Local Network Properties Research Articles

Large language models recover scientific collaboration networks from text

Science is a collaborative endeavor. Yet, unlike co-authorship, interactions within and across teams are seldom reported in a structured way, making them hard to study at scale. We show that Large Language Models (LLMs) can solve this problem, vastly improving the efficiency and quality of network data collection. Our approach iteratively applies filtering with few-shot learning, allowing us to identify and categorize different types of relationships from text. We compare this approach to manual annotation and fuzzy matching using a corpus of digital laboratory notebooks, examining inference quality at the level of edges (recovering a single link), labels (recovering the relationship context) and at the whole-network level (recovering local and global network properties). Large Language Models perform impressively well at each of these tasks, with edge recall rate ranging from 0.8 for the highly contextual case of recovering the task allocation structure of teams from their unstructured attribution page to 0.9 for the more explicit case of retrieving the collaboration with other teams from direct mentions, showing a 32% improvement over a fuzzy matching approach. Beyond science, the flexibility of LLMs means that our approach can be extended broadly through minor prompt revision.

Topological structural characteristics in patients with systemic lupus erythematosus with and without neuropsychiatric symptoms

PurposeThis study investigated the topological structural characteristics of systemic lupus erythematosus (SLE) with and without neuropsychiatric symptoms (NPSLE and non-NPSLE), and explore their clinical implications.MethodsWe prospectively recruited 50 patients with...

Feature-aware ultra-low dimensional reduction of real networks

In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. This study introduces FiD-Mercator, a model-based ultra-low dimensional reduction technique that integrates node features with network structure to create D-dimensional maps of complex networks in a hyperbolic space. This embedding method efficiently uses features as an initial condition, guiding the search of nodes’ coordinates toward an optimal solution. The research reveals that downstream task performance improves with the correlation between network connectivity and features, emphasizing the importance of such correlation for enhancing the description and predictability of real networks. Simultaneously, hyperbolic embedding’s ability to reproduce local network properties remains unaffected by the inclusion of features. The findings highlight the necessity for developing network embedding techniques capable of exploiting such correlations to optimize both network structure and feature association jointly in the future.

Higher-Order Null Models as a Lens for Social Systems

Despite the widespread adoption of higher-order mathematical structures such as hypergraphs, methodological tools for their analysis lag behind those for traditional graphs. This work addresses a critical gap in this context by proposing two microcanonical random null models for directed hypergraphs: the directed hypergraph degree model () and the directed hypergraph JOINT model (). These models preserve essential structural properties of directed hypergraphs such as node in- and out-degree sequences and hyperedge head- and tail-size sequences, or their joint tensor. We also describe two efficient Markov chain Monte Carlo algorithms, - and -, to sample random hypergraphs from these ensembles. To showcase the interdisciplinary applicability of the proposed null models, we present three distinct use cases in sociology, epidemiology, and economics. First, we reveal the oscillatory behavior of increased homophily in opposition parties in the U.S. Congress over a 40-year span, emphasizing the role of higher-order structures in quantifying political group homophily. Second, we investigate a nonlinear contagion in contact hypernetworks, demonstrating that disparities between simulations and theoretical predictions can be explained by considering higher-order joint degree distributions. Last, we examine the economic complexity of countries in the global trade network, showing that local network properties preserved by explain the main structural economic complexity indexes. This work advances the development of null models for directed hypergraphs, addressing the intricate challenges posed by their complex entity relations, and providing a versatile suite of tools for researchers across various domains. Published by the American Physical Society 2024

Graph structure estimation neural network-based service classification

PurposeWith the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.Design/methodology/approachFirst, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.FindingsThe authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.Originality/valueThis paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

Dynamical changes of interaction across functional brain communities during propofol-induced sedation.

It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Rail Transit Networks and Network Motifs: A Review and Research Agenda

The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.

Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study.

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann-Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA.

Xylem-dwelling pathogen unaffected by local xylem vessel network properties in grapevines (Vitis spp.).

