Integral Metrics Research Articles

Formation of flight zones of UAV according to the degree of stability in order to increase the reliability of the command radio control line in the conditions of the use of air defense and electronic suppression

The combat use of small and medium-class unmanned aerial vehicles (UAVs) in recent local military conflicts, as well as the experience of using UAVs in the operation of the Russian Aerospace Forces in Syria and Ukraine, has shown that they are mainly assigned the tasks of aerial reconnaissance in those areas of the theater of operations (Theater of Operations) where The use of manned aircraft is unjustified or impractical due to the high probability of damage to manned aircraft. The main threats to UAVs in modern theater are the possibility of hitting them with anti-aircraft missile systems (SAMs) of air defense (air defense), as well as the suppression of the command radio control line (CRU) of the UAV by means of electronic suppression (RAP) of the enemy. The purpose of the work is to develop theoretical solutions aimed at the formalized formation of air defense and RAP zones in the theater based on clustering theory methods for subsequent consideration of these zones in the automated formation of UAV flight routes. The paper proposes a methodology for the formalized formation of air defense and RAP zones based on the methods of clustering theory. In the future, the air defense and RAP zones are taken into account in the automated route control of the UAV by forming the flight route of the UAV bypassing these zones. The novelty of this solution, which distinguishes it from well–known works in the field of formation of UAV flight routes, is the consideration of two types of destabilizing factors as obstacles to UAV flight - the impact of air defense and REP means. These factors are formalized in the form of an integral metric of nodes in the graph of the geotopological model of the theater flight zone. At the same time, the Lance-Williams mathematical algorithm of hierarchical clustering is used to form «closed for flights» zones in which the probability of UAV damage is high, and areas of control violation due to the impact of RAP means. The connectivity check of the route network is based on the method of determining the strongly connected areas of the graph. The resulting solution makes it possible to increase the stability of UAV control during their combat use in theater equipped with air defense and RAP systems.

Blockchain Technology: Revolutionizing Data Integrity and Security in Digital Environments

This study explores the transformative impact of blockchain technology on data integrity and security in digital environments. Through a comprehensive assessment of data integrity metrics across prominent blockchain networks, including Bitcoin, Ethereum, and Hyperledger Fabric, we unveil nuanced differences in immutability and reliability. Our security analysis delves into the cryptographic strength and resistance to unauthorized access, showcasing the outstanding security features of Hyperledger Fabric and Bitcoin, with Ethereum exhibiting commendable yet moderate security levels. The discussions underscore the multifaceted nature of blockchain technology, emphasizing the importance of selecting a platform aligned with specific use cases. Hyperledger Fabric and Bitcoin emerge as strong contenders for applications requiring high integrity and robust security, while Ethereum offers a reliable but moderate alternative. As blockchain technology continues to evolve, this study provides valuable insights for practitioners and researchers, guiding the strategic selection of blockchain platforms to harness their transformative potential in diverse digital environments.

Systematic Review of the Circular Economy Performance Assessment System Under International Management Paradigms

Objective: To present a Systematic Literature Review (SLR) on Performance Evaluation in the Circular Economy (PECE), addressing its temporal evolution and critically analyzing the metrics found in empirical studies. Theoretical Framework: The research starts from theoretical contributions on the characteristics of a metric and the levels of performance assessment systems, compared with the evolution of performance assessment in the management of natural resources until we reach the circular economy. Method: Utilization of Systematic Literature Review following the Knowledge Development Process-Constructivist approach (ProKnow-C), assessing secondary data from articles comprising the Research Portfolio (RP). Results and Discussion: The literature presents a variety of circularity metrics at different scales, yet there is a predominance of individual metrics and a lack of comprehensive focus on Performance Evaluation Systems (PES). Research Implications: Identifies the need for more comprehensive and integrative metrics aligned with CE principles and capable of encompassing multiple levels of action. Future research has the potential to significantly enrich society by exploring the development of more comprehensive metrics, considering the interaction between levels of circularity and environmental performance. Originality/Value: Highlights the growing concern with metrics in the Circular Economy (CE), identifies gaps in understanding the full impacts of each approach in the supply chain, and underscores the need for more robust and integrative metrics aligned with CE principles.

