Connectivity Measures Research Articles

Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance.

Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.

Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms

BackgroundCompared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve throughout typical and abnormal neurodevelopment remains elusive. The 22q11.2 deletion syndrome (22q11.2DS) offers an important opportunity to study the development of FSDs and their specific association with the pathophysiology of psychosis. MethodsPreviously, we used graph signal processing to combine brain activity and structural connectivity measures in adults, quantifying FSD. Here, we combined FSD with longitudinal multivariate partial least squares correlation to evaluate FSD alterations across groups and among patients with and without mild to moderate positive psychotic symptoms. We assessed 391 longitudinally repeated resting-state functional and diffusion-weighted magnetic resonance images from 194 healthy control participants and 197 deletion carriers (ages 7–34 years, data collected over a span of 12 years). ResultsCompared with control participants, patients with 22q11.2DS showed a persistent developmental offset from childhood, with regions of hyper- and hypocoupling across the brain. Additionally, a second deviating developmental pattern showed an exacerbation during adolescence, presenting hypocoupling in the frontal and cingulate cortices and hypercoupling in temporal regions for patients with 22q11.2DS. Interestingly, the observed aggravation during adolescence was strongly driven by the group with positive psychotic symptoms. ConclusionsThese results confirm a central role of altered FSD maturation in the emergence of psychotic symptoms in 22q11.2DS during adolescence. The FSD deviations precede the onset of psychotic episodes and thus offer a potential early indication for behavioral interventions in individuals at risk.

Connectivity measures across scales differentially influence dryland sediment and seed movement

Drylands makeup over 40% of the terrestrial land surface area and are highly vulnerable to degradation. The drivers of dryland degradation can lead to shifts in vegetation, such as woody plant encroachment into historic arid grasslands. Encroachment often creates connected bare plant interspaces where wind and water erosion can redistribute resources, including sediment and seeds. Dryland restoration can incorporate methods to reduce these connected pathways, thus mitigating erosion and retaining resources locally. One method to reduce connectivity is through connectivity modifier (ConMod) structures. Quantifying sediment and seeds captured in ConMod structures provides insight into resource movement on the landscape and system‐level resilience. We quantified sediment and germinable seeds captured in ConMods in relation to vegetation along a grassland‐to‐shrubland gradient, measured at multiple scales, in the Northern Chihuahuan desert, United States. We found (1) a significant but weak correlation between ConMod sediment and seed capture; (2) connectivity in the form of bare ground cover at the large and small scale correlated with sediment capture but not seed capture; and (3) sediment and seed capture were both influenced by previously implemented restoration treatments, though differentially. When investigating the capture of different seed functional groups and sizes, we found that grass seed capture increased with proximity to shrubs and that smaller seeds were both captured more frequently and more closely correlated to sediment capture. These findings have implications for the use of ConMods as restoration tools in shrub‐encroached systems.

Application of individual brain connectome in chronic ischemia: mapping symptoms before and after reperfusion.

How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno-occlusive vasculopathy (CSOV) to map both ischemic symptoms and their postbypass changes. A total of 499 bypasses in 455 CSOV patients were collected and followed up for 47.8±20.5 months. Using multimodal parcellation with connectivity-based and pathological distortion-independent approach, areal MR features of brain connectome were generated with three measurements of functional connectivity (FC), structural connectivity, and PageRank centrality at the single-subject level. Thirty-three machine-learning models were then trained with clinical and areal MR features to obtain acceptable classifiers for both ischemic symptoms and their postbypass changes, among which, 11 were deemed acceptable (AUC>0.7). Notably, the FC feature-based model for long-term neurological outcomes performed very well (AUC>0.8). Finally, a Shapley additive explanations plot was adopted to extract important individual features in acceptable models to generate "fingerprints" of brain connectome. This study not only establishes brain connectomic fingerprint databases for brain ischemia with distortion, but also provides informative insights for how brain functions before and after reperfusion.

Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks

Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy. Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier’s features. Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis. Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.