Xylella fastidiosa (Xf) is the xylem-dwelling bacterium associated with Pierce's disease (PD), which causes mortality in agriculturally important species, such as grapevine (Vitis vinifera). The development of PD symptoms in grapevines depends on the ability of Xf to produce cell-wall-degrading enzymes to break up intervessel pit membranes and systematically spread through the xylem vessel network. Our objective here was to investigate whether PD resistance could be mechanistically linked to xylem vessel network local connectivity. We used high-resolution X-ray micro-computed tomography (microCT) imaging to identify and describe the type, area and spatial distribution of intervessel connections for six different grapevine genotypes from three genetic backgrounds, with varying resistance to PD (four PD resistant and two PD susceptible). Our results suggest that PD resistance is unlikely to derive from local xylem network connectivity. The intervessel pit area (Ai) varied from 0.07 ± 0.01 mm2 mm-3 in Lenoir to 0.17 ± 0.03 mm2 mm-3 in Blanc do Bois, both PD resistant. Intervessel contact fraction (Cp) was not statically significant, but the two PD-susceptible genotypes, Syrah (0.056 ± 0.015) and Chardonnay (0.041 ± 0.013), were among the most highly connected vessel networks. Neither Ai nor Cp explained differences in PD resistance among the six genotypes. Bayesian re-analysis of our data shows moderate evidence against the effects of the traits analysed: Ai (BF01 = 4.88), mean vessel density (4.86), relay diameter (4.30), relay density (3.31) and solitary vessel proportion (3.19). Our results show that radial and tangential xylem network connectivity is highly conserved within the six different Vitis genotypes we sampled. The way that Xf traverses the vessel network may limit the importance of local network properties to its spread and may confer greater importance on host biochemical responses.

In patients with mild disability NMOSD: is the alteration in the cortical morphological or functional network topological properties more significant.

To assess the alteration of individual brain morphological and functional network topological properties and their clinical significance in patients with neuromyelitis optica spectrum disorder (NMOSD). Eighteen patients with NMOSD and twenty-two healthy controls (HCs) were included. The clinical assessment of NMOSD patients involved evaluations of disability status, cognitive function, and fatigue impact. For each participant, brain images, including high-resolution T1-weighted images for individual morphological brain networks (MBNs) and resting-state functional MR images for functional brain networks (FBNs) were obtained. Topological properties were calculated and compared for both MBNs and FBNs. Then, partial correlation analysis was performed to investigate the relationships between the altered network properties and clinical variables. Finally, the altered network topological properties were used to classify NMOSD patients from HCs and to analyses time- to-progression of the patients. The average Expanded Disability Status Scale score of NMOSD patients was 1.05 (range from 0 to 2), indicating mild disability. Compared to HCs, NMOSD patients exhibited a higher normalized characteristic path length (λ) in their MBNs (P = 0.0118, FDR corrected) but showed no significant differences in the global properties of FBNs (p: 0.405-0.488). Network-based statistical analysis revealed that MBNs had more significantly altered connections (P< 0.01, NBS corrected) than FBNs. Altered nodal properties of MBNs were correlated with disease duration or fatigue scores (P< 0.05/6 with Bonferroni correction). Using the altered nodal properties of MBNs, the accuracy of classification of NMOSD patients versus HCs was 96.4%, with a sensitivity of 93.3% and a specificity of 100%. This accuracy was better than that achieved using the altered nodal properties of FBNs. Nodal properties of MBN significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD. The results indicated that patients with mild disability NMOSD exhibited compensatory increases in local network properties to maintain overall stability. Furthermore, the alterations in the morphological network nodal properties of NMOSD patients not only had better relevance for clinical assessments compared with functional network nodal properties, but also exhibited predictive values of EDSS worsening.

Identifying the perceived local properties of networks reconstructed from biased random walks.