White Matter Microstructural Underpinnings of Mild Behavioral Impairment in Parkinson's Disease.

Patients with Parkinson's disease (PD) experience changes in behavior, personality, and cognition that can manifest even in the initial stages of the disease. Previous studies have suggested that mild behavioral impairment (MBI) should be considered an early marker of cognitive decline. However, the precise neurostructural underpinnings of MBI in early- to mid-stage PD remain poorly understood. The aim was to explore the changes in white matter microstructure linked to MBI and mild cognitive impairment (MCI) in early- to mid-stage PD using diffusion magnetic resonance imaging (dMRI). A total of 91 PD patients and 36 healthy participants were recruited and underwent anatomical MRI and dMRI, a comprehensive neuropsychological battery, and the completion of the Mild Behavioral Impairment-Checklist. Metrics of white matter integrity included tissue fractional anisotropy (FAt) and radial diffusivity (RDt), free water (FW), and fixel-based apparent fiber density (AFD). The connection between the left amygdala and the putamen was disrupted when comparing PD patients with MBI (PD-MBI) to PD-non-MBI, as evidenced by increased RDt (η2 = 0.09, P = 0.004) and both decreased AFD (η2 = 0.05, P = 0.048) and FAt (η2 = 0.12, P = 0.014). Compared to controls, PD patients with both MBI and MCI demonstrated increased FW for the connection between the left orbitofrontal gyrus (OrG) and the hippocampus (η2 = 0.22, P = 0.008), augmented RDt between the right OrG and the amygdala (η2 = 0.14, P = 0.008), and increased RDt (η2 = 0.25, P = 0.028) with decreased AFD (η2 = 0.10, P = 0.046) between the right OrG and the caudate nucleus. MBI is associated with abnormal microstructure of connections involving the orbitofrontal cortex, putamen, and amygdala. To our knowledge, this is the first assessment of the white matter microstructure in PD-MBI using dMRI. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

High-altitude hypoxia promotes BRD4-mediated activation of the Wnt/β-catenin pathway and disruption of intestinal barrier

Hypobaric hypoxia, commonly experienced at elevated altitudes, presents significant physiological challenges. Our investigation is centered on the impact of the bromodomain protein 4 (BRD4) under these conditions, especially its interaction with the Wnt/β-Catenin pathway and resultant effects on glycolytic inflammation and intestinal barrier stability. By combining transcriptome sequencing with bioinformatics, we identified BRD4's key role in hypoxia-related intestinal anomalies. Clinical parameters of altitude sickness patients, including serum BRD4 levels, inflammatory markers, and barrier integrity metrics, were scrutinized. In vitro studies using CCD 841 CoN cells depicted expression changes in BRD4, Interleukin (IL)-1β, IL-6, and β-Catenin. Transepithelial electrical resistance (TEER) and FD4 analyses assessed barrier resilience. Hypoxia-induced mouse models, analyzed via H&E staining and Western blot, provided insights into barrier and protein alterations. Under hypoxic conditions, marked BRD4 expression variations emerged. Elevated serum BRD4 in patients coincided with intensified Wnt signaling, inflammation, and barrier deterioration. In vitro, findings showed hypoxia-induced upregulation of BRD4 and inflammatory markers but a decline in Occludin and ZO1, affecting barrier strength—effects mitigated by BRD4 inhibition. Mouse models echoed these patterns, linking BRD4 upregulation in hypoxia to barrier perturbations. Hypobaric hypoxia-induced BRD4 upregulation disrupts the Wnt/β-Catenin signaling, sparking glycolysis-fueled inflammation and weakening intestinal tight junctions and barrier degradation.

Free water-corrected fractional anisotropy of the fornix and parahippocampal cingulum predicts longitudinal memory change in cognitively healthy older adults

Prior studies have reported inconsistent results regarding the relationships between the integrity of the fornix and parahippocampal cingulum and both memory performance and longitudinal change in performance. In the present study, we examined associations in a sample of cognitively healthy older adults between free water-corrected fractional anisotropy (FA) metrics derived from the fornix and cingulum, baseline memory performance, and 3-year memory change. Neither fornix nor cingulum FA correlated with memory performance at baseline. By contrast, FA of each tract was predictive of memory change, such that greater FA was associated with less longitudinal decline. These associations remained significant after controlling for FA of other white matter tracts and for performance in other cognitive domains. Furthermore, fornix and cingulum FA explained unique variance in memory change. These results suggest that free water-corrected measures of fornix and parahippocampal cingulum integrity are reliable predictors of future memory change in cognitively healthy older adults. The findings for the fornix in particular highlight the utility of correcting for free water when estimating diffusion tensor imaging metrics of white matter integrity.