Brain Responses to Real and Imagined Interpretation of Tonal Versus Atonal Music

Professional musicians have been teaching/learning/interpreting Western classical tonal music for longer than atonal music. This may be reflected in their brain plasticity and playing efficiency. To test this idea, EEG connectivity networks (EEG-CNs) of expert cellists at rest and during real and imagined musical interpretation of tonal and atonal excerpts were analyzed. Graphs and connectomes were constructed as models of EEG-CNs, using functional connectivity measurements of EEG phase synchronization in different frequency bands. Tonal and atonal interpretation resulted in a global desynchronization/dysconnectivity versus resting—irrespective of frequency bands—particularly during imagined-interpretation. During the latter, the normalized local information-transfer efficiency (NLE) of graph-EEG-CN’s small-world structure at rest increased significantly during both tonal and atonal interpretation, and more significantly during atonal-interpretation. Regional results from the graphs/connectomes supported previous findings, but only certain EEG frequency bands. During imagined-interpretation, the number of disconnected regions and subnetworks, as well as regions with higher NLE, were greater in atonal-interpretation than in tonal-interpretation for delta/theta/gamma-EEG-CNs. The opposite was true during real-interpretation, specifically limited to alpha-EEG-CN. Our EEG-CN experimental paradigm revealed perceptual differences in musicians’ brains during tonal and atonal interpretations, particularly during imagined-interpretation, potentially due to differences in cognitive roots and brain plasticity for tonal and atonal music, which may affect the musicians’ interpretation.

An improved linear systems model of hydrothermal isometric tension testing to aid in assessing bone collagen quality: Effects of ribation and type-2 diabetes.

This study sought to further develop and validate a previously proposed physics-based model that maps denaturation kinetics from differential scanning calorimetry (DSC) to the isometric tension generated during hydrothermal isometric tension (HIT) testing of collagenous tissues. The primary objectives of this study were to verify and validate two physics-based model parameters: α, which indicates the amount of instantaneous isometric tension developed per unit of collagen denaturation, and β, which captures the proportionality between temperature and the generated isometric tension post denaturation initiation. These parameters were used as measures of bone collagen quality, employing data from HIT and DSC testing of human bone collagen from two previous studies. Additionally, given the physical basis of the model, the study aimed to further validate Max.Slope, the rate of change in isometric tensile stress with change in temperature, as an independent measure of collagen network connectivity. Max.Slope has previously been positively correlated with measures of cortical bone fracture resistance. Towards this verification and validation, the hypotheses were a) that α would correlate strongly with HIT denaturation temperature, Td, and the enthalpy of melting (ΔH) from DSC, and b) that β would correlate positively and strongly with Max.Slope.The model was employed in the analysis of HIT-DSC data from the testing of demineralized bone collagen isolated from cadaveric human femurs in two prior studies. In one study, data were collected from HIT-DSC testing of cortical bone collagen from 74 donors. Among them, 38 had a history of type 2 diabetes +/− chronic kidney disease, while the remaining 36 had no history of T2D again with or without CKD. Cortical bone specimens were extracted from the lateral mid-shaft. The second study involved 15 donor femora, with four cortical bone specimens extracted from each. Of these four, two specimens underwent a 4-week incubation in 0.1 M ribose at 37 °C to induce non-enzymatic ribation and advanced glycation endproducts, while the other two served as non-ribated controls.The examination involved investigating correlations between the model parameters α and β and various measures, such as Max.Slope, Td, ΔH, age, and duration of type 2 diabetes. The results revealed positive correlations between the model parameter β and Max.Slope (r = 0.55–0.58). The parameter α was found to be associated with Td, but also sensitive to the shape of the HIT curve around Td resulting in difficulties with variability and interpretation. As a result, while both hypotheses are confirmed, Max.Slope and β are better indicators of bone collagen quality because they are measures of the connectivity or, more generally, the integrity of the bone collagen network.

The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and the underlying patterns of neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the amygdala of two male macaques. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5–14.5 Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-modal approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.

Incorporating flowpaths as an explicit measure of river-floodplain connectivity to improve estimates of floodplain sediment deposition

Variation in floodplain topography can lead to gradual flooding and increase river-floodplain connectivity. We show that incorporating flowpaths as an explicit measure of river-floodplain connectivity can improve estimates of floodplain sediment deposition. We focus on the floodplain of the South River, downstream of Waynesboro, Virginia, where measurements of mercury accumulation have been used to estimate decadal-scale sedimentation rates. We developed a two-dimensional Hydrologic Engineering Center's River Analysis System (2D HEC-RAS) hydrodynamic model and used simulated model results with sediment deposition data to create regression models describing sedimentation across the floodplain. All of our statistical models incorporated a flowpath length from the location on the floodplain downstream to the riverbank as an explicit measure of river-floodplain connectivity that improved our estimates of floodplain sediment deposition (r2 = 0.514). We applied our best regression model to our hydrodynamic model results to create a map of floodplain sedimentation rate and discuss differences of three separate sections of floodplain. We found that floodplains with variable topography had wider, bimodal probability distribution functions (PDFs) of sedimentation rate (aggregated spatially) than floodplains without this topographic relief (with narrower log-normal PDFs). Our work highlights how floodplain topography and river-floodplain connectivity affect sedimentation rates and can help inform the development of floodplain sediment budgets.