Many real-world systems give rise to a time series of symbols. The elements in a sequence can be generated by agents walking over a networked space so that whenever a node is visited the corresponding symbol is generated. In many situations the underlying network is hidden, and one aims to recover its original structure and/or properties. For example, when analyzing texts, the underlying network structure generating a particular sequence of words is not available. In this paper, we analyze whether one can recover the underlying local properties of networks generating sequences of symbols for different combinations of random walks and network topologies. We found that the reconstruction performance is influenced by the bias of the agent dynamics. When the walker is biased toward high-degree neighbors, the best performance was obtained for most of the network models and properties. Surprisingly, this same effect is not observed for the clustering coefficient and eccentric, even when large sequences are considered. We also found that the true self-avoiding displayed similar performance as the one preferring highly-connected nodes, with the advantage of yielding competitive performance to recover the clustering coefficient. Our results may have implications for the construction and interpretation of networks generated from sequences.

Central limit theorems for local network statistics

Summary Subgraph counts, in particular the number of occurrences of small shapes such as triangles, characterize properties of random networks. As a result, they have seen wide use as network summary statistics. Subgraphs are typically counted globally, making existing approaches unable to describe vertex-specific characteristics. In contrast, rooted subgraphs focus on vertex neighbourhoods, and are fundamental descriptors of local network properties. We derive the asymptotic joint distribution of rooted subgraph counts in inhomogeneous random graphs, a model that generalizes most statistical network models. This result enables a shift in the statistical analysis of graphs, from estimating network summaries to estimating models linking local network structure and vertex-specific covariates. As an example, we consider a school friendship network and show that gender and race are significant predictors of local friendship patterns.

Neural Network Connectivity Following Opioid Dependence is Altered by a Common Genetic Variant in the µ-Opioid Receptor, OPRM1 A118G.

Opioid use disorder is a chronic, relapsing disease associated with persistent changes in brain plasticity. A common single nucleotide polymorphism (SNP) in the µ-opioid receptor gene, OPRM1 A118G, is associated with altered vulnerability to opioid addiction. Reconfiguration of neuronal connectivity may explain dependence risk in individuals with this SNP. Mice with the equivalent Oprm1 variant, A112G, demonstrate sex-specific alterations in the rewarding properties of morphine and heroin. To determine whether this SNP influences network-level changes in neuronal activity, we compared FOS expression in male and female mice that were opioid-naive or opioid-dependent. Network analyses identified significant differences between the AA and GG Oprm1 genotypes. Based on several graph theory metrics, including small-world analysis and degree centrality, we show that GG females in the opioid-dependent state exhibit distinct patterns of connectivity compared to other groups of the same genotype. Using a network control theory approach, we identified key cortical brain regions that drive the transition between opioid-naive and opioid-dependent brain states; however, these regions are less influential in GG females leading to sixfold higher average minimum energy needed to transition from the acute to the dependent state. In addition, we found that the opioid-dependent brain state is significantly less stable in GG females compared to other groups. Collectively, our findings demonstrate sex- and genotype-specific modifications in local, mesoscale, and global properties of functional brain networks following opioid exposure and provide a framework for identifying genotype differences in specific brain regions that play a role in opioid dependence.

Preoperative white matter network organization and memory decline after epilepsy surgery.

Risk for memory decline is a common concern for individuals with temporal lobe epilepsy (TLE) undergoing surgery. Global and local network abnormalities are well documented in TLE. However, it is less known whether network abnormalities predict postsurgical memory decline. The authors examined the role of preoperative global and local white matter network organization and risk of postoperative memory decline in TLE. One hundred one individuals with TLE (n = 51 with left TLE and 50 with right TLE) underwent preoperative T1-weighted MRI, diffusion MRI, and neuropsychological memory testing in a prospective longitudinal study. Fifty-six age- and sex-matched controls completed the same protocol. Forty-four patients (22 with left TLE and 22 with right TLE) subsequently underwent temporal lobe surgery and postoperative memory testing. Preoperative structural connectomes were generated via diffusion tractography and analyzed using measures of global and local (i.e., medial temporal lobe [MTL]) network organization. Global metrics measured network integration and specialization. The local metric was calculated as an asymmetry of the mean local efficiency between the ipsilateral and contralateral MTLs (i.e., MTL network asymmetry). Higher preoperative global network integration and specialization were associated with higher preoperative verbal memory function in patients with left TLE. Higher preoperative global network integration and specialization, as well as greater leftward MTL network asymmetry, predicted greater postoperative verbal memory decline for patients with left TLE. No significant effects were observed in right TLE. Accounting for preoperative memory score and hippocampal volume asymmetry, MTL network asymmetry uniquely explained 25%-33% of the variance in verbal memory decline for left TLE and outperformed hippocampal volume asymmetry and global network metrics. MTL network asymmetry alone produced good diagnostic classification of memory decline in left TLE (i.e., an area under the receiver operating characteristic curve of 0.80-0.84 and correct classification of 65%-76% of cases with cross-validation). These preliminary data suggest that global white matter network disruption contributes to verbal memory impairment preoperatively and predicts postsurgical verbal memory outcomes in left TLE. However, a leftward asymmetry of MTL white matter network organization may confer the highest risk for verbal memory decline. Although this requires replication in a larger sample, the authors demonstrate the importance of characterizing preoperative local white matter network properties within the to-be-operated hemisphere and the reserve capacity of the contralateral MTL network, which may eventually be useful in presurgical planning.

