Bulletin of ANPA

Abstract submitted to ANPA Conference July 14–16, 2023

Volume 5, Number 1

Data Science, Quantum Computing, Artificial Intelligence

Abstract ID: ANPA2023-N00080

Abstract:

ANPA2023-N00080: Promises of imaging physics and machine learning in mental disorders

Authors:

• Puskar Bhattarai; Washington University in St. Louis

• Ahmed Taha; Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA

• Bhavin Soni;

• Deepa S. Thakuri;

• Erin Ritter;

• Ganesh B. Chand;

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are some of the greatest inventions of physics and high-level engineering designs. MRI provides anatomical and functional information based on proton interactions with a strong magnetic field and radiofrequency pulses. PET offers the spatial distribution of positron-emitting radionuclides based on capturing the gamma rays emitted upon the annihilation of electron-positron pair, allowing the quantification of various physiological and molecular processes. These non-invasive techniques have revolutionized many fields, including neuroimaging of mental disorders. However, MRI and PET neuroimaging data are highly complex and difficult to interpret. This makes drawing clinical conclusions challenging that are critical for clinical diagnostics, prognostics, and precision medicine. To overcome such barriers, modern machine learning methods have shown great promise in characterizing neuroimaging data. The large-scale resources in mental disorders involve images from multiple sites, scanners, protocols, and diverse populations. One of the biggest challenges for using such research resources is that quantitative measures are not easily reproducible and are highly sensitive to PET and MRI acquisition differences and other sources of variance. Our team has been at the forefront of building machine learning and statistical harmonization methods to study brain patterns in various diseases, including Alzheimer’s disease and schizophrenia. We will highlight various statistical harmonization and machine learning approaches, such as control-based regression, supervised learning, semi-supervised learning, feature importance, and dimensionality reduction, in the context of MRI and PET data. We will show their broad applications in detecting subtle structural and functional changes in the brain and identifying the novel relationships of these changes with behavioral, clinical, and genetic measures. Overall, our findings demonstrate that physics-based imaging technologies integrated with advanced machine learning approaches provide a multidisciplinary perspective in understanding neurobiological mechanisms in health and diseases, and aid in developing future computer-aided therapeutic efforts in mental disorders.

To cite this abstract, use the following reference: https://anpaglobal.org/conference/2023/ANPA2023-N00080