The Impact of the Opening of China-Europe Railway Express On the Development of Foreign Trade in Anhui Province

Since the "Belt and Road" initiative was put forward, China-Europe freight trains, as an important international transport channel for the Eurasian continent connectivity, have enhanced the level of bilateral trade facilitation and economic and trade cooperation, and had a profound impact on the development of foreign trade. Central trains is China to Europe and the "Belt and Road" international transport trains, central trains opening is the "Belt and Road" initiative China strengthen and countries along the important measures of connectivity, is an important gripper to promote the "Belt and Road" initiative, is an important part of "unicom" facilities, the opening of changed the previous central trains rely solely on maritime transport of export, greatly improve the level of export facilitation and management efficiency, for the improvement of foreign trade development provides strong conditions. The performance of China-Europe freight trains is even more impressive under the epidemic. As a huge potential mode of transportation between Europe and Europe, its importance is gradually emerging.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Magnetic Resonance Imaging in the Study of Catatonia: Use of DWI and Resting State fMRI

Background: catatonia is the focus of neurophysiological research as a syndrome with unspecified pathogenesis. Modern neuroimaging techniques contribute to the understanding of the pathophysiological mechanisms of this disorder. The aim was to conduct a systematic review of the scientific literature to confirm that catatonia is associated with structural and functional changes in the brain. The analysis made up researches using diffusion MRI for judgement on indirect measure of changes in white or gray matter density using a fractional anisotropy (FA) and resting state functional MRI for assessment a measure of connectivity. Materials and methods: PubMed, ScienceDirect and Mendeley databases were searched using the search terms (and their derivatives) for: “catatonia”, “resting state functional magnetic resonance imaging” and “catatonia”, “diffusion weighted magnetic resonance imaging”. The search yielded 147 publications for preliminary screening, of which 96 were on fMRI of catatonia and 51 on dMRI. During the screening stage, duplicates and articles that could not be accessed were removed. This left 21 fMRI articles and 18 dMRI articles. After which the articles were checked for compliance with the inclusion criteria: 1) original full-text articles; 2) identification of catatonia not caused by a somatic disease and verified using the Bush–Francis and/or Northoff psychometric scales; 3) age of the examined 18 years and over. 3 fMRI and 3 dMRI articles were included. Conclusions: aberrations of FA indicators were found in catatonia, which may be associated with the density of the white matter of the brain. Changes in connectivity in the somatosensory network have been identified, which allows to consider these disorders as potential markers of catatonia. To confirm the hypothesis and results obtained, further research is required due to the small number of publications on this topic.

#659 Analysis of functional brain connectivity in patient with end-stage kidney disease undergoing peritoneal dialysis

Abstract Background and Aims Neurological complications are common in patients with end-stage kidney disease (ESKD). However, the mechanisms underlying neurological complications of ESKD are poorly understood. Research on brain connectivity in patients undergoing peritoneal dialysis (PD) is limited. In this study, we aimed to examine alterations in functional brain connectivity in patients with ESKD undergoing PD compared to the control group using functional near infrared spectroscopy (fNIRS). Method We prospectively enrolled 20 patients with ESKD who had been receiving PD for more than 6 months and had no prior history of psychiatric or neurological diseases, along with 20 healthy controls. The fNIRS data were obtained using an NIRSIT Lite instrument. After processing all the data, we used Pearson correlation analysis to create a weighted connectivity matrix. Functional connectivity measures were derived from the connectivity matrix using graph theory. Functional connectivity measures were compared between the controls and patients with ESKD undergoing PD. Results The average degree, average strength, and small-worldness were significantly lower in patients with ESKD undergoing PD than in the controls (9.333 [8.000∼11.433] vs. 12.733 [9.600∼13.400], p = 0.030; 6.865 [4.768∼7.560] vs. 8.432 [6.593∼9.432], p = 0.036; 0.836 [0.724∼0.900] vs. 0.949 [0.882∼0.972], p = 0.025, respectively). Conclusion This study demonstrated significant alterations in functional brain connectivity in patients with ESKD undergoing PD, suggesting that functional brain connectivity is significantly reduced in patients with ESKD undergoing PD when compared with that in healthy controls.

