List of Abbreviations by Gursimran Singh (575288)

Subjects:

The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

Subjects:

Data_Sheet_1_A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks.docx by Maria Grazia Puxeddu (10211162)

“…Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. …”

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

Subjects:

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

The workflow used to analyze imbalanced and balanced sequences. by Solomon Shiferaw Beyene (9143178)

Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). by Solomon Shiferaw Beyene (9143178)

Accuracy, sensitivity, specificity and F-score. by Solomon Shiferaw Beyene (9143178)

The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. by Solomon Shiferaw Beyene (9143178)

Out-of-distribution prediction and performance of predictive models as a function of sample diversity. by Oualid Benkarim (9707986)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

Subjects:

Penalty functions used in different methods. by Souvik Seal (12437166)

Application of Cell2Spatial to 10× Genomics Xenium mouse brain data. by Huamei Li (8815955)

Estimation accuracy across different structural and functional components of the BVEP model. by Meysam Hashemi (4114738)

“…<p>(<b>A</b>) Excitability values used for simulation () versus the estimated values () for a selected brain region (node number 6). As the node dynamics are varied by changing the excitability parameter (functional component), the model inversion by NUTS algorithm demonstrates an accurate and robust estimation by recovering the ground truth. …”

Image_1_Test-retest reliability of modular-relevant analysis in brain functional network.PDF by Xuyun Wen (414454)

“…Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection.…”

Hierarchical decomposition of covariance in gene expression space brain is mirrored by a matching decomposition in brain space. by Stan Kerstjens (13410092)

Data_Sheet_1_Abnormal Brain Functional Network Dynamics in Acute CO Poisoning.docx by Hongyi Zheng (816158)

“…</p><p>Methods: Combining the sliding window method and k-means algorithm, we identified four recurrent dynamic functional cognitive impairment states from resting-state functional magnetic resonance imaging data from 29 patients in the acute phase of carbon monoxide poisoning and 29 healthy controls. …”

Explained variance as a function of number of inner dimensions, <i>Q</i>. by Kai J. Miller (8846117)

Table_1_Quantification and Selection of Ictogenic Zones in Epilepsy Surgery.DOCX by Petroula Laiou (7446842)

“…<p>Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. …”

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)