♫musicjinni

Cambridge Neuroscience - Duncan Astle - Transdiagnostic approaches to understanding neurodevelopment

video thumbnail
Theme: Lifelong Brain Development

Transdiagnostic approaches to understanding neurodevelopment

Abstract: Macroscopic brain organisation emerges early in life, even prenatally, and continues to develop through adolescence and into early adulthood. The emergence and continual refinement of large-scale brain networks, connecting neuronal populations across anatomical distance, allows for increasing functional integration and specialisation. This process is thought crucial for the emergence of complex cognitive processes. But how and why is this process so diverse? We used structural neuroimaging collected from a large diverse cohort, to explore how different features of macroscopic brain organisation are associated with diverse cognitive trajectories. We used diffusion-weighted imaging (DWI) to construct whole-brain white-matter connectomes. A simulated attack on each child’s connectome revealed that some brain networks were strongly organized around highly connected ‘hubs’. The more children’s brains were critically dependent on hubs, the better their cognitive skills. Conversely, having poorly integrated hubs was a very strong risk factor for cognitive and learning difficulties across the sample. We subsequently developed a computational framework, using generative network modelling (GNM), to model the emergence of this kind of connectome organisation. Relatively subtle changes within the wiring rules of this computational framework give rise to differential developmental trajectories, because of small biases in the preferential wiring properties of different nodes within the network. Finally, we were able to use this GNM to implicate the molecular and cellular processes that govern these different growth patterns.

Biography: Duncan is a Programme Leader at the Medical Research Council’s Cognition and Brain Sciences Unit, and a Fellow of Robinson College, University of Cambridge. Prior to this he completed his training at Durham and Nottingham, and held fellowships at Oxford, Royal Holloway and Cambridge. His research uses multiple methods to explore how brain systems develop through childhood, and how they vary across children and adolescents. This programme of work has been supported by the Royal Society, the British Academy, the Medical Research Council, the Economic and Social Research Council and various charitable foundations.

The Cambridge Neuroscience Interdisciplinary Seminar Series provides a forum for neuroscientists across Cambridge and beyond to discuss contemporary and interdisciplinary research topics and issues.

The seminars are open to both members of the University, external academics and members of the public. We have tried to reflect the diversity of people’s interests at the University with our programme, and the breadth of the research taking place in Cambridge. Registration and more details are available here: http://talks.cam.ac.uk/show/index/125062

For more information on Cambridge Neuroscience, please see www.neuroscience.cam.ac.uk or follow us on Twitter @CamNeuro

Kristin Branson: “Machine Learning in Neuroscience”

Karen Moxon, Ph.D. — The Relationship Between A.I., Machine Learning and Brain Health

Machine Learning for Brain Health and Understanding at Starlab Neuroscience | Starlab Consulting

Machine-Learning Assisted Directed Evolution - Viviana Gradinaru - 10/25/2019

A Machine Learning Collaboration with Neuroscience: Opportunities & Challenges | Francesco Casalegno

Neuroscience+AI can unlock hidden visual interface for the emotional brain | James DiCarlo | TEDxMIT

A Fruitful Reciprocity: The Neuroscience-AI Connection

CARTA: Computational Neuroscience and Anthropogeny with Terry Sejnowski

Modern AI and the state of interdisciplinary exchange with neuroscience - Greg Corrado

Neuroscience and Artificial Intelligence Need Each Other | Marvin Chun | TEDxKFAS

AI for Neuroscience & Neuroscience for AI

Neuroscience with Azure Machine Learning

Neuroscience through the lens of machine learning

Machine learning + neuroscience = biologically feasible computing | Benjamin Migliori | TEDxSanDiego

AI vs Machine Learning

Artificial Intelligence for Mental Health and Neuroscience

How Advances in Technology are Transforming Neuroscience | Dr. Kafui Dzirasa

But what is a neural network? | Chapter 1, Deep learning

Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn

Machine Learning in Neuroscience

Will Debello, Ph.D. — Using Machine Learning to Unravel the Brain’s Wiring Diagram

How to learn Computational Neuroscience on your Own (a self-study guide)

From neuroscience to a simple software - Machine Learning history lecture

How Multi-Brain Neuroscience can inform Artificial Intelligence and Computational Psychiatry

W0 V2 - History of neuroscience and machine learning

Meet AI 7 - Past, Present and Future of Machine Learning x Neuroscience

Brain Hack: 6 secrets to learning faster, backed by neuroscience | Lila Landowski | TEDxHobart

WEAVING TOGETHER MACHINE LEARNING, THEORETICAL PHYSICS, AND NEUROSCIENCE

WEBINAR: Intersections between Deep Learning and Neuroscience

Decoding Depression: How AI is Revolutionizing Mental Health | Mariam Khayretdinova | TEDxBoston

Disclaimer DMCA