Overlap Metrics Research Articles

App-based data collection, mental imagery, and naming performance in adults with aphasia

Background and purposeNaming impairments are often found in aphasia. Combining mHealth technology with mental imagery may offer new insights into novel approaches to naming interventions in persons with aphasia. The aims of this study are to (1) explore whether mental imagery can improve performance during a naming task in adults with aphasia; and, (2) demonstrate the use of app-based data collection for research in aphasia rehabilitation through utilization of an app designed specifically for this study. Materials and methodsA single subject non-concurrent multiple baseline design across participants was utilized. Four participants with anomic and conduction aphasia were included. Each participant's percentage of naming accuracy was measured before the initiating intervention to establish a baseline level of performance. ResultsPer overlap metrics, two of the participants demonstrated a strong effect in improving naming from baseline to intervention on items using the app. Two participants showed no effect in naming accuracy from the imagery intervention. ConclusionSome participants showed improvement in naming items following an imagery script, suggesting that this complementary health practice should continue to be explored. Additionally, this study demonstrates the successful use of app-based data collection.

Multimodal biometric authentication for mobile edge computing

In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) system designed for Mobile Edge Computing (MEC) and multimodal biometrics. We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user’s secret parameters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead. Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing.

Exploiting Global Structure Information to Improve Medical Image Segmentation.

In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts.

Food web structure in relation to environmental drivers across a continental shelf ecosystem

AbstractQuantification of the physical and biological factors that influence the spatial structuring of food webs is central to inform effective resource management. We used baseline‐corrected stable isotope ratios (δ13C and δ15N) of 63 invertebrate and fish to investigate food web structure across a continental shelf gradient—the Celtic Sea Shelf in the Northeast Atlantic Ocean. Hierarchical clustering on δ13C and δ15N showed that the shelf food web is characterized by four trophic levels with trophic groups spread across pelagic and benthic trophic pathways. Four biomass‐weighted isotopic diversity metrics provided indicators on the status of the system, showing a relatively complex food web with high trophic redundancy at intermediate trophic levels suggesting resilience to disturbances. Two sets of statistical models, at the community scale and for each trophic group, identified five distinct trophic assemblages associated with different chlorophyll a concentrations, water depth, and bottom temperature. A cold, vertically mixed‐water assemblage over the outer shelf comprised the largest habitat and most diverse assemblage, highlighting the importance of cold productive conditions in the Celtic Sea. Trophic group model results were used to generate spatial area predictions to compare functioning of groups using isotopic overlap (similarity and nestedness) metrics. Isotopic niche area was larger (spanning two trophic levels) in shallow habitats, but not in habitats underlying high primary production or nutrient‐rich water masses, suggesting stronger benthic‐pelagic trophic coupling in inner shelf habitats. Results suggest that depth and intensity of pelagic production are major drivers of trophic structure and functioning of Celtic Sea communities.

Repeatability of Automated Image Segmentation with BraTumIA in Patients with Recurrent Glioblastoma.

Despite high interest in machine-learning algorithms for automated segmentation of MRIs of patients with brain tumors, there are few reports on the variability of segmentation results. The purpose of this study was to obtain benchmark measures of repeatability for a widely accessible software program, BraTumIA (Versions 1.2 and 2.0), which uses a machine-learning algorithm to segment tumor features on contrast-enhanced brain MR imaging. Automatic segmentation of enhancing tumor, tumor edema, nonenhancing tumor, and necrosis was performed on repeat MR imaging scans obtained approximately 2 days apart in 20 patients with recurrent glioblastoma. Measures of repeatability and spatial overlap, including repeatability and Dice coefficients, are reported. Larger volumes of enhancing tumor were obtained on later compared with earlier scans (mean, 26.3 versus 24.2 mL for BraTumIA 1.2; P < .05; and 24.9 versus 22.9 mL for BraTumIA 2.0, P < .01). In terms of percentage change, repeatability coefficients ranged from 31% to 46% for enhancing tumor and edema components and from 87% to 116% for nonenhancing tumor and necrosis. Dice coefficients were highest (>0.7) for enhancing tumor and edema components, intermediate for necrosis, and lowest for nonenhancing tumor and did not differ between software versions. Enhancing tumor and tumor edema were smaller, and necrotic tumor larger using BraTumIA 2.0 rather than 1.2. Repeatability and overlap metrics varied by segmentation type, with better performance for segmentations of enhancing tumor and tumor edema compared with other components. Incomplete washout of gadolinium contrast agents could account for increasing enhancing tumor volumes on later scans.

