Connectomic Medicine

Guide to Brain AI in Treatment Decision Planning

1st Edition - December 1, 2023
Authors: Michael E. Sughrue, Jacky T. Yeung, Nicholas B. Dadario
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 9 0 8 9 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 9 0 9 0 - 2

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

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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.

Cover image
Title page
Table of Contents
Copyright
Dedication
Introduction: What is this book trying to say?
Section 1. Foundations
Chapter 1. Why should you learn something new?
Introduction: One step forward or two steps back?
Question 1: Do you feel we have effective therapies for most patients with back and neck pain?
Question 2: Do you feel that given the ∼60,000 completed suicides in the United States annually suggests that our treatments are truly optimized?
Question 3: If you, personally, were paralyzed from a stroke, would you view a few weeks of rehab alone, and then hoping for the best to occur naturally, is the best anyone on earth could come up with?
Our biases: What we claim by writing this book
Point 1: Much of our failure to successfully treat patients results from poor knowledge about how the brain works, specifically the brain of the patient in front of us
Point 2: Connectomics and associated neuroimaging are ready for clinical translation
Point 3: There is no such thing as “Evidence based medicine”
So what is connectomic medicine?
Conclusions
Chapter 2. How to map a connectome
What does the connectome approach add to previous neuroimaging techniques?
What does it mean to map the connectome?
Stage 1: Methods for creating the maps
The benefits of crossing fiber tractography over DTI
Functional MRI
Phase 2: Making the maps make sense
Using prior information
Using machine learning approaches
Conclusion
Chapter 3. The anatomy of human brain networks
A better anatomically specific nomenclature for structure and function
What do we mean by a brain network?
The basic anatomy of the human brain
The cortical parcels
Which brain networks should we study?
The anatomy of the seven main brain network axes
Conclusion
Chapter 4. Updating the traditional Brodmann’s Atlas based on structural and functional connectivity
Introduction
Brodmann areas 1, 2, and 3 (BA1, BA2, BA3)
Brodmann area 4 (BA4)
Brodmann area 5 (BA5)
Brodmann area 6 (BA6)
Brodmann area 7 (BA7)
Brodmann area 8 (BA8)
Brodmann area 9 (BA9)
Brodmann area 10 (BA10)
Brodmann area 11l (BA11l)
Brodmann area 13 (BA13)
Brodmann area 17 (BA17)
Brodmann area 18 (BA18)
Brodmann area 19 (BA19)
Brodmann area 20 (BA20)
Brodmann area 21 (BA21)
Brodmann area 22 (BA22)
Brodmann area 23 (BA23)
Brodmann area 24 (BA24)
Brodmann area 25 (BA25)
Brodmann areas 26, 29, and 30 (BA26, BA29, BA30)
Brodmann area 27 (BA27)
Brodmann area 28 (BA28)
Brodmann area 31 (BA31)
Brodmann area 32 (BA32)
Brodmann area 33 (BA33)
Brodmann area 34 (BA34)
Brodmann area 37 (BA37)
Brodmann area 38 (BA38)
Brodmann area 39
Brodmann area 40 (BA40)
Brodmann area 41 (BA41)
Brodmann area 42 (BA42)
Brodmann area 43 (BA43)
Brodmann area 44 (BA44)
Brodmann area 45 (BA45)
Brodmann area 46 (BA46)
Brodmann area 47 (BA47)
Brodmann area 52 (BA52)
Conclusion
Chapter 5. The transdiagnostic model of mental illness and cognitive dysfunction
Introducing a transdiagnostic hypothesis
Brain function as a series of states
How does functional connectivity become disturbed in brain disease?
The RDoc framework
Negative valence domains
Positive valence domains
Cognitive system domains
Social process system domains
Alertness system domains
Sensorimotor system domains
Conclusion
Chapter 6. Reimagining neurocognitive functions as emergent phenomena: What resting state is really showing us
Introduction
What do we mean by the term network dynamics?
Where do network dynamics come from?
Network control theory
What does this tell us about the clinical utility of resting state fMRI?
Conclusion
Chapter 7. Brain stimulation techniques
Introduction
Deep brain stimulation (DBS)
Transcranial direct current stimulation (tDCS)
Transcranial magnetic stimulation (TMS)
How does TMS work?
Conclusions
Chapter 8. Machine learning and its utility in connectomic medicine
Introduction
Machine learning in medicine
What do we need machine learning for in the brain?
What does a connectome question look like?
What is machine learning’s real promise in connectomic medicine?
Conclusions
Chapter 9. Fundamentals of connectome based decision making and targeting
Introduction
Our four-step approach to any patient
Step 1: define the problem
Step 2: look at the relevant parts of structural connectome
Step 3: look at the relevant parts of the functional connectome
Picking targets
Step 4: determine a treatment which addresses these issues and integrates into the overall care plan
Conclusion
Section 2. Applications
Chapter 10. Using the connectome in psychotherapy and other psychiatric therapies
Introduction
How could a brain MRI impact psychotherapy?
Why would a patient benefit by adding an MRI to this process?
How could a brain MRI impact pharmacotherapy?
In order to alleviate a symptom, a therapy must make a functionally relevant circuit somewhere in the brain fire more normally
Conclusion
Chapter 11. How to organize a connectomics-driven neuroscience clinic
Introduction
Floor plan of the clinic
Processes to consider for running a TMS clinic
Conclusion
Chapter 12. Connectomic approaches to neurosurgical planning
Introduction
Explanations of noncanonical postoperative deficits
Conclusions
Chapter 13. Connectomic strategies for post-neurosurgical applications
Introduction
Personalized, multinetwork rTMS treatment for postsurgical neurorehabilitation
Conclusions
Chapter 14. Connectomic strategies for stroke patients
Introduction
Conclusions
Chapter 15. Connectomic strategies for depression and anxiety
Introduction
Moving forward with a better approach
A personalized approach to target the heterogeneity of MDD
Conclusion
Chapter 16. Connectomic strategy for the treatment of postconcussive syndrome
Introduction
Conclusion
Chapter 17. Connectomic strategies for treating chronic tinnitus associated with psychiatric disorders
Introduction
TMS treatment
Our experience
Conclusion
Chapter 18. Connectomic approach to treating everything else
Introduction
General advice for tackling unique cases
A compendium of treatment approaches
Conclusions
Index

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.

Affiliations and expertise

Associate Professor, Department of Neurosurgery, Prince of Wales Hospital, Sydney, Australia

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.

Affiliations and expertise

Assistant Professor, Department of Neurosurgery, Yale University, USA

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.

Affiliations and expertise

Robert Wood Johnson Medical School, Rutgers University, USA

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Connectomic Medicine

Guide to Brain AI in Treatment Decision Planning

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Michael E. Sughrue

Jacky T. Yeung

Nicholas B. Dadario