RNet: a network strategy to predict RNA binding preferences.

Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community.

GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks

Local overlapping community detection is a hot problem in the field of studying complex networks. It is the process of finding dense clusters based on local network information. This paper proposes a method called local greedy extended dynamic overlapping community detection (GLOD) to address the challenges of detecting high-quality overlapping communities in complex networks. The goal is to improve the accuracy of community detection by considering the dynamic nature of community boundaries and leveraging local network information. The GLOD method consists of several steps. First, a coupling seed is constructed by selecting nodes from blank communities (i.e., nodes not assigned to any community) and their similar neighboring nodes. This seed serves as the starting point for community detection. Next, the seed boundaries are extended by applying multiple community fitness functions. These fitness functions determine the likelihood of nodes belonging to a specific community based on various local network properties. By iteratively expanding the seed boundaries, communities with higher density and better internal structure are formed. Finally, the overlapping communities are merged using an improved version of the Jaccard coefficient, which is a measure of similarity between sets. This step ensures that overlapping nodes between communities are properly identified and accounted for in the final community structure. The proposed method is evaluated using real networks and three sets of LFR (Lancichinetti–Fortunato–Radicchi) networks, which are synthetic benchmark networks widely used in community detection research. The experimental results demonstrate that GLOD outperforms existing algorithms and achieves a 2.1% improvement in the F-score, a community quality evaluation metric, compared to the LOCD framework. It outperforms the best existing LOCD algorithm on the real provenance network. In summary, the GLOD method aims to overcome the limitations of existing community detection algorithms by incorporating local network information, considering overlapping communities, and dynamically adjusting community boundaries. The experimental results suggest that GLOD is effective in improving the quality of community detection in complex networks.

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes

BackgroundWe aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct...

Decreased functional integration in higher‐order association areas in individuals with autosomal‐dominant Alzheimer’s disease: Findings from the Colombia‐Boston (COLBOS) biomarker study