Assembly of species’ climatic niches of coastal communities does not shift after invasion

AbstractQuestionDo invasions by invasive plant species with contrasting trait profiles (Arctotheca calendula, Carpobrotus spp., Conyza bonariensis, and Opuntia dillenii) change the climatic niche of coastal plant communities?LocationAtlantic coastal habitats in Huelva (Spain).MethodsWe identified the species composition of 216 paired (non‐invaded and invaded) 10 × 10 m plots along the coast. For each species, we calculated its climatic niche based on the two main axis of a PCA constructed with nine climatic variables. We defined the community ensemble niche by the union of the overall climate niches of co‐occurring species within a plot. We compared niche overlap metrics between non‐invaded and invaded paired communities.ResultsThere was an almost complete overlap in the community ensemble niches between non‐invaded and invaded plots for the four invaders. Plots invaded by Carpobrotus spp. presented the lowest niche stability and those invaded by A. calendula had the highest. Plots invaded by Carpobrotus spp. showed the highest values of niche unfilling and expansion. In contrast, plots invaded by O. dillenii exhibited the lowest niche unfilling. Species similarity between non‐invaded and invaded plots was on average 58%. The community ensemble niches differed depending on the invasive species and were related to differences in community species similarity between non‐invaded and invaded plots. Overall, there was a positive correlation between community species similarity and climatic niche stability, and a negative correlation between community difference in taxonomic richness and climatic niche stability.ConclusionsSpecies assemblages in coastal vegetation did not change their community ensemble climatic niches after invasion by plants with contrasted life forms. This pattern is likely the result of invasion which did not trigger major changes in species richness and composition, or alternatively, because the species that were locally displaced by invasion have been substituted by others with similar climatic requirements.

Ecological niche differentiation in Chiroxiphia and Antilophia manakins (Aves: Pipridae).

Species distribution models are useful for identifying the ecological characteristics that may limit a species’ geographic range and for inferring patterns of speciation. Here, we test a hypothesis of niche conservatism across evolutionary time in a group of manakins (Aves: Pipridae), with a focus on Chiroxiphia boliviana, and examine the degree of ecological differentiation with other Chiroxiphia and Antilophia manakins. We tested whether allopatric sister species were more or less similar in environmental space than expected given their phylogenetic distances, which would suggest, respectively, ecological niche conservatism over time or ecologically mediated selection (i.e. niche divergence). We modeled the distribution of nine manakin taxa (C. boliviana, C. caudata, C. lanceolata, C. linearis, C. p. pareola, C. p. regina, C. p. napensis, Antilophia galeata and A. bokermanni) using Maxent. We first performed models for each taxon and compared them. To test our hypothesis we followed three approaches: (1) we tested whether C. boliviana could predict the distribution of the other manakin taxa and vice versa; (2) we compared the ecological niches by using metrics of niche overlap, niche equivalency and niche similarity; and (3) lastly, we tested whether niche differentiation corresponded to phylogenetic distances calculated from two recent phylogenies. All models had high training and test AUC values. Mean AUC ratios were high (>0.8) for most taxa, indicating performance better than random. Results suggested niche conservatism, and high niche overlap and equivalency between C. boliviana and C. caudata, but we found very low values between C. boliviana and the rest of the taxa. We found a negative, but not significant, relationship between niche overlap and phylogenetic distance, suggesting an increase in ecological differentiation and niche divergence over evolutionary time. Overall, we give some insights into the evolution of C. boliviana, proposing that ecological selection may have influenced its speciation.

