1st Edition
Statistical Methods in Epilepsy
Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery. Statistical Methods in Epilepsy provides a comprehensive introduction to statistical methods used in epilepsy research. Written in a clear, accessible style by leading authorities, this textbook demystifies introductory and advanced statistical methods, providing a practical roadmap that will be invaluable for learners and experts alike.
Topics include a primer on version control and coding, pre-processing of imaging and electrophysiological data, hypothesis testing, generalized linear models, survival analysis, network analysis, time-series analysis, spectral analysis, spatial statistics, unsupervised and supervised learning, natural language processing, prospective trial design, pharmacokinetic and pharmacodynamic modeling, and randomized clinical trials.
Features:
- Provides a comprehensive introduction to statistical methods employed in epilepsy research
- Divided into four parts: Basic Processing Methods for Data Analysis; Statistical Models for Epilepsy Data Types; Machine Learning Methods; and Clinical Studies
- Covers methodological and practical aspects, as well as worked-out examples with R and Python code provided in the online supplement
- Includes contributions by experts in the field
- https://github.com/sharon-chiang/Statistics-Epilepsy-Book/
The handbook targets clinicians, graduate students, medical students, and researchers who seek to conduct quantitative epilepsy research. The topics covered extend broadly to quantitative research in other neurological specialties and provide a valuable reference for the field of neurology.
1. Coding Basics
Emilian R. Vankov, Rob M. Sylvester and Christfried H. Focke
2. Preprocessing Electrophysiological Data: EEG, iEEG and MEG Data
Kristin K. Sellers, Joline M. Fan, Leighton B.N. Hinkley and Heidi E. Kirsch
3. Acquisition and Preprocessing of Neuroimaging MRI Data
Hsiang J. Yeh
4. Hypothesis Testing and Correction for Multiple Testing
Doug Speed
5. Introduction to Linear, Generalized Linear and Mixed-Effects Models
Omar Vazquez, Xiangmin Xu and Zhaoxia Yu
6. Survival Analysis
Fei Jiang and Elan Guterman
7. Graph and Network Control Theoretic Frameworks
Ankit N. Khambhati and Sharon Chiang
8. Time-Series Analysis
Sharon Chiang, John Zito, Vikram R. Rao, and Marina Vannucci
9. Spectral Analysis of Electrophysiological Data
Hernando Ombao and Marco Antonio Pinto-Orellana
10. Spatial Modeling of Imaging and Electrophysiological Data
Rongke Lyu, Michele Guindani and Marina Vannucci
11. Unsupervised Learning
Giuseppe Vinci
12. Supervised Learning
Emilian R. Vankov and Kais Gadhoumi
13. Natural Language Processing
Christfried H. Focke and Rob M. Sylvester
14. Prospective Observational Study Design and Analysis
Carrie Brown, Kimford J. Meador, Page Pennell, and Abigail G. Matthews
15. Pharmacokinetic and Pharmacodynamic Modeling
Ashwin Karanam, Yuhan Long and Angela Birnbaum
16. Randomized Clinical Trial Analysis
Joseph E. Sullivan and Michael Lock
Biography
Sharon Chiang is a research fellow in the Department of Physiology and instructor in the Epilepsy Division in the Department of Neurology at the University of California, San Francisco, USA. Her research focuses on development of methods for state-space models in the estimation of seizure risk and neural mechanisms of memory consolidation in epilepsy.
Vikram R. Rao is Associate Professor of Clinical Neurology, Ernest Gallo Distinguished Professor, and Chief of the Epilepsy Division in the Department of Neurology at the University of California, San Francisco, USA. His clinical and research interests involve applications of neurostimulation devices for drug-resistant epilepsy, neuropsychiatric disorders, and seizure forecasting.
Marina Vannucci is Noah Harding Professor of Statistics at Rice University, Houston, TX, USA, and also holds an Adjunct Professor appointment at the MD Anderson Cancer Center, Houston, TX, USA. Her research is focused on the development of Bayesian statistical methodologies for application in genomics, neuroscience and neuroimaging.
