# Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images

arXiv: https://arxiv.org/abs/2410.18506 · HTML: https://mlsanity.com/paper/2410.18506/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 105 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data](https://mlsanity.com/paper/2009.04681/) (2020)
- [Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks](https://mlsanity.com/paper/2011.08261/) (2020)
- [A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering](https://mlsanity.com/paper/1407.3809/) (2014)
- [Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI](https://mlsanity.com/paper/2101.01832/) (2021)
- [Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)](https://mlsanity.com/paper/2101.09354/) (2021)
- [Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality](https://mlsanity.com/paper/2101.10471/) (2021)
- [A Prospective Randomized Clinical Trial for Measuring Radiology Study Reporting Time on Artificial Intelligence-Based Detection of Intracranial Hemorrhage in Emergent Care Head CT](https://mlsanity.com/paper/2002.12515/) (2020)
- [Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis in Biomedical Imaging](https://mlsanity.com/paper/2004.01185/) (2020)
- [Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments](https://mlsanity.com/paper/2106.00842/) (2021)

## Abstract (arXiv)

In the present research, the effectiveness of large-scale Augmented Granger Causality (lsAGC) as a tool for gauging brain network connectivity was examined to differentiate between marijuana users and typical controls by utilizing resting-state functional Magnetic Resonance Imaging (fMRI). The relationship between marijuana consumption and alterations in brain network connectivity is a recognized fact in scientific literature. This study probes how lsAGC can accurately discern these changes. The technique used integrates dimension reduction with the augmentation of source time-series in a model that predicts time-series, which helps in estimating the directed causal relationships among fMRI time-series. As a multivariate approach, lsAGC uncovers the connection of the inherent dynamic system while considering all other time-series. A dataset of 60 adults with an ADHD diagnosis during childhood, drawn from the Addiction Connectome Preprocessed Initiative (ACPI), was used in the study. The brain connections assessed by lsAGC were utilized as classification attributes. A Graph Attention Neural Network (GAT) was chosen to carry out the classification task, particularly for its ability to harness graph-based data and recognize intricate interactions between brain regions, making it appropriate for fMRI-based brain connectivity data. The performance was analyzed using a five-fold cross-validation system. The average accuracy achieved by the correlation coefficient method was roughly 52.98%, with a 1.65 standard deviation, whereas the lsAGC approach yielded an average accuracy of 61.47%, with a standard deviation of 1.44. The suggested method enhances the body of knowledge in the field of neuroimaging-based classification and emphasizes the necessity to consider directed causal connections in brain network connectivity analysis when studying marijuana's effects on the brain.