Little interannual variability in gray seal (Halichoerus grypus) trophic niches during pregnancy despite variable environmental conditions

The successful recruitment of juveniles into a population is often dependent on receiving sufficient nutrition pre- and post-parturition. Thus, variation in maternal foraging ecology during pregnancy, whether due to prey choice, prey availability or fluctuations in environmental conditions, can impact offspring fitness in the subsequent breeding season. As many pinnipeds spend the majority of the gestation period at sea, past studies have used the biogeochemical analysis of pup tissues to infer female diets during this critical period. The objective of our study was to examine the trophic niches of a population of pregnant female gray sealsHalichoerus grypusover a 4 yr period in coastal Massachusetts, USA by analyzing the stable carbon (δ13C) and nitrogen (δ15N) values of pup lanugo. In addition, we sought to determine if individual and inter-annual variation in pup body condition could be explained by differences in female trophic niches, prey availability, or environmental conditions. Stable isotope-based metrics of trophic niche position, width, and overlap indicated little to no interannual variability in female foraging ecology at the population level despite variation in environmental conditions and prey availability in the fall on Georges Bank. Model selection indicated a positive relationship between pup body condition and δ13C values, which is indicative of pregnant females foraging on benthic, demersal, or nearshore prey species during the fall prior to parturition. This indicates that individual variation in female foraging ecology during pregnancy has a carry-over effect on offspring condition with possible implications for first-year survival, and ultimately recruitment to the adult population.

Match-mismatch dynamics in the Norwegian-Barents Sea system

A key process affecting variation in the recruitment of fish into fisheries is the spatio-temporal overlap between prey and predator (match-mismatch hypothesis, MMH). The Northeast Arctic cod Gadus morhua and its dominant prey, the copepod Calanus finmarchicus, have long been studied in the Norwegian-Barents Sea system. However, the mechanistic explanation of how cod survival is affected by MMH dynamics remains unclear. Most MMH studies have focused on either the time synchrony or the spatial overlap between trophic levels. Here, we used G. morhua larvae and C. finmarchicus data collected in the Norwegian-Barents Sea via ichthyoplankton surveys from 1959-1992 to assess the effect of the predator-prey relationship on predator recruitment to the fisheries at age 3 (as a measure of survival) and to develop a metric of predator-prey overlap using spatio-temporal statistical models. We then compared the interannual variability of the predator-prey overlap with the predator’s abundance at recruitment to assess how MMH dynamics explain the survival of cod during its early life stages. We found that the amount of overlap between cod larvae (length: 11-15 mm) and their prey explained 29% of cod recruitment variability. Positive correlations between predator-prey overlap and subsequent recruitment were also found for predators of 6-10 and 16-20 mm, but not for 21+ mm. This improved predator-prey overlap metric is thus (1) useful to better understand how predator-prey dynamics at early life stages of fish impact the survival of later stages; and (2) a valuable tool for assessing the state of an ecosystem.

