Dr. Ali Moradi Amani Profile

Dr. Ali Moradi Amani

at RMIT Univ

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Author

Publications (2)

Proceedings Article | 21 May 2020 Paper

Large-scale graph networks and AI applied to medical image data processing

Anke Meyer-Bäse, Simon Foo, Amirhessam Tahmassebi, Uwe Meyer-Bäse, Ali Moradi Amani, Theresa Götz, Doris Leithner, Andreas Stadlbauer, Katja Pinker

Proceedings Volume 11396, 1139605 (2020) https://doi.org/10.1117/12.2557813

KEYWORDS: Brain, Artificial intelligence, Medical imaging, Radiology, Machine learning, Tumors, Data processing, Cancer, Feature extraction, Data modeling

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Proceedings Article | 12 March 2018 Paper

Determining disease evolution driver nodes in dementia networks

Amirhessam Tahmassebi, Ali Moradi Amani, Katja Pinker-Domenig, Anke Meyer-Baese

Proceedings Volume 10578, 1057829 (2018) https://doi.org/10.1117/12.2293865

KEYWORDS: Dementia, Brain, Control systems, Magnetic resonance imaging, Alzheimer's disease, Analytical research, Neuroimaging, Network security, Neural networks, Mathematical modeling

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Imaging connectomics emerged as an important field in modern neuroimaging to represent the interaction of structural and functional brain areas. Static graph networks are the mathematical structure to capture these interactions modeled by Pearson correlations between the representative area signals. Dynamical functional resting state networks seen in most fMRI experiments can not be represented by the classic correlation graph network. The changes in brain connectivity observed in many neuro-degenerative diseases in longitudinal data series suggest that more sophisticated graph networks to capture the dynamical properties of the brain networks are required. Furthermore, certain brain areas seem to act as ”disease epicenters” being responsible for the spread of neuro-degenerative diseases. To mathematically describe these aspects, we propose a novel framework based on pinning controllability applied to dynamic graphs and seek to determine the changes in the ”driver nodes” during the course of the disease. In contrast to other current research in pinning controllability, we aim to identify the best driver nodes describing disease evolution with respect to connectivity changes and location of the best driver nodes in functional ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography (¹⁸FDG-PET) and structural Magnetic Resonance Imaging (MRI) connectivity graphs in healthy controls (CN), and patients with mild cognitive impairment (MCI), and Alzheimer’s disease (AD). We present the theoretical framework for determining the best driver nodes in connectivity graphs and their relation to disease evolution in dementia. We revolutionize the current graph analysis in brain networks and apply the concept of dynamic graph theory in connection with pinning controllability to reveal differences in the location of ”disease epicenters” that play an important role in the temporal evolution of dementia. The described research will constitute a leap in biomedical research related to novel disease prediction trajectories and precision dementia therapies.

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