AbstractBackgroundThe disruption of brain functional connections has been linked to cognitive impairment in Alzheimer’s disease. The functional architecture of the brain can be modeled using graph theory, which provides metrics to describe both local and global network properties. Autosomal dominant Alzheimer’s disease (ADAD) provides a unique opportunity to study brain changes in preclinical AD since carriers of ADAD mutations are destined to develop dementia at young age and have a well‐characterized disease trajectory. Here, we applied a graph theory approach to examine the functional network topology in Presenilin‐1 (PSEN1) E280A carriers and non‐carriers.MethodsA total of 43 cognitively‐unimpaired individuals (18 PSEN1 carriers, 25 non‐carriers; mean age = 36.6±5.2) from the Colombia‐Boston biomarker study were included. Participants underwent resting‐state fMRI in a 3T scanner, and a comprehensive multi‐domain neuropsychological battery. fMRI data were preprocessed using C‐PAC. Global functional connectivity (FC) was derived from the nodal strength of the Pearson correlation matrix between time‐series of 400 ROIs [Shaeffer‐2016] within 6 different functional networks [Yeo‐2011]. The strength of within‐ (z) and between‐network (h) functional integration was derived from a consensus modularity analysis [Dwyer‐2014]. Differences in cognitive variables were evaluated using Mann‐Whitney tests. Differences in FC were assessed using permutation methods: global FC using FSL‐randomize and integration parameters were based on a consensus analysis applied to random controlled matrices. Both methods were repeated 1000 times, with a p&lt;0.05 significance threshold.ResultsThere were no group differences in cognitive variables. Compared to non‐carriers, carriers exhibited decreased local FC in higher‐order association areas, and increased connectivity in somatosensory areas. There were no group differences in the within‐network integration. Carriers showed decreased between‐network integration, especially in higher‐order association networks, mainly the default mode network (Fig.1).ConclusionsFindings support the hypothesis that alterations of functional network topology are evident in preclinical ADAD, primarily in the higher‐order cognitive networks. Early identification of abnormalities within these networks may be useful in understanding how FC changes may lead to subsequent cognitive decline in individuals at increased risk for AD. Future work with larger samples should investigate the relationships between FC changes and the neurobiological factors underlying AD.

Characterization of FTLD spectrum through advanced diffusion-weighted MRI metrics: a connectome approach (P11-6.003)

<h3>Objective:</h3> To investigate structural alterations in brain network of patients within the frontotemporal lobar degeneration (FTLD) spectrum using connectome analysis with advanced diffusion-weighted MRI metrics. <h3>Background:</h3> MRI connectomics is a promising tool to describe network-based degeneration in neurodegenerative diseases. Neurite orientation dispersion and density imaging (NODDI) has shown to provide novel insights on structural connectivity alterations. <h3>Design/Methods:</h3> Thirty-four behavioral-variant frontotemporal dementia (bvFTD), 11 semantic variant primary progressive aphasia (svPPA), 11 nonfluent (nfvPPA) patients and 48 healthy controls underwent multi-shell diffusion MRI. Fractional anisotropy (FA) maps were computed. Intra-cellular Volume Fraction (ICVF) maps were also estimated using NODDI, providing a direct quantification of neurite integrity. Graph analysis and connectomics assessed global and local structural topological network properties and regional structural connectivity. <h3>Results:</h3> Overall, widespread structural changes were observed in bvFTD patients relative to healthy controls and svPPA patients in terms of FA-derived network properties. nfvPPA patients also showed altered FA topological properties at global level compared to healthy controls. Sum of node weights (SN) in the sensorimotor lobe revealed that nfvPPA patients showed an altered connection strength compared to svPPA. Moreover, nfvPPA patients showed a significant decreased FA in the left hemispheric fronto-temporal-insular regions relative to controls. Considering ICVF, more severe alterations compared to FA maps allowed to find differences also between svPPA and nfvPPA patients. ICVF graph analysis measures also showed that svPPA had a lower degree centrality and SN in the temporal lobe compared to controls. <h3>Conclusions:</h3> These findings suggest that conventional diffusion-tensor measures might be sensitive enough to highlight vulnerable connections in the FTLD spectrum. However, ICVF demonstrated to be a more specific biomarker to differentiate FTLD syndromes. Specifically, the benefits emerged in the differentiation between svPPA patients and other groups. <b>Disclosure:</b> Federica Agosta has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Philips. Federica Agosta has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier INC. Silvia Basaia has nothing to disclose. Dr. Facente has nothing to disclose. Ms. Cividini has nothing to disclose. Dr. Spinelli has nothing to disclose. The institution of Elisa Canu has received research support from Italian Ministry of Health . Dr. Castelnovo has nothing to disclose. Giuseppe Magnani has nothing to disclose. Dr. Caso has nothing to disclose. Paola Caroppo has nothing to disclose. Sara Prioni has nothing to disclose. Miss Villa has nothing to disclose. Lucio Tremolizzo has nothing to disclose. Dr. Appollonio has nothing to disclose. Dr. Silani has nothing to disclose. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Springer Nature. The institution of Dr. Filippi has received research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA).