Topological Measures Research Articles

Density topologies and the marginal strong lifting problem for hyperstonian spaces

We investigate properties of density topologies and of topological measure spaces whose topologies are density topologies, motivated by the search for necessary as well as sufficient conditions for the canonical strong lifting of a hyperstonian space to be a marginal. For topological probability spaces we establish relations between the existence of strong liftings and the marginal property of the canonical strong lifting of their hyperstonian space, building on earlier work.

Experimental study of single-translated field-reversed configuration in KMAX

For collisional merging field-reversed configurations (FRCs), it is desired to have both FRCs tuned to be approximately the same, as well as to optimize each FRC to have high temperature and high translation speed so as to retain most of the equilibrium flux after traveling a distance to the middle plane for merging. The present study reports the experimental study of a single-translated FRC in the KMAX-FRC device with various diagnostics, including a triple probe, a bolometer, several magnetic probe arrays, and a novel 2D internal magnetic probe array. According to the measurements conducted in the present study, a maximum toroidal magnetic field equal to ∼1/3 of the external magnetic field inside the FRC separatrix radius is observed, and the typical parameters of a single-translated FRC near the device’s mid-plane are n e ∼ (2–4)×1019 m−3, T e ∼ 8 eV, T i ∼ 5 eV, r s ∼ 0.2 m, l s ∼ 0.6 m and ϕ p(RR) ∼ 0.2 mWb. The 2D magnetic topology measurement revealed, for the first time, the time evolution of the overall internal magnetic fields of a single-translated FRC, and an optimized operation regime is given in the paper.

Weighted Complex Network Analysis of the Difference Between Nodal Centralities of the Beijing Subway System

The centrality of stations is one of the most important issues in urban transit systems. The central stations of such networks have often been identified using network to-pological centrality measures. In real networks, passenger flows arise from an interplay between the dynamics of the individual person movements and the underlying physical structure. In this paper, we apply a two-layered model to identify the most central stations in the Beijing Subway System, in which the lower layer is the physical infrastruc-ture and the upper layer represents the passenger flows. We compare various centrality indicators such as degree, strength and betweenness centrality for the two-layered model. To represent the influence of exogenous factors of stations on the subway system, we reference the al-pha centrality. The results show that the central stations in the geographic system in terms of the betweenness are not consistent with the central stations in the network of the flows in terms of the alpha centrality. We clarify this difference by comparing the two centrality measures with the real load, indicating that the alpha centrality approx-imates the real load better than the betweenness, as it can capture the direction and volume of the flows along links and the flows into and out of the systems. The empirical findings can give us some useful insights into the node cen-trality of subway systems.

Emerging functional connectivity patterns during sevoflurane anaesthesia in the developing human brain

BackgroundSpectral-based EEG is used to monitor anaesthetic state during surgical procedures in adults. Spectral EEG features that can resemble the patterns seen in adults emerge in children after the age of 10 months and cannot distinguish wakefulness and anaesthesia in the youngest children. There is a need to explore alternative EEG measures. We hypothesise that functional connectivity is one of the measures that can help distinguish between consciousness states in children. MethodsAn EEG data set of children undergoing sevoflurane general anaesthesia (age 0–3 yr) was reanalysed using debiased weighted phase lag index as a measure of functional connectivity in wakefulness (n=38) and anaesthesia (n=73). Network topology measures were compared between states in 0- to 6-, 6- to 10-, and >10-month-old children. ResultsFunctional connectivity was reduced in anaesthesia vs wakefulness in delta band (n=cluster of 17 significant connections; P=0.013; 58% connections surviving thresholding in wakefulness and 49% in anaesthesia). Network density and node degree were lower in anaesthesia even in the youngest children (0.57 in wakefulness; 0.48 in anaesthesia; t [9]=3.39; P=0.029; G=0.98; confidence interval [CI] [0.25–1.77]). Modularity was higher in anaesthesia (0–6 months: 0.16 in wakefulness and 0.19 in anaesthesia, t [9]=–2.95, P=0.04, G=–0.85, CI [–1.60 to –0.16]; >10 months: 0.16 vs 0.21, t [13]=–6.45, P<0.001, G=–1.62, CI [–2.49 to –0.85]) and decreased with age (ρ [73]=–0.456; P<0.001). ConclusionsAnaesthesia modulates functional connectivity. Increased segregation into a more modular structure in anaesthesia decreases with age as adult-like features develop. These findings advance our understanding of the network architecture underlying the effects of anaesthesia on the developing brain.

Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls.

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2–5 Hz) (P = 0.0007, hedges’ g = 0.55) and low-alpha (6–9 Hz) (P = 2.9 × 10−8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10−8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.

Altered Functional Connectivity and Topological Organization of Brain Networks Correlate to Cognitive Impairments After Sleep Deprivation.

IntroductionSleep deprivation (SD) has a detrimental effect on cognitive functions. Numerous studies have indicated the mechanisms underlying cognitive impairments after SD in brain networks. However, the findings based on the functional connectivity (FC) and topological architecture of brain networks are inconsistent.MethodsIn this study, we recruited 30 healthy participants with regular sleep (aged 25.20 ± 2.20 years). All participants performed the repeatable battery for the assessment of neuropsychological status and resting-state fMRI scans twice, during the rested wakefulness (RW) state and after 24 h of total SD. Using the Dosenbach atlas, both large-scale FC and topological features of brain networks (ie nodal, global and local efficiency) were calculated for the RW and SD states. Furthermore, the correlation analysis was conducted to explore the relationship between the changes in FC and topological features of brain networks and cognitive performances.ResultsCompared to the RW state, the large-scale brain network results showed decreased between-network FC in somatomotor network (SMN)-default mode network (DMN), SMN-frontoparietal network (FPN), and SMN-ventral attention network (VAN), and increased between-network FC in the dorsal attention network (DAN)-VAN, DAN-SMN after SD. The clustering coefficient, characteristic path length and local efficiency decreased after SD. Moreover, the decreased attention score positively correlated with the decreased topological measures and negatively correlated with the FC of DAN-SMN.ConclusionOur results suggested that the increased FC of DAN-SMN and decreased topological features of brain networks may act as neural indicators for the decrease in attention after SD.Clinical Trial RegistrationThe study was registered at the Chinese Clinical Trial Registry, registration ID: ChiCTR2000039858, China.

An automatic graph-based method for characterizing multichannel networks

Assessment and quantitative description of river morphology using widely recognized river planview measures (e.g., length, width and sinuosity of channels, bifurcation angles and island shape) for multichannel rivers are regarded as fundamental parts of the toolkit of geomorphologists and river engineers. However, conventional assessment methods including field surveys or exiting algorithms for the extraction of multichannel planviews might be suboptimal. More recently, the potential for the application of complex network analysis to the study of river morphology has led to emphasis on the accurate characterization and definition of multichannel network topology. Therefore, we developed a novel algorithm called RivMACNet (River Morphological Analysis based on Complex Networks) that enables the extraction of multichannel network topology using satellite sensor images as the input. We applied RivMACNet to a meandering reach of the Yangtze River and a strongly anastomosing reach of the Indus River to construct their network topologies, and then calculated a series of common topological measures including weighted degree (WD), clustering coefficient (CC) and weighted characteristic path length (WCPL). The network analysis indicated that both networks exhibit poor transitivity with small clustering coefficients. The topological properties of the Indus at the reach scale are independent of flow conditions, while they vary across space at the subnetwork scale. In addition, comparison between RivMACNet and an alternative common river network analysis engine (RivaMap) demonstrated that RivMACNet is superior in terms of representation accuracy and network connectivity and, thus, is more suitable for multichannel fluvial systems with complex planviews. RivMACNet is, thus, a useful tool to support further investigation of multichannel river networks using graph theory.

Quantification on the Generalization Performance of Deep Neural Network with Tychonoff Separation Axioms

Learning in Deep Neural Network (DNN) identifies the geometric structure of data. Hence there is a need for topological measures for quantifying this geometric structure. Based on the transformations in DNN, a metric is introduced with certain conditions to generate a base for a suitable topology. The locally indiscrete nature of the topology is applied to quantify the generalization gap in DNN approximations based on Tychonoff separation axioms. It develops a new measure T2 complexity that quantifies the model’s ability to distinguish classes irrespective of the class imbalance and the threshold applied. The efficiency of the new measure on a real dataset for binary and multiclass classification problems compared with commonly used classification metrics Accuracy, Precision, Recall, F1 score, Matthews Correlation coefficient, AUC-ROC score, and AUC-PR score. The experimental analysis shows that the proposed measure gives a necessary condition for an optimal classification model. The study shows that the measure T2 complexity can be included as a performance metric to analyze the model’s ability to distinguish classes on any binary and multiclass classification problem when the existing measures show misleading results due to class imbalance.

Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle

In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a “co-expression pattern recognizer” (CEPR) and “module classifier”. The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering.

Whole community invasions and the integration of novel ecosystems.

The impact of invasion by a single non-native species on the function and structure of ecological communities can be significant, and the effects can become more drastic–and harder to predict–when multiple species invade as a group. Here we modify a dynamic Boolean model of plant-pollinator community assembly to consider the invasion of native communities by multiple invasive species that are selected either randomly or such that the invaders constitute a stable community. We show that, compared to random invasion, whole community invasion leads to final stable communities (where the initial process of species turnover has given way to a static or near-static set of species in the community) including both native and non-native species that are larger, more likely to retain native species, and which experience smaller changes to the topological measures of nestedness and connectance. We consider the relationship between the prevalence of mutualistic interactions among native and invasive species in the final stable communities and demonstrate that mutualistic interactions may act as a buffer against significant disruptions to the native community.

Global competitiveness analysis of industrial robot technology innovations market layout using visibility graph

The competition degree and characteristics of the target markets based on a global vision provide references for market entrants and policymakers. This study describes a method for identifying the global technology innovations market layout competitiveness based on time series data analysis. We map the time series data from 30 countries or areas of the global industrial robot technology innovations market through retrieval strategy and data mining onto complex networks by visibility graph algorithm. We analyze the dynamic characteristics by the VGNs’ topological measures after the development trend overview and stage division analyses. We use cosine similarity to evaluate the differences and similarities between countries or areas. To further uncover the relationships among them, a similarity complex network is constructed by setting a link threshold. Seven community categories as sub-markets are found through community division. CN and US rank as the top two largest industrial robot innovation markets. Most European countries share the same community because of their similarity of economic development brought by geographical proximity except for several earlier developed economies such as DE, GB, FR, and IT. Some catching-up countries, for example, IN and PH, show potential similar dynamic trends respectively in their group for the sharing characteristics probably with the similar economic development type as the reason behind, whereas RU is distinct for its unique economy type.

Computerized features of tumor diversity on pre-chemoradiation MRI are associated with pathologic complete response in rectal cancers: A multi-institutional study.

3608 Background: Neoadjuvant chemoradiotherapy (nCRT) prior to total mesorectal excision (TME) reduces the risk of local recurrence and is a standard-of-care treatment for patients with locally advanced rectal cancer. Previous studies have indicated up to 30% of patients who undergo TME demonstrate a pathologic complete response (pCR) to nCRT [2], but current serum markers and clinical evaluation have limited utility in predicting benefit from nCRT. In this study, we evaluated if tumor diversity features on baseline pre-treatment MRI were predictive of pCR in rectal cancers. Methods: Pre-nCRT T2-weighted MRIs from 126 patients who later underwent nCRT and TME (without neoadjuvant chemotherapy) were retrospectively collected from 2 institutions. 69 patients (14 pCR, Institution 1) formed the training cohort D1, while 57 patients (10 pCR, Institution 2) formed the independent holdout validation cohort D2. Response groupings were based on AJCC tumor regression grade (TRG) evaluation where pCR corresponded to no residual tumor cells in excised histopathologic specimens after nCRT (i.e. TRG0) and non/partial response otherwise (TRG1-3). Computerized tumor diversity algorithms extracted measurements of fractal dimensions, surface topology, and persistent homology from annotated tumor regions on MRI to quantify visual patterns, spatial geometry, and prolonged structural similarity. Top tumor diversity features identified via Wilcoxon rank-sum testing were used to train a random forest (RF) machine learning classifier to predict the likelihood of pCR using D1 and then independently evaluated for predicting pCR in D2. Results: Machine learning analysis identified 5 tumor diversity measurements associated with pCR on pre-nCRT MRI. The associated RF classifier for pCR achieved an AUC of 0.98 (95% CI, 0.9808-0.9949) in D1 and an AUC of 0.9117 in hold-out validation on D2. This corresponded to predicting 100% of non/partial response prior to nCRT as well as 80% of pCR patients via tumor diversity features on pre-nCRT MRI. Conclusions: Computerized tumor diversity features on pre-nCRT MRI can predict pathologic complete response to nCRT in rectal cancers. These findings need to be validated in larger multi-institutional cohorts to establish independent predictive utility of MRI-based biomarkers, which could aid in the selection of patients who will see maximal response from neoadjuvant therapy and avoid unnecessary surgery.