Ecological river health assessments, based on fish ordination analysis of ecological indicator entities and the biological integrity metrics, responding to the chemical water pollution

Evaluation of the ecological health of rivers requires a focused examination of how biological indicators respond to chemical stressors to offer key insights for effective conservation strategies. We examined the influence of stressors on aquatic ecosystems by analyzing various ecological entities and biotic integrity metrics of fish communities. A nonmetric multidimensional scaling (NMDS) approach was applied to determine scores based on 19 fish ecological entities (FEs) and a fish-based multi-metric index of biotic integrity (mIBI-F). The composition of fish communities in reference clusters differed from the disturbed clusters due to instream chemical stressors. These chemical stressors, including high levels of nutrients, organic matter, and ionic/suspended solids, were linked to variation in the key indicator FEs, whose guild identities were closely associated with instream chemical degradation. The scores of FEs (abundance weighted) and mIBI-F metrics in the first NMDS axis (NMDS1) were significantly linked with chemical health indicators (p < 0.001), such as total phosphorus (R2 = 0.67 and 0.47), electrical conductivity (R2 = 0.59 and 0.49), and chlorophyll-a (R2 = 0.48 and 0.25). These NMDS1 scores showed better accuracy than the conventional mIBI-F score in capturing river ecological health linked with chemical health status as determined by a multi-metric index of water pollution. Our study suggests that based on the ordination approach, the biological integrity of these systems reflected the chemical health.

Velocity gradient analysis of a head-on vortex ring collision

We simulate the head-on collision between vortex rings with circulation Reynolds numbers of 4000 using an adaptive, multiresolution solver based on the lattice Green's function. The simulation fidelity is established with integral metrics representing symmetries and discretization errors. Using the velocity gradient tensor and structural features of local streamlines, we characterize the evolution of the flow with a particular focus on its transition and turbulent decay. Transition is excited by the development of the elliptic instability, which grows during the mutual interaction of the rings as they expand radially at the collision plane. The development of antiparallel secondary vortex filaments along the circumference mediates the proliferation of small-scale turbulence. During turbulent decay, the partitioning of the velocity gradients approaches an equilibrium that is dominated by shearing and agrees well with previous results for forced isotropic turbulence. We also introduce new phase spaces for the velocity gradients that reflect the interplay between shearing and rigid rotation and highlight geometric features of local streamlines. In conjunction with our other analyses, these phase spaces suggest that, while the elliptic instability is the predominant mechanism driving the initial transition, its interplay with other mechanisms, e.g. the Crow instability, becomes more important during turbulent decay. Our analysis also suggests that the geometry-based phase space may be promising for identifying the effects of the elliptic instability and other mechanisms using the structure of local streamlines. Moving forward, characterizing the organization of these mechanisms within vortices and universal features of velocity gradients may aid in modelling turbulent flows.

DeepReality: An open source framework to develop AI-based augmented reality applications

