospitalized patients affected 2 and advanced aging (> 65yrs) being a significant risk-factor due to neuroinflammatory changes 3 . Critically it is associated with increased morbidity, mortality, and costs 4 . Amongst the prevailing theories, one suggests that delirium can be described as a state of ‘cognitive disintegration’ due to a reduction or dysfunctional connectivity between specific cortical networks that are necessary for maintaining normal cognitive function 5 . The theory predicts that delirium results when the network connectivity disintegrates below a critical threshold. While this theory is supported by recent EEG studies, showing a reduction in feedback cortical connectivity 6 and resting state functional connectivity (FC) in delirium 7–10 , data linking connectivity to vulnerability are less forthcoming. Several studies have revealed that global and regional cortical atrophy, white-matter hyperintensities and structural based differences may predict postoperative delirium 7,11–13 . These studies revealed that neuropathological changes in postoperative delirium are detectable with standard functional imaging techniques; however they have not established directly whether there are pre-existing dynamic and static FC signatures that are indicative of vulnerability to delirium, that may provide important insights into disturbed neural dynamics. Aligning with the cognitive disintegration theory is a graph theoretical measure popularised in network neuroscience; participation coefficient. Participation coefficient quantifies how evenly distributed a region’s connections are within its own network/subnetwork compared to its connections to regions outside of their network 14 . As such, this is used to define whether brain network topology is either integrated (strong connections across brain networks) or segregated. Network topology has been shown to be disrupted across several neurodegenerative diseases of aging, specifically in Alzheimer’s disease 15 (AD) and Dementia with Lewy Bodies 16 (DLB). This highlights that in other neurodegenerative conditions, that delirium populations are more at risk of developing 1 , there is an overlap in underlying network topological changes. Patterns of brain wide integration and segregation have been associated with underlying neuromodulatory systems, the noradrenergic and cholinergic neuromodulators. Previous work has evidenced that activity within the noradrenergic locus coeruleus (LC) can imbue the system towards a more integrated network topology 17,18 and is critical in exploitation/exploration cognitive processes 19 . Whereas, the cholinergic system has been thought to facilitate a more segregated network topology due to its more targeted projection patterns across the cortex, which is critical for selective attentional control 20,21 . This highlights a preoperative vulnerability in the ascending arousal systems that relates to cognitive disintegration theory. The noradrenergic and cholinergic systems are critical for normal cognitive function and; they are sites of substantial pathology in neurodegenerative diseases 22 . Specifically, the noradrenergic locus coeruleus undergoes early pathological degeneration across neurodegenerative disease 23–25 . The integrity of the locus coeruleus has been extensively linked to decline in memory performance in ageing and in Alzheimer’s disease 26–29 . Given noradrenaline’s critical role in facilitating selective attention 30,31 ; preoperative vulnerability within the locus coeruleus could underly the neuropathological insult prior to surgery. In addition to the loss of the noradrenergic system integrity, there is substantial evidence of cholinergic associated cognitive decline in both Lewy Body Dementia 32–34 and Alzheimer’s disease 35–38 . Cholinergic pathology precedes degeneration in other associated brain regions and has been shown to predict future memory impairment in non-symptomatic aging individuals 39,40 . This results in dysfunction in learning, memory, sensory and attentional processing 41 as the cholinergic system is critical in modulating feedforward processing within the cortex 42–44 . Recent literature has also linked non-linear interactions of the cholinergic system: increases in cholinergic presynaptic VAChT levels colocalized with tau is predictive of cognitive resilience in older adults at risk of AD 45 . Consistent with this, we have recently observed exaggerated feedforward processing in individuals who are vulnerable to delirium 46 and in patients with tau disease on PET imaging 47 . As such, dysfunctional noradrenergic and cholinergic neuromodulation could underly vulnerability to delirium, with early degeneration of the locus coeruleus and subsequent non-linear compensatory hyperactivity of the cholinergic system. Given the cognitive disintegration theory, we hypothesise that preoperative participants that go onto develop delirium will have dysfunctional network topology, with a greater degree of segregation (disintegration) related to disturbed function of the ascending arousal system. Results A total of 296 participants were recruited for this study. 153 participants did not undergo preoperative MRI scans due to refusal of consent or ineligibility. Delirium occurred in 32.4% of scanned participants and 37.0% of non-scanned participants. After performing pre-processing and denoising of the resting state fMRI data, a total of 120 participants (see methods for exclusion) were analysed. The delirious scanned participants were matched to non-delirious participants across most characteristics (see supplementary materials Table 1), except for operative severity (National Surgical Quality Improvement Program risk of Surgical Complications or Death) and a marker of peak delirium severity, overall peak Delirium Rating Scale-98 (DRS). These analyses compare before surgery (preoperative) between participants who later developed postoperative delirium (termed delirious) and who did not develop postoperative delirium (termed non-delirious), scanning was conducted when no patient was actively delirious. To disentangle the contributions of the ascending arousal system to segregated network topology in development of postoperative delirium; time-series were extracted across 400 cortical regions 48 , the pontine locus coeruleus (cortical source of noradrenaline) 49 and basal forebrain nucleus basalis of Meynert (source of acetylcholine) 50 . Pearson’s correlation was calculated to estimate static (FC) and dynamic functional connectivity (dFC) was calculated using the multiplication of temporal derivatives 51 . We calculated graph theoretical derived segregated network topology using participation coefficient and determined distinct spatio-temporal dFC patterns using linear discriminant analysis in those delirious compared to non-delirious. Lastly, we related cholinergic gene expression density maps 52 to regional segregation across the brain (Fig. 1 – graphical methods). Segregated Network Topology in Postoperative Delirium We explored time-varying network topology by calculating the dynamic functional connectivity (dFC) and used participation coefficient (PC) 14,53 to quantify the distribution of connections across different network modules. We determined whether there were significant differences in integration (larger PC) versus segregation for the delirious compared to non-delirious participants (1000 iterations of permuted p-value). We found that on average across the time-series, the delirium participants had a relatively decreased PC score compared to the non-delirious participants (mean pc delirium = 0.39 ± 0.05, mean pc non-delirious = 0.41 ± 0.04; Fig. 2A), suggesting that the delirium population had more segregated network topology. Specifically, this was driven by a greater segregation across the salience attention and default mode networks. To further interrogate this, we ran a k-means clustering 53 across the entire time-series for all cortical regions and found that the delirium population was more frequently in a segregated state compared to the non-delirious population (Fig. 2B). In addition, to determine whether the preoperative PC was an important biological predictor of delirium, we adjusted for the main biological neural correlate during delirium – postoperative EEG slow wave activity 7,54 . This adjustment is important to confirm a robust relationship between segregation and delirium. We verified that the preoperative PC value interacted with difference in pre versus postoperative EEG slow wave activity (change Oz-delta power) in their association with postoperative delirium using logistic regression (Fig. 2C, evidenced with fitted curved plot in Fig. 2D). These data demonstrate a negative interaction between PC and EEG slow wave activity: as PC falls and slow wave activity increases, the probability of delirium increases. Furthermore, the adjustment for slow wave activity confirms that PC predisposes to delirium independent of the change in brain electrophysiology that occurs during delirium. Noradrenergic and Cholinergic Dysfunctional Static Connectivity We found significant hypoconnectivity between the LC and the pre-frontal regions and anterior cingulate of the cortex for the delirium population compared to non-delirious (Fig. 3A). We found a significant hyperconnectivity between the nbM and the posterior and superior temporal cortical regions in the delirium compared to non-delirious participants (Fig. 3B). In addition, the simple logistic regression results for predicting delirium outcome based on ascending arousal nuclei connectivity found that hypoconnectivity from the LC to salience attentional (p = 0.041), and control networks (p = 0.034) were associated with delirium. Decreases in connectivity from nbM to regions within peripheral visual (p = 0.017) and default network (p = 0.045) and increased connectivity from nbM to central visual network (p = 0.016) were also associated with delirium (Supplementary Materials, Fig.S3). In addition, inspired by a recent report 45 , in a post hoc analysis we show a positive linear relationship between stronger nBM FC and pTau (blood biomarker), (Supplementary Materials, Fig. S9); however the regional differences were not significant after FDR correction. Dynamic Functional Connectivity Ascending Arousal Patterns We next explored time-varying features of brain connectivity. We used eigen-mode linear discriminant analysis (LDA) to determine specific spatio-temporal patterns that are unique to the delirium and non-delirious populations across the LC dFC and nbM dFC (Fig. 4A-B). The dFC matrices were concatenated across subjects and time, scaled and balanced-class priors for LDA was used to identify features contributing to maximal separation between the groups. Using the highest-order magnitude (top 5%, based LDA coefficients) time-points across the dFC that separate the two groups, the LDA-embedded average dFC were compared for noradrenergic and cholinergic connectivity between delirious and non-delirious groups. We found greater anti-coupling between the LC and frontal regions in the delirium population compared to non-delirious (Fig. 4C). The nbM had greater coupling across posterior-temporal regions and distinct regions within the default mode network (DMN) in the delirium group compared to non-delirious, and there was anti-coupling across regions within the salience attentional and somato-motor networks (Fig. 4D). These heterogenous patterns were mirrored when looking at average dFC differences between the groups (Supplementary Materials, Fig. S4). We were interested in determining whether there are time-dependent relationships between the global brain integration/segregation compared to ascending arousal BOLD dynamics. Leveraging an approach from Munn et al., (2021) derived from younger individuals 55 , we calculated the cross-correlation between the phasic peaks (defined by 2-standard deviations) of the BOLD signal for the LC and nbM against the average (global) participation coefficients for each subject. Similar to Munn et al., (2021), we found a mirroring pattern, wherein post-phasic LC activity was positively correlated to greater integration (Fig. 5A) and post-cholinergic activity was related to more segregation (Fig. 5B-D). However, the cross-correlation between nbM and global PC occurred in bursts: there was greater segregation for the delirium population for time-lag 0 (p = 0.04) and time-lag + 1 (p = 0.015) compared to non-delirious (Fig. 5B), but the relationship became flipped at greater time-lags. This finding is consistent with a hyperactive cholinergic response in the delirium population driving greater segregation. Relationship between Segregation and Gene Expression Receptors Lastly, we explored whether regional segregation related to gene expression derived from the Allen Human Brain Atlas 52,56,57 . We selected genes relating cholinergic receptor expression, neurotransmission and clearance enzymes and performed principal component analysis to reduce the dimensionality of the number of genes (see Methods). Then, we correlated the principal component loadings (across 8 principal components, Supplementary Materials, Fig. S6) to the regional average PC across the delirious and non-delirious participants combined (and distinct; Supplementary Materials, Fig. S7). The top performing linear relationship was ranked and validated based upon largest effect size ( r = 0.3, p = medium effect) and greatest absolute correlation coefficient (see Methods). We found a strong negative relationship between the second principal component of the cholinergic related gene expression correlated with the average PC across both the delirium (rho=-0.456, p spin = 0.001, p-FDR = 0.004) and non-delirious (rho=-0.396), p spin = 0.001, p-FDR = 0.004) participations (Fig. 6B-C). Importantly, the second principal component embeds nicotinic receptors (nAChRs) and cholinergic neurotransmission gene-related expression (Fig. 6A) with higher gene loadings across frontal cortical regions (Fig. 6D). In addition, when observing the individual cholinergic gene expression related to the average PC; several genes that contribute to the loading in the second principal component are significantly negatively related to PC across both groups (Fig. 6E - H), across nicotinic receptors (Fig. 6-F -G) and butyrylcholinesterase (Fig. 6E). Interestingly, two distinctive genes that have negative loadings in the second principal component show a positive relationship with average PC (Fig. 6I – Ji) and the gene expression is inverted with low expression across frontal and high loadings across posterior cortex compared to all the other expression maps (Fig. 6I - J). This is additional evidence that there is a mechanistic relationship between regional cholinergic gene expression and segregated network topology. We also examined the relationship between cholinergic gene expression and nbM functional connectivity, however none of these results passed FDR correction (Supplementary Materials, Fig. S8). Discussion In this study, we found signatures of disrupted ascending arousal modulation in preoperative resting-state scans of participations who developed postoperative delirium. Preoperatively, delirious participants had more segregated network topology and evidence of disrupted ascending arousal modulation compared with non-delirious participants, with decreased connectivity between the LC and cortex and hyperconnectivity between cholinergic nbM and the posterior regions of the cortex. The relative reduction in the preoperative participation coefficient across the entire cortex for individuals with postoperative delirium was explored through a data-driven approach to categorise the segregated and integrated states across the entire time-series for all cortical brain regions. We found that the participants who were vulnerable to postoperative delirium spent more time in a segregated state compared to non-delirious participants (Fig. 2B). Furthermore, we established a relationship between the interaction with slow-wave activity (a key biological correlate of the precipitant of postoperative delirium 7 ) and segregated brain regions in predicting postoperative delirium outcomes (Fig. 2C). This establishes a robust relationship between delirium and the segregated network topology of the population. Segregated network topology has been evidenced across several other neurodegenerative conditions, including Dementia with Lewy Bodies 16 , Alzheimer’s disease. 58 Within the postoperative population there is evidence of segregated networks in EEG analysis. 59 Previous large data analysis of familial Alzheimer’s disease (ADNI study) has found that greater segregation of higher-order cognitive networks is critical in mediating cognitive resilience despite neuropathology 60 . This finding parallels our findings as there were no preoperative differences in cognition between those who became delirious compared to those that did not. Hence, greater segregation in the delirium population, highlights a potential marker of compensatory cholinergic activity. These findings support the cognitive disintegration theory 5 and extend it to include pre-existing ascending arousal changes prior to surgery. We further interrogated the relationship between the ascending arousal nuclei activity and the network topology by performing a time-lagged cross-correlation analysis between peaks in BOLD activity of the ascending arousal system nuclei and the global brain segregation. Our results replicated previous findings 20,55 in which increased integration is positively related to peaks in LC activity (Fig. 5A), across both delirium and non-delirium participants. However, we found that the cholinergic nuclei exhibited an inverse relationship, suggesting that post phasic peaks in cholinergic activity are related to greater segregation; with a significant disproportionate relationship between greater segregation post-phasic nbM activity in the delirium population (Fig. 5C), this result provides a potential explanation for the overall segregated topology in the delirium participants (Fig. 2A). It also supports the theory that the cholinergic system may drive a more segregated network topology through facilitation of normalization 61 by emphasizing patterns of neuronal activity through modulation of inhibition and an increase in the signal-to-noise ratio in these targeted regions 61,62 . Given the hyperconnectivity of the nbM to the cortex in the delirium population, we speculate that vulnerability to delirium relates to mechanistic compensatory changes in the cholinergic system that drive greater segregation. We extended this analysis to determine whether there is a relationship between the regional participation coefficient and gene-expression for cholinergic receptors and clearance enzymes. We found a negative relationship across multiple nicotinic sub-unit receptor genes (Fig. 6F-G) indicating that the more segregated regions have specific cholinergic receptor-related gene expression and may therefore be more responsive to changes in cholinergic modulation 63 . Importantly, nAChRs have been related to executive control and higher-order cognition, mirroring by the pattern of gene expression loadings across frontal-cortical regions (Fig. 6D). 63,64 There was observational overlap between greater segregation across the frontal-cortical regions (Fig. 2A) and the heterogeneity of distinct cholinergic gene expression across the frontal-cortical regions. Also, there was a strong negative correlation between butyrylcholinestrase (BCHE) gene expression and segregation in the delirium population (rho= -0.32, p spin = 0.001, FDR = 0.003). Previously, higher levels butyrylcholinestrase activity (in blood) have been associated with delirium 65 , indicating a potential relationship with butyrylcholinestrase activity and associated vulnerability in these regions. In addition to these findings, we also established a weakly positive relationship (rho = 0.157, p spin =0.003, FDR p = 0.008) between hyperconnectivity of the nbM and the first principal component of cholinergic gene expression, which proportionally represent muscarinic receptors M1 and M3 (Supplementary Materials, Fig. S6). These findings support the anatomical disparate projection patterns of the cholinergic system 66 – specifically in relation to how the cholinergic system is driving segregated patterns; it may be facilitating greater segregation specifically in relation to regions that have higher-levels of nicotinic gene-expression. We also found that the delirium population had heterogenous time-varying coupling patterns between the LC and nbM. We found differences in the functional connectivity of the noradrenergic system, with a relative decrease in connectivity for the delirium population preoperatively and anti-coupling in the dFC. These results further suggests that there is imbalanced ascending arousal function in the preoperative delirium population, as evidenced by dynamic features of the functional connectivity. The nbM appears to have more coupling across posterior cortical regions in the delirium participants compared to the non-delirious participants (Fig. 4B/D). This pattern is consistent across different functional connectivity profiles, including in static functional connectivity (Fig. 3B). The occipital/posterior related hyperconnectivity differences with the nbM could relate to an underlying vulnerability across these regions, as we see changes in slow-wave activity (from EEG) propagating across these posterior cortical regions. 7 Our findings also parallel previous work that has shown potential compensatory hyperconnectivity in the preoperative at-risk delirium populations and across early AD pathology 67,68 . Additionally, microstructural white-matter changes within the basal forebrain (containing the nbM) predisposes individuals to postoperative delirium independent of cognitive function 69 . This suggests that perhaps in conjunction with functional related changes, there are axonal and structural based changes that could be related to mechanistic changes in neuronal vulnerability prior to the neuroinflammatory insult that occurs during surgery. Taken together, these findings lead us to speculate that the cholinergic system predisposes to selective vulnerability in part due to early presynaptic adaptations aimed at maintaining plasticity and network function in the face of other neuronal pathologies 68,70,71 . Basal forebrain cholinergic synapses show an adaptive response to early tau pathology—potentially a compensatory mechanism—whereby presynaptic VAChT (Vesicular Acetylcholine Transporter) is upregulated in at-risk, cognitively resilient older adults, particularly when colocalized with tau rather than amyloid 45 . This adaptive response appears to mitigate tau-induced synaptotoxicity in cognitively resilient individuals 45 . Along these lines, in an preliminary exploratory analysis we showed a positive linear relationship between pTau (within the blood) and increased cholinergic connectivity 72 (Supplementary Materials, Fig. S9) which could suggest a relationship with underlying cholinergic driven synaptic modulation and neuropathological accumulation; as our participants are cognitively healthy (see Supplementary Table S1 ). Given that acute inflammation is known to drive central cholinergic suppression, our findings suggest that those predisposed to delirium can be conceptualised as more susceptible to the neuroinflammatory insult during surgery and unable to compensate for loss of cholinergic tone in the brain. While this reinvigorates the decades old cholinergic hypotheses for the mechanisms of delirium 73 , our data suggests that the nicotinic receptor family should be specifically considered as well as protocols that allow for dose-dependent modification. A limitation of this study is the potential selection bias between scanned and non-scanned participants. Individuals who were not scanned are likely to have undergone more complex surgeries and/or presented with more severe comorbidities, which may reduce the generalisability of our findings. All participants were offered scanning, unless contraindicated (e.g. by surgical implants), and so the difference in the population primarily relates to participant choice. Nonetheless this does limit the broader generalisability of our work. Additionally, there are limitations in relation to other unknown confounders that cannot be fully excluded. Importantly, cognitive measures previously associated with delirium risk showed no significant differences between delirious and non-delirious participants (Table 1), nor between scanned and non-scanned individuals (Supplementary Table 1). This enhances confidence that any observed resting-state functional connectivity differences reflect preoperative neural vulnerabilities, rather than cognitive disparities. Our findings diverge from prior work suggesting that known delirium risk factors (e.g., age, cognitive impairment, depression) are not associated with delirium-related network changes in fMRI. 9 However, that prior study employed distinct methodological approaches, that likely account for the discrepancies in our findings. In addition, the use of the Human Allen Brain Atlas cholinergic gene expression does not represent individual variations in gene expression, which is a limiting factor of this analysis. Future research could aim to incorporate multimodal neuroimaging, such as simultaneous PET-fMRI and structural imaging, to further elucidate potential neuropathological mechanisms underlying ascending arousal related differences in populations at risk for delirium. Conclusion Given that postoperative delirium is associated with later-life cognitive decline 74 , and neuronal injury 75 , a postoperative delirium event might act as a further insult to an already vulnerable ascending arousal system. Further research needs to interrogate whether there are lasting disruptions to the ascending arousal system postoperatively. This would be critical in revealing the relationship between postoperative delirium and associated cognitive decline. Declarations Data availability The data that support the findings of this study are available upon reasonable request from the corresponding author. The data are not publicly available due to clinically relevant data under ethics restrictions. All code for the analysis is available: https://github.com/NatashaLTaylor/Preoperative_fMRI_AscendingArousal Acknowledgements We acknowledge the U.S. National Institute of Health (NIH) for their funding of this project, which facilitated the funding relevant for the associated costs for data collection. We acknowledge and thank all the volunteer participants that consented and participated in this study. Funding This study was funded by the U.S. National Institute of Health (NIH) (2R01AG063849-06). V. N is supported by NIH (R01NS123378). V. P is supported by NIH (R01NS117568). J.M.S is supported by National Health and Medical Research Council (1193857) and Australian Research Council (DP240101295). Competing interests The authors disclose no conflicts of interest. Supplementary material Supplementary materials are available online. References Morandi, A. et al. Delirium, Dementia, and In-Hospital Mortality: The Results From the Italian Delirium Day 2016, A National Multicenter Study. J. Gerontol. A. Biol. Sci. Med. Sci. 74 , 910–916 (2019). Fong, T. G., Tulebaev, S. R. & Inouye, S. K. Delirium in elderly adults: diagnosis, prevention and treatment. Nat. Rev. Neurol. 5 , 210–220 (2009). Thedim, M. & Vacas, S. Postoperative delirium and the older adult: untangling the confusion. J. Neurosurg. Anesthesiol. 36 , 184–189 (2024). Racine, A. M. et al. Delirium Burden in Patients and Family Caregivers: Development and Testing of New Instruments. The Gerontologist 59 , e393–e402 (2019). Sanders, R. D. Hypothesis for the pathophysiology of delirium: role of baseline brain network connectivity and changes in inhibitory tone. Med. Hypotheses 77 , 140–143 (2011). Gjini, K. et al. Delirium is associated with loss of feedback cortical connectivity. Alzheimers Dement. 20 , 511 (2024). Tanabe, S. et al. Cohort study into the neural correlates of postoperative delirium: the role of connectivity and slow-wave activity. Br. J. Anaesth. 125 , 55–66 (2020). van Dellen, E. et al. Decreased functional connectivity and disturbed directionality of information flow in the electroencephalography of intensive care unit patients with delirium after cardiac surgery. Anesthesiology 121 , 328–335 (2014). van Montfort, S. J. T. et al. fMRI network correlates of predisposing risk factors for delirium: A cross-sectional study. NeuroImage Clin. 27 , 102347 (2020). Ditzel, F. L. et al. Functional brain network and trail making test changes following major surgery and postoperative delirium: a prospective, multicentre, observational cohort study. Br. J. Anaesth. 130 , e281–e288 (2023). Cavallari, M. et al. Neural substrates of vulnerability to postsurgical delirium as revealed by presurgical diffusion MRI. Brain 139 , 1282–1294 (2016). White, M. F. et al. Relationships between preoperative cortical thickness, postoperative electroencephalogram slowing, and postoperative delirium. Br. J. Anaesth. 127 , 236–244 (2021). Shioiri, A. et al. White Matter Abnormalities as a Risk Factor for Postoperative Delirium Revealed by Diffusion Tensor Imaging. Am. J. Geriatr. Psychiatry 18 , 743–753 (2010). Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52 , 1059–1069 (2010). Supekar, K., Menon, V., Rubin, D., Musen, M. & Greicius, M. D. Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer’s Disease. PLOS Comput. Biol. 4 , e1000100 (2008). Matar, E. et al. Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia. Npj Park. Dis. 8 , 16 (2022). Shine, J. M., van den Brink, R. L., Hernaus, D., Nieuwenhuis, S. & Poldrack, R. A. Catecholaminergic manipulation alters dynamic network topology across cognitive states. Netw. Neurosci. 2 , 381–396 (2018). Shine, J. M. et al. The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron 92 , 544–554 (2016). Aston-Jones, G. & Cohen, J. D. AN INTEGRATIVE THEORY OF LOCUS COERULEUS-NOREPINEPHRINE FUNCTION: Adaptive Gain and Optimal Performance. Annu. Rev. Neurosci. 28 , 403–450 (2005). Taylor, N. L. et al. Structural connections between the noradrenergic and cholinergic system shape the dynamics of functional brain networks. NeuroImage 260 , 119455 (2022). Coronel-Oliveros, C., Cofré, R. & Orio, P. Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models. PLOS Comput. Biol. 17 , e1008737 (2021). Orlando, I. F., Shine, J. M., Robbins, T. W., Rowe, J. B. & O’Callaghan, C. Noradrenergic and cholinergic systems take centre stage in neuropsychiatric diseases of ageing. Neurosci. Biobehav. Rev. 149 , 105167 (2023). Braak, H. & Del Tredici, K. The pathological process underlying Alzheimer’s disease in individuals under thirty. Acta Neuropathol. (Berl.) 121 , 171–181 (2011). Ehrenberg, A. J. et al. Quantifying the accretion of hyperphosphorylated tau in the locus coeruleus and dorsal raphe nucleus: the pathological building blocks of early Alzheimer’s disease. Neuropathol. Appl. Neurobiol. 43 , 393–408 (2017). Surmeier, D. J., Obeso, J. A. & Halliday, G. M. Selective neuronal vulnerability in Parkinson disease. Nat. Rev. Neurosci. 18 , 101–113 (2017). Prokopiou, P. C. et al. Lower novelty-related locus coeruleus function is associated with Aβ-related cognitive decline in clinically healthy individuals. Nat. Commun. 13 , 1571 (2022). Dahl, M. J. et al. Locus coeruleus integrity is related to tau burden and memory loss in autosomal-dominant Alzheimer’s disease. Neurobiol. Aging 112 , 39–54 (2022). Hämmerer, D. et al. Locus coeruleus integrity in old age is selectively related to memories linked with salient negative events. Proc. Natl. Acad. Sci. 115 , 2228–2233 (2018). Jacobs, H. I. L. et al. In vivo and neuropathology data support locus coeruleus integrity as indicator of Alzheimer’s disease pathology and cognitive decline. Sci. Transl. Med. 13 , eabj2511 (2021). Aston-Jones, G., Rajkowski, J., Kubiak, P. & Alexinsky, T. Locus coeruleus neurons in monkey are selectively activated by attended cues in a vigilance task. J. Neurosci. 14 , 4467–4480 (1994). Rajkowski, J., Majczynski, H., Clayton, E. & Aston-Jones, G. Activation of Monkey Locus Coeruleus Neurons Varies With Difficulty and Performance in a Target Detection Task. J. Neurophysiol. 92 , 361–371 (2004). Bohnen, N. I. et al. Heterogeneity of Cholinergic Denervation in Parkinson’s Disease without Dementia. J. Cereb. Blood Flow Metab. 32 , 1609–1617 (2012). Hepp, D. H. et al. Pedunculopontine Cholinergic Cell Loss in Hallucinating Parkinson Disease Patients but Not in Dementia With Lewy Bodies Patients. J. Neuropathol. Exp. Neurol. 72 , 1162–1170 (2013). Manganelli, F. et al. Functional involvement of central cholinergic circuits and visual hallucinations in Parkinson’s disease. Brain 132 , 2350–2355 (2009). Grothe, M., Heinsen, H. & Teipel, S. Longitudinal measures of cholinergic forebrain atrophy in the transition from healthy aging to Alzheimer’s disease. Neurobiol. Aging 34 , 1210–1220 (2013). Hampel, H. et al. The cholinergic system in the pathophysiology and treatment of Alzheimer’s disease. Brain 141 , 1917–1933 (2018). Mesulam, M., Shaw, P., Mash, D. & Weintraub, S. Cholinergic nucleus basalis tauopathy emerges early in the aging-MCI-AD continuum. Ann. Neurol. 55 , 815–828 (2004). Vana, L. et al. Progression of Tau Pathology in Cholinergic Basal Forebrain Neurons in Mild Cognitive Impairment and Alzheimer’s Disease. Am. J. Pathol. 179 , 2533–2550 (2011). Grothe, M., Heinsen, H. & Teipel, S. J. Atrophy of the Cholinergic Basal Forebrain Over the Adult Age Range and in Early Stages of Alzheimer’s Disease. Biol. Psychiatry 71 , 805–813 (2012). Schmitz, T. W. & Nathan Spreng, R. Basal forebrain degeneration precedes and predicts the cortical spread of Alzheimer’s pathology. Nat. Commun. 7 , 13249 (2016). Hasselmo, M. E. Neuromodulation: acetylcholine and memory consolidation. Trends Cogn. Sci. 3 , 351–359 (1999). Hasselmo, M. E. & Sarter, M. Modes and Models of Forebrain Cholinergic Neuromodulation of Cognition. Neuropsychopharmacology 36 , 52–73 (2011). Sarter, M. & Lustig, C. Cholinergic double duty: cue detection and attentional control. Curr. Opin. Psychol. 29 , 102–107 (2019). Howe, W. M. et al. Prefrontal cholinergic mechanisms Instigating shifts from monitoring for cues to Cue-Guided performance: Converging electrochemical and fMRI evidence from rats and humans. J. Neurosci. 33 , 8742–8752 (2013). Onuska, K. M. et al. Cholinergic synaptic plasticity shapes resilience and vulnerability to tau. 2025.05.27.656174 Preprint at https://doi.org/10.1101/2025.05.27.656174 (2025). Gjini, K. et al. Delirium is associated with loss of feedback cortical connectivity. Alzheimers Dement. 20 , 511–524 (2023). Gjini, K. et al. Greater tau pathology is associated with altered predictive coding. Brain Commun. 4 , fcac209 (2022). Schaefer, A. et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex 28 , 3095–3114 (2018). Ye, R. et al. An in vivo probabilistic atlas of the human locus coeruleus at ultra-high field. NeuroImage 225 , 117487 (2021). Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25 , 1325–1335 (2005). Shine, J. M. et al. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage 122 , 399–407 (2015). Ding, S.-L. et al. Comprehensive cellular-resolution atlas of the adult human brain. J. Comp. Neurol. 524 , 3127–3481 (2016). Shine, J. M. et al. The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron 92 , 544–554 (2016). Kimchi, E. Y. et al. Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes. Neurology 93 , e1260–e1271 (2019). Munn, B. R., Müller, E. J., Wainstein, G. & Shine, J. M. The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states. Nat. Commun. 12 , 6016 (2021). Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489 , 391–399 (2012). Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18 , 1832–1844 (2015). Ng, A. S. L. et al. Distinct network topology in Alzheimer’s disease and behavioral variant frontotemporal dementia. Alzheimers Res. Ther. 13 , 13 (2021). van Montfort, S. et al. Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis. NeuroImage Clin. 23 , (2019). Ewers, M. et al. Segregation of functional networks is associated with cognitive resilience in Alzheimer’s disease. Brain 144 , 2176–2185 (2021). Schmitz, T. W. & Duncan, J. Normalization and the Cholinergic Microcircuit: A Unified Basis for Attention. Trends Cogn. Sci. 22 , 422–437 (2018). Shine, J. M. Neuromodulatory Influences on Integration and Segregation in the Brain. Trends Cogn. Sci. 23 , 572–583 (2019). Dasilva, M. et al. Cell class-specific modulation of attentional signals by acetylcholine in macaque frontal eye field. Proc. Natl. Acad. Sci. 116 , 20180–20189 (2019). Mansvelder, H. D., van Aerde, K. I., Couey, J. J. & Brussaard, A. B. Nicotinic modulation of neuronal networks: from receptors to cognition. Psychopharmacology (Berl.) 184 , 292–305 (2006). Caeiro, L. et al. The role of acetylcholinesterase and butyrylcholinesterase activity in the development of delirium in acute stroke. Cereb. Circ. - Cogn. Behav. 2 , 100017 (2021). Zaborszky, L. et al. Neurons in the Basal Forebrain Project to the Cortex in a Complex Topographic Organization that Reflects Corticocortical Connectivity Patterns: An Experimental Study Based on Retrograde Tracing and 3D Reconstruction. Cereb. Cortex 25 , 118–137 (2015). Winterer, J. M. et al. Neurocognitive disorders in the elderly: altered functional resting-state hyperconnectivities in postoperative delirium patients. Transl. Psychiatry 11 , 1–12 (2021). Zeng, Q. et al. Increased functional connectivity between nucleus basalis of Meynert and amygdala in cognitively intact elderly along the Alzheimer’s continuum. NeuroImage Clin. 36 , 103256 (2022). Cavallari, M. et al. Neural substrates of vulnerability to postsurgical delirium as revealed by presurgical diffusion MRI. Brain 139 , 1282–1294 (2016). Marder, E. & Goaillard, J.-M. Variability, compensation and homeostasis in neuron and network function. Nat. Rev. Neurosci. 7 , 563–574 (2006). Pozo, K. & Goda, Y. Unraveling mechanisms of homeostatic synaptic plasticity. Neuron 66 , 337–351 (2010). Gjini, K. et al. Greater tau pathology is associated with altered predictive coding. Brain Commun. 4 , fcac209 (2022). Hshieh, T. T., Fong, T. G., Marcantonio, E. R. & Inouye, S. K. Cholinergic Deficiency Hypothesis in Delirium: A Synthesis of Current Evidence. J. Gerontol. A. Biol. Sci. Med. Sci. 63 , 764–772 (2008). Davis, D. H. J. et al. Association of Delirium With Cognitive Decline in Late Life: A Neuropathologic Study of 3 Population-Based Cohort Studies. JAMA Psychiatry 74 , 244–251 (2017). Casey, C. P. et al. Postoperative delirium is associated with increased plasma neurofilament light. Brain 143 , 47–54 (2020). Methods Clinical Data All data were collected as part of the perioperative registered trial (2015-0374, NCT01980511 and NCT03124303) cohort study, Interventions for Postoperative Delirium: Biomarker-3 (IPOD-B3), approved by University of Wisconsin-Madison Institutional Review Board (2015-0374). 5519 All participants provided informed consent, and work was conducted in accordance with the Code of Ethics of World Medical Association (Declaration of Helsinki) for experiments involving humans. 5519 individuals were screened, and 296 patients were enrolled for participation in the perioperative cohort study. All participants were aged 65yrs or older and underwent major elective non-intracranial surgery, under general anaesthesia. 153 participants did not undergo preoperative MRI scans (due to refusal of consent or ineligibility). Delirium occurred in 32.35% of scanned participants and 37.04% of non-scanned participants. Exclusion criteria consist of any documented history of dementia or residing in nursing aged care facilities. Participants underwent pre-operative MRI scans across five sites. They were all assessed for up to 4 days post-operatively which included twice daily (between 05:00–10:00 and 16:00–20:00) delirium assessment using 3-minute Diagnostic Confusion Assessment Method (3D-CAM) 1 , or CAM for intensive care unit (CAM-ICU, if intubated), bloods were collected pre-operatively and, on each day, post-operatively for 4 days. After surgery, participants underwent delirium assessments twice daily using the 3D-Confusion Assessment Method (3D-CAM or CAM-ICU (dependent on whether patients were intubated or not) to define delirium. Additionally, in patients who were able to verbalise, delirium severity was assessed by delirium rating scale 98 and 3D-CAM-severity score. All assessors were trained in 3D-CAM 2 , CAM-ICU 3 and delirium rating scale delirium assessments. Assessments were discussed in weekly meetings to review diagnoses and harmonise delirium scoring. Coma was assessed by Richmond Agitation/Sedation Scoring (RASS). 4 All patients who scored for coma, later scored as delirious on formal testing. Daily blood sample collection was also conducted. Samples were analysed by NULISA 5 but only pTau181 based on our prior work using this biomarker in delirium 6 and a prior report implicating Tau associated changes with cholinergic signalling. EEG were collected preoperatively and on postoperative day 1. If patients were in a coma and could not be assessed for delirium the EEG was deferred until a delirium assessment was possible (see Tanabe et al, BJA 2020 7 ). Three minutes of average re-referenced clean signals were obtained from high-density EEG data acquired at a 250 Hz sampling rate using a 256-channel system (Electrical Geodesics, Inc.). EEG data from a resting-state, awake, and eyes-closed condition were processed in EEGLAB 8 , a toolbox running in MATLAB (The MathWorks Inc., Natick, Massachusetts). Artifact detection and rejection was performed using independent component analysis (ICA). Delta band [1-3.5 Hz] power in dB units was calculated for electrode Oz using EEGLAB's function ‘spectopo’ from the power spectral density (PSD) with Welch’s method (2-sec ‘hamming’ windows and with 50% overlap). Cognition was assessed preoperatively using Montreal Cognitive Assessment (MoCA) Trail Making Test A/B (TMT-A & TMT-B), Boston Naming Test, Digit Symbol Substitution Test (DSST), Controlled Oral Word Association Test (COWAT) and years of education. The following intraoperative assessments were collected as the American College of Surgeons’ National Quality Improvement Program for surgical risk of death (NSQIP-D), National Quality Improvement Program for surgical risk of serious complications (NSQIP-SC) and American Society of Anesthesiologists Physical Status Classification (ASA). From a total of 142 scanned participants, seven subjects were removed due to incomplete overall peak DRS scores, as surgery was cancelled (n=3), two patients died during or shortly after surgery, one patient withdrew, and one patient surgery was converted to an outpatient procedure. A subsequent twelve subjects were removed due to substantial denoising/pre-processing errors in fMRI, and incomplete fMRI scan protocols (no resting-state scans completed, n=3). Functional MRI acquisition and preprocessing Participants were scanned across five different sites with the following General Electric MRI scanner types: group 0 was scanned on a 3T GE 750 (n=35), group 1 was scanned on a 1.5T Optima MR 450 wide bore (n=2), group 2 was scanned on 3T GE 750 & 3Tw Premier (n=4), group 3 was scanned on a 3T Signa PETMR (n=77), group 4 was scanned on a 3Tw Premier (n=1) and group 5 was unknown (n=1). A General Electric MRI scanner was used to acquire T1-weighted axial structural images at beginning of each scan using FSPGR BRAVO sequence; repetition time = 8.132ms; echo-time = 450ms, 256 x 256 matrix, 156 slices, flip-angle = 60 o , and slice thickness = 1mm. Resting state fMRI scanning was done using T 2 -weighted echo-planar functional images, acquired in interleaved order with: repetition time = 2600ms; echo-time = 22ms; flip-angle = 60 o ; 40 axial slices covering the entire brain; interslice gap = 3.5 mm; field of view = 224mm; and the raw voxel size = 3.5 x 3.5 x 3.5mm . Pre-processing of images was performed using fMRIPrep (version stable 20.0.2, 9 ), run through NiPype (version 1.8.1, 10 ). Anatomical data preprocessing Each T1w (T1-weighted) volume was corrected for INU (intensity non-uniformity) using N4BiasFieldCorrection v2.1.0 11 and skull-stripped using antsBrainExtraction.sh v2.1.0 (using the OASIS template). Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c 12 was performed through nonlinear registration with the antsRegistration tool of ANTs v2.1.0 13 , using brain-extracted versions of both T1w volume and template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast 14 (FSL v5.0.9, RRID:SCR_002823). The following templates were selected for spatial normalization: FSL's MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model [@mni152nlin6asym, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym], “ICBM 152 Nonlinear Asymmetrical template version 2009c” [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym]. Functional data preprocessing For each BOLD resting-state run found per subject, the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Next, head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) were estimated before any spatiotemporal filtering using `mcflirt` [FSL 6.0.3:b862cdd5, 15 ]. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The BOLD reference was then co-registered to the T1w reference using `mri_coreg` (FreeSurfer) followed by `flirt` [FSL 6.0.3:b862cdd5, 15 ] with the boundary-based registration 16 cost-function. Co-registration was configured with six degrees of freedom. Several confounding time-series were calculated based on the preprocessed BOLD including framewise displacement (FD), DVARS and three anatomically derived global signals. FD was computed using two formulations following Power (absolute sum of relative motions, 17 ) and Jenkinson (relative root mean square displacement between affines, 18 ). FD and DVARS were calculated for each functional run, both using their implementations in *Nipype* [following the definitions by 17 ]. The three global signals were extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction [CompCor, 19 ]. Principal components were estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor).tCompCor components are then calculated from the top 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, the aCompCor masks were a subtracted mask of pixels that likely contain a volume fraction of GM. This mask was obtained by thresholding the corresponding partial volume map at 0.05, and it ensures components were not extracted from voxels containing a minimal fraction of GM. Finally, these masks were resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each 20 . Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. The BOLD time-series were resampled into several standard spaces, correspondingly generating the following spatially-normalized, preprocessed BOLD runs: MNI152NLin6Asym (for realignment to same resolution as atlas), MNI152NLin2009cAsym. All resamplings were performed with a single interpolation step by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels 13 . Non-gridded (surface) resamplings were performed using `mri_vol2surf`(FreeSurfer). Many internal operations of fMRIPrep use Nilearn 0.9.1 [ 21 , RRID:SCR_001362], mostly within the functional processing workflow. For more details of the pipeline, see [the section corresponding to workflows in fMRIPrep's documentation (https://fmriprep.readthedocs.io/en/latest/workflows.html). All BOLD signal was denoised by regressing out the following noise parameters 12 measures of head motion and their corresponding derivatives, white-matter and CSF measures and frame-wise displacement and a high and low-pass filter (0.01 Hz & 0.1 Hz) was applied (using customised code generated through Nilearn, version 0.9.1). Note, we do not use CompCor anatomically derived physiological noises as a regressor in this preprocessing. Functional MRI Analysis Region of Interest Selection & Functional Connectivity The denoised functional data underwent BOLD time-series extraction for the 17-network 400-regions of interest (ROI) defined by the Schaefer cortical parcellation 22 . We categorised the regions into the specific cortical networks, based upon the Yeo’s 7-network grouping of the 400 Schaefer atlas parcellation (with an additional network for regions labelled as the tempo-parietal regions from the 17-network parcellation), locus coeruleus 23 and nucleus basalis of Meynert. 24 To determine the functional connectivity for each participant, the Pearson’s correlation was calculated for each average BOLD time-series for each unique ROI-to-ROI connection (excluding self-connections for statistical analysis, given that self-connections are correlated as 1). Dynamic Functional Connectivity Time-varying functional connectivity was calculated for each pair of ROIs across temporal windows. We used the multiplication of temporal derivatives which calculates dynamic functional connectivity for each pair of ROIs. 25 For each region, n , with time points, t , a vector of t-1 , temporal derivatives were calculated and normalized. A matrix of functional coupling between the ith and jth regions for each time point is multiplied by the temporal derivatives of each pairs of regions across temporal windows (5TRs, 40.66 ms) (code is freely available at https://github.com/macshine/coupling/). Note that we removed one subject from the time-varying analysis due to the entire time-series being slightly shorter in length – to ease further calculations with graph theoretical metrics that are time-varying in nature. Linear Discriminant Analysis Linear discriminant analysis (LDA) was used to capture the spatio-temporal dynamics of functional connectivity from the noradrenergic (LC), and cholinergic (nbM) nuclei to rest of the 400 cortical ROIs. Dynamic functional connectivity from the specific seeds mentioned above are then concatenated across subjects and time to generate a 119 x 210 (time point) matrix, by ROIs; in order to reduce instability, we scaled the concatenated data prior to fitting the LDA. Using an eigen-mode LDA (scikit learn), we accounted for imbalances in the number of participants in each group by setting the priors to (0.5:0.5), which forces an ‘equal’ assumption for class specification within the model. Then, we calculated the group-based separation by taking the average LDA coefficients to determine the directionality of the LDA-axis for group-based separation. We back-projected the original functional connectivity as the weights assigned to each region (‘feature’) in the LDA; to determine how the features contribute to the group-separation. From the LDA, we select the top 5% discriminating time-points (largest order of magnitude) across the entire dFC for the corresponding nuclei. We then took the average of those corresponding top-performing time-points from the LDA-separation and look at differences in the overall dFC for the noradrenergic and cholinergic connections across the delirious and non-delirious. Graph Theoretical Measures In addition, we wanted to quantify whether participants had overall more integrated or segregated network topology. We applied the Louvain algorithm to identify modules of strongly intra-correlated brain regions. Then, to quantify each regions’ integrations across the modules, we calculated the participation coefficient (B T ) for each time-resolved dynamic functional connectivity matrix (using the Brain Connectivity Toolbox 26 ). For a given time-point, T, represents the strength of positive connections from region( i ) to regions within module( s ), and is the total strength of all positive connections of region( i ). A participation coefficient near 1 indicates that a region is functionally connected across multiple modules (integration), whereas a value near 0 reflects connections confined largely to its own module (segregation) 27–30 . Module assignments were based on the Yeo’s 17-Networks parcellation. To further categorise integrated and segregated network states over time, we applied k -means clustering to the region-wise time-resolved participation coefficient data. For this analysis, data from all subjects were concatenated across time, resulting in a matrix of size (subjects × time) × regions, where each row corresponded to the brain state of a region at a specific time point. This approach enabled us to treat each regional time point as an independent observation in clustering space. K -means clustering was implemented using the KMeans algorithm (scikit-learn python 3.12.8). The number of clusters was fixed at k = 2, consistent with prior work distinguishing between integrated and segregated states (based of Shine et al., 2016 31 ). We used the default parameters, including the k-means++ initialisation method to improve convergence, and ran the algorithm with 100 initialisations (n_init=100) to ensure robust clustering solutions. The algorithm iteratively minimised the within-cluster sum of squares (inertia) to partition the data into two non-overlapping clusters, which were then labelled based on their average participation coefficient values: clusters with higher average values were designated as integrated states, and those with lower values as segregated states (Supplementary Materials, Fig. S1A). This approach allowed us to identify recurring, distinct network states across the entire time series. Phasic Increases in Noradrenergic and Cholinergic BOLD Signal To identify the relationship between phasic BOLD dynamics of the noradrenergic and cholinergic system in relation to fluctuations in network topology 32,33 , we identified phasic peaks in BOLD signal by identifying time-points in which the original BOLD time-series was greater than or equal to two standard deviations above the mean for the timeseries within a 10TR window; and we excluded any timepoints within the first or last 10TRs of the time-series (avoiding boundary related effects). We then looked at the cross-correlation between the participation coefficient (time-varying) and the proceeding five TRs prior and after peak in BOLD signal for the LC and nbM respectively. We then calculated the statistical difference between the cross-correlation values for the delirious and non-delirious groups, by running a permutation-based independence t-test and determine whether there were significant differences in the cross-correlation between the BOLD dynamics of the LC and nbM in relation to fluctuations in the participation coefficient. Statistical Analysis To determine significant differences in FC between the two groups, we performed a permutation-based independent samples t-test. Group labels were randomly shuffled, and the t-test was conducted for each permutation. The process was repeated for a large number of iterations (e.g., 1,000 permutations) to generate a null distribution, allowing us to identify significant group-wise differences in connectivity at each unique ROI-to-ROI edge. This approach controlled for multiple comparisons across all pairwise connections between cortical regions. Logistic Regression To determine the relationship between multiple different variables we ran a logistic regression to disentangle whether greater segregation relates to delirium-based interactions with other predictive measures. D is the postoperative delirium outcome (binarized outcome, binomial methods), and PC i is the regional( i ) participation coefficient and C is the predictive measure. We also verified that the preoperative PC values interacted with the difference in postoperative EEG slow-wave activity (Postoperative – Preoperative Oz-delta power) in their association with postoperative delirium. For appropriate model selection, we verified the best performing model by using Akaike Information Criterion (AIC) comparison for 3 versions of the model including predicting delirium outcome from just PC (AIC = 140), difference in postoperative EEG slow-wave activity (AIC = 77.7), PC interacting with difference in EEG slow-wave activity (AIC= 65.56), and for PC covariates with difference in EEG slow-wave activity (AIC = 79.66) (Supplementary Materials, Fig. 2A). In addition, we ran supplementary analysis predicting delirium outcome from average FC for LC and nbM, as well as their relationship with interaction with difference in slow-wave EEG activity (Supplementary Materials, Fig.S3A-D). Lastly, we conducted a preliminary exploratory analysis to disentangle the relationship between cholinergic functional connectivity and blood-biomarkers that are indicative of neuropathological insult. We ran Pearson’s correlation between the average nbM FC for each subject compared to the blood-biomarker phosphorylated Tau Protein (pTau-181) 34 , we confirmed the linear relationship significance with FDR corrected p-values (Supplementary Materials, Fig. 9). Gene-expression Analysis To test the relationship between cholinergic gene expression data obtained from the Allen Human Brain Atlas 35,36 (AHBA); we preselected expression profiles relating to the cholinergic system (18 relevant genes) including nicotinic receptor (nAChRs) subunits (CHRNA1, CHRNA2, CHRNA3, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNB1, CHRNB2, CHRNB3, CHRNB4), muscarinic receptors (CHRM1, CHRM2, CHRM3, CHRM4, CHRM5), cholinergic synthesis (CHAT), vesicular transport (SLC18A3, SLC44A2, SLC44A5, SLC5A7, SLC44A1) and cholinergic degradation enzymes (ACHE, BCHE). All data was pre-processed 37–39 and microarray probes were filtered to retain only expression levels above background threshold of more than 50%. For each gene, the probe with highest differential stability across donors were selected as representative. Then, gene expression values were aggregated and normalised across the cortex using an outlier-robust, scaled sigmoid transformation. The gene data were registered to standard MNI 2mm space, to enable mapping between the AHBA gene expression and our 400 cortical Schaefer parcellation. Expression profiles of our preselected genes were extracted for each region and z-scored. We performed a principal component analysis (PCA) to capture a whole brain low-dimensional resolution of cholinergic gene expression and selected the top principal components that explained 80% variance in the data (Supplementary Materials, Fig. S5). We then ran Pearson correlation across the average participation coefficient for each group (delirium and non-delirious); and combined both groups together and the top 8 principal components, with spin permutation testing (1000 iterations) to deal with spatial autocorrelation and FDR correction (all correlations plotted Supplementary Materials, Fig. 6 & 7). We also performed validation of the best performing principal component in relation to computing the lowest FDR p-value, strongest absolute correlation (ranking the principal components in accordance) and calculated the effect-size (determined small, medium and large based upon Cohen’s D guidelines) (Supplementary Materials, Fig. S5B). In addition, using the Williams-Hotelling formula, we compared the correlation coefficients between the principal components (with shared common variable the average participation coefficient), to determine whether the principal component correlations with average participation coefficient are significantly distinct. We performed 28 pairwise comparison (comparing each principal component correlation with each other); 14 remained significant after FDR correction. For instance, the second principal component had the largest William’s T-Test T =+8.98, p=0.001 (FDR) (when compared against principal component 1) (Supplementary Materials, Fig. S5E-F). We also ran correlation analysis across selected genes that contributed to the top-performing principal components and strongest rho correlation (number 2). We also ran the above analysis with the average functional connectivity from the nbM to the cortex (Supplementary Materials, Fig. S8). References Oberhaus, J. et al. Evaluation of the 3-Minute Diagnostic Confusion Assessment Method for Identification of Postoperative Delirium in Older Patients. JAMA Netw Open 4 , e2137267 (2021). Vasunilashorn, S. M. et al. A New Severity Scoring Scale for the 3-Minute Confusion Assessment Method (3D-CAM). J Am Geriatr Soc 68 , 1874–1876 (2020). Ely, E. W. et al. Evaluation of delirium in critically ill patients: validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Crit Care Med 29 , 1370–1379 (2001). Rivera, C. et al. The 3-Minute Diagnostic Confusion Assessment Method severity score correlates with the Delirium Rating Scale–Revised–98 and with biomarkers of delirium. BJA Open 14 , 100398 (2025). Feng, W. et al. NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing. Nat Commun 14 , 7238 (2023). Parker, M. et al. Cohort Analysis of the Association of Delirium Severity With Cerebrospinal Fluid Amyloid-Tau-Neurodegeneration Pathologies. J Gerontol A Biol Sci Med Sci 77 , 494–501 (2022). Tanabe, S. et al. Cohort study into the neural correlates of postoperative delirium: the role of connectivity and slow-wave activity. British Journal of Anaesthesia 125 , 55–66 (2020). Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134 , 9–21 (2004). Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16 , 111–116 (2019). Gorgolewski, K. J. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13.1. Zenodo https://doi.org/10.5281/ZENODO.581704 (2017). Tustison, N. J. et al. N4ITK: Improved N3 Bias Correction. IEEE Trans. Med. Imaging 29 , 1310–1320 (2010). Fonov, V., Evans, A., McKinstry, R., Almli, C. & Collins, D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47 , S102 (2009). Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54 , 2033–2044 (2011). Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20 , 45–57 (2001). Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage 17 , 825–841 (2002). Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48 , 63–72 (2009). Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage 84 , 320–341 (2014). Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Medical Image Analysis 5 , 143–156 (2001). Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37 , 90–101 (2007). Satterthwaite, T. D. et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage 64 , 240–256 (2013). Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8 , (2014). Schaefer, A. et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex 28 , 3095–3114 (2018). Ye, R. et al. An in vivo probabilistic atlas of the human locus coeruleus at ultra-high field. NeuroImage 225 , 117487 (2021). Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25 , 1325–1335 (2005). Shine, J. M. et al. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage 122 , 399–407 (2015). Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52 , 1059–1069 (2010). Bassett, D. S. & Bullmore, E. Small-World Brain Networks. Neuroscientist 12 , 512–523 (2006). Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10 , 186–198 (2009). Shine, J. M. et al. The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron 92 , 544–554 (2016). Shine, J. M., Aburn, M. J., Breakspear, M. & Poldrack, R. A. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain. eLife 7 , e31130 (2018). Shine, J. M. et al. The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron 92 , 544–554 (2016). Taylor, N. L. et al. Structural connections between the noradrenergic and cholinergic system shape the dynamics of functional brain networks. NeuroImage 260 , 119455 (2022). Munn, B. R., Müller, E. J., Wainstein, G. & Shine, J. M. The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states. Nat Commun 12 , 6016 (2021). Gjini, K. et al. Greater tau pathology is associated with altered predictive coding. Brain Commun 4 , fcac209 (2022). Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489 , 391–399 (2012). Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat Neurosci 18 , 1832–1844 (2015). Arnatkevic̆iūtė, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage 189 , 353–367 (2019). Markello, R. D. et al. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Preprint at https://doi.org/10.1101/2021.07.08.451635 (2021). Fornito, A., Arnatkevičiūtė, A. & Fulcher, B. D. Bridging the Gap between Connectome and Transcriptome. Trends in Cognitive Sciences 23 , 34–50 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterials.docx Supplementary Results Cite Share Download PDF Status: Under Review 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. 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B) Preprocessed and denoised fMRI BOLD signal was extracted across 400 cortical regions using the Schaefer parcellation and 2 ascending arousal nuclei (locus coeruleus and nucleus basalis of Meynert). C) Sliding-window approach dynamic functional connectivity using multiplication temporal derivatives. D/G) Dynamic functional connectivity produces correlation matrices across time. E) Graph Topology, using the Louvain community detection algorithm to calculate participation coefficient. F) K-Means clustering was performed to differentiate states of integration or segregation. H) Linear discriminant Analysis was used to maximally separate spatio-temporal patterns of dFC across the delirious and non-delirious. I) Top 5% of separating time-points were selected from the LDA embedded time-series. J) Average of the top-selected time points across the dFC for the delirium and non-delirious groups. K) Coupling patterns of the LDA differentiate dFC patterns for the LC and nbM. L) Selecting BOLD time-series from the LC and nbM. M) Peak BOLD time-series for the LC and nbM were calculated based of 2 standard deviations. N) Cross-correlation was performed with the peaks in BOLD time-series from the LC and nbM versus changes in the participation coefficient overtime. O) PCA reduced gene expression maps of the cholinergic system were preselected from the Human Allen Brain Atlas. F) Correlation between the cholinergic gene expression and the average participation coefficient value was calculated.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FinalFiguresAAS1.png","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/a11450df11bf09b82fed3bec.png"},{"id":96566276,"identity":"4f73bc09-0fcd-483a-aedd-1fccd92021cc","added_by":"auto","created_at":"2025-11-23 15:51:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6342428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificant difference in participation coefficient values between delirium and non-delirious.\u003c/strong\u003e \u003cem\u003eA) Significant difference in average participation coefficient values, delirium – non-delirious (p\u0026lt;0.05, 1000 iterations). B) K-means clustering of the entire participant coefficient across all regions over time, clustered into integrated and segregated states and grouped into delirium (light blue) and non-delirious (yellow). C) Logistic regression of the significant beta coefficients for predicting delirium outcome and interaction between participation coefficient and slow-wave EEG activity. D) Exemplar fitted curve plot of logistic regression of predicting delirium outcome and interaction between regional average participation coefficient (Default B network) and difference in slow-wave EEG activity.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FinalFiguresAAS2.png","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/6402fa7f0ff8aa347efb81f4.png"},{"id":96605843,"identity":"3c0cb20b-4316-4b44-8e10-85172125c560","added_by":"auto","created_at":"2025-11-24 09:24:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8094922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificant difference in functional connectivity in noradrenergic and cholinergic systems.\u003c/strong\u003e \u003cem\u003eA) Significant difference in functional connectivity from locus coeruleus, delirium – non-delirious (p\u0026lt;0.05, 1000 permutation test). B) Significant difference in functional connectivity from PPN. C) Significant difference in functional connectivity from nbM to whole cortex.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FinalFiguresAAS3.png","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/18250961437df1851bb856b4.png"},{"id":96566284,"identity":"4fbd868d-7ea0-4a4d-8ab2-8670aa2655c5","added_by":"auto","created_at":"2025-11-23 15:51:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14762422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLinear Discriminant Analysis of dynamic functional connectivity of ascending arousal nuclei that differentiate delirium and non-delirious preoperatively. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA-B) Linear Discriminant Analysis coefficients that determine which regions are distinct in differentiating the delirium and non-delirious participants ran on the time-varying components of the dynamic functional connectivity for LC (A), and nbM (B) (blue indicates delirium related, orange indicates non-delirious). C-D) LDA defined time-points that maximally differentiate the delirium and non-delirious participants (through LDA embedding of the dFC), taking an average of these time-points for the dFC and calculating the difference between the delirium and non-delirious groups across ascending arousal nuclei from LC (C) and nbM (D) to entire cortex.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FinalFiguresAAS4.png","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/9240219ad91907a20ecb8f34.png"},{"id":96566298,"identity":"5f72eca7-6de7-448e-9afb-5d2fa710dbbb","added_by":"auto","created_at":"2025-11-23 15:51:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3644784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePeak activity in BOLD time-series for ascending arousal nuclei associated with greater integration \u0026amp; segregation. \u003c/strong\u003e\u003c/em\u003ePeak time-series cross-correlated to global participation coefficient across time separated into delirium and\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003enon-delirious, for LC (A), nbM (B). Peak difference in time-series cross-correlated\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003ewith global participation coefficient separated into delirium and non-delirious for C) LC – nbM (D) nbM – LC. ** significant difference between two groups for time-lags for nbM peak p=0.04 (Lag 0), p=0.0150 (Lag +1), p=0.02 (Lag +4), p=0.049 (Lag +5).\u003c/p\u003e","description":"","filename":"FinalFiguresAAS5.png","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/c7b2b2001fc8ebc3358f284d.png"},{"id":96566289,"identity":"e2e2b691-d51a-408c-bda3-175af42328ca","added_by":"auto","created_at":"2025-11-23 15:51:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32864199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eRelationship between Human Allen Brain Atlas derived Cholinergic Gene Expression and Segregated Network Topology\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. A) The principal component coefficient loadings for the pre-selected cholinergic genes in the second principal component. B) The Pearson correlation between the average participation coefficient for each 400 cortical ROIs for the delirium population versus second principal component loadings of pre-selected gene expression, p\u003c/em\u003e\u003csub\u003e\u003cem\u003espin\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e = 0.001 (1000 iterations) and FDR corrected p=0.004. C) The Pearson correlation between the average participation coefficient for each 400 cortical ROIs for the non-delirious population versus second principal component loadings of pre-selected gene expression, p\u003c/em\u003e\u003csub\u003e\u003cem\u003espin\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e = 0.001 (1000 iterations) and FDR corrected p=0.00. D) Preselected gene expression second principal component loadings across the Schaefer cortical parcellation. E-J) Visualising the selected gene expression loadings on the Schaefer cortical parcellation. i)the Pearson correlation between average participation coefficient for all participants compared to selected (E-J) gene expression (p\u003c/em\u003e\u003csub\u003e\u003cem\u003espin\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e \u0026lt;0.05, FDR correction). E) BCHE = butyrylcholinesterase. F) CHRNA3 = nAChR \u003c/em\u003e\u003cimg width=\"10\" height=\"22\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA8AAAAhCAMAAAAxrgE+AAAAAXNSR0IArs4c6QAAAGBQTFRFAAAAAAAAAAA6AABmADpmADqQAGa2OgAAOjpmOma2OpDbZgAAZjpmZmZmZrb/kDoAkGY6kGaQkNv/tmYAtmY6tpA6ttv/tv/btv//25A62////7Zm/9uQ/9u2//+2///b0Y4JfAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAcUlEQVQoU92QaxKDMAiE2ValVeu7ahJS739LJegdnO4f5oNdZoDoDxU7IBsoIPd6XeB8Jvdc2segKKxt4Q8axa1N9VehTK+w8cEWJmd94eQmGi12lisubMvPwdZ/Lw4ofKxXhybWsxom4O1JKrwS3ks77IMFe9sxkDEAAAAASUVORK5CYII=\"/\u003e\u003cem\u003e3 subunit. G) CHRNB1 = nAChR ß subunit. H) SLC44A2 = choline transporter-like protein 2. I) CHRNA2 = nAChR \u003c/em\u003e\u003cimg width=\"10\" height=\"22\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA8AAAAhCAMAAAAxrgE+AAAAAXNSR0IArs4c6QAAAGBQTFRFAAAAAAAAAAA6AABmADpmADqQAGa2OgAAOjpmOma2OpDbZgAAZjpmZmZmZrb/kDoAkGY6kGaQkNv/tmYAtmY6tpA6ttv/tv/btv//25A62////7Zm/9uQ/9u2//+2///b0Y4JfAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAcUlEQVQoU92QaxKDMAiE2ValVeu7ahJS739LJegdnO4f5oNdZoDoDxU7IBsoIPd6XeB8Jvdc2segKKxt4Q8axa1N9VehTK+w8cEWJmd94eQmGi12lisubMvPwdZ/Lw4ofKxXhybWsxom4O1JKrwS3ks77IMFe9sxkDEAAAAASUVORK5CYII=\"/\u003e\u003cem\u003e2 subunit. J) SLC44A5 = choline transporter-like protein 5.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FinalFiguresAAS6.png","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/6e45bc882230da06f1e4e8da.png"},{"id":97135435,"identity":"d7d3122d-6e4d-4769-b44f-d5953e76af50","added_by":"auto","created_at":"2025-12-01 09:45:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":95394334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/47a8e6b4-fe1a-4bb5-90bf-ddf22342546f.pdf"},{"id":96566282,"identity":"e498e4ca-7da4-4c41-b28c-a8422dd87a7a","added_by":"auto","created_at":"2025-11-23 15:51:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33822604,"visible":true,"origin":"","legend":"Supplementary Results","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7881643/v1/8ec646656985558d08b7604f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Preoperative Cholinergic Signatures Drive Segregated Brain Architecture in Postoperative Delirium","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDelirium is an acute state of cognitive impairment, attention, and/or arousal that has significant impact on overall long-term cognitive outcomes and is associated with an increased risk of dementia\u003csup\u003e1\u003c/sup\u003e. It disproportionality impacts the aging population with up to 56% of elderly hospitalized patients affected\u003csup\u003e2\u003c/sup\u003e and advanced aging (\u0026gt;\u0026thinsp;65yrs) being a significant risk-factor due to neuroinflammatory changes\u003csup\u003e3\u003c/sup\u003e. Critically it is associated with increased morbidity, mortality, and costs\u003csup\u003e4\u003c/sup\u003e. Amongst the prevailing theories, one suggests that delirium can be described as a state of \u0026lsquo;cognitive disintegration\u0026rsquo; due to a reduction or dysfunctional connectivity between specific cortical networks that are necessary for maintaining normal cognitive function\u003csup\u003e5\u003c/sup\u003e. The theory predicts that delirium results when the network connectivity disintegrates below a critical threshold. While this theory is supported by recent EEG studies, showing a reduction in feedback cortical connectivity\u003csup\u003e6\u003c/sup\u003e and resting state functional connectivity (FC) in delirium\u003csup\u003e7\u0026ndash;10\u003c/sup\u003e, data linking connectivity to vulnerability are less forthcoming. Several studies have revealed that global and regional cortical atrophy, white-matter hyperintensities and structural based differences may predict postoperative delirium\u003csup\u003e7,11\u0026ndash;13\u003c/sup\u003e. These studies revealed that neuropathological changes in postoperative delirium are detectable with standard functional imaging techniques; however they have not established directly whether there are pre-existing dynamic and static FC signatures that are indicative of vulnerability to delirium, that may provide important insights into disturbed neural dynamics.\u003c/p\u003e\u003cp\u003eAligning with the cognitive disintegration theory is a graph theoretical measure popularised in network neuroscience; participation coefficient. Participation coefficient quantifies how evenly distributed a region\u0026rsquo;s connections are within its own network/subnetwork compared to its connections to regions outside of their network\u003csup\u003e14\u003c/sup\u003e. As such, this is used to define whether brain network topology is either integrated (strong connections across brain networks) or segregated. Network topology has been shown to be disrupted across several neurodegenerative diseases of aging, specifically in Alzheimer\u0026rsquo;s disease\u003csup\u003e15\u003c/sup\u003e (AD) and Dementia with Lewy Bodies\u003csup\u003e16\u003c/sup\u003e (DLB). This highlights that in other neurodegenerative conditions, that delirium populations are more at risk of developing\u003csup\u003e1\u003c/sup\u003e, there is an overlap in underlying network topological changes.\u003c/p\u003e\u003cp\u003ePatterns of brain wide integration and segregation have been associated with underlying neuromodulatory systems, the noradrenergic and cholinergic neuromodulators. Previous work has evidenced that activity within the noradrenergic locus coeruleus (LC) can imbue the system towards a more integrated network topology\u003csup\u003e17,18\u003c/sup\u003e and is critical in exploitation/exploration cognitive processes\u003csup\u003e19\u003c/sup\u003e. Whereas, the cholinergic system has been thought to facilitate a more segregated network topology due to its more targeted projection patterns across the cortex, which is critical for selective attentional control\u003csup\u003e20,21\u003c/sup\u003e. This highlights a preoperative vulnerability in the ascending arousal systems that relates to cognitive disintegration theory.\u003c/p\u003e\u003cp\u003eThe noradrenergic and cholinergic systems are critical for normal cognitive function and; they are sites of substantial pathology in neurodegenerative diseases\u003csup\u003e22\u003c/sup\u003e. Specifically, the noradrenergic locus coeruleus undergoes early pathological degeneration across neurodegenerative disease\u003csup\u003e23\u0026ndash;25\u003c/sup\u003e. The integrity of the locus coeruleus has been extensively linked to decline in memory performance in ageing and in Alzheimer\u0026rsquo;s disease\u003csup\u003e26\u0026ndash;29\u003c/sup\u003e. Given noradrenaline\u0026rsquo;s critical role in facilitating selective attention \u003csup\u003e30,31\u003c/sup\u003e; preoperative vulnerability within the locus coeruleus could underly the neuropathological insult prior to surgery. In addition to the loss of the noradrenergic system integrity, there is substantial evidence of cholinergic associated cognitive decline in both Lewy Body Dementia\u003csup\u003e32\u0026ndash;34\u003c/sup\u003e and Alzheimer\u0026rsquo;s disease \u003csup\u003e35\u0026ndash;38\u003c/sup\u003e. Cholinergic pathology precedes degeneration in other associated brain regions and has been shown to predict future memory impairment in non-symptomatic aging individuals\u003csup\u003e39,40\u003c/sup\u003e. This results in dysfunction in learning, memory, sensory and attentional processing\u003csup\u003e41\u003c/sup\u003e as the cholinergic system is critical in modulating feedforward processing within the cortex\u003csup\u003e42\u0026ndash;44\u003c/sup\u003e. Recent literature has also linked non-linear interactions of the cholinergic system: increases in cholinergic presynaptic VAChT levels colocalized with tau is predictive of cognitive resilience in older adults at risk of AD\u003csup\u003e45\u003c/sup\u003e. Consistent with this, we have recently observed exaggerated feedforward processing in individuals who are vulnerable to delirium\u003csup\u003e46\u003c/sup\u003e and in patients with tau disease on PET imaging\u003csup\u003e47\u003c/sup\u003e. As such, dysfunctional noradrenergic and cholinergic neuromodulation could underly vulnerability to delirium, with early degeneration of the locus coeruleus and subsequent non-linear compensatory hyperactivity of the cholinergic system. Given the cognitive disintegration theory, we hypothesise that preoperative participants that go onto develop delirium will have dysfunctional network topology, with a greater degree of segregation (disintegration) related to disturbed function of the ascending arousal system.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 296 participants were recruited for this study. 153 participants did not undergo preoperative MRI scans due to refusal of consent or ineligibility. Delirium occurred in 32.4% of scanned participants and 37.0% of non-scanned participants. After performing pre-processing and denoising of the resting state fMRI data, a total of 120 participants (see methods for exclusion) were analysed. The delirious scanned participants were matched to non-delirious participants across most characteristics (see supplementary materials Table\u0026nbsp;1), except for operative severity (National Surgical Quality Improvement Program risk of Surgical Complications or Death) and a marker of peak delirium severity, overall peak Delirium Rating Scale-98 (DRS). These analyses compare \u003cem\u003ebefore surgery\u003c/em\u003e (preoperative) between participants who later developed postoperative delirium (termed delirious) and who did not develop postoperative delirium (termed non-delirious), scanning was conducted when no patient was actively delirious.\u003c/p\u003e\n\u003cp\u003eTo disentangle the contributions of the ascending arousal system to segregated network topology in development of postoperative delirium; time-series were extracted across 400 cortical regions\u003csup\u003e48\u003c/sup\u003e, the pontine locus coeruleus (cortical source of noradrenaline)\u003csup\u003e49\u003c/sup\u003e and basal forebrain nucleus basalis of Meynert (source of acetylcholine)\u003csup\u003e50\u003c/sup\u003e. Pearson\u0026rsquo;s correlation was calculated to estimate static (FC) and dynamic functional connectivity (dFC) was calculated using the multiplication of temporal derivatives\u003csup\u003e51\u003c/sup\u003e. We calculated graph theoretical derived segregated network topology using participation coefficient and determined distinct spatio-temporal dFC patterns using linear discriminant analysis in those delirious compared to non-delirious. Lastly, we related cholinergic gene expression density maps\u003csup\u003e52\u003c/sup\u003e to regional segregation across the brain (Fig.\u0026nbsp;1 \u0026ndash; graphical methods).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eSegregated Network Topology in Postoperative Delirium\u003c/h2\u003e\n\u003cp\u003eWe explored time-varying network topology by calculating the dynamic functional connectivity (dFC) and used participation coefficient (PC)\u003csup\u003e14,53\u003c/sup\u003e to quantify the distribution of connections across different network modules. We determined whether there were significant differences in integration (larger PC) versus segregation for the delirious compared to non-delirious participants (1000 iterations of permuted p-value). We found that on average across the time-series, the delirium participants had a relatively decreased PC score compared to the non-delirious participants (mean pc delirium\u0026thinsp;=\u0026thinsp;0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, mean pc non-delirious\u0026thinsp;=\u0026thinsp;0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04; Fig.\u0026nbsp;2A), suggesting that the delirium population had more segregated network topology. Specifically, this was driven by a greater segregation across the salience attention and default mode networks. To further interrogate this, we ran a k-means clustering\u003csup\u003e53\u003c/sup\u003e across the entire time-series for all cortical regions and found that the delirium population was more frequently in a segregated state compared to the non-delirious population (Fig.\u0026nbsp;2B). In addition, to determine whether the preoperative PC was an important biological predictor of delirium, we adjusted for the main biological neural correlate \u003cem\u003eduring\u003c/em\u003e delirium \u0026ndash; postoperative EEG slow wave activity\u003csup\u003e7,54\u003c/sup\u003e. This adjustment is important to confirm a robust relationship between segregation and delirium. We verified that the preoperative PC value interacted with difference in pre versus postoperative EEG slow wave activity (change Oz-delta power) in their association with postoperative delirium using logistic regression (Fig.\u0026nbsp;2C, evidenced with fitted curved plot in Fig.\u0026nbsp;2D). These data demonstrate a negative interaction between PC and EEG slow wave activity: as PC falls and slow wave activity increases, the probability of delirium increases. Furthermore, the adjustment for slow wave activity confirms that PC predisposes to delirium independent of the change in brain electrophysiology that occurs during delirium.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNoradrenergic and Cholinergic Dysfunctional Static Connectivity\u003c/h3\u003e\n\u003cp\u003eWe found significant hypoconnectivity between the LC and the pre-frontal regions and anterior cingulate of the cortex for the delirium population compared to non-delirious (Fig.\u0026nbsp;3A). We found a significant hyperconnectivity between the nbM and the posterior and superior temporal cortical regions in the delirium compared to non-delirious participants (Fig.\u0026nbsp;3B). In addition, the simple logistic regression results for predicting delirium outcome based on ascending arousal nuclei connectivity found that hypoconnectivity from the LC to salience attentional (p\u0026thinsp;=\u0026thinsp;0.041), and control networks (p\u0026thinsp;=\u0026thinsp;0.034) were associated with delirium. Decreases in connectivity from nbM to regions within peripheral visual (p\u0026thinsp;=\u0026thinsp;0.017) and default network (p\u0026thinsp;=\u0026thinsp;0.045) and increased connectivity from nbM to central visual network (p\u0026thinsp;=\u0026thinsp;0.016) were also associated with delirium (Supplementary Materials, Fig.S3). In addition, inspired by a recent report\u003csup\u003e45\u003c/sup\u003e, in a post hoc analysis we show a positive linear relationship between stronger nBM FC and pTau (blood biomarker), (Supplementary Materials, Fig. S9); however the regional differences were not significant after FDR correction.\u003c/p\u003e\n\u003ch3\u003eDynamic Functional Connectivity Ascending Arousal Patterns\u003c/h3\u003e\n\u003cp\u003eWe next explored time-varying features of brain connectivity. We used eigen-mode linear discriminant analysis (LDA) to determine specific spatio-temporal patterns that are unique to the delirium and non-delirious populations across the LC dFC and nbM dFC (Fig.\u0026nbsp;4A-B). The dFC matrices were concatenated across subjects and time, scaled and balanced-class priors for LDA was used to identify features contributing to maximal separation between the groups. Using the highest-order magnitude (top 5%, based LDA coefficients) time-points across the dFC that separate the two groups, the LDA-embedded average dFC were compared for noradrenergic and cholinergic connectivity between delirious and non-delirious groups. We found greater anti-coupling between the LC and frontal regions in the delirium population compared to non-delirious (Fig.\u0026nbsp;4C). The nbM had greater coupling across posterior-temporal regions and distinct regions within the default mode network (DMN) in the delirium group compared to non-delirious, and there was anti-coupling across regions within the salience attentional and somato-motor networks (Fig.\u0026nbsp;4D). These heterogenous patterns were mirrored when looking at average dFC differences between the groups (Supplementary Materials, Fig. S4).\u003c/p\u003e\n\u003cp\u003eWe were interested in determining whether there are time-dependent relationships between the global brain integration/segregation compared to ascending arousal BOLD dynamics. Leveraging an approach from Munn et al., (2021) derived from younger individuals\u003csup\u003e55\u003c/sup\u003e, we calculated the cross-correlation between the phasic peaks (defined by 2-standard deviations) of the BOLD signal for the LC and nbM against the average (global) participation coefficients for each subject. Similar to Munn et al., (2021), we found a mirroring pattern, wherein post-phasic LC activity was positively correlated to greater integration (Fig.\u0026nbsp;5A) and post-cholinergic activity was related to more segregation (Fig.\u0026nbsp;5B-D). However, the cross-correlation between nbM and global PC occurred in bursts: there was greater segregation for the delirium population for time-lag 0 (p\u0026thinsp;=\u0026thinsp;0.04) and time-lag\u0026thinsp;+\u0026thinsp;1 (p\u0026thinsp;=\u0026thinsp;0.015) compared to non-delirious (Fig.\u0026nbsp;5B), but the relationship became flipped at greater time-lags. This finding is consistent with a hyperactive cholinergic response in the delirium population driving greater segregation.\u003c/p\u003e\n\u003ch3\u003eRelationship between Segregation and Gene Expression Receptors\u003c/h3\u003e\n\u003cp\u003eLastly, we explored whether regional segregation related to gene expression derived from the Allen Human Brain Atlas\u003csup\u003e52,56,57\u003c/sup\u003e. We selected genes relating cholinergic receptor expression, neurotransmission and clearance enzymes and performed principal component analysis to reduce the dimensionality of the number of genes (see Methods). Then, we correlated the principal component loadings (across 8 principal components, Supplementary Materials, Fig. S6) to the regional average PC across the delirious and non-delirious participants combined (and distinct; Supplementary Materials, Fig. S7). The top performing linear relationship was ranked and validated based upon largest effect size (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3, p\u0026thinsp;=\u0026thinsp;medium effect) and greatest absolute correlation coefficient (see Methods). We found a strong negative relationship between the second principal component of the cholinergic related gene expression correlated with the average PC across both the delirium (rho=-0.456, p\u003csub\u003espin\u003c/sub\u003e = 0.001, p-FDR\u0026thinsp;=\u0026thinsp;0.004) and non-delirious (rho=-0.396), p\u003csub\u003espin\u003c/sub\u003e = 0.001, p-FDR\u0026thinsp;=\u0026thinsp;0.004) participations (Fig.\u0026nbsp;6B-C). Importantly, the second principal component embeds nicotinic receptors (nAChRs) and cholinergic neurotransmission gene-related expression (Fig.\u0026nbsp;6A) with higher gene loadings across frontal cortical regions (Fig.\u0026nbsp;6D). In addition, when observing the individual cholinergic gene expression related to the average PC; several genes that contribute to the loading in the second principal component are significantly negatively related to PC across both groups (Fig.\u0026nbsp;6E - H), across nicotinic receptors (Fig.\u0026nbsp;6-F -G) and butyrylcholinesterase (Fig.\u0026nbsp;6E). Interestingly, two distinctive genes that have negative loadings in the second principal component show a positive relationship with average PC (Fig.\u0026nbsp;6I \u0026ndash; Ji) and the gene expression is inverted with low expression across frontal and high loadings across posterior cortex compared to all the other expression maps (Fig.\u0026nbsp;6I - J). This is additional evidence that there is a mechanistic relationship between regional cholinergic gene expression and segregated network topology. We also examined the relationship between cholinergic gene expression and nbM functional connectivity, however none of these results passed FDR correction (Supplementary Materials, Fig. S8).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found signatures of disrupted ascending arousal modulation in preoperative resting-state scans of participations who developed postoperative delirium. Preoperatively, delirious participants had more segregated network topology and evidence of disrupted ascending arousal modulation compared with non-delirious participants, with decreased connectivity between the LC and cortex and hyperconnectivity between cholinergic nbM and the posterior regions of the cortex.\u003c/p\u003e\u003cp\u003eThe relative reduction in the preoperative participation coefficient across the entire cortex for individuals with postoperative delirium was explored through a data-driven approach to categorise the segregated and integrated states across the entire time-series for all cortical brain regions. We found that the participants who were vulnerable to postoperative delirium spent more time in a segregated state compared to non-delirious participants (Fig.\u0026nbsp;2B). Furthermore, we established a relationship between the interaction with slow-wave activity (a key biological correlate of the precipitant of postoperative delirium\u003csup\u003e7\u003c/sup\u003e) and segregated brain regions in predicting postoperative delirium outcomes (Fig.\u0026nbsp;2C). This establishes a robust relationship between delirium and the segregated network topology of the population. Segregated network topology has been evidenced across several other neurodegenerative conditions, including Dementia with Lewy Bodies\u003csup\u003e16\u003c/sup\u003e, Alzheimer\u0026rsquo;s disease.\u003csup\u003e58\u003c/sup\u003e Within the postoperative population there is evidence of segregated networks in EEG analysis.\u003csup\u003e59\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePrevious large data analysis of familial Alzheimer\u0026rsquo;s disease (ADNI study) has found that greater segregation of higher-order cognitive networks is critical in mediating cognitive resilience despite neuropathology\u003csup\u003e60\u003c/sup\u003e. This finding parallels our findings as there were no preoperative differences in cognition between those who became delirious compared to those that did not. Hence, greater segregation in the delirium population, highlights a potential marker of compensatory cholinergic activity. These findings support the cognitive disintegration theory\u003csup\u003e5\u003c/sup\u003e and extend it to include pre-existing ascending arousal changes prior to surgery.\u003c/p\u003e\u003cp\u003eWe further interrogated the relationship between the ascending arousal nuclei activity and the network topology by performing a time-lagged cross-correlation analysis between peaks in BOLD activity of the ascending arousal system nuclei and the global brain segregation. Our results replicated previous findings\u003csup\u003e20,55\u003c/sup\u003e in which increased integration is positively related to peaks in LC activity (Fig.\u0026nbsp;5A), across both delirium and non-delirium participants. However, we found that the cholinergic nuclei exhibited an inverse relationship, suggesting that post phasic peaks in cholinergic activity are related to greater segregation; with a significant disproportionate relationship between greater segregation post-phasic nbM activity in the delirium population (Fig.\u0026nbsp;5C), this result provides a potential explanation for the overall segregated topology in the delirium participants (Fig.\u0026nbsp;2A). It also supports the theory that the cholinergic system may drive a more segregated network topology through facilitation of normalization\u003csup\u003e61\u003c/sup\u003e by emphasizing patterns of neuronal activity through modulation of inhibition and an increase in the signal-to-noise ratio in these targeted regions\u003csup\u003e61,62\u003c/sup\u003e. Given the hyperconnectivity of the nbM to the cortex in the delirium population, we speculate that vulnerability to delirium relates to mechanistic compensatory changes in the cholinergic system that drive greater segregation.\u003c/p\u003e\u003cp\u003eWe extended this analysis to determine whether there is a relationship between the regional participation coefficient and gene-expression for cholinergic receptors and clearance enzymes. We found a negative relationship across multiple nicotinic sub-unit receptor genes (Fig.\u0026nbsp;6F-G) indicating that the more segregated regions have specific cholinergic receptor-related gene expression and may therefore be more responsive to changes in cholinergic modulation\u003csup\u003e63\u003c/sup\u003e. Importantly, nAChRs have been related to executive control and higher-order cognition, mirroring by the pattern of gene expression loadings across frontal-cortical regions (Fig.\u0026nbsp;6D).\u003csup\u003e63,64\u003c/sup\u003e There was observational overlap between greater segregation across the frontal-cortical regions (Fig.\u0026nbsp;2A) and the heterogeneity of distinct cholinergic gene expression across the frontal-cortical regions. Also, there was a strong negative correlation between butyrylcholinestrase (BCHE) gene expression and segregation in the delirium population (rho= -0.32, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003espin\u003c/em\u003e\u003c/sub\u003e= 0.001, FDR\u0026thinsp;=\u0026thinsp;0.003). Previously, higher levels butyrylcholinestrase activity (in blood) have been associated with delirium\u003csup\u003e65\u003c/sup\u003e, indicating a potential relationship with butyrylcholinestrase activity and associated vulnerability in these regions. In addition to these findings, we also established a weakly positive relationship (rho\u0026thinsp;=\u0026thinsp;0.157, p\u003csub\u003espin\u003c/sub\u003e=0.003, FDR p\u0026thinsp;=\u0026thinsp;0.008) between hyperconnectivity of the nbM and the first principal component of cholinergic gene expression, which proportionally represent muscarinic receptors M1 and M3 (Supplementary Materials, Fig. S6). These findings support the anatomical disparate projection patterns of the cholinergic system\u003csup\u003e66\u003c/sup\u003e \u0026ndash; specifically in relation to how the cholinergic system is driving segregated patterns; it may be facilitating greater segregation specifically in relation to regions that have higher-levels of nicotinic gene-expression.\u003c/p\u003e\u003cp\u003eWe also found that the delirium population had heterogenous time-varying coupling patterns between the LC and nbM. We found differences in the functional connectivity of the noradrenergic system, with a relative decrease in connectivity for the delirium population preoperatively and anti-coupling in the dFC. These results further suggests that there is imbalanced ascending arousal function in the preoperative delirium population, as evidenced by dynamic features of the functional connectivity. The nbM appears to have more coupling across posterior cortical regions in the delirium participants compared to the non-delirious participants (Fig.\u0026nbsp;4B/D). This pattern is consistent across different functional connectivity profiles, including in static functional connectivity (Fig.\u0026nbsp;3B). The occipital/posterior related hyperconnectivity differences with the nbM could relate to an underlying vulnerability across these regions, as we see changes in slow-wave activity (from EEG) propagating across these posterior cortical regions.\u003csup\u003e7\u003c/sup\u003e Our findings also parallel previous work that has shown potential compensatory hyperconnectivity in the preoperative at-risk delirium populations and across early AD pathology\u003csup\u003e67,68\u003c/sup\u003e. Additionally, microstructural white-matter changes within the basal forebrain (containing the nbM) predisposes individuals to postoperative delirium independent of cognitive function\u003csup\u003e69\u003c/sup\u003e. This suggests that perhaps in conjunction with functional related changes, there are axonal and structural based changes that could be related to mechanistic changes in neuronal vulnerability prior to the neuroinflammatory insult that occurs during surgery.\u003c/p\u003e\u003cp\u003eTaken together, these findings lead us to speculate that the cholinergic system predisposes to selective vulnerability in part due to early presynaptic adaptations aimed at maintaining plasticity and network function in the face of other neuronal pathologies\u003csup\u003e68,70,71\u003c/sup\u003e. Basal forebrain cholinergic synapses show an adaptive response to early tau pathology\u0026mdash;potentially a compensatory mechanism\u0026mdash;whereby presynaptic VAChT (Vesicular Acetylcholine Transporter) is upregulated in at-risk, cognitively resilient older adults, particularly when colocalized with tau rather than amyloid\u003csup\u003e45\u003c/sup\u003e. This adaptive response appears to mitigate tau-induced synaptotoxicity in cognitively resilient individuals\u003csup\u003e45\u003c/sup\u003e. Along these lines, in an preliminary exploratory analysis we showed a positive linear relationship between pTau (within the blood) and increased cholinergic connectivity\u003csup\u003e72\u003c/sup\u003e (Supplementary Materials, Fig. S9) which could suggest a relationship with underlying cholinergic driven synaptic modulation and neuropathological accumulation; as our participants are cognitively healthy (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Given that acute inflammation is known to drive central cholinergic suppression, our findings suggest that those predisposed to delirium can be conceptualised as more susceptible to the neuroinflammatory insult during surgery and unable to compensate for loss of cholinergic tone in the brain. While this reinvigorates the decades old cholinergic hypotheses for the mechanisms of delirium\u003csup\u003e73\u003c/sup\u003e, our data suggests that the nicotinic receptor family should be specifically considered as well as protocols that allow for dose-dependent modification.\u003c/p\u003e\u003cp\u003eA limitation of this study is the potential selection bias between scanned and non-scanned participants. Individuals who were not scanned are likely to have undergone more complex surgeries and/or presented with more severe comorbidities, which may reduce the generalisability of our findings. All participants were offered scanning, unless contraindicated (e.g. by surgical implants), and so the difference in the population primarily relates to participant choice. Nonetheless this does limit the broader generalisability of our work. Additionally, there are limitations in relation to other unknown confounders that cannot be fully excluded. Importantly, cognitive measures previously associated with delirium risk showed no significant differences between delirious and non-delirious participants (Table\u0026nbsp;1), nor between scanned and non-scanned individuals (Supplementary Table\u0026nbsp;1). This enhances confidence that any observed resting-state functional connectivity differences reflect preoperative neural vulnerabilities, rather than cognitive disparities. Our findings diverge from prior work suggesting that known delirium risk factors (e.g., age, cognitive impairment, depression) are not associated with delirium-related network changes in fMRI.\u003csup\u003e9\u003c/sup\u003e However, that prior study employed distinct methodological approaches, that likely account for the discrepancies in our findings. In addition, the use of the Human Allen Brain Atlas cholinergic gene expression does not represent individual variations in gene expression, which is a limiting factor of this analysis. Future research could aim to incorporate multimodal neuroimaging, such as simultaneous PET-fMRI and structural imaging, to further elucidate potential neuropathological mechanisms underlying ascending arousal related differences in populations at risk for delirium.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGiven that postoperative delirium is associated with later-life cognitive decline\u003csup\u003e74\u003c/sup\u003e, and neuronal injury\u003csup\u003e75\u003c/sup\u003e, a postoperative delirium event might act as a further insult to an already vulnerable ascending arousal system. Further research needs to interrogate whether there are lasting disruptions to the ascending arousal system postoperatively. This would be critical in revealing the relationship between postoperative delirium and associated cognitive decline.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available upon reasonable request from the corresponding author. The data are not publicly available due to clinically relevant data under ethics restrictions. All code for the analysis is available: https://github.com/NatashaLTaylor/Preoperative_fMRI_AscendingArousal\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe acknowledge the U.S. National Institute of Health (NIH) for their funding of this project, which facilitated the funding relevant for the associated costs for data collection. We acknowledge and thank all the volunteer participants that consented and participated in this study.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the U.S. National Institute of Health (NIH) (2R01AG063849-06). \u0026nbsp;V. N is supported by NIH (R01NS123378). V. P is supported by NIH (R01NS117568). J.M.S is supported by National Health and Medical Research Council (1193857) and Australian Research Council (DP240101295).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors disclose no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eSupplementary material\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSupplementary materials are available online.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorandi, A. \u003cem\u003eet al.\u003c/em\u003e Delirium, Dementia, and In-Hospital Mortality: The Results From the Italian Delirium Day 2016, A National Multicenter Study. \u003cem\u003eJ. Gerontol. A. Biol. Sci. Med. Sci.\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 910\u0026ndash;916 (2019).\u003c/li\u003e\n\u003cli\u003eFong, T. G., Tulebaev, S. R. \u0026amp; Inouye, S. K. Delirium in elderly adults: diagnosis, prevention and treatment. \u003cem\u003eNat. Rev. Neurol.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 210\u0026ndash;220 (2009).\u003c/li\u003e\n\u003cli\u003eThedim, M. \u0026amp; Vacas, S. Postoperative delirium and the older adult: untangling the confusion. \u003cem\u003eJ. Neurosurg. Anesthesiol.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 184\u0026ndash;189 (2024).\u003c/li\u003e\n\u003cli\u003eRacine, A. M. \u003cem\u003eet al.\u003c/em\u003e Delirium Burden in Patients and Family Caregivers: Development and Testing of New Instruments. \u003cem\u003eThe Gerontologist\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, e393\u0026ndash;e402 (2019).\u003c/li\u003e\n\u003cli\u003eSanders, R. D. Hypothesis for the pathophysiology of delirium: role of baseline brain network connectivity and changes in inhibitory tone. \u003cem\u003eMed. Hypotheses\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 140\u0026ndash;143 (2011).\u003c/li\u003e\n\u003cli\u003eGjini, K. \u003cem\u003eet al.\u003c/em\u003e Delirium is associated with loss of feedback cortical connectivity. \u003cem\u003eAlzheimers Dement.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 511 (2024).\u003c/li\u003e\n\u003cli\u003eTanabe, S. \u003cem\u003eet al.\u003c/em\u003e Cohort study into the neural correlates of postoperative delirium: the role of connectivity and slow-wave activity. \u003cem\u003eBr. J. Anaesth.\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 55\u0026ndash;66 (2020).\u003c/li\u003e\n\u003cli\u003evan Dellen, E. \u003cem\u003eet al.\u003c/em\u003e Decreased functional connectivity and disturbed directionality of information flow in the electroencephalography of intensive care unit patients with delirium after cardiac surgery. \u003cem\u003eAnesthesiology\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 328\u0026ndash;335 (2014).\u003c/li\u003e\n\u003cli\u003evan Montfort, S. J. T. \u003cem\u003eet al.\u003c/em\u003e fMRI network correlates of predisposing risk factors for delirium: A cross-sectional study. \u003cem\u003eNeuroImage Clin.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 102347 (2020).\u003c/li\u003e\n\u003cli\u003eDitzel, F. L. \u003cem\u003eet al.\u003c/em\u003e Functional brain network and trail making test changes following major surgery and postoperative delirium: a prospective, multicentre, observational cohort study. \u003cem\u003eBr. J. Anaesth.\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, e281\u0026ndash;e288 (2023).\u003c/li\u003e\n\u003cli\u003eCavallari, M. \u003cem\u003eet al.\u003c/em\u003e Neural substrates of vulnerability to postsurgical delirium as revealed by presurgical diffusion MRI. \u003cem\u003eBrain\u003c/em\u003e \u003cstrong\u003e139\u003c/strong\u003e, 1282\u0026ndash;1294 (2016).\u003c/li\u003e\n\u003cli\u003eWhite, M. F. \u003cem\u003eet al.\u003c/em\u003e Relationships between preoperative cortical thickness, postoperative electroencephalogram slowing, and postoperative delirium. \u003cem\u003eBr. J. Anaesth.\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 236\u0026ndash;244 (2021).\u003c/li\u003e\n\u003cli\u003eShioiri, A. \u003cem\u003eet al.\u003c/em\u003e White Matter Abnormalities as a Risk Factor for Postoperative Delirium Revealed by Diffusion Tensor Imaging. \u003cem\u003eAm. J. Geriatr. Psychiatry\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 743\u0026ndash;753 (2010).\u003c/li\u003e\n\u003cli\u003eRubinov, M. \u0026amp; Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 1059\u0026ndash;1069 (2010).\u003c/li\u003e\n\u003cli\u003eSupekar, K., Menon, V., Rubin, D., Musen, M. \u0026amp; Greicius, M. D. Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer\u0026rsquo;s Disease. \u003cem\u003ePLOS Comput. Biol.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e1000100 (2008).\u003c/li\u003e\n\u003cli\u003eMatar, E. \u003cem\u003eet al.\u003c/em\u003e Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia. \u003cem\u003eNpj Park. Dis.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 16 (2022).\u003c/li\u003e\n\u003cli\u003eShine, J. M., van den Brink, R. L., Hernaus, D., Nieuwenhuis, S. \u0026amp; Poldrack, R. A. Catecholaminergic manipulation alters dynamic network topology across cognitive states. \u003cem\u003eNetw. Neurosci.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 381\u0026ndash;396 (2018).\u003c/li\u003e\n\u003cli\u003eShine, J. M. \u003cem\u003eet al.\u003c/em\u003e The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 544\u0026ndash;554 (2016).\u003c/li\u003e\n\u003cli\u003eAston-Jones, G. \u0026amp; Cohen, J. D. AN INTEGRATIVE THEORY OF LOCUS COERULEUS-NOREPINEPHRINE FUNCTION: Adaptive Gain and Optimal Performance. \u003cem\u003eAnnu. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 403\u0026ndash;450 (2005).\u003c/li\u003e\n\u003cli\u003eTaylor, N. L. \u003cem\u003eet al.\u003c/em\u003e Structural connections between the noradrenergic and cholinergic system shape the dynamics of functional brain networks. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e260\u003c/strong\u003e, 119455 (2022).\u003c/li\u003e\n\u003cli\u003eCoronel-Oliveros, C., Cofr\u0026eacute;, R. \u0026amp; Orio, P. Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models. \u003cem\u003ePLOS Comput. Biol.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, e1008737 (2021).\u003c/li\u003e\n\u003cli\u003eOrlando, I. F., Shine, J. M., Robbins, T. W., Rowe, J. B. \u0026amp; O\u0026rsquo;Callaghan, C. Noradrenergic and cholinergic systems take centre stage in neuropsychiatric diseases of ageing. \u003cem\u003eNeurosci. Biobehav. Rev.\u003c/em\u003e \u003cstrong\u003e149\u003c/strong\u003e, 105167 (2023).\u003c/li\u003e\n\u003cli\u003eBraak, H. \u0026amp; Del Tredici, K. The pathological process underlying Alzheimer\u0026rsquo;s disease in individuals under thirty. \u003cem\u003eActa Neuropathol. (Berl.)\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 171\u0026ndash;181 (2011).\u003c/li\u003e\n\u003cli\u003eEhrenberg, A. J. \u003cem\u003eet al.\u003c/em\u003e Quantifying the accretion of hyperphosphorylated tau in the locus coeruleus and dorsal raphe nucleus: the pathological building blocks of early Alzheimer\u0026rsquo;s disease. \u003cem\u003eNeuropathol. Appl. Neurobiol.\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 393\u0026ndash;408 (2017).\u003c/li\u003e\n\u003cli\u003eSurmeier, D. J., Obeso, J. A. \u0026amp; Halliday, G. M. Selective neuronal vulnerability in Parkinson disease. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 101\u0026ndash;113 (2017).\u003c/li\u003e\n\u003cli\u003eProkopiou, P. C. \u003cem\u003eet al.\u003c/em\u003e Lower novelty-related locus coeruleus function is associated with A\u0026beta;-related cognitive decline in clinically healthy individuals. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1571 (2022).\u003c/li\u003e\n\u003cli\u003eDahl, M. J. \u003cem\u003eet al.\u003c/em\u003e Locus coeruleus integrity is related to tau burden and memory loss in autosomal-dominant Alzheimer\u0026rsquo;s disease. \u003cem\u003eNeurobiol. Aging\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 39\u0026ndash;54 (2022).\u003c/li\u003e\n\u003cli\u003eH\u0026auml;mmerer, D. \u003cem\u003eet al.\u003c/em\u003e Locus coeruleus integrity in old age is selectively related to memories linked with salient negative events. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, 2228\u0026ndash;2233 (2018).\u003c/li\u003e\n\u003cli\u003eJacobs, H. I. L. \u003cem\u003eet al.\u003c/em\u003e In vivo and neuropathology data support locus coeruleus integrity as indicator of Alzheimer\u0026rsquo;s disease pathology and cognitive decline. \u003cem\u003eSci. Transl. Med.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, eabj2511 (2021).\u003c/li\u003e\n\u003cli\u003eAston-Jones, G., Rajkowski, J., Kubiak, P. \u0026amp; Alexinsky, T. Locus coeruleus neurons in monkey are selectively activated by attended cues in a vigilance task. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 4467\u0026ndash;4480 (1994).\u003c/li\u003e\n\u003cli\u003eRajkowski, J., Majczynski, H., Clayton, E. \u0026amp; Aston-Jones, G. Activation of Monkey Locus Coeruleus Neurons Varies With Difficulty and Performance in a Target Detection Task. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 361\u0026ndash;371 (2004).\u003c/li\u003e\n\u003cli\u003eBohnen, N. I. \u003cem\u003eet al.\u003c/em\u003e Heterogeneity of Cholinergic Denervation in Parkinson\u0026rsquo;s Disease without Dementia. \u003cem\u003eJ. Cereb. Blood Flow Metab.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 1609\u0026ndash;1617 (2012).\u003c/li\u003e\n\u003cli\u003eHepp, D. H. \u003cem\u003eet al.\u003c/em\u003e Pedunculopontine Cholinergic Cell Loss in Hallucinating Parkinson Disease Patients but Not in Dementia With Lewy Bodies Patients. \u003cem\u003eJ. Neuropathol. Exp. Neurol.\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 1162\u0026ndash;1170 (2013).\u003c/li\u003e\n\u003cli\u003eManganelli, F. \u003cem\u003eet al.\u003c/em\u003e Functional involvement of central cholinergic circuits and visual hallucinations in Parkinson\u0026rsquo;s disease. \u003cem\u003eBrain\u003c/em\u003e \u003cstrong\u003e132\u003c/strong\u003e, 2350\u0026ndash;2355 (2009).\u003c/li\u003e\n\u003cli\u003eGrothe, M., Heinsen, H. \u0026amp; Teipel, S. Longitudinal measures of cholinergic forebrain atrophy in the transition from healthy aging to Alzheimer\u0026rsquo;s disease. \u003cem\u003eNeurobiol. Aging\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 1210\u0026ndash;1220 (2013).\u003c/li\u003e\n\u003cli\u003eHampel, H. \u003cem\u003eet al.\u003c/em\u003e The cholinergic system in the pathophysiology and treatment of Alzheimer\u0026rsquo;s disease. \u003cem\u003eBrain\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 1917\u0026ndash;1933 (2018).\u003c/li\u003e\n\u003cli\u003eMesulam, M., Shaw, P., Mash, D. \u0026amp; Weintraub, S. Cholinergic nucleus basalis tauopathy emerges early in the aging-MCI-AD continuum. \u003cem\u003eAnn. Neurol.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 815\u0026ndash;828 (2004).\u003c/li\u003e\n\u003cli\u003eVana, L. \u003cem\u003eet al.\u003c/em\u003e Progression of Tau Pathology in Cholinergic Basal Forebrain Neurons in Mild Cognitive Impairment and Alzheimer\u0026rsquo;s Disease. \u003cem\u003eAm. J. Pathol.\u003c/em\u003e \u003cstrong\u003e179\u003c/strong\u003e, 2533\u0026ndash;2550 (2011).\u003c/li\u003e\n\u003cli\u003eGrothe, M., Heinsen, H. \u0026amp; Teipel, S. J. Atrophy of the Cholinergic Basal Forebrain Over the Adult Age Range and in Early Stages of Alzheimer\u0026rsquo;s Disease. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 805\u0026ndash;813 (2012).\u003c/li\u003e\n\u003cli\u003eSchmitz, T. W. \u0026amp; Nathan Spreng, R. Basal forebrain degeneration precedes and predicts the cortical spread of Alzheimer\u0026rsquo;s pathology. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 13249 (2016).\u003c/li\u003e\n\u003cli\u003eHasselmo, M. E. Neuromodulation: acetylcholine and memory consolidation. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 351\u0026ndash;359 (1999).\u003c/li\u003e\n\u003cli\u003eHasselmo, M. E. \u0026amp; Sarter, M. Modes and Models of Forebrain Cholinergic Neuromodulation of Cognition. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 52\u0026ndash;73 (2011).\u003c/li\u003e\n\u003cli\u003eSarter, M. \u0026amp; Lustig, C. Cholinergic double duty: cue detection and attentional control. \u003cem\u003eCurr. Opin. Psychol.\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 102\u0026ndash;107 (2019).\u003c/li\u003e\n\u003cli\u003eHowe, W. M. \u003cem\u003eet al.\u003c/em\u003e Prefrontal cholinergic mechanisms Instigating shifts from monitoring for cues to Cue-Guided performance: Converging electrochemical and fMRI evidence from rats and humans. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 8742\u0026ndash;8752 (2013).\u003c/li\u003e\n\u003cli\u003eOnuska, K. M. \u003cem\u003eet al.\u003c/em\u003e Cholinergic synaptic plasticity shapes resilience and vulnerability to tau. 2025.05.27.656174 Preprint at https://doi.org/10.1101/2025.05.27.656174 (2025).\u003c/li\u003e\n\u003cli\u003eGjini, K. \u003cem\u003eet al.\u003c/em\u003e Delirium is associated with loss of feedback cortical connectivity. \u003cem\u003eAlzheimers Dement.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 511\u0026ndash;524 (2023).\u003c/li\u003e\n\u003cli\u003eGjini, K. \u003cem\u003eet al.\u003c/em\u003e Greater tau pathology is associated with altered predictive coding. \u003cem\u003eBrain Commun.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, fcac209 (2022).\u003c/li\u003e\n\u003cli\u003eSchaefer, A. \u003cem\u003eet al.\u003c/em\u003e Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. \u003cem\u003eCereb. Cortex\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 3095\u0026ndash;3114 (2018).\u003c/li\u003e\n\u003cli\u003eYe, R. \u003cem\u003eet al.\u003c/em\u003e An in vivo probabilistic atlas of the human locus coeruleus at ultra-high field. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e225\u003c/strong\u003e, 117487 (2021).\u003c/li\u003e\n\u003cli\u003eEickhoff, S. B. \u003cem\u003eet al.\u003c/em\u003e A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1325\u0026ndash;1335 (2005).\u003c/li\u003e\n\u003cli\u003eShine, J. M. \u003cem\u003eet al.\u003c/em\u003e Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e122\u003c/strong\u003e, 399\u0026ndash;407 (2015).\u003c/li\u003e\n\u003cli\u003eDing, S.-L. \u003cem\u003eet al.\u003c/em\u003e Comprehensive cellular-resolution atlas of the adult human brain. \u003cem\u003eJ. Comp. Neurol.\u003c/em\u003e \u003cstrong\u003e524\u003c/strong\u003e, 3127\u0026ndash;3481 (2016).\u003c/li\u003e\n\u003cli\u003eShine, J. M. \u003cem\u003eet al.\u003c/em\u003e The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 544\u0026ndash;554 (2016).\u003c/li\u003e\n\u003cli\u003eKimchi, E. Y. \u003cem\u003eet al.\u003c/em\u003e Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes. \u003cem\u003eNeurology\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, e1260\u0026ndash;e1271 (2019).\u003c/li\u003e\n\u003cli\u003eMunn, B. R., M\u0026uuml;ller, E. J., Wainstein, G. \u0026amp; Shine, J. M. The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 6016 (2021).\u003c/li\u003e\n\u003cli\u003eHawrylycz, M. J. \u003cem\u003eet al.\u003c/em\u003e An anatomically comprehensive atlas of the adult human brain transcriptome. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e489\u003c/strong\u003e, 391\u0026ndash;399 (2012).\u003c/li\u003e\n\u003cli\u003eHawrylycz, M. \u003cem\u003eet al.\u003c/em\u003e Canonical genetic signatures of the adult human brain. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1832\u0026ndash;1844 (2015).\u003c/li\u003e\n\u003cli\u003eNg, A. S. L. \u003cem\u003eet al.\u003c/em\u003e Distinct network topology in Alzheimer\u0026rsquo;s disease and behavioral variant frontotemporal dementia. \u003cem\u003eAlzheimers Res. Ther.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 13 (2021).\u003c/li\u003e\n\u003cli\u003evan Montfort, S. \u003cem\u003eet al.\u003c/em\u003e Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis. \u003cem\u003eNeuroImage Clin.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, (2019).\u003c/li\u003e\n\u003cli\u003eEwers, M. \u003cem\u003eet al.\u003c/em\u003e Segregation of functional networks is associated with cognitive resilience in Alzheimer\u0026rsquo;s disease. \u003cem\u003eBrain\u003c/em\u003e \u003cstrong\u003e144\u003c/strong\u003e, 2176\u0026ndash;2185 (2021).\u003c/li\u003e\n\u003cli\u003eSchmitz, T. W. \u0026amp; Duncan, J. Normalization and the Cholinergic Microcircuit: A Unified Basis for Attention. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 422\u0026ndash;437 (2018).\u003c/li\u003e\n\u003cli\u003eShine, J. M. Neuromodulatory Influences on Integration and Segregation in the Brain. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 572\u0026ndash;583 (2019).\u003c/li\u003e\n\u003cli\u003eDasilva, M. \u003cem\u003eet al.\u003c/em\u003e Cell class-specific modulation of attentional signals by acetylcholine in macaque frontal eye field. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 20180\u0026ndash;20189 (2019).\u003c/li\u003e\n\u003cli\u003eMansvelder, H. D., van Aerde, K. I., Couey, J. J. \u0026amp; Brussaard, A. B. Nicotinic modulation of neuronal networks: from receptors to cognition. \u003cem\u003ePsychopharmacology (Berl.)\u003c/em\u003e \u003cstrong\u003e184\u003c/strong\u003e, 292\u0026ndash;305 (2006).\u003c/li\u003e\n\u003cli\u003eCaeiro, L. \u003cem\u003eet al.\u003c/em\u003e The role of acetylcholinesterase and butyrylcholinesterase activity in the development of delirium in acute stroke. \u003cem\u003eCereb. Circ. - Cogn. Behav.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 100017 (2021).\u003c/li\u003e\n\u003cli\u003eZaborszky, L. \u003cem\u003eet al.\u003c/em\u003e Neurons in the Basal Forebrain Project to the Cortex in a Complex Topographic Organization that Reflects Corticocortical Connectivity Patterns: An Experimental Study Based on Retrograde Tracing and 3D Reconstruction. \u003cem\u003eCereb. Cortex\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 118\u0026ndash;137 (2015).\u003c/li\u003e\n\u003cli\u003eWinterer, J. M. \u003cem\u003eet al.\u003c/em\u003e Neurocognitive disorders in the elderly: altered functional resting-state hyperconnectivities in postoperative delirium patients. \u003cem\u003eTransl. Psychiatry\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1\u0026ndash;12 (2021).\u003c/li\u003e\n\u003cli\u003eZeng, Q. \u003cem\u003eet al.\u003c/em\u003e Increased functional connectivity between nucleus basalis of Meynert and amygdala in cognitively intact elderly along the Alzheimer\u0026rsquo;s continuum. \u003cem\u003eNeuroImage Clin.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 103256 (2022).\u003c/li\u003e\n\u003cli\u003eCavallari, M. \u003cem\u003eet al.\u003c/em\u003e Neural substrates of vulnerability to postsurgical delirium as revealed by presurgical diffusion MRI. \u003cem\u003eBrain\u003c/em\u003e \u003cstrong\u003e139\u003c/strong\u003e, 1282\u0026ndash;1294 (2016).\u003c/li\u003e\n\u003cli\u003eMarder, E. \u0026amp; Goaillard, J.-M. Variability, compensation and homeostasis in neuron and network function. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 563\u0026ndash;574 (2006).\u003c/li\u003e\n\u003cli\u003ePozo, K. \u0026amp; Goda, Y. Unraveling mechanisms of homeostatic synaptic plasticity. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 337\u0026ndash;351 (2010).\u003c/li\u003e\n\u003cli\u003eGjini, K. \u003cem\u003eet al.\u003c/em\u003e Greater tau pathology is associated with altered predictive coding. \u003cem\u003eBrain Commun.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, fcac209 (2022).\u003c/li\u003e\n\u003cli\u003eHshieh, T. T., Fong, T. G., Marcantonio, E. R. \u0026amp; Inouye, S. K. Cholinergic Deficiency Hypothesis in Delirium: A Synthesis of Current Evidence. \u003cem\u003eJ. Gerontol. A. Biol. Sci. Med. Sci.\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 764\u0026ndash;772 (2008).\u003c/li\u003e\n\u003cli\u003eDavis, D. H. J. \u003cem\u003eet al.\u003c/em\u003e Association of Delirium With Cognitive Decline in Late Life: A Neuropathologic Study of 3 Population-Based Cohort Studies. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 244\u0026ndash;251 (2017).\u003c/li\u003e\n\u003cli\u003eCasey, C. P. \u003cem\u003eet al.\u003c/em\u003e Postoperative delirium is associated with increased plasma neurofilament light. \u003cem\u003eBrain\u003c/em\u003e \u003cstrong\u003e143\u003c/strong\u003e, 47\u0026ndash;54 (2020).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eClinical Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were collected as part of the perioperative registered trial (2015-0374, NCT01980511 and NCT03124303) cohort study, Interventions for Postoperative Delirium: Biomarker-3 (IPOD-B3), approved by University of Wisconsin-Madison Institutional Review Board (2015-0374). 5519\u0026nbsp;All participants provided informed consent, and work was conducted in accordance with the Code of Ethics of World Medical Association (Declaration of Helsinki) for experiments involving humans. 5519 individuals were screened, and 296 patients were enrolled for participation in the perioperative cohort study. All participants were aged 65yrs or older and underwent major elective non-intracranial surgery, under general anaesthesia.\u0026nbsp;153 participants did not undergo preoperative MRI scans (due to refusal of consent or ineligibility). Delirium occurred in 32.35% of scanned participants and 37.04% of non-scanned participants.\u0026nbsp;Exclusion criteria consist of any documented history of dementia or residing in nursing aged care facilities. Participants underwent pre-operative MRI scans across five sites. They were all assessed for up to 4 days post-operatively which included twice daily (between 05:00\u0026ndash;10:00 and 16:00\u0026ndash;20:00) delirium assessment using 3-minute Diagnostic Confusion Assessment Method (3D-CAM)\u003csup\u003e1\u003c/sup\u003e, or CAM for intensive care unit (CAM-ICU, if intubated), bloods were collected pre-operatively and, on each day, post-operatively for 4 days.\u0026nbsp;After surgery, participants underwent delirium assessments twice daily using the 3D-Confusion Assessment Method (3D-CAM or CAM-ICU (dependent on whether patients were intubated or not) to define delirium. Additionally, in patients who were able to verbalise, delirium severity was assessed by delirium rating scale 98 and 3D-CAM-severity score.\u0026nbsp;All assessors were trained in 3D-CAM\u003csup\u003e2\u003c/sup\u003e, CAM-ICU\u003csup\u003e3\u003c/sup\u003e and delirium rating scale delirium assessments. Assessments were discussed in weekly meetings to review diagnoses and harmonise delirium scoring. Coma was assessed by Richmond Agitation/Sedation Scoring (RASS).\u003csup\u003e4\u003c/sup\u003e All patients who scored for coma, later scored as delirious on formal testing. Daily blood sample collection was also conducted. Samples were analysed by NULISA\u003csup\u003e5\u003c/sup\u003e but only pTau181 based on our prior work using this biomarker in delirium\u003csup\u003e6\u003c/sup\u003e and a prior report implicating Tau associated changes with cholinergic signalling. EEG were collected preoperatively and on postoperative day 1. If patients were in a coma and could not be assessed for delirium the EEG was deferred until a delirium assessment was possible (see Tanabe et al, BJA 2020\u003csup\u003e7\u003c/sup\u003e).\u0026nbsp;Three minutes of average re-referenced clean signals were obtained from high-density EEG data acquired at a 250 Hz sampling rate using a 256-channel system (Electrical Geodesics, Inc.). EEG data from a resting-state, awake, and eyes-closed condition were processed in EEGLAB\u003csup\u003e8\u003c/sup\u003e, a toolbox running in MATLAB (The MathWorks Inc., Natick, Massachusetts). Artifact detection and rejection was performed using independent component analysis (ICA). Delta band [1-3.5 Hz] power in dB units was calculated for electrode Oz using EEGLAB\u0026apos;s function \u0026lsquo;spectopo\u0026rsquo; from the power spectral density (PSD) with Welch\u0026rsquo;s method (2-sec \u0026lsquo;hamming\u0026rsquo; windows and with 50% overlap).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCognition was assessed preoperatively using Montreal Cognitive Assessment (MoCA) Trail Making Test A/B (TMT-A \u0026amp; TMT-B), Boston Naming Test, Digit Symbol Substitution Test (DSST), Controlled Oral Word Association Test (COWAT) and years of education. The following intraoperative assessments were collected as the American College of Surgeons\u0026rsquo; National Quality Improvement Program for surgical risk of death (NSQIP-D), National Quality Improvement Program for surgical risk of serious complications (NSQIP-SC) and American Society of Anesthesiologists Physical Status Classification (ASA). From a total of 142 scanned participants, seven subjects were removed due to incomplete overall peak DRS scores, as surgery was cancelled (n=3), two patients died during or shortly after surgery, one patient withdrew, and one patient surgery was converted to an outpatient procedure. A subsequent twelve subjects were removed due to substantial denoising/pre-processing errors in fMRI, and incomplete fMRI scan protocols (no resting-state scans completed, n=3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional MRI acquisition and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were scanned across five different sites with the following General Electric MRI scanner types: group 0 was scanned on a 3T GE 750 (n=35), group 1 was scanned on a 1.5T Optima MR 450 wide bore (n=2), group 2 was scanned on 3T GE 750 \u0026amp; 3Tw Premier (n=4), group 3 was scanned on a 3T Signa PETMR (n=77), group 4 was scanned on a 3Tw Premier (n=1) and group 5 was unknown (n=1). A General Electric MRI scanner was used to acquire T1-weighted axial structural images at beginning of each scan using FSPGR BRAVO sequence; repetition time = 8.132ms; echo-time = 450ms, 256 x 256 matrix, 156 slices, flip-angle = 60\u003csup\u003eo\u003c/sup\u003e, and slice thickness = 1mm. Resting state fMRI scanning was done using T\u003csub\u003e2\u003c/sub\u003e-weighted echo-planar functional images, acquired in interleaved order with: repetition time = 2600ms; echo-time = 22ms; flip-angle = 60\u003csup\u003eo\u003c/sup\u003e; 40 axial slices covering the entire brain; interslice gap = 3.5 mm; field of view = 224mm; and the raw voxel size = 3.5 x 3.5 x 3.5mm .\u0026nbsp;Pre-processing of images was performed using fMRIPrep (version stable 20.0.2,\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e), run through NiPype (version 1.8.1,\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnatomical data preprocessing\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach T1w (T1-weighted) volume was corrected for INU (intensity non-uniformity) using N4BiasFieldCorrection v2.1.0\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e and skull-stripped using antsBrainExtraction.sh v2.1.0 (using the OASIS template). Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e was performed through nonlinear registration with the antsRegistration tool of ANTs v2.1.0\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e, using brain-extracted versions of both T1w volume and template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e (FSL v5.0.9, RRID:SCR_002823). The following templates were selected for spatial normalization: FSL\u0026apos;s MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model [@mni152nlin6asym, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym], \u0026ldquo;ICBM 152 Nonlinear Asymmetrical template version 2009c\u0026rdquo; [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional data preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each BOLD resting-state run found per subject, the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep.\u003c/p\u003e\n\u003cp\u003eNext, head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) were estimated before any spatiotemporal filtering using `mcflirt` [FSL 6.0.3:b862cdd5,\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e]. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The BOLD reference was then co-registered to the T1w reference using `mri_coreg` (FreeSurfer) followed by `flirt` [FSL 6.0.3:b862cdd5,\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e] with the boundary-based registration\u0026nbsp;\u003csup\u003e16\u003c/sup\u003e cost-function. Co-registration was configured with six degrees of freedom. Several confounding time-series were calculated based on the preprocessed BOLD including framewise displacement (FD), DVARS and three anatomically derived global signals.\u003c/p\u003e\n\u003cp\u003eFD was computed using two formulations following Power (absolute sum of relative motions,\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e) and Jenkinson (relative root mean square displacement between affines,\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e). FD and DVARS were calculated for each functional run, both using their implementations in *Nipype* [following the definitions by\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e]. The three global signals were extracted within the CSF, the WM, and the whole-brain masks.\u003c/p\u003e\n\u003cp\u003eAdditionally, a set of physiological regressors were extracted to allow for component-based noise correction [CompCor,\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e]. Principal components were estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor).tCompCor components are then calculated from the top 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, the aCompCor masks were a subtracted mask of pixels that likely contain a volume fraction of GM. This mask was obtained by thresholding the corresponding partial volume map at 0.05, and it ensures components were not extracted from voxels containing a minimal fraction of GM. Finally, these masks were resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation).\u003c/p\u003e\n\u003cp\u003eThe head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. The BOLD time-series were resampled into several standard spaces, correspondingly generating the following spatially-normalized, preprocessed BOLD runs: MNI152NLin6Asym (for realignment to same resolution as atlas), MNI152NLin2009cAsym.\u003c/p\u003e\n\u003cp\u003eAll resamplings were performed with a single interpolation step by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. Non-gridded (surface) resamplings were performed using `mri_vol2surf`(FreeSurfer). Many internal operations of fMRIPrep use Nilearn 0.9.1 [\u003csup\u003e21\u003c/sup\u003e, RRID:SCR_001362], mostly within the functional processing workflow. For more details of the pipeline, see [the section corresponding to workflows in fMRIPrep\u0026apos;s documentation (https://fmriprep.readthedocs.io/en/latest/workflows.html).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll BOLD signal was denoised by regressing out the following noise parameters 12 measures of head motion and their corresponding derivatives, white-matter and CSF measures and frame-wise displacement and a high and low-pass filter (0.01 Hz \u0026amp; 0.1 Hz) was applied (using customised code generated through Nilearn, version 0.9.1). Note, we do not use CompCor anatomically derived physiological noises as a regressor in this preprocessing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional MRI Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegion of Interest Selection \u0026amp; Functional Connectivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe denoised functional data underwent BOLD time-series extraction for the 17-network 400-regions of interest (ROI) defined by the Schaefer cortical parcellation\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. We categorised the regions into the specific cortical networks, based upon the Yeo\u0026rsquo;s 7-network grouping of the 400 Schaefer atlas parcellation (with an additional network for regions labelled as the tempo-parietal regions from the 17-network parcellation), locus coeruleus\u003csup\u003e23\u003c/sup\u003e and nucleus basalis of Meynert.\u003csup\u003e24\u003c/sup\u003e\u0026nbsp; To determine the functional connectivity for each participant, the Pearson\u0026rsquo;s correlation was calculated for each average BOLD time-series for each unique ROI-to-ROI connection (excluding self-connections for statistical analysis, given that self-connections are correlated as 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDynamic Functional Connectivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime-varying functional connectivity was calculated for each pair of ROIs across temporal windows. We used the multiplication of temporal derivatives which calculates dynamic functional connectivity for each pair of ROIs.\u003csup\u003e25\u003c/sup\u003e For each region, \u003cem\u003en\u003c/em\u003e, with time points, \u003cem\u003et\u003c/em\u003e, a vector of \u003cem\u003et-1\u003c/em\u003e, temporal derivatives were calculated and normalized. A matrix of functional coupling between the \u003cem\u003eith\u003c/em\u003e and \u003cem\u003ejth\u003c/em\u003e regions for each time point is multiplied by the temporal derivatives of each pairs of regions across temporal windows (5TRs, 40.66 ms) (code is freely available at https://github.com/macshine/coupling/).\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"203\" height=\"57\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eNote that we removed one subject from the time-varying analysis due to the entire time-series being slightly shorter in length \u0026ndash; to ease further calculations with graph theoretical metrics that are time-varying in nature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinear Discriminant Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear discriminant analysis (LDA) was used to capture the spatio-temporal dynamics of functional connectivity from the noradrenergic (LC), and cholinergic (nbM) nuclei to rest of the 400 cortical ROIs. Dynamic functional connectivity from the specific seeds mentioned above are then concatenated across subjects and time to generate a 119 x 210 (time point) matrix, by ROIs; in order to reduce instability, we scaled the concatenated data prior to fitting the LDA. Using an eigen-mode LDA (scikit learn), we accounted for imbalances in the number of participants in each group by setting the priors to (0.5:0.5), which forces an \u0026lsquo;equal\u0026rsquo; assumption for class specification within the model. Then, we calculated the group-based separation by taking the average LDA coefficients to determine the directionality of the LDA-axis for group-based separation. We back-projected the original functional connectivity as the weights assigned to each region (\u0026lsquo;feature\u0026rsquo;) in the LDA; to determine how the features contribute to the group-separation. From the LDA, we select the top 5% discriminating time-points (largest order of magnitude) across the entire dFC for the corresponding nuclei. We then took the average of those corresponding top-performing time-points from the LDA-separation and look at differences in the overall dFC for the noradrenergic and cholinergic connections across the delirious and non-delirious.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraph Theoretical Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition, we wanted to quantify whether participants had overall more integrated or segregated network topology. We applied the Louvain algorithm to identify modules of strongly intra-correlated brain regions. Then, to quantify each regions\u0026rsquo; integrations across the modules, we calculated the participation coefficient (B\u003cem\u003e\u003csub\u003eT\u003c/sub\u003e\u003c/em\u003e) for each time-resolved dynamic functional connectivity matrix (using the Brain Connectivity Toolbox\u003csup\u003e26\u003c/sup\u003e). For a given time-point, T,\u0026nbsp;\u003cimg width=\"33\" height=\"22\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;represents the strength of positive connections from region(\u003cem\u003ei\u003c/em\u003e) to regions within module(\u003cem\u003es\u003c/em\u003e), and\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cimg width=\"26\" height=\"22\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;is the total strength of all positive connections of region(\u003cem\u003ei\u003c/em\u003e). A participation coefficient near 1 indicates that a region is functionally connected across multiple modules (integration), whereas a value near 0 reflects connections confined largely to its own module (segregation)\u003csup\u003e27\u0026ndash;30\u003c/sup\u003e. Module assignments were based on the Yeo\u0026rsquo;s 17-Networks parcellation.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"153\" height=\"57\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eTo further categorise integrated and segregated network states over time, we applied \u003cem\u003ek\u003c/em\u003e-means clustering to the region-wise time-resolved participation coefficient data. For this analysis, data from all subjects were concatenated across time, resulting in a matrix of size (subjects \u0026times; time) \u0026times; regions, where each row corresponded to the brain state of a region at a specific time point. This approach enabled us to treat each regional time point as an independent observation in clustering space.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eK\u003c/em\u003e-means clustering was implemented using the KMeans algorithm (scikit-learn python 3.12.8). The number of clusters was fixed at \u003cem\u003ek\u003c/em\u003e = 2, consistent with prior work distinguishing between integrated and segregated states (based of Shine et al., 2016\u003csup\u003e31\u003c/sup\u003e). We used the default parameters, including the k-means++ initialisation method to improve convergence, and ran the algorithm with 100 initialisations (n_init=100) to ensure robust clustering solutions. The algorithm iteratively minimised the within-cluster sum of squares (inertia) to partition the data into two non-overlapping clusters, which were then labelled based on their average participation coefficient values: clusters with higher average values were designated as integrated states, and those with lower values as segregated states (Supplementary Materials, Fig. S1A). This approach allowed us to identify recurring, distinct network states across the entire time series.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhasic Increases in Noradrenergic and Cholinergic BOLD Signal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the relationship between phasic BOLD dynamics of the noradrenergic and cholinergic system in relation to fluctuations in network topology\u003csup\u003e32,33\u003c/sup\u003e, we identified phasic peaks in BOLD signal by identifying time-points in which the original BOLD time-series was greater than or equal to two standard deviations above the mean for the timeseries within a 10TR window; and we excluded any timepoints within the first or last 10TRs of the time-series (avoiding boundary related effects). We then looked at the cross-correlation between the participation coefficient (time-varying) and the proceeding five TRs prior and after peak in BOLD signal for the LC and nbM respectively. We then calculated the statistical difference between the cross-correlation values for the delirious and non-delirious groups, by running a permutation-based independence t-test and determine whether there were significant differences in the cross-correlation between the BOLD dynamics of the LC and nbM in relation to fluctuations in the participation coefficient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine significant differences in FC between the two groups, we performed a permutation-based independent samples t-test. Group labels were randomly shuffled, and the t-test was conducted for each permutation. The process was repeated for a large number of iterations (e.g., 1,000 permutations) to generate a null distribution, allowing us to identify significant group-wise differences in connectivity at each unique ROI-to-ROI edge. This approach controlled for multiple comparisons across all pairwise connections between cortical regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the relationship between multiple different variables we ran a logistic regression to disentangle whether greater segregation relates to delirium-based interactions with other predictive measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"89\" height=\"22\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIYAAAAhCAMAAAArviP1AAAAAXNSR0IArs4c6QAAAJBQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGa2OgAAOjoAOjo6OjpmOjqQOmZmOmaQOma2OpDbZgAAZjoAZpC2ZpDbZrbbZrb/kDoAkGY6kLbbkNvbkNv/tmYAtmY6tpBmtrbbttv/tv/btv//25A625Bm27Zm27aQ29u22////7Zm/7a2/9uQ/9u2/9vb//+2///bcR8XGgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAACBElEQVRYR+1W21bCMBBsCmi8It7qJSAaqtQi+f+/M81u7gHSgw8+tE+cdGYz2cxsKYrhGTowdGDoQP8ONEQ94+tlBhfB5MKAxeJMss/v1hnsAOJTazL6KERNy/uMSgpcfFLyCOCaTl4KwchJfxkBtYEaLS1fD+tAMCenCmu4N4epASKkcgI1GJbeWxDBgXRRYXM8bntpWrSKOq1PbagMG9yofh94EMyhgwxPkCYhSL7kZBpCQqqo8DJyZGgwFGnpfuFah75CR0pEbSnaK0cGghGaKO8fGnSkYNFaoy3hy1hRMnmGot9LU1yBfxbgZlHpvOy8yU6H3DGKUUzVDpWindTx6Vos6LTjb2fWfwxmDEyJnGxxMk6oiKlGl/xhUwcm39zKhmwqu2x8pI4f3CJLGUWOlNRyZIDtDGHe2TZvaiPZEFI+2JYbMMpwh5aoUiOs60ZiHQNvKxuH5owNnx3Vii3S+cK7bIREVDsPM6aG8ZHXDdc8YVI6dyZ0aBmGivMox25hNPQVKVNwEsdGZyTOikdVMeiy9/VEJjaVO+NXU39qqvJiDpfPo27a2TCPvhMeVWZBJbC8whGxUwEkQz42NnKl7j7yYyCzyImNneBzx+ewh0vdu2u/l9uZJ7Af+e/QLU19ZP+ufmYlnvNvJbPWETA2ep//g3401B23R5xnoA4dcDrwC3tLLD4Kfdq5AAAAAElFTkSuQmCC\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eD\u003c/em\u003e is the postoperative delirium outcome (binarized outcome, binomial methods), and \u003cem\u003ePC\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is the regional(\u003cem\u003ei\u003c/em\u003e) participation coefficient and \u003cem\u003eC\u003c/em\u003e is the predictive measure. We also verified that the preoperative PC values interacted with the difference in postoperative EEG slow-wave activity (Postoperative \u0026ndash; Preoperative Oz-delta power) in their association with postoperative delirium. For appropriate model selection, we verified the best performing model by using Akaike Information Criterion (AIC) comparison for 3 versions of the model including predicting delirium outcome from just PC (AIC = 140), difference in postoperative EEG slow-wave activity (AIC = 77.7), PC interacting with difference in EEG slow-wave activity (AIC= 65.56), and for PC covariates with difference in EEG slow-wave activity (AIC = 79.66) (Supplementary Materials, Fig. 2A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, we ran supplementary analysis predicting delirium outcome from average FC for LC and nbM, as well as their relationship with interaction with difference in slow-wave EEG activity (Supplementary Materials, Fig.S3A-D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLastly, we conducted a preliminary exploratory analysis to disentangle the relationship between cholinergic functional connectivity and blood-biomarkers that are indicative of neuropathological insult. We ran Pearson\u0026rsquo;s correlation between the average nbM FC for each subject compared to the blood-biomarker phosphorylated Tau Protein (pTau-181)\u003csup\u003e34\u003c/sup\u003e, we confirmed the linear relationship significance with FDR corrected p-values (Supplementary Materials, Fig. 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene-expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the relationship between cholinergic gene expression data obtained from the Allen Human Brain Atlas\u003csup\u003e35,36\u003c/sup\u003e (AHBA); we preselected expression profiles relating to the cholinergic system (18 relevant genes) including nicotinic receptor (nAChRs) subunits (CHRNA1, CHRNA2, CHRNA3, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNB1, CHRNB2, CHRNB3, CHRNB4), muscarinic receptors (CHRM1, CHRM2, CHRM3, CHRM4, CHRM5), cholinergic synthesis (CHAT), vesicular transport (SLC18A3, SLC44A2, SLC44A5, SLC5A7, SLC44A1) and cholinergic degradation enzymes (ACHE, BCHE). All data was pre-processed\u003csup\u003e37\u0026ndash;39\u003c/sup\u003e and microarray probes were filtered to retain only expression levels above background threshold of more than 50%. For each gene, the probe with highest differential stability across donors were selected as representative. Then, gene expression values were aggregated and normalised across the cortex using an outlier-robust, scaled sigmoid transformation. The gene data were registered to standard MNI 2mm space, to enable mapping between the AHBA gene expression and our 400 cortical Schaefer parcellation. Expression profiles of our preselected genes were extracted for each region and z-scored. We performed a principal component analysis (PCA) to capture a whole brain low-dimensional resolution of cholinergic gene expression and selected the top principal components that explained 80% variance in the data (Supplementary Materials, Fig. S5). We then ran Pearson correlation across the average participation coefficient for each group (delirium and non-delirious); and combined both groups together and the top 8 principal components, with spin permutation testing (1000 iterations) to deal with spatial autocorrelation and FDR correction (all correlations plotted Supplementary Materials, Fig. 6 \u0026amp; 7). We also performed validation of the best performing principal component in relation to computing the lowest FDR p-value, strongest absolute correlation (ranking the principal components in accordance) and calculated the effect-size (determined small, medium and large based upon Cohen\u0026rsquo;s D guidelines) (Supplementary Materials, Fig. S5B). In addition, using the Williams-Hotelling formula, we compared the correlation coefficients between the principal components (with shared common variable the average participation coefficient), to determine whether the principal component correlations with average participation coefficient are significantly distinct. We performed 28 pairwise comparison (comparing each principal component correlation with each other); 14 remained significant after FDR correction. For instance, the second principal component had the largest William\u0026rsquo;s T-Test \u003cem\u003eT\u003c/em\u003e=+8.98, p=0.001 (FDR) (when compared against principal component 1) (Supplementary Materials, Fig. S5E-F). We also ran correlation analysis across selected genes that contributed to the top-performing principal components and strongest rho correlation (number 2). We also ran the above analysis with the average functional connectivity from the nbM to the cortex (Supplementary Materials, Fig. S8).\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOberhaus, J. \u003cem\u003eet al.\u003c/em\u003e Evaluation of the 3-Minute Diagnostic Confusion Assessment Method for Identification of Postoperative Delirium in Older Patients. \u003cem\u003eJAMA Netw Open\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, e2137267 (2021).\u003c/li\u003e\n\u003cli\u003eVasunilashorn, S. M. \u003cem\u003eet al.\u003c/em\u003e A New Severity Scoring Scale for the 3-Minute Confusion Assessment Method (3D-CAM). \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 1874\u0026ndash;1876 (2020).\u003c/li\u003e\n\u003cli\u003eEly, E. W. \u003cem\u003eet al.\u003c/em\u003e Evaluation of delirium in critically ill patients: validation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). \u003cem\u003eCrit Care Med\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1370\u0026ndash;1379 (2001).\u003c/li\u003e\n\u003cli\u003eRivera, C. \u003cem\u003eet al.\u003c/em\u003e The 3-Minute Diagnostic Confusion Assessment Method severity score correlates with the Delirium Rating Scale\u0026ndash;Revised\u0026ndash;98 and with biomarkers of delirium. \u003cem\u003eBJA Open\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 100398 (2025).\u003c/li\u003e\n\u003cli\u003eFeng, W. \u003cem\u003eet al.\u003c/em\u003e NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 7238 (2023).\u003c/li\u003e\n\u003cli\u003eParker, M. \u003cem\u003eet al.\u003c/em\u003e Cohort Analysis of the Association of Delirium Severity With Cerebrospinal Fluid Amyloid-Tau-Neurodegeneration Pathologies. \u003cem\u003eJ Gerontol A Biol Sci Med Sci\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 494\u0026ndash;501 (2022).\u003c/li\u003e\n\u003cli\u003eTanabe, S. \u003cem\u003eet al.\u003c/em\u003e Cohort study into the neural correlates of postoperative delirium: the role of connectivity and slow-wave activity. \u003cem\u003eBritish Journal of Anaesthesia\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 55\u0026ndash;66 (2020).\u003c/li\u003e\n\u003cli\u003eDelorme, A. \u0026amp; Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. \u003cem\u003eJournal of Neuroscience Methods\u003c/em\u003e \u003cstrong\u003e134\u003c/strong\u003e, 9\u0026ndash;21 (2004).\u003c/li\u003e\n\u003cli\u003eEsteban, O. \u003cem\u003eet al.\u003c/em\u003e fMRIPrep: a robust preprocessing pipeline for functional MRI. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 111\u0026ndash;116 (2019).\u003c/li\u003e\n\u003cli\u003eGorgolewski, K. J. \u003cem\u003eet al.\u003c/em\u003e Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13.1. Zenodo https://doi.org/10.5281/ZENODO.581704 (2017).\u003c/li\u003e\n\u003cli\u003eTustison, N. J. \u003cem\u003eet al.\u003c/em\u003e N4ITK: Improved N3 Bias Correction. \u003cem\u003eIEEE Trans. Med. Imaging\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1310\u0026ndash;1320 (2010).\u003c/li\u003e\n\u003cli\u003eFonov, V., Evans, A., McKinstry, R., Almli, C. \u0026amp; Collins, D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, S102 (2009).\u003c/li\u003e\n\u003cli\u003eAvants, B. B. \u003cem\u003eet al.\u003c/em\u003e A reproducible evaluation of ANTs similarity metric performance in brain image registration. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 2033\u0026ndash;2044 (2011).\u003c/li\u003e\n\u003cli\u003eZhang, Y., Brady, M. \u0026amp; Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. \u003cem\u003eIEEE Trans. Med. Imaging\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 45\u0026ndash;57 (2001).\u003c/li\u003e\n\u003cli\u003eJenkinson, M., Bannister, P., Brady, M. \u0026amp; Smith, S. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 825\u0026ndash;841 (2002).\u003c/li\u003e\n\u003cli\u003eGreve, D. N. \u0026amp; Fischl, B. Accurate and robust brain image alignment using boundary-based registration. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 63\u0026ndash;72 (2009).\u003c/li\u003e\n\u003cli\u003ePower, J. D. \u003cem\u003eet al.\u003c/em\u003e Methods to detect, characterize, and remove motion artifact in resting state fMRI. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 320\u0026ndash;341 (2014).\u003c/li\u003e\n\u003cli\u003eJenkinson, M. \u0026amp; Smith, S. A global optimisation method for robust affine registration of brain images. \u003cem\u003eMedical Image Analysis\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 143\u0026ndash;156 (2001).\u003c/li\u003e\n\u003cli\u003eBehzadi, Y., Restom, K., Liau, J. \u0026amp; Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 90\u0026ndash;101 (2007).\u003c/li\u003e\n\u003cli\u003eSatterthwaite, T. D. \u003cem\u003eet al.\u003c/em\u003e An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 240\u0026ndash;256 (2013).\u003c/li\u003e\n\u003cli\u003eAbraham, A. \u003cem\u003eet al.\u003c/em\u003e Machine learning for neuroimaging with scikit-learn. \u003cem\u003eFront. Neuroinform.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, (2014).\u003c/li\u003e\n\u003cli\u003eSchaefer, A. \u003cem\u003eet al.\u003c/em\u003e Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. \u003cem\u003eCerebral Cortex\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 3095\u0026ndash;3114 (2018).\u003c/li\u003e\n\u003cli\u003eYe, R. \u003cem\u003eet al.\u003c/em\u003e An in vivo probabilistic atlas of the human locus coeruleus at ultra-high field. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e225\u003c/strong\u003e, 117487 (2021).\u003c/li\u003e\n\u003cli\u003eEickhoff, S. B. \u003cem\u003eet al.\u003c/em\u003e A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1325\u0026ndash;1335 (2005).\u003c/li\u003e\n\u003cli\u003eShine, J. M. \u003cem\u003eet al.\u003c/em\u003e Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e122\u003c/strong\u003e, 399\u0026ndash;407 (2015).\u003c/li\u003e\n\u003cli\u003eRubinov, M. \u0026amp; Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 1059\u0026ndash;1069 (2010).\u003c/li\u003e\n\u003cli\u003eBassett, D. S. \u0026amp; Bullmore, E. Small-World Brain Networks. \u003cem\u003eNeuroscientist\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 512\u0026ndash;523 (2006).\u003c/li\u003e\n\u003cli\u003eBullmore, E. \u0026amp; Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 186\u0026ndash;198 (2009).\u003c/li\u003e\n\u003cli\u003eShine, J. M. \u003cem\u003eet al.\u003c/em\u003e The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 544\u0026ndash;554 (2016).\u003c/li\u003e\n\u003cli\u003eShine, J. M., Aburn, M. J., Breakspear, M. \u0026amp; Poldrack, R. A. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain. \u003cem\u003eeLife\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, e31130 (2018).\u003c/li\u003e\n\u003cli\u003eShine, J. M. \u003cem\u003eet al.\u003c/em\u003e The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 544\u0026ndash;554 (2016).\u003c/li\u003e\n\u003cli\u003eTaylor, N. L. \u003cem\u003eet al.\u003c/em\u003e Structural connections between the noradrenergic and cholinergic system shape the dynamics of functional brain networks. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e260\u003c/strong\u003e, 119455 (2022).\u003c/li\u003e\n\u003cli\u003eMunn, B. R., M\u0026uuml;ller, E. J., Wainstein, G. \u0026amp; Shine, J. M. The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 6016 (2021).\u003c/li\u003e\n\u003cli\u003eGjini, K. \u003cem\u003eet al.\u003c/em\u003e Greater tau pathology is associated with altered predictive coding. \u003cem\u003eBrain Commun\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, fcac209 (2022).\u003c/li\u003e\n\u003cli\u003eHawrylycz, M. J. \u003cem\u003eet al.\u003c/em\u003e An anatomically comprehensive atlas of the adult human brain transcriptome. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e489\u003c/strong\u003e, 391\u0026ndash;399 (2012).\u003c/li\u003e\n\u003cli\u003eHawrylycz, M. \u003cem\u003eet al.\u003c/em\u003e Canonical genetic signatures of the adult human brain. \u003cem\u003eNat Neurosci\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1832\u0026ndash;1844 (2015).\u003c/li\u003e\n\u003cli\u003eArnatkevic̆iūtė, A., Fulcher, B. D. \u0026amp; Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e189\u003c/strong\u003e, 353\u0026ndash;367 (2019).\u003c/li\u003e\n\u003cli\u003eMarkello, R. D. \u003cem\u003eet al.\u003c/em\u003e Standardizing workflows in imaging transcriptomics with the abagen toolbox. Preprint at https://doi.org/10.1101/2021.07.08.451635 (2021).\u003c/li\u003e\n\u003cli\u003eFornito, A., Arnatkevičiūtė, A. \u0026amp; Fulcher, B. D. Bridging the Gap between Connectome and Transcriptome. \u003cem\u003eTrends in Cognitive Sciences\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 34\u0026ndash;50 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Delirium, Resting-state fMRI, Cholinergic, Segregation, Gene expression, Ascending Arousal Systems","lastPublishedDoi":"10.21203/rs.3.rs-7881643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7881643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDelirium affects up to 50% of hospitalised, older patients and is linked to increased risk of death and long-term cognitive decline. Age-related changes in the ascending arousal system (AAS), including cholinergic and noradrenergic nuclei, may contribute to delirium vulnerability. Static and dynamic functional connectivity, across cortical and subcortical regions, was extracted from preoperative resting-state fMRI from 120 older adults (aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years old). Participants who developed delirium showed more segregated brain networks, cholinergic hyperconnectivity and noradrenergic hypoconnectivity. Dynamic patterns from these systems separated groups in low-dimensional space, suggesting altered temporal network dynamics. Normative maps of cholinergic gene expression density were associated with increased network segregation. These results suggest that aging-related AAS alterations\u0026mdash;particularly compensatory cholinergic overactivity\u0026mdash;may drive network changes that increase delirium risk. This work provides new insights into the neural mechanisms linking aging, arousal system dysfunction, and brain network disruption in delirium, mandating re-appraisal of leading delirium theories.\u003c/p\u003e","manuscriptTitle":"Preoperative Cholinergic Signatures Drive Segregated Brain Architecture in Postoperative Delirium","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 15:51:32","doi":"10.21203/rs.3.rs-7881643/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ecdd05d3-e0aa-432e-8b1b-d17d2a328597","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58315718,"name":"Health sciences/Neurology/Neurological disorders/Neurodegeneration"},{"id":58315719,"name":"Health sciences/Neurology/Neurological disorders/Dementia"}],"tags":[],"updatedAt":"2026-03-11T17:32:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-23 15:51:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7881643","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7881643","identity":"rs-7881643","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}