Principal Investigators //
- David Bridwell, PhD >
- Vince Calhoun, PhD >
- Arvind Caprihan, PhD >
- Zikuan Chen, PhD >
- Vince Clark, PhD >
- Eric D. Claus, PhD >
- Carla Harenski, PhD >
- Kent Hutchison, PhD >
- Kent A. Kiehl, PhD >
- Jeffrey D. Lewine, PhD >
- Jingyu Liu, PhD >
- Andrew R. Mayer, PhD >
- John Phillips, MD >
- Sergey Plis, PhD
- Matthew Shane, PhD >
- Julia M. Stephen, PhD >
- Jing Sui, PhD >
- Jessica Turner, PhD >
- Qingbao Yu, PhD >
Sergey Plis, PhD
Assistant Professor of Translational Neuroscience, Director of Machine Learning in Neuroscience Lab
Dr. Plis researches novel techniques and approaches to analyzing multimodal brain imaging datasets. The main tool source is the field of machine learning. The main goal is to be able to infer structures and patterns in brain function that are hard to obtain non-invasively and/or are unavailable for direct observation. In the long term, this develops methods able to teach us about mechanisms used by the brain for forming task specific transient interaction networks. Dr. Plis' current research is on inferring probabilistic descriptions of these function induced networks based on fusion of fast and slow imaging modalities: MEG and fMRI.
Selected Publications //
- Cortical Sensitivity to Guitar Note Patterns: EEG Entrainment to Repetition and Key >
- Causal Discovery from Subsampled Time Series Data by Constraint Optimization >
- COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Im >
- Task-specific feature extraction and classification of fMRI volumes using a deep neural network… >
- Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia >
- Group-level component analyses of EEG: validation and evaluation >
- A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI >
- Impact of autocorrelation on functional connectivity >
- Disrupted correlation between low frequency power and connectivity strength of resting state brain n >
- Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schiz >
- Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. >
- Directional Statistics on Permutations >
- Analysis of Multimodal Neuroimaging Data >
- Permutations as Angular Data: Efficient Inference in Factorial Spaces >
- MEG and fMRI fusion for non-linear estimation of neural and BOLD signal changes >
Capturing Complex Interactions in Neuroimaging Data
Unsupervised data analysis approaches have been widely used in recent years and have become instrumental in establishing new research directions impossible with more traditional supervised approaches, such as the study of the default mode network of the brain. Growing interest in unsupervised analysis of multi-modal data, multi-subject studies, whole brain activit and other datasets involving multiple interacting variables, have increased demand for multivariate unsupervised techniques. However there is still relatively little work on the examination of the full relationships among interacting variables in an unsupervised fashion. In this project, we work on a novel approach to address the problem of identifying these higher-order interactions. Unlike existing approaches, we directly identify sets of inter-dependent random variables without explicitly modeling interactions within these sets.
Multimodal Data Fusion for Brain Connectivity Analysis
Despite enormous strides in our understanding of neural physiology, the transition from cellular and subcellular functional variability to variations in human behavior is poorly understood. Current evidence ties this transition to complex interactions within brain functional networks. These networks are not easy to estimate from the data due to their dynamic nature. However, accurately characterizing them is of the uttermost importance for diagnosis and prediction of mental disorders at their early stages (e.g. schizophrenia). This project is aiming to improve estimation of brain functional networks by taking advantage of the complementary nature of multiple brain imaging modalities. The main application is in the study of mental disorders linked to brain network dysfunction (schizophrenia, Alzheimer's, ADHD, bipolar disorder and others).