Evaluating Indirect Effects of Hunting on Mule Deer Spatial Behavior

ABSTRACTMule deer (Odocoileus hemionus) are widely hunted throughout western North America and are experiencing population declines across much of their range. Consequently, understanding the direct and indirect effects of hunting is important for management of mule deer populations. Managers can influence deer mortality rates through changes in hunting season length or authorized tag numbers. Little is known, however, about how hunting can affect site fidelity patterns and subsequent habitat use and movement patterns of mule deer. Understanding these patterns is especially important for adult females because changes in behavior may influence their ability to acquire resources and ultimately affect their productivity. Between 2008 and 2013, we obtained global positioning system locations for 42 adult female deer at the Starkey Experimental Forest and Range in northeast Oregon, USA, during 5‐day control and treatment periods in which hunters were absent (pre‐hunt), present but not actively hunting (scout and post‐hunt), and actively hunting male mule deer (hunt) on the landscape. We estimated summer home ranges and 5‐day use areas during pre‐hunt and hunt periods and calculated overlap metrics across home ranges and use areas to assess site fidelity within and across years. We used step selection functions to evaluate whether female mule deer responded to human hunters by adjusting fine‐scale habitat selection and movement patterns during the hunting season compared to the pre‐hunt period. Mule deer maintained site fidelity despite disturbance by hunters with 72 ± 4% (SE) within‐year overlap between summer home ranges and hunt use areas and 54 ± 7% inter‐annual overlap among pre‐hunt use areas and 56 ± 7% among hunt use areas. Mule deer diurnal movement rates, when hunters are active on the landscape, were higher during the hunting period versus pre‐hunt or scout periods. In contrast, nocturnal movement rates, when hunters are inactive on the landscape, were similar between hunting and non‐hunting periods. Additionally, during the hunt, female mule deer hourly movements increased in areas with high greenness values, indicating that mule deer spent less time in areas with more vegetative productivity. Female mule deer maintained consistent habitat selection patterns before and during hunts, selecting areas that offered more forest canopy cover and high levels of vegetative productivity. Our results indicate that deer at Starkey are adopting behavioral strategies in response to hunters by increasing their movement rates and selecting habitat in well‐established ranges. Therefore, considering site fidelity behavior in management planning could provide important information about the spatial behavior of animals and potential energetic costs incurred, especially by non‐target animals during hunting season. © 2020 The Wildlife Society.

Predicting Labor Market Competition: Leveraging Interfirm Network and Employee Skills

Human capital is a key component of the knowledge economy. Firms compete not only for consumers in the product market but also for human capital in the labor market. In this study, we perform an interfirm labor market competitor analysis using the online profiles of more than 89,000 employees and their career histories that span more than 3,000 public firms. Using these profiles, we characterize firms through the granular skill distribution of their employees. Also, using employee migrations across firms, we derive and analyze a human capital flow (HCF) network. Such information allows us to measure the interfirm human capital overlap in terms of similarity in their employees’ skills and HCF network structure. We show that our proposed human capital overlap metrics have superior predictive power over conventional firm-level measures in predicting future labor market competitors. We further demonstrate how our proposed metrics and the prediction framework can be incorporated into a comprehensive two-dimensional competitor analysis that includes both product and labor overlap between firms. By evaluating interfirm relationships in the product and labor markets simultaneously, this two-dimensional competitor analysis framework can help managers make strategic decisions beyond human resources, such as product development and customer relationship management.

Advancing interpretation of stable isotope assignment maps: comparing and summarizing origins of known-provenance migratory bats

AbstractProbability-of-origin maps deduced from stable isotope data are important for inferring broad-scale patterns of animal migration, but few resources and tools for interpreting and validating these maps exist. For example, quantitative tools for comparing multiple probability-of-origin maps do not exist, and many existing approaches for geographic assignment of individuals have not been validated or compared with respect to precision and accuracy. To address these challenges, we created and analyzed probability-of-origin maps using stable hydrogen isotope values from known-origin individuals of three species of migratory bat. We used a metric of spatial overlap to group individuals by areas of origin without a priori knowledge of such regions. The metric of spatial similarity allowed for quantitative comparison of geographic origins and grouping of individuals with similar origins. We then compared four approaches for inferring origins (cumulative-sum, odds-ratio, quantile-only, and quantile-simulation) across a range of thresholds and probable minimum distance traveled. The accuracy of geographic origins and minimum distance traveled varied across species at most threshold values for most approaches. The cumulative-sum and quantile-simulation approaches had generally higher precision at a given level of accuracy than the odds-ratio and quantile-only approaches, and many threshold values were associated with a relatively high degree (&gt; 300 km) of variation in minimum distance traveled. Overall, these results reinforce the importance of validating assignment techniques with known-origin individuals when possible. We present the tools discussed as part of an R package, ‘isocat’ (“Isotope Origin Clustering and Assignment Tools”).

Optimizing data processing to improve the reproducibility of single-subject functional magnetic resonance imaging.