Generating Synthetic Systems of Interdependent Critical Infrastructure Networks

The lack of data on critical infrastructure systems has hindered the research progress in modeling and optimizing the system performance. This work develops a method for generating Synthetic Interdependent Critical Infrastructure Networks (SICIN) using simulation and non-linear optimization techniques. SICIN consists of three components: (i) determining the location of facilities in individual networks via a modified simulated annealing algorithm, (ii) generating interdependent links based on a novel pseudo-tripartite graph algorithm, and (iii) simulating network flow using nonlinear optimization considering the operations of individual networks and their interdependencies. Two existing systems of interdependent infrastructure networks are used to validate the proposed method. The results demonstrate that SICIN outperforms state-of-the-art simulation methods according to multiple topological and flow measures of similarity between the simulated and real networks.

Transfer learning for autonomous chatter detection in machining

Large-amplitude chatter vibrations are one of the most important phenomena in machining processes. It is often detrimental in cutting operations causing a poor surface finish and decreased tool life. Therefore, chatter detection using machine learning has been an active research area over the last decade. Three challenges can be identified in applying machine learning for chatter detection at large in industry: an insufficient understanding of the universality of chatter features across different processes, the need for automating feature extraction, and the existence of limited data for each specific workpiece-machine tool combination, e.g., when machining one-off products. These three challenges can be grouped under the umbrella of transfer learning, which is concerned with studying how knowledge gained from one setting can be leveraged to obtain information in new settings. This paper studies automating chatter detection by evaluating transfer learning of prominent as well as novel chatter detection methods. We investigate chatter classification accuracy using a variety of features extracted from turning and milling experiments with different cutting configurations. The studied methods include Fast Fourier Transform (FFT), Power Spectral Density (PSD), the Auto-correlation Function (ACF), and decomposition based tools such as Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). We also examine more recent approaches based on Topological Data Analysis (TDA) and similarity measures of time series based on Discrete Time Warping (DTW). We evaluate transfer learning potential of each approach by training and testing both within and across the turning and milling data sets. Four supervised classification algorithms are explored: support vector machine (SVM), logistic regression, random forest classification, and gradient boosting. In addition to accuracy, we also comment on the automation potential of feature extraction for each approach which is integral to creating autonomous manufacturing centers. Our results show that carefully chosen time-frequency features can lead to high classification accuracies albeit at the cost of requiring manual pre-processing and the tagging of an expert user. On the other hand, we found that the TDA and DTW approaches can provide accuracies and F1-scores on par with the time-frequency methods without the need for manual preprocessing via completely automatic pipelines. Further, we discovered that the DTW approach outperforms all other methods when trained using the milling data and tested on the turning data. Therefore, TDA and DTW approaches may be preferred over the time-frequency-based approaches for fully automated chatter detection schemes. DTW and TDA also can be more advantageous when pooling data from either limited workpiece-machine tool combinations, or from small data sets of one-off processes.

Structure-based approach to identifying small sets of driver nodes in biological networks.

