Saltar al contenido principal
Elsevier

Connectomic Medicine

Guide to Brain AI in Treatment Decision Planning

  • 1 Edición - 1 de diciembre de 2023
  • Última edición
  • Autores: Michael E. Sughrue, Jacky T. Yeung, Nicholas B. Dadario
  • Idioma: Inglés

Connectomic Medicine: A Guide to Brain AI in Treatment Decision Planning examines how to apply connectomics to clinical medicine, including discussions on techniques, applic… Leer más

Descripción

Connectomic Medicine: A Guide to Brain AI in Treatment Decision Planning examines how to apply connectomics to clinical medicine, including discussions on techniques, applications, novel ideas, and in case examples that highlight the state-of-the-art. Written by pioneers, this volume serves as the foundation for all neuroscience clinicians/researchers venturing into the field of AI medicine, its realistic applications, and how to integrate AI connectomics into clinical practice. With widespread applications in neurology, neurosurgery and psychiatry, this book is appropriate for anyone interested in cerebral network anatomy, imaging techniques, and insights into this emerging field.

Puntos claves

  • Empowers readers to utilize clinically applicable AI platforms to enhance current neurological and psychiatric practices
  • Provides understanding on how brain connectomics pertain to patients with brain-related ailments
  • Serves as a guide towards maximally using existing connectomics software
  • Details relevant clinical and radiological background

De interès para

Neurosurgeons, neurologists, neuropsychiatrists, psychiatrists, researchers, rehab physicians, pain management physicians

Índice

Part 1: Foundations1. What is connectomic medicine and why should you upskill?2. Basic Concepts in Connectomic neuroimaging and modelling approaches3. Human brain network anatomy and function4. The Transdiagnostic model of Neuropsychological dysfunction5. Thinking about Neurocognitive functions as emergent phenomena6. Brain stimulation techniques and targeting principles7. Industrial grade Machine learning techniques for brain data analysis8. Novel ways to think about brain data

Part 2: Applications9. How to organize a Connectomics driven neuroscience clinic10. How to pick targets and brain stimulation approaches11. Connectomic approaches to neurosurgical planning12. Connectomic strategies for post-neurosurgical applications13. Connectomic strategies for stroke patients14. Connectomic strategies for depression and anxiety 15. Connectomic strategies for behavioral/psychiatric disorders 16. Connectomic strategies for movement disorders patients17. Connectomic strategies for dementia

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 1 de diciembre de 2023
  • Idioma: Inglés

Sobre los autores

MS

Michael E. Sughrue

Dr. Sughrue is Associate Professor at the Department of Neurosurgery at Prince of Wales Hospital and Community Health Services in Sydney, Australia. One of the world’s leading neurosurgeons and researchers in connectomics, he is the former Director of the Brain Tumor Center at the University of Oklahoma, during which it grew to become one of the largest clinics in the US. He has lectured around the world on the management of glioma and the use of connectomics, having performed over 3,000 brain tumor surgeries to date and published over 250 peer-reviewed articles. In addition, he cofounded Omniscient Neurotechnology which is an innovative technology startup aimed at using AI to improve the care of patients with mental illness and brain disease.
Afiliaciones y experiencia
Associate Professor, Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia

JY

Jacky T. Yeung

Dr. Yeung is a fellowship-trained neurosurgeon who started his residency in neurosurgery at Yale University in 2013 after completing undergraduate studies at University of British Columbia in Honors Physiology, and his MD degree at Michigan State University College of Human Medicine. He is a member of the American Association of Neurological Surgeons. He obtained his fellowship training at the Centre for Minimally Invasive Neurosurgery in Sydney, Australia under Dr. Charles Teo. During that time, he published widely on the use of machine learning to study the human functional connectome and was the first neurosurgeon to publish on the use of personalized pre-operative brain mapping during intracerebral surgeries. He is currently a funded neurosurgeon-scientist, mentored by pioneer immunologist Dr. Lieping Chen, with an active research focus at Yale University on identifying novel immunotherapies for the treatment of brain cancers. He acts as the Chief Research Officer for Cingulum Health, which is the first clinic in the world to leverage personalized connectomics with transcranial magnetic stimulation to treat patients with wide-ranging neurological and psychiatric conditions.
Afiliaciones y experiencia
Assistant Professor, Department of Neurosurgery, Yale University, USA

ND

Nicholas B. Dadario

Nicholas Dadario is an MD candidate at Robert Wood Johnson Medical School at Rutgers University. He graduated magna cum laude with President’s Honors in 2020 from Binghamton University in Integrative Neuroscience. In just three years of medical school, Nicholas has already published over 50 peer-reviewed articles and numerous book chapters on the use of machine learning and connectomics in neurosurgery under the mentorship of Dr. Michael Sughrue, MD. Nicholas was the recipient of the prestigious Neurosurgery Research & Education Foundation (NREF) Research Fellowship in which he studied the effect of a novel chemotherapy treatment on the brain connectome in patients with glioblastoma under the mentorship of Dr. Jeffrey Bruce, MD, and Dr. Peter Canoll, MD, PhD at Columbia University, NY. Currently, he is focused on understanding how a multi-omic perspective of the glioblastoma environment can leverage new therapeutic insights for brain tumor patients.
Afiliaciones y experiencia
Robert Wood Johnson Medical School, Rutgers University, USA

Ver libro en ScienceDirect

Lee Connectomic Medicine en ScienceDirect