IntroductionHigh reproducibility is critical for ensuring the confidence needed to use functional magnetic resonance imaging (fMRI) activation maps for presurgical planning.MethodsIn this study, the comparison of different motion correction methods, spatial smoothing methods, regression methods, and thresholding methods was performed to see whether specific data processing methods can be employed to improve the reproducibility of single‐subject fMRI activation. Three test–retest metrics were used: the percent difference in activation volume (PDAV), the difference in the center of mass (DCM), and the Dice Similarity Coefficient (DSC).ResultsThe PDAV was minimized when using little or no spatial smoothing and AMPLE thresholding. The DCM was minimized when using affine motion correction and little or no spatial smoothing. The DSC was improved when using affine motion correction and generous spatial smoothing. However, it is believed that the overlap metric may be unsuitable for testing fMRI reproducibility.ConclusionProcessing methods to improve fMRI reproducibility were determined. Importantly, the processing methods needed to improve reproducibility were dependent on the fMRI activation metric of interest.

An enhanced protein secondary structure prediction using deep learning framework on hybrid profile based features

Accurate protein secondary structure prediction (PSSP) is essential to identify structural classes, protein folds, and its tertiary structure. To identify the secondary structure, experimental methods exhibit higher precision with the trade-off of high cost and time. In this study, we propose an effective prediction model which consists of hybrid features of 42-dimensions with the combination of convolutional neural network (CNN) and bidirectional recurrent neural network (BRNN). The proposed model is accessed on four benchmark datasets such as CB6133, CB513, CASP10, and CAP11 using Q3, Q8, and segment overlap (Sov) metrics. The proposed model reported Q3 accuracy of 85.4%, 85.4%, 83.7%, 81.5%, and Q8 accuracy 75.8%, 73.5%, 72.2%, and 70% on CB6133, CB513, CASP10, and CAP11 datasets respectively. The results of the proposed model are improved by a minimum factor of 2.5% and 2.1% in Q3 and Q8 accuracy respectively, as compared to the popular existing models on CB513 dataset. Further, the quality of the Q3 results is validated by structural class prediction and compared with PSI-PRED. The experiment showed that the quality of the Q3 results of the proposed model is higher than that of PSI-PRED.

Revisiting the identification of tumor sub-volumes predictive of residual uptake after (chemo)radiotherapy: influence of segmentation methods on 18F-FDG PET/CT images

Our aim was to evaluate the impact of the accuracy of image segmentation techniques on establishing an overlap between pre-treatment and post-treatment functional tumour volumes in 18FDG-PET/CT imaging. Simulated images and a clinical cohort were considered. Three different configurations (large, small or non-existent overlap) of a single simulated example was used to elucidate the behaviour of each approach. Fifty-four oesophageal and head and neck (H&N) cancer patients treated with radiochemotherapy with both pre- and post-treatment PET/CT scans were retrospectively analysed. Images were registered and volumes were determined using combinations of thresholds and the fuzzy locally adaptive Bayesian (FLAB) algorithm. Four overlap metrics were calculated. The simulations showed that thresholds lead to biased overlap estimation and that accurate metrics are obtained despite spatially inaccurate volumes. In the clinical dataset, only 17 patients exhibited residual uptake smaller than the pre-treatment volume. Overlaps obtained with FLAB were consistently moderate for esophageal and low for H&N cases across all metrics. Overlaps obtained using threshold combinations varied greatly depending on thresholds and metrics. In both cases overlaps were variable across patients. Our findings do not support optimisation of radiotherapy planning based on pre-treatment 18FDG-PET/CT image definition of high-uptake sub-volumes. Combinations of thresholds may have led to overestimation of overlaps in previous studies.

