1st Edition

Statistical Methods in Epilepsy

Edited By Sharon Chiang, Vikram Rao, Marina Vannucci Copyright 2024
    418 Pages 95 B/W Illustrations
    by Chapman & Hall

    418 Pages 95 B/W Illustrations
    by Chapman & Hall

    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.


    • 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


    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.