Computational and Network Modeling of Neuroimaging Data 1st Edition

Grab the attention of every neuroscientist, data scientist, and clinician: Computational and Network Modeling of Neuroimaging Data, 1st Edition by Kendrick Kay is a definitive, contemporary guide to extracting meaning from complex brain imaging datasets.

Dive into a clear, methodical exploration of computational modeling, graph and network analysis, and statistical techniques tailored for neuroimaging modalities such as fMRI, EEG, and MEG. Kay blends rigorous mathematical foundations with accessible explanations, making advanced topics—dynamic functional connectivity, generative models, and multivariate pattern analysis—usable for researchers and graduate students alike.

This book is designed for practical impact: it translates theory into interpretable insights for mapping brain function, diagnosing disorders, and designing experiments. Readers will gain skills to build principled models, evaluate network architecture, and interpret results across populations and individual subjects. The writing emphasizes reproducibility and best practices, helping labs and classrooms worldwide adopt robust computational workflows.

Whether you’re in academia, industry, or a clinical research setting, this edition equips you to: improve model fidelity, compare network metrics, and integrate multimodal data for richer interpretations. Its balance of depth and clarity makes it suitable as a course text or a hands-on reference for ongoing projects.

Elevate your neuroimaging research with a resource that connects computational rigor to real-world applications. Add Computational and Network Modeling of Neuroimaging Data by Kendrick Kay to your library today and take the next step in mastering brain data analysis.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.