Community structure of a Neotropical bat fauna as revealed by stable isotope analysis: Not all species fit neatly into predicted guilds

AbstractNeotropical bat communities are among the most diverse mammal communities in the world, and a better understanding of these assemblages may permit inferences about how so many species coexist. While broad trophic guilds (e.g., frugivore, insectivore) of bats are recognized, details of diet and similarities among species remain largely unknown. We used stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) to characterize the community structure of a diverse Neotropical bat fauna from Belize to test predictions of niche theory and the competitive exclusion principle. We predicted that (1) interspecific variation in isotopic overlap would be greater within guilds than between guilds, and (2) no two sympatric populations would have isotopic niches that overlap completely, unless there is variation along some other axis (e.g., temporal, spatial). We additionally tested body size as an explanatory metric of potential overlap and predicted that larger‐bodied animals would have greater niche breadths. Results suggest that while guild‐level characterizations of communities are at least somewhat informative, there are multiple examples of intra‐ and inter‐guild species pairs with significantly overlapping isotopic niches, suggesting that, counter to predictions, they may compete for resources. Understanding the trophic structure of animal communities is fundamental to conservation and management of endangered species and ecosystems and important for evolutionary studies, and stable isotope analyses can provide key insights as well as informing hypotheses of the diet of species that are not well known.Abstract in Spanish is available with online material.

A review of methods for quantifying spatial predator–prey overlap

AbstractBackgroundStudies that attempt to measure shifts in species distributions often consider a single species in isolation. However, understanding changes in spatial overlap between predators and their prey might provide deeper insight into how species redistribution affects food web dynamics.Predator–prey overlap metricsHere, we review a suite of 10 metrics [range overlap, area overlap, the local index of collocation (Pianka's O), Hurlbert's index, biomass‐weighted overlap, asymmetrical alpha, Schoener's D, Bhattacharyya's coefficient, the global index of collocation and the AB ratio] that describe how two species overlap in space, using concepts such as binary co‐occurrence, encounter rates, spatial niche similarity, spatial independence, geographical similarity and trophic transfer. We describe the specific ecological insights that can be gained using each overlap metric, in order to determine which is most appropriate for describing spatial predator–prey interactions for different applications.Simulation and case studyWe use simulated predator and prey distributions to demonstrate how the 10 metrics respond to variation in three types of predator–prey interactions: changing spatial overlap between predator and prey, changing predator population size and changing patterns of predator aggregation in response to prey density. We also apply these overlap metrics to a case study of a predatory fish (arrowtooth flounder, Atheresthes stomias) and its prey (juvenile walleye pollock, Gadus chalcogrammus) in the Eastern Bering Sea, AK, USA. We show how the metrics can be applied to understand spatial and temporal variation in the overlap of species distributions in this rapidly changing Arctic ecosystem.ConclusionsUsing both simulated and empirical data, we provide a roadmap for ecologists and other practitioners to select overlap metrics to describe particular aspects of spatial predator–prey interactions. We outline a range of research and management applications for which each metric may be suited.

Making Sense of Single‐Case Design Effect Sizes

AbstractSingle‐case research methods provide a basis for demonstrating that an intervention produces a reliable change in a targeted outcome for individual cases. To supplement visual analysis of data in single‐case studies, researchers frequently report statistics—often referred to as effect sizes—to summarize study findings. The recent proliferation of effect sizes used in single‐case research can be confusing. In this article, after reviewing single‐case research, we provide an overview of common types of effect sizes used in single‐case research, including overlap metrics and within‐ and between‐participant effect sizes, and conclude with examples of these effect sizes in the single‐case literature. Our take‐home message is that effect sizes are useful complements to visual analysis when interpreting results of single‐case design research studies.

Approximate Multiparametric Mixed-Integer Convex Programming

We propose an algorithm for generating explicit solutions of multiparametric mixed-integer convex programs to within a given suboptimality tolerance. The algorithm is applicable to a very general class of optimization problems, but is most useful for hybrid model predictive control, where online implementation is hampered by the worst-case exponential complexity of mixed-integer solvers. The output is a simplicial partition which defines a static map from the current state to a suboptimal solution. The primary theoretical contribution of this letter is to introduce a non-zero optimal cost overlap metric which is necessary and sufficient for convergence. The overlap size is also linked to partition complexity. The algorithm is massively parallelizable and our implementation, which is publicly available, is run on a cluster of several hundred processors. Not only does our solution have a deterministic runtime, simulations show that our approach is faster than online optimization by up to three orders of magnitude.

A framework for evaluating correspondence between brain images using anatomical fiducials.

Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.