Augmented reality (AR) and Artificial Intelligence (AI) are technologies pioneers in innovation and alteration in several domains. AR allows the creation of an entirely new and interactive experience for users. However, there are several drawbacks in developing AR applications, such as the marker identification process and the creation of content itself. These are very time-consuming procedures and require ad-hoc development. The advantages of using AI to solve AR limitations have recently been explored in literature. Motivated by these findings, in this paper it is proposed DeepReality, a software toolkit plug-in for Unity 3D. It is conceived for allowing developers to integrate any Deep Learning (DL) models into Unity, through AR Foundation and Barracuda inference engine. DeepReality is aimed at simplifying and streamlining the usage of DL models in conjunction with AR. As such, users skilled in Unity and DL can easily create mobile applications (iOS and Android) to: extract visual features of real-world objects (framed with the device camera) via DL; Show on-screen content on top of those real-world objects, via AR. DeepReality performs object semantic processing within the scene, and extended semantic effects for incongruent objects, overcoming the environmental tracking, which is feature-based. In order to test DeepReality usability, experiments have been performed on the execution time and memory usage data, demonstrating the feasibility and possibility of integrating and using DNNs models in mobile applications for AR. The complexity analysis confirms that DeepReality can be completely executed on mobile devices. DeepReality is also open-source and it is freely available in the Unity asset store. By fostering accessible AI-AR integration, DeepReality addresses key shortcomings in existing approaches, encapsulating contributions such as versatile DL integration, open-source accessibility, operational validation, and comprehensive metrics analysis. DeepReality empowers developers to transcend boundaries, enriching AR applications with AI’s transformative potential. Our proposed framework fosters benchmarking, comparison, and a future harmonised by AR-AI synergy.

Normative High-Resolution Pharyngeal Manometry: Impact of Age, Size of System, and Sex on Primary Metrics and Pressure Stability.

There have been many reports of normative pharyngeal swallowing pressures using high-resolution pharyngeal manometry, but there is a fair amount of between-subject variance in reported pressure parameters. The purpose of this study was to put forward normative pharyngeal high-resolution manometry measures across the lifespan and investigate the effects of age, size of system, and sex. High-resolution pharyngeal manometry was performed on 98 healthy adults (43 males) between the ages 21 and 89. Pressure duration, maxima, integral, and within-individual variability metrics were averaged over 10 swallows of 10-ml thin liquid. Multiple linear and logistic regressions with model fitting were used to examine how pharyngeal pressures relate to age, pharyngeal size, and sex. Age was associated with tongue base maximum pressure, tongue base maximum variability, and upper esophageal sphincter-integrated relaxation pressure (F3,92 = 6.69; p < 0.001; adjusted R2 = 0.15). Pharyngeal area during bolus hold was associated with velopharynx integral (F1,89 = 5.362; p = 0.02; adjusted R2 = 0.05), and there was no significant model relating pharyngeal pressures to C2-C4 length (p < 0.05). Sex differences were best described by tongue base integral and hypopharynx maximum variability (χ2 = 10.27; p = 0.006; pseudo R2 = 0.14). Normative data reveal the distribution of swallow pressure metrics which need to be accounted for when addressing dysphagia patients, the importance of pressure interactions in normal swallow, and address the relative stability of swallow metrics with normal aging.

Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis

This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoost) model was developed to effectively classify individuals with ASD and typical developing children (TDC). Multi-shell diffusion weighted images were acquired from 62 children with ASD and 44 TDC. Using the Pydesigner procedure, diffusion tensor (DT), diffusion kurtosis (DK), and white matter tract integrity (WMTI) metrics were computed. Subsequently, TBSS analysis was applied to discern differences in these diffusion parameters between ASD and TDC groups. The XGBoost model was then trained using metrics showing significant differences, and Shapley Additive explanations (SHAP) values were computed to assess the feature importance in the model's predictions. TBSS analysis revealed a significant reduction in axonal diffusivity (AD) in the left posterior corona radiata and the right superior corona radiata. Among the DK indicators, mean kurtosis, axial kurtosis, and kurtosis fractional anisotropywere notably increased in children with ASD, with no significant difference in radial kurtosis. WMTI metrics such as axonal water fraction, axonal diffusivity of the extra-axonal space (EAS_AD), tortuosity of the extra-axonal space (EAS_TORT), and diffusivity of intra-axonal space (IAS_Da) were significantly increased, primarily in the corpus callosum and fornix. Notably, there was no significant difference in radial diffusivity of the extra-axial space (EAS_RD). The XGBoost model demonstrated excellent classification ability, and the SHAP analysis identified EAS_TORT as the feature with the highest importance in the model's predictions. This study utilized TBSS analyses with multi-shell diffusion data to examine white matter abnormalities in pediatric autism. Additionally, the developed XGBoost model showed outstanding performance in classifying ASD and TDC. The ranking of SHAP values based on the XGBoost model underscored the significance of features in influencing model predictions.

