Search Results for author: Anton Orlichenko

Found 7 papers, 4 papers with code

An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis

no code implementations29 Mar 2024 Ziyu Zhou, Anton Orlichenko, Gang Qu, Zening Fu, Vince D Calhoun, Zhengming Ding, Yu-Ping Wang

Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder.

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies

no code implementations18 Jan 2024 Gang Qu, Anton Orlichenko, Junqi Wang, Gemeng Zhang, Li Xiao, Aiying Zhang, Zhengming Ding, Yu-Ping Wang

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain.

Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort

1 code implementation15 Aug 2023 Anton Orlichenko, Kuan-Jui Su, Qing Tian, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

Using the full FC and a training set of 2, 000 subjects, one is able to predict which scan is older 82. 5\% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

1 code implementation2 Aug 2023 Anton Orlichenko, Gang Qu, Kuan-Jui Su, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10, 000 training subjects without double-dipping.

Angle Basis: a Generative Model and Decomposition for Functional Connectivity

1 code implementation17 May 2023 Anton Orlichenko, Gang Qu, Ziyu Zhou, Zhengming Ding, Yu-Ping Wang

We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%.

ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound

no code implementations1 Feb 2023 Anton Orlichenko, Grant Daly, Ziyu Zhou, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.

Data Visualization Navigate

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction

1 code implementation30 Aug 2022 Anton Orlichenko, Gang Qu, Gemeng Zhang, Binish Patel, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang

Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data.

Computational Efficiency Metric Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.