Mapping correlations of psychological and structural connectome properties of the dataset of the human connectome project with the maximum spanning tree method

2018 ◽  
Vol 13 (5) ◽  
pp. 1185-1192 ◽  
Author(s):  
Balázs Szalkai ◽  
Bálint Varga ◽  
Vince Grolmusz
Keyword(s):  
Spanning Tree ◽  
Structural Connectome ◽  
Human Connectome Project ◽  
Maximum Spanning Tree ◽  
Tree Method

scholarly journals Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data

2021 ◽  
Vol 2021 (1) ◽  
pp. 1054-1064
Author(s):  
Salwa Rizqina Putri ◽  
Thosan Girisona Suganda ◽  
Setia Pramana
Keyword(s):  
Big Data ◽  
Bayesian Network ◽  
Data Model ◽  
Spanning Tree ◽  
Cross Validation ◽  
Parameter Learning ◽  
Maximum Spanning Tree

Untuk mendukung pertumbuhan ekonomi hijau Indonesia, diperlukan analisis lebih lanjut terkait aktivitas ekonomi di masa pandemi dan keterkaitannya dengan kondisi lingkungan. Penelitian ini bertujuan untuk menerapkan pendekatan Bayesian Network dalam memodelkan kondisi ekonomi hijau Indonesia di masa pandemi berdasarkan variabel-variabel yang disinyalir dapat berpengaruh seperti aktivitas ekonomi, kualitas udara, tingkat mobilitas penduduk, dan kasus positif COVID-19 yang diperoleh melalui big data. Model Bayesian Network yang dikonstruksi secara manual dengan algoritma Maximum Spanning Tree dipilih sebagai model terbaik dengan rata-rata akurasi 5-cross validation dalam memprediksi empat kelas PDRB sebesar 0,83. Model terbaik yang dipilih menunjukkan bahwa kondisi ekonomi Indonesia di era pandemi secara langsung dipengaruhi oleh intensitas cahaya malam (NTL) yang menunjukkan aktivitas ekonomi, kualitas udara (AQI), dan kasus positif COVID-19. Analisis parameter learning menunjukkan bahwa pertumbuhan ekonomi provinsi-provinsi Indonesia masih cenderung belum sejalan dengan terpeliharanya kualitas udara sehingga usaha untuk mencapai kondisi ekonomi hijau masih harus ditingkatkan.

scholarly journals ANALYSIS OF MENTAL ARITHMETIC TASK BY THE “MINIMUM SPANNING TREE” METHOD

2016 ◽  
Vol 69 (5-6) ◽  
Author(s):  
Roland Boha ◽  
Brigitta Tóth ◽  
Zsófia Kardos ◽  
Bálint File ◽  
Zsófia Anna Gaál ◽  
...  
Keyword(s):  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Mental Arithmetic ◽  
Arithmetic Task ◽  
Mental Arithmetic Task ◽  
Tree Method

scholarly journals Network communication models improve the behavioral and functional predictive utility of the human structural connectome

2020 ◽  
Author(s):  
Caio Seguin ◽  
Ye Tian ◽  
Andrew Zalesky
Keyword(s):  
Structure Function ◽  
Shortest Paths ◽  
Brain Regions ◽  
Interindividual Variation ◽  
Network Communication ◽  
Resting State Functional Connectivity ◽  
Predictive Utility ◽  
Communication Models ◽  
Structural Connectome ◽  
Human Connectome Project

The connectome provides a structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication paths in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Structural connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (i) used to perform predictions of five data-driven behavioral dimensions and (ii) correlated to interregional resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, network communication models, in particular communicability and navigation, improved the performance of structural connectomes. Accounting for polysynaptic communication also significantly strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. Combining behavioral and functional results into a single ranking of communication models positioned navigation as the top model, suggesting that it may more faithfully recapitulate underlying neural signaling patterns. We conclude that network communication models augment the functional and behavioral predictive utility of the human structural connectome and contribute to narrowing the gap between brain structure and function.

scholarly journals Graphical Optimization of Spectral Shift Reconstructions for Optical Backscatter Reflectometry

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6154
Author(s):  
Daniel C. Sweeney ◽  
Dennis M. Sweeney ◽  
Christian M. Petrie
Keyword(s):  
Adaptive Algorithm ◽  
Spanning Tree ◽  
Mechanical Strain ◽  
Adaptive Algorithms ◽  
Spectral Shift ◽  
Reference Measurement ◽  
Interferometric Technique ◽  
Quality Metric ◽  
Spectral Shifts ◽  
Maximum Spanning Tree

Optical backscatter reflectometry (OBR) is an interferometric technique that can be used to measure local changes in temperature and mechanical strain based on spectral analyses of backscattered light from a singlemode optical fiber. The technique uses Fourier analyses to resolve spectra resulting from reflections occurring over a discrete region along the fiber. These spectra are cross-correlated with reference spectra to calculate the relative spectral shifts between measurements. The maximum of the cross-correlated spectra—termed quality—is a metric that quantifies the degree of correlation between the two measurements. Recently, this quality metric was incorporated into an adaptive algorithm to (1) selectively vary the reference measurement until the quality exceeds a predefined threshold and (2) calculate incremental spectral shifts that can be summed to determine the spectral shift relative to the initial reference. Using a graphical (network) framework, this effort demonstrated the optimal reconstruction of distributed OBR measurements for all sensing locations using a maximum spanning tree (MST). By allowing the reference to vary as a function of both time and sensing location, the MST and other adaptive algorithms could resolve spectral shifts at some locations, even if others can no longer be resolved.

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