Selective logging intensity and time since logging drive tropical bird and dung beetle diversity: a case study from Amazonia

SummaryUnderstanding how selective logging affects biodiversity is crucial to planning cost-effective conservation strategies in tropical forests, yet there is limited understanding of its impacts on fauna functional diversity in the tropical Americas. We assessed how selective logging intensification and time since logging influence multiple metrics of fauna functional integrity in the Brazilian Amazon by collecting bird and dung beetle data within 48 logging management units that had experienced varying exploitation intensities between 1.5–2.0 and 5.0–6.0 years before the faunal surveys. We investigated assemblage responses and used functional attributes to assess changes in functional diversity, specialization, originality and species rarity. Using point counts and pitfall traps, we collected 5081 records of 182 bird species and 7892 dung beetles from 45 species. Logging intensification led to significant declines in most dung beetle responses and bird body mass, whereas time since logging was a key driver of dung beetle and bird functional and taxonomic diversity. Our study presents novel empirical evidence of how logging intensification effects vary across taxa, ecological metrics and time since logging, which is key to informing forest conservation and further investigating environmental degradation impacts on tropical biodiversity.

Lee metrics on groups

In this work we consider interval metrics on groups; that is, integral invariant metrics whose associated weight functions do not have gaps. We give conditions for a group to have and not to have interval metrics. Then we study Lee metrics on general groups, that is interval metrics having the finest unitary symmetric associated partition. These metrics generalize the classic Lee metric on cyclic groups. In the case that $ G $ is a torsion-free group or a finite group of odd order, we prove that $ G $ has a Lee metric if and only if $ G $ is cyclic. Also, if $ G $ is a group admitting Lee metrics then $ G \times \mathbb{Z}_2^k $ always has Lee metrics for every $ k \in \mathbb{N} $. Then, we show that some families of metacyclic groups, such as cyclic, dihedral, and dicyclic groups, always have Lee metrics. Finally, we give conditions for non-cyclic groups such that they do not have Lee metrics. We end with tables of all groups of order $ \le 31 $ indicating which of them have (or have not) Lee metrics and why (not).

Islamic-Nuanced Mathematics Teaching Material: An Alternative Tool to Improve Students' Understanding on Integers

The acquisition of mathematical proficiency among university students, particularly within the Department of Elementary Education at Universitas Syiah Kuala, Aceh – Indonesia, often presents challenges. Integrating religious undertones into mathematics pedagogy represents a constructive approach to engender student engagement and augment comprehension. Such integration affords students a familiar contextual backdrop, fostering a more meaningful learning milieu. This article delineates the development of an instructional resource imbued with Islamic motifs, specifically addressing concepts related to integers. The study cohort comprises 115 second-year students enrolled in the Department of Elementary Education at Universitas Syiah Kuala. Methodological stages encompassed preliminary inquiry, developmental endeavors, and evaluative assessments. Research instrumentation encompassed validation protocols for assessing integrity and observation metrics capturing students' instructional engagements. Additionally, assessments, accompanied by post-instructional questionnaires, were administered to gauge student perceptions. Qualitative data analyses were conducted, evaluating the product's viability across dimensions of validity, applicability, and efficacy. Iterative revisions were undertaken during the instructional resource's refinement process. Outcomes underscore the aptness of the Islamic-imbued mathematics instructional material for second-year students within the elementary education domain. The implications of these findings underscore the advantages conferred by familiar-contextualized learning materials in facilitating students' mathematical comprehension.

Basal forebrain integrity, cholinergic innervation and cognition in idiopathic Parkinson's disease.

Most individuals with Parkinson's disease experience cognitive decline. Mounting evidence suggests this is partially caused by cholinergic denervation due to α-synuclein pathology in the cholinergic basal forebrain. Alpha-synuclein deposition causes inflammation, which can be measured with free water fraction, a diffusion MRI-derived metric of extracellular water. Prior studies have shown an association between basal forebrain integrity and cognition, cholinergic levels and cognition, and basal forebrain volume and acetylcholine, but no study has directly investigated whether basal forebrain physiology mediates the relationship between acetylcholine and cognition in Parkinson's disease. We investigated the relationship between these variables in a cross-sectional analysis of 101 individuals with Parkinson's disease. Cholinergic levels were measured using fluorine-18 fluoroethoxybenzovesamicol (18F-FEOBV) PET imaging. Cholinergic innervation regions of interest included the medial, lateral capsular and lateral perisylvian regions and the hippocampus. Brain volume and free water fraction were quantified using T1 and diffusion MRI, respectively. Cognitive measures included composites of attention/working memory, executive function, immediate memory and delayed memory. Data were entered into parallel mediation analyses with the cholinergic projection areas as predictors, cholinergic basal forebrain volume and free water fraction as mediators and each cognitive domain as outcomes. All mediation analyses controlled for age, years of education, levodopa equivalency dose and systolic blood pressure. The basal forebrain integrity metrics fully mediated the relationship between lateral capsular and lateral perisylvian acetylcholine and attention/working memory, and partially mediated the relationship between medial acetylcholine and attention/working memory. Basal forebrain integrity metrics fully mediated the relationship between medial, lateral capsular and lateral perisylvian acetylcholine and free water fraction. For all mediations in attention/working memory and executive function, the free water mediation was significant, while the volume mediation was not. The basal forebrain integrity metrics fully mediated the relationship between hippocampal acetylcholine and delayed memory and partially mediated the relationship between lateral capsular and lateral perisylvian acetylcholine and delayed memory. The volume mediation was significant for the hippocampal and lateral perisylvian models, while free water fraction was not. Free water fraction in the cholinergic basal forebrain mediated the relationship between acetylcholine and attention/working memory and executive function, while cholinergic basal forebrain volume mediated the relationship between acetylcholine in temporal regions in memory. These findings suggest that these two metrics reflect different stages of neurodegenerative processes and add additional evidence for a relationship between pathology in the basal forebrain, acetylcholine denervation and cognitive decline in Parkinson's disease.

Екстремальні значення найкращих наближень лінійних комбінацій гармонічних функцій

Extreme problems and their practical applications have been under the scrutiny of mathematicians since ancient times. An important step in the development of extreme problems was made by P. L. Chebyshev, who in the 50s of the 19th century laid the foundations of a section of destructive function theory – the theory of approximation. A significant role of the formation of the theory of approximation of functions was played by Carl Weierstrass’s theorem on the convergence to zero of best approximations by polynomials of a continuous function. As is well known, Weierstrass’s theorem is not constructive – it does not contained estimates of the approach speed. Thanks to the work of D. Jackson, S. N. Bernstein, Vallee-Poussin and others, such estimates began to appear in works on approximation theory. At the same time, at the first stages of the development of the theory of approximation, approximations of individual functions were studied. That beginning of a new period, a dipper study of the deviation values of functions from their approximating polynomials, dates back to the 30s and 40s of the 20th century and is associated with the names of A. M. Kolmogorov, S. M. Nikolsky, J. Favard, N. I. Achieser, M. G. Crane and B. Nagy. Thanks to their works, the main emphasis in the theory of approximations is shifted to the study of the best approximations or other approximation characteristics of functions that have certain differential-difference or smoothness properties. In particular, in 1936, J. Favard calculated the exact values of the best uniform approximations by trigonometric polynomials of order no higher than n – 1 on classes of differentiable 2π-periodic functions, whose r-th (r – natural) derivatives are in a unit sphere of the space of essentially bounded functions. The problem of obtaining exact values of the best approximations in uniform and integral metrics for various functional compacts was in sight of many prominent mathematicians of the XX century. General issues related to the study of the best approximation functional: the existence of a polynomial of the best approximation, its characteristic properties, are destribed in detail in many monographs, in particular, for example, in the book by M. P. Korneichuk [1]. In the 80s and 90s of the XX century, O. I. Stepanets (see, [2, section III]) developed a new approach to the classification of periodic functions, which allowed for a fairly fine classification of extremely wide sets of periodic functions. At the same time, the results obtained for these classes are, on the one hand, general, and on the other hand, they give a number of new, hitherto unknown results that were impossible to obtain on previously known classes. Following the approaches to the requirements of function classification, we can consider a linear combination of function classes of a more complex nature. And then the problem of finding the exact values of the upper bounds of the best joint approximations will be reduced to the problem of the best approximation of this composite class corresponding to convolutions with the composite kernel.

Spatial navigation metrics differentiating preclinical and prodromal Alzheimer’s disease: a systematic review

AbstractBackgroundIncreasing evidence suggests that impairments of spatial navigation, orientation and memory may represent one of the earliest features Alzheimer’s disease (AD), preceding deficits in other cognitive domains. This is consistent with early pathological tau and amyloid deposition in temporal and parietal brain regions implicated in spatial processing. However, it remains unclear which metrics and spatial behaviours can i) differentiate high‐risk, preclinical or prodromal AD individuals, and ii) may be captured with digital devices suitable for future deployment at scale in clinical practice. We addressed this knowledge gap with a systematic review.MethodTwo databases (PubMed/Web of Science) were searched following PRISMA guidelines. Medical subheading keywords for different dementia types were combined with prodromal, preclinical and genetic or disorder risk factor terms. We included studies published until October 2022 that collected digital or physical objective measures of egocentric or allocentric processing (wayfinding, orientation, reference frame translation, route learning or path integration). We excluded diagnoses of other conditions or dementia, cross‐sectional MCI studies without biomarkers, young adults, case studies, interventional studies or studies without appropriate controls.Result25 articles were included from 316 identified abstracts. None investigated non‐AD dementias. 131 metrics were extracted from 38 different tasks, of which 31 where digital (81%). 82 metrics showed differentiation of predementia AD groups (63%). We harmonised these into 21 distinct summary metrics covering four domains of active or passively tracked spatial behaviours. Across all domains, metrics capturing decreased navigation efficiency (i.e. distance or time required to find goals) and accuracy (i.e. distance or angle from goal) were most frequently reported, but egocentric better differentiated prodromal than preclinical groups. Metrics for path integration and passive tracking of everyday GPS data were promising but relatively under‐explored.ConclusionFor future clinical use, spatial tests will require standardisation and validation against established markers of disease. These review data will inform the selection of digital tools to assess spatial behaviours in at‐risk, preclinical and prodromal AD populations as part of the EDoN Initiative.

Clinical Manifestations.

Increasing evidence suggests that impairments of spatial navigation, orientation and memory may represent one of the earliest features Alzheimer's disease (AD), preceding deficits in other cognitive domains. This is consistent with early pathological tau and amyloid deposition in temporal and parietal brain regions implicated in spatial processing. However, it remains unclear which metrics and spatial behaviours can i) differentiate high-risk, preclinical or prodromal AD individuals, and ii) may be captured with digital devices suitable for future deployment at scale in clinical practice. We addressed this knowledge gap with a systematic review. Two databases (PubMed/Web of Science) were searched following PRISMA guidelines. Medical subheading keywords for different dementia types were combined with prodromal, preclinical and genetic or disorder risk factor terms. We included studies published until October 2022 that collected digital or physical objective measures of egocentric or allocentric processing (wayfinding, orientation, reference frame translation, route learning or path integration). We excluded diagnoses of other conditions or dementia, cross-sectional MCI studies without biomarkers, young adults, case studies, interventional studies or studies without appropriate controls. 25 articles were included from 316 identified abstracts. None investigated non-AD dementias. 131 metrics were extracted from 38 different tasks, of which 31 where digital (81%). 82 metrics showed differentiation of predementia AD groups (63%). We harmonised these into 21 distinct summary metrics covering four domains of active or passively tracked spatial behaviours. Across all domains, metrics capturing decreased navigation efficiency (i.e. distance or time required to find goals) and accuracy (i.e. distance or angle from goal) were most frequently reported, but egocentric better differentiated prodromal than preclinical groups. Metrics for path integration and passive tracking of everyday GPS data were promising but relatively under-explored. For future clinical use, spatial tests will require standardisation and validation against established markers of disease. These review data will inform the selection of digital tools to assess spatial behaviours in at-risk, preclinical and prodromal AD populations as part of the EDoN Initiative.

Containerizing neuroimaging workflows for scalable and reproducible analyses

AbstractBackgroundLarge multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.MethodWe demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.ResultContainerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.ConclusionContainerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.

Containerizing neuroimaging workflows for scalable and reproducible analyses

AbstractBackgroundLarge multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.MethodWe demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.ResultContainerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.ConclusionContainerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.