Precision Functional Mapping

Prantik Kundu, PhD is a neuroscientist and biophysicist with expertise in advanced neuroimaging, and software systems based on AI and cloud architectures. His career has bridged academia and industry. After obtaining his PhD from Cambridge, he joined the faculty of radiology and psychiatry at Mount Sinai School of Medicine, and then served in senior roles in the development of portable MRI, and executive roles in brain-imaging based multi-site clinical trials. His neuroscience and neuroimaging research has led to numerous papers laying the foundational for advanced functional MRI, precision functional mapping, in broad theoretical underpinning and widespread applications in the neuropsychology, neuropsychiatry, and neuroengineering.

• Part 1: Precision Functional Mapping1. Precision from a Neuroimaging Perspective: Definitions of precision for fMRI and comparisons to other techniques2. Myriad Measures of Multi-Echo fMRI: Key measurements enabled by this imaging strategy3. TE-Dependence Analysis: Understanding what adding the TE-domain to measurement adds to interpretation4. Understanding Noise: Historical precedents to the definition of noise in fMRI and a modern take5. Resolving Resolution: Understand imaging resolution for effect size and avoiding hype6. A Tale of Two Global Signals: A deep dive on physical mechanisms and mitigation strategies related to the global signal7. A Stable Basis: Using the emergence of stable functional neuroarchitecture to changing experimental strategies and possibilities8. Surprises in the Subcortex: New insights into cerebellar and subcortical roles9. Strategic Power Analysis in PFM: Optimizing experimental powerPart 2: Temporal PFM 10. Slow BOLD: An underestimated yet essential measurement capability11. Free Time: The possibilities of experiments that the temporally multi-scale12. pace-Time: Modelling spatio-temporal brain dynamics13. Sparsity Theory: The essence of information in brain data14. BOLD Rhythms: Under-represented temporal dynamics in BOLD signals in health and diseasePart 3. Scalable PFM15. What is Scalable?: Defining scalability for functional neuroimaging and its importance in application16. Neuroengineering principles of PFM: Systematic approaches to bringing engineering discipline to functional neuroimaging17. PFM for Controlled Endpoints: Regulatory pathways and considerations for bringing PFM strategies to market18. Scalability Engineering for PFM Systems: Software and hardware innovations19. Brain Factors Framework: Extending the engineering concept of human factors to neuroengineering strategy based on PFM20. Future of Field Strength: Realizing PFM at multiple field strengths by combining the strategies discussed throughout the book