In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) by node-state override forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular networks, while their FVS may contain more than 10 nodes. Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the use of seven topological prediction measures sorted into three categories-centrality measures, propagation measures, and cycle-based measures. Using each measure, every subset was ranked and then evaluated against two dynamics-based metrics that measure the ability of interventions to drive the system toward or away from its attractors: To Control and Away Control. After examining an array of biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can most effectively control the network. This result was independently corroborated on a second array of different Boolean models of biological networks. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and this method provides a way to reliably identify effective FVS subsets without the knowledge of the network dynamics.

Two-dimensional coronene fractal structures: topological entropy measures, energetics, NMR and ESR spectroscopic patterns and existence of isentropic structures

Molecular fractals are geometric patterns that appear self-similar across all length scales and are constructed by repeating a single unit on a regular basis. The benzenoid compounds are found to have a natural pattern that helps to analyse and explore the potential attributes of these molecular fractals. Here, we focus on coronene-based fractals where coronene is a benzenoid molecule with a symmetric graphite-like structure and challenging synthesis methods. Topological molecular descriptors are obtained in order to provide structure-activity relations for the physico-chemical properties. We obtain these descriptors for the molecular fractals that are constructed by repeating a fixed unit of coronene on different stages systematically. The structural characterisation of the two-dimensional coronene fractals in various tessellations is presented through a wide range of degree-based topological indices. The entropy measures of these significant frameworks are also computed for estimating their potential attributes, and the comparison of their values among various degree-based indices is performed, which shows the existence of isentropic structures. We have developed machine learning techniques for robust computations of the enthalpies, NMR and ESR spectroscopic patterns of coronene fractals.

Online handwritten signature verification via network analysis

In this paper, a model for online handwritten signature verification via networks analysis is proposed, in which centrality metrics are used as predictive characteristics. Supervised learning methods were used to classify the veracity of the signatures. The study uses the MCYT-100 and SVC2004 task2 signature databases, which are widely used in signature verification problems. In the framework, signatures were considered as nodes of a complex network in which the existing connection relationship between two signatures is measured through Pearson correlations between time series of signature coordinates that exceed a threshold of comparison. The attributes for the model were topological measures of centralities of the networks and the entropy of the time series signature. Some metrics are used to measure classification errors, including the False Rejection Rate (FRR) which identifies the percentage of true signatures that are rejected by the model, the False Acceptance Rate (FAR) as the percentage of false signatures that are accepted by the learning engine and the Average Error Rate (AER) as the average error. In this framework, we obtained a false positive rate of 6.19%, a false negative rate of 6.39% and an average error of approximately 6.28%. This result is close to ones obtained by state-of-the-art models for this problem, showing that the use of complex networks for online handwritten signature verification is a promising venue of research.

Numerical study of the porosity-permeability evolution due to chmically-active fluid injection

The paper presents a numerical algorithm for simulation of the reactive transport at the pore scale. The algorithm allows simulating pore space evolution, porosity, absolute permeability, and form factor changes due to core matrix dissolution or precipitation. We also, introduce the topological measure; the persistence diagrams of independent cycles in pore space to classify different dissolution scenarios. Using derived classification, we constructed the statistically reliable porosity-permeability relations for different dissolution scenarios.

The usefulness of topological indices

A huge number of topological graph measures have been defined and investigated. It turned out that various graph measures failed to solve problems meaningfully in the context of characterizing graphs. Reasons for this range from selecting redundant and unfavorable graph invariants and the fact that many of those measures have been defined in an unreflected manner. In this paper, we extend the debate in the literature to find useful properties of structural graph measures. For this, we investigate the usefulness of topological indices for graphs quantitatively by assigning a feature vector to graph that contains ‘useful’ properties represented by certain measures. We show examples and compare the usefulness by using this apparatus based on distance measures and on a agglomerative clustering task.

Simulated Attack Reveals How Lesions Affect Network Properties in Poststroke Aphasia.

Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomic network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomic network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores [the Western Aphasia Battery Aphasia Quotient (WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum], we compared the behavioral variance accounted for by simulated poststroke connectomes to that observed in the randomly permuted data. Global measures of anatomic network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.SIGNIFICANCE STATEMENT Poststroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often use small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. "Simulated attack" models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomic networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.