Hybrid parcellation mapping approach for the extraction of connectivity measures in autism spectrum disorder fMRI data

Hybrid parcellation mapping approach for the extraction of connectivity measures in autism spectrum disorder fMRI data

Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus

Objective: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS−). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. Methods: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/− patients by enrolling 57 MCS patients (MCS−: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs’ metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/−. Results: A lower level of brain activity integration was found in the MCS− group in the α band. Instead, in the δ band MCS− group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/− through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively). Conclusion: Despite tackling the MCS+/− differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. Significance: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

Longitudinal Assessment of Outcome Measures of Diaphragm Motor Unit Connectivity As Biomarkers of Phrenic Motor Unit Degeneration and Compensation

Phrenic motor neurons (MNs) form the final link from the central nervous system (CNS) to the diaphragm muscle. Ventilatory function is modulated by the CNS via groups of muscle fibers each controlled by a single MN (motor units). Processes such as injury, aging, and neurodegeneration can result in losses of MNs, and their motor unit (MU) counterparts, but MNs and MUs have some capacity for compensation through mechanisms such as axonal sprouting and collateral sprouting to regain connectivity with denervated muscle. Motor unit number (motor unit number estimation, MUNE), MU size (single motor unit potential amplitude, SMUP), and summated muscle excitation (compound muscle action potential amplitude, CMAP) have been used clinically and preclinically for decades to track MU connectivity but efforts have largely focused on limb muscles. We aimed to apply these measures to a model of phrenic MN degeneration (intrapleural injection of cholera toxin B fragment conjugated to saporin, CTB-SAP).We hypothesized that CTB-SAP-induced phrenic motor neuron death would result in loss of diaphragm MU number and compensatory increases in MU size. Under isoflurane anesthesia, rats were bilaterally, intrapleurally injected with CTB-SAP (25 μg) to target phrenic MNs and extra CTB (25 μg) to label surviving phrenic MNs, and control rats received unconjugated CTB (25 μg) and SAP (25 μg). Diaphragm CMAP, SMUP, and MUNE were acquired bilaterally and averaged at baseline/pre-injection (n=14), as well as 7 (n=14), 14 (n=14), 21 (n=6), and 28 (n=14) days post-injection. No baseline differences between groups were found. MUNE showed a significant change for time (p&lt;0.05), treatment (p&lt;0.001), and interaction time x treatment (p&lt;0.01) with ~40% reduction of MUNE in CTB-SAP rats. Average SMUP of CTB-SAP rats demonstrated a significant change with time (p&lt;0.05) and treatment (p&lt;0.01), but no significant interaction with ~50-60% increase in SMUP amplitude. CMAP showed no significant change. Our results show a significant ~40% loss of MUs in CTB-SAP rats, similar to previous studies on morphological MN counts. In parallel with MU loss, MUs enlarged (increased SMUP), consistent with compensatory increases of MU size likely through MU collateral sprouting. In conclusion, our findings suggest that the use of diaphragm MU electrophysiological measures can provide insights into the onset and progression of CTB-SAP-induced pathology concerning the loss of phrenic MU integrity. Hence, these biomarkers can be used to investigate mechanisms of phrenic motor neuron degeneration and compensation, and to expedite the translation of experimental findings to clinical testing. Supported by a Spinal Cord Injury and Disease Research grant. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

EEG connectivity and network analyses predict outcome in patients with disorders of consciousness – A systematic review and meta-analysis

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60–0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70–0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70–0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition.

Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5-11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.

Age-dependent coupling characteristics of bilateral frontal EEG during desflurane anesthesia.

Objectives.The purpose of this study is to investigate the age dependence of bilateral frontal electroencephalogram (EEG) coupling characteristics, and find potential age-independent depth of anesthesia monitoring indicators for the elderlies.Approach.We recorded bilateral forehead EEG data from 41 patients (ranged in 19-82 years old), and separated into three age groups: 18-40 years (n= 12); 40-65 years (n= 14), >65 years (n= 15). All these patients underwent desflurane maintained general anesthesia (GA). We analyzed the age-related EEG spectra, phase amplitude coupling (PAC), coherence and phase lag index (PLI) of EEG data in the states of awake, GA, and recovery.Main results.The frontal alpha power shows age dependence in the state of GA maintained by desflurane. Modulation index in slow oscillation-alpha and delta-alpha bands showed age dependence and state dependence in varying degrees, the PAC pattern also became less pronounced with increasing age. In the awake state, the coherence in delta, theta and alpha frequency bands were all significantly higher in the >65 years age group than in the 18-40 years age group (p< 0.05 for three frequency bands). The coherence in alpha-band was significantly enhanced in all age groups in GA (p< 0.01) and then decreased in recovery state. Notably, the PLI in the alpha band was able to significantly distinguish the three states of awake, GA and recovery (p< 0.01) and the results of PLI in delta and theta frequency bands had similar changes to those of coherence.Significance.We found the EEG coupling and synchronization between bilateral forehead are age-dependent. The PAC, coherence and PLI portray this age-dependence. The PLI and coherence based on bilateral frontal EEG functional connectivity measures and PAC based on frontal single-channel are closely associated with anesthesia